{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:22:46Z","timestamp":1775578966048,"version":"3.50.1"},"reference-count":287,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals\u2019 driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.<\/jats:p>","DOI":"10.1007\/s11432-021-3487-x","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T06:04:38Z","timestamp":1667455478000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey"],"prefix":"10.1007","volume":"65","author":[{"given":"Qimei","family":"Cui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingxing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kwang-Cheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Haenggi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"3487_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1109\/COMST.2014.2339817","volume":"17","author":"S Djahel","year":"2015","unstructured":"Djahel S, Doolan R, Muntean G M, et al. A communications-oriented perspective on traffic management systems for smart cities: challenges and innovative approaches. IEEE Commun Surv Tut, 2015, 17: 125\u2013151","journal-title":"IEEE Commun Surv Tut"},{"key":"3487_CR2","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MCOM.2017.1600514","volume":"55","author":"Y Mehmood","year":"2017","unstructured":"Mehmood Y, Ahmad F, Yaqoob I, et al. Internet-of-Things-based smart cities: recent advances and challenges. IEEE Commun Mag, 2017, 55: 16\u201324","journal-title":"IEEE Commun Mag"},{"key":"3487_CR3","doi-asserted-by":"publisher","first-page":"2908","DOI":"10.1007\/s11432-012-4725-1","volume":"55","author":"Z Xiong","year":"2012","unstructured":"Xiong Z, Sheng H, Ro N, et al. Intelligent transportation systems for smart cities: a progress review. Sci China Inf Sci, 2012, 55: 2908\u20132914","journal-title":"Sci China Inf Sci"},{"key":"3487_CR4","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1093\/nsr\/nwx099","volume":"5","author":"Z Xu","year":"2018","unstructured":"Xu Z, Sun J. Model-driven deep-learning. Nat Sci Rev, 2018, 5: 26\u201328","journal-title":"Nat Sci Rev"},{"key":"3487_CR5","unstructured":"Bonawitz K, Eichner H, Grieskamp W, et al. Towards federated learning at scale: system design. 2019. ArXiv:1902.01046"},{"key":"3487_CR6","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1109\/MWC.2019.1800447","volume":"26","author":"H He","year":"2019","unstructured":"He H, Jin S, Wen C K, et al. Model-driven deep learning for physical layer communications. IEEE Wireless Commun, 2019, 26: 77\u201383","journal-title":"IEEE Wireless Commun"},{"key":"3487_CR7","unstructured":"Lee M, Yu G, Li G Y. Learning to branch: accelerating resource allocation in wireless networks. 2019. ArXiv:1903.01819"},{"key":"3487_CR8","doi-asserted-by":"crossref","unstructured":"He H, Wen C K, Jin S, et al. A model-driven deep learning network for MIMO detection. In: Proceedings of IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2018","DOI":"10.1109\/GlobalSIP.2018.8646357"},{"key":"3487_CR9","doi-asserted-by":"crossref","unstructured":"Liu S, Su H, Zhao Y, et al. Lane change scheduling for autonomous vehicle: a prediction-and-search framework. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021. 3343\u20133353","DOI":"10.1145\/3447548.3467072"},{"key":"3487_CR10","doi-asserted-by":"crossref","unstructured":"Leutzbach W. Introduction to the Theory of Traffic Flow. Berlin: Springer, 1988","DOI":"10.1007\/978-3-642-61353-1"},{"key":"3487_CR11","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/S0965-8564(97)00048-7","volume":"32","author":"M Papageorgiou","year":"1998","unstructured":"Papageorgiou M. Some remarks on macroscopic traffic flow modelling. Transport Res Part A-Policy Pract, 1998, 32: 323\u2013329","journal-title":"Transport Res Part A-Policy Pract"},{"key":"3487_CR12","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1177\/095965180121500402","volume":"215","author":"S P Hoogendoorn","year":"2001","unstructured":"Hoogendoorn S P, Bovy P H L. State-of-the-art of vehicular traffic flow modelling. Proc Inst Mech Eng Part I-J Syst Control Eng, 2001, 215: 283\u2013303","journal-title":"Proc Inst Mech Eng Part I-J Syst Control Eng"},{"key":"3487_CR13","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s13676-014-0045-5","volume":"4","author":"F van Wageningen-Kessels","year":"2014","unstructured":"van Wageningen-Kessels F, van Lint H, Vuik K, et al. Genealogy of traffic flow models. EURO J Transport Log, 2014, 4: 445\u2013473","journal-title":"EURO J Transport Log"},{"key":"3487_CR14","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s11768-012-9221-z","volume":"10","author":"Y Li","year":"2012","unstructured":"Li Y, Sun D. Microscopic car-following model for the traffic flow: the state of the art. J Control Theor Appl, 2012, 10: 133\u2013143","journal-title":"J Control Theor Appl"},{"key":"3487_CR15","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/TITS.2013.2272074","volume":"14","author":"M Rahman","year":"2013","unstructured":"Rahman M, Chowdhury M, Xie Y, et al. Review of microscopic lane-changing models and future research opportunities. IEEE Trans Intell Transport Syst, 2013, 14: 1942\u20131956","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR16","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.trb.2013.11.009","volume":"60","author":"Z Zheng","year":"2014","unstructured":"Zheng Z. Recent developments and research needs in modeling lane changing. Transport Res Part B-Meth, 2014, 60: 16\u201332","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40648-014-0001-z","volume":"1","author":"S Lef\u00e8vre","year":"2014","unstructured":"Lef\u00e8vre S, Vasquez D, Laugier C. A survey on motion prediction and risk assessment for intelligent vehicles. Robomech J, 2014, 1: 1","journal-title":"Robomech J"},{"key":"3487_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2016\/6952791","volume":"2016","author":"A N Abu","year":"2016","unstructured":"Abu A N, Abou-zeid H. Driver behavior modeling: developments and future directions. Int J Veh Tech, 2016, 2016: 1\u201312","journal-title":"Int J Veh Tech"},{"key":"3487_CR19","first-page":"107","volume-title":"Vehicular Mobility Modeling for VANET","author":"J H\u00e4rri","year":"2009","unstructured":"H\u00e4rri J. Vehicular Mobility Modeling for VANET. Hoboken: John Wiley and Sons, Ltd., 2009. 107\u2013156"},{"key":"3487_CR20","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/SURV.2009.090403","volume":"11","author":"J H\u00e4rri","year":"2009","unstructured":"H\u00e4rri J, Filali F, Bonnet C. Mobility models for vehicular ad hoc networks: a survey and taxonomy. IEEE Commun Surv Tut, 2009, 11: 19\u201341","journal-title":"IEEE Commun Surv Tut"},{"key":"3487_CR21","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1109\/JSAC.2019.2904363","volume":"37","author":"C Zhang","year":"2019","unstructured":"Zhang C, Zhang H, Qiao J, et al. Deep transfer learning for intelligent cellular traffic prediction based on cross-domain big data. IEEE J Sel Areas Commun, 2019, 37: 1389\u20131401","journal-title":"IEEE J Sel Areas Commun"},{"key":"3487_CR22","first-page":"2637","volume":"21","author":"Y Yang","year":"2022","unstructured":"Yang Y, Xie X, Fang Z, et al. VeMo: enabling transparent vehicular mobility modeling at individual levels with full penetration. IEEE Trans Mobile Comput, 2022, 21: 2637\u20132651","journal-title":"IEEE Trans Mobile Comput"},{"key":"3487_CR23","first-page":"157","volume-title":"Random Forests","author":"A Cutler","year":"2012","unstructured":"Cutler A, Cutler D R, Stevens J R. Random Forests. Boston: Springer, 2012. 157\u2013175"},{"key":"3487_CR24","first-page":"349","volume-title":"Statistics and Its Interface","author":"J Zhu","year":"2006","unstructured":"Zhu J, Rosset S, Zou H, et al. Multi-class AdaBoost. In: Statistics and Its Interface. Boston: International Press 2006. 2: 349\u2013360"},{"key":"3487_CR25","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1023\/A:1007515423169","volume":"36","author":"E Bauer","year":"1999","unstructured":"Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learn, 1999, 36: 105\u2013139","journal-title":"Machine Learn"},{"key":"3487_CR26","first-page":"1303","volume-title":"Support Vector Machine","author":"M M Adankon","year":"2009","unstructured":"Adankon M M, Cheriet M. Support Vector Machine. Boston: Springer, 2009. 1303\u20131308"},{"key":"3487_CR27","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"S J Pan","year":"2010","unstructured":"Pan S J, Yang Q. A survey on transfer learning. IEEE Trans Knowl Data Eng, 2010, 22: 1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3487_CR28","unstructured":"Bonawitz K, Eichner H, Grieskamp W, et al. Towards federated learning at scale: system design. 2019. ArXiv:1902.01046"},{"key":"3487_CR29","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1109\/ACCESS.2015.2452576","volume":"3","author":"S Zhou","year":"2015","unstructured":"Zhou S, Lee D, Leng B, et al. On the spatial distribution of base stations and its relation to the traffic density in cellular networks. IEEE Access, 2015, 3: 998\u20131010","journal-title":"IEEE Access"},{"key":"3487_CR30","doi-asserted-by":"publisher","first-page":"100278","DOI":"10.1016\/j.vehcom.2020.100278","volume":"26","author":"O Rehman","year":"2020","unstructured":"Rehman O, Qureshi R, Ould-Khaoua M, et al. Analysis of mobility speed impact on end-to-end communication performance in VANETs. Vehicular Commun, 2020, 26: 100278","journal-title":"Vehicular Commun"},{"key":"3487_CR31","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1061\/(ASCE)0733-947X(1997)123:4(261)","volume":"123","author":"B L Smith","year":"1997","unstructured":"Smith B L, Demetsky M J. Traffic flow forecasting: comparison of modeling approaches. J Transport Eng, 1997, 123: 261\u2013266","journal-title":"J Transport Eng"},{"key":"3487_CR32","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/MVT.2016.2645318","volume":"12","author":"C Chen","year":"2017","unstructured":"Chen C, Luan T H, Guan X, et al. Connected vehicular transportation: data analytics and traffic-dependent networking. IEEE Veh Technol Mag, 2017, 12: 42\u201354","journal-title":"IEEE Veh Technol Mag"},{"key":"3487_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-618-6","volume-title":"Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems","author":"P C Cacciabue","year":"2007","unstructured":"Cacciabue P C. Modelling Driver Behaviour in Automotive Environments: Critical Issues in Driver Interactions with Intelligent Transport Systems. Berlin: Springer-Verlag, 2007"},{"key":"3487_CR34","first-page":"17","volume-title":"Data-Driven Modelling: Concepts, Approaches and Experiences","author":"D Solomatine","year":"2008","unstructured":"Solomatine D, See L, Abrahart R. Data-Driven Modelling: Concepts, Approaches and Experiences. Berlin: Springer, 2008. 17\u201330"},{"key":"3487_CR35","doi-asserted-by":"publisher","DOI":"10.1002\/9781118658222","volume-title":"Stochastic Geometry and Its Applications","author":"S N Chiu","year":"2013","unstructured":"Chiu S N, Stoyan D, Kendall W S, et al. Stochastic Geometry and Its Applications. 3rd ed. Hoboken: Wiley, 2013","edition":"3rd ed."},{"key":"3487_CR36","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139043816","volume-title":"Stochastic Geometry for Wireless Networks","author":"M Haenggi","year":"2012","unstructured":"Haenggi M. Stochastic Geometry for Wireless Networks. Cambridge: Cambridge University Press, 2012"},{"key":"3487_CR37","doi-asserted-by":"publisher","first-page":"223301","DOI":"10.1007\/s11432-020-2875-7","volume":"63","author":"X L Yu","year":"2020","unstructured":"Yu X L, Cui Q M, Wang Y J, et al. Stochastic geometry based analysis for heterogeneous networks: a perspective on meta distribution. Sci China Inf Sci, 2020, 63: 223301","journal-title":"Sci China Inf Sci"},{"key":"3487_CR38","first-page":"281","volume":"229","author":"M J Lighthill","year":"1955","unstructured":"Lighthill M J, Whitham G B. On kinematic waves. I. Flood movement in long rivers. In: Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences, 1955. 229: 281\u2013316","journal-title":"Proceedings of the Royal Society of London"},{"key":"3487_CR39","doi-asserted-by":"crossref","unstructured":"Chhabra R, Verma S, Krishna C R. A survey on driver behavior detection techniques for intelligent transportation systems. In: Proceedings of the 7th International Conference on Cloud Computing, Data Science Engineering \u2014 Confluence, 2017. 36\u201341","DOI":"10.1109\/CONFLUENCE.2017.7943120"},{"key":"3487_CR40","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1049\/iet-its.2014.0248","volume":"9","author":"J Engelbrecht","year":"2015","unstructured":"Engelbrecht J, Booysen M J, Rooyen G-J, et al. Survey of smartphone-based sensing in vehicles for intelligent transportation system applications. IET Intell Transp Syst, 2015, 9: 924\u2013935","journal-title":"IET Intell Transp Syst"},{"key":"3487_CR41","doi-asserted-by":"publisher","first-page":"10176","DOI":"10.1109\/TVT.2018.2865679","volume":"67","author":"Q Cui","year":"2018","unstructured":"Cui Q, Wang N, Haenggi M. Vehicle distributions in large and small cities: spatial models and applications. IEEE Trans Veh Technol, 2018, 67: 10176\u201310189","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR42","doi-asserted-by":"crossref","unstructured":"Cui Q, Wang N, Haenggi M. Spatial point process modeling of vehicles in large and small cities. In: Proceedings of IEEE Global Communications Conference, 2017. 1\u20137","DOI":"10.1109\/GLOCOM.2017.8254666"},{"key":"3487_CR43","doi-asserted-by":"crossref","unstructured":"Jeyaraj J P, Haenggi M. Reliability analysis of V2V communications on orthogonal street systems. In: Proceedings of IEEE Global Communications Conference, 2017. 1\u20136","DOI":"10.1109\/GLOCOM.2017.8254665"},{"key":"3487_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2010.11.002","volume":"499","author":"M Barth\u00e9lemy","year":"2011","unstructured":"Barth\u00e9lemy M. Spatial networks. Phys Reports, 2011, 499: 1\u2013101","journal-title":"Phys Reports"},{"key":"3487_CR45","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/TITS.2006.869595","volume":"7","author":"J C McCall","year":"2006","unstructured":"McCall J C, Trivedi M M. Video-based lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Trans Intell Transport Syst, 2006, 7: 20\u201337","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR46","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1002\/rob.20255","volume":"25","author":"C Urmson","year":"2008","unstructured":"Urmson C, Anhalt J, Bagnell D, et al. Autonomous driving in urban environments: boss and the urban challenge. J Field Robot, 2008, 25: 425\u2013466","journal-title":"J Field Robot"},{"key":"3487_CR47","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1109\/JSAC.2013.SUP.0513045","volume":"31","author":"L-W Chen","year":"2013","unstructured":"Chen L-W, Sharma P, Tseng Y. Dynamic traffic control with fairness and throughput optimization using vehicular communications. IEEE J Sel Areas Commun, 2013, 31: 504\u2013512","journal-title":"IEEE J Sel Areas Commun"},{"key":"3487_CR48","unstructured":"Jin P, Zhang X. A new approach to modeling city road network. In: Proceedings of International Conference on Computer Application and System Modeling, 2010. 305\u2013309"},{"key":"3487_CR49","doi-asserted-by":"publisher","first-page":"407","DOI":"10.3233\/FI-2012-745","volume":"119","author":"J Rzesz\u00f3tko","year":"2012","unstructured":"Rzesz\u00f3tko J, Nguyen S H. Machine learning for traffic prediction. Fundamenta Informaticae, 2012, 119: 407\u2013420","journal-title":"Fundamenta Informaticae"},{"key":"3487_CR50","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1145\/3231541.3231544","volume":"10","author":"Y Li","year":"2018","unstructured":"Li Y, Shahabi C. A brief overview of machine learning methods for short-term traffic forecasting and future directions. SIGSPATIAL Spec, 2018, 10: 3\u20139","journal-title":"SIGSPATIAL Spec"},{"key":"3487_CR51","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1109\/MITS.2009.933860","volume":"1","author":"Q Q Li","year":"2009","unstructured":"Li Q Q, Zeng Z, Yang B S. Hierarchical model of road network for route planning in vehicle navigation systems. IEEE Intell Transp Syst Mag, 2009, 1: 20\u201324","journal-title":"IEEE Intell Transp Syst Mag"},{"key":"3487_CR52","doi-asserted-by":"publisher","first-page":"057101","DOI":"10.1103\/PhysRevE.76.057101","volume":"76","author":"A Y Abul-Magd","year":"2007","unstructured":"Abul-Magd A Y. Modeling highway-traffic headway distributions using superstatistics. Phys Rev E, 2007, 76: 057101","journal-title":"Phys Rev E"},{"key":"3487_CR53","doi-asserted-by":"crossref","unstructured":"Muhlethaler P, Bouchaala Y, Shagdar O, et al. A simple stochastic geometry model to test a simple adaptive CSMA protocol: application for VANETs. In: Proceedings of International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks (PEMWN), 2016. 1\u20136","DOI":"10.1109\/PEMWN.2016.7842899"},{"key":"3487_CR54","doi-asserted-by":"crossref","unstructured":"Farooq M J, ElSawy H, Alouini M S. Modeling inter-vehicle communication in multi-lane highways a stochastic geometry approach. In: Proceedings of IEEE 82nd Vehicular Technology Conference (VTC2015-Fall), 2015. 1\u20135","DOI":"10.1109\/VTCFall.2015.7391025"},{"key":"3487_CR55","doi-asserted-by":"crossref","unstructured":"Muhammed Ajeer V K, Neelakantan P C, Babu A V. Network connectivity of one-dimensional vehicular ad hoc network. In: Proceedings of International Conference on Communications and Signal Processing, 2011. 241\u2013245","DOI":"10.1109\/ICCSP.2011.5739311"},{"key":"3487_CR56","doi-asserted-by":"crossref","unstructured":"Ejaz W, Naeem M, Ramzan M R, et al. Charging infrastructure placement for electric vehicles: an optimization prospective. In: Proceedings of the 27th International Telecommunication Networks and Applications Conference (ITNAC), 2017. 1\u20136","DOI":"10.1109\/ATNAC.2017.8215427"},{"key":"3487_CR57","doi-asserted-by":"crossref","unstructured":"Busanelli S, Ferrari G, Gruppini R. Performance analysis of broadcast protocols in VANETs with Poisson vehicle distribution. In: Proceedings of the 11th International Conference on ITS Telecommunications, 2011. 133\u2013138","DOI":"10.1109\/ITST.2011.6060040"},{"key":"3487_CR58","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1016\/j.trb.2006.11.002","volume":"41","author":"X Wang","year":"2007","unstructured":"Wang X. Modeling the process of information relay through inter-vehicle communication. Transport Res Part B-Meth, 2007, 41: 684\u2013700","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR59","doi-asserted-by":"crossref","unstructured":"Bouchaala Y, Muhlethaler P, Shagdar O, et al. Optimized spatial CSMA for VANETs: a comparative study using a simple stochastic model and simulation results. In: Proceedings of the 14th IEEE Annual Consumer Communications Networking Conference (CCNC), 2017. 293\u2013298","DOI":"10.1109\/CCNC.2017.7983122"},{"key":"3487_CR60","doi-asserted-by":"publisher","first-page":"4401","DOI":"10.1109\/TWC.2018.2824832","volume":"17","author":"V V Chetlur","year":"2018","unstructured":"Chetlur V V, Dhillon H S. Coverage analysis of a vehicular network modeled as Cox process driven by Poisson line process. IEEE Trans Wireless Commun, 2018, 17: 4401\u20134416","journal-title":"IEEE Trans Wireless Commun"},{"key":"3487_CR61","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1109\/TASE.2015.2422852","volume":"12","author":"J Chen","year":"2015","unstructured":"Chen J, Low K H, Yao Y, et al. Gaussian process decentralized data fusion and active sensing for spatiotemporal traffic modeling and prediction in mobility-on-demand systems. IEEE Trans Automat Sci Eng, 2015, 12: 901\u2013921","journal-title":"IEEE Trans Automat Sci Eng"},{"key":"3487_CR62","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1109\/TITS.2016.2632309","volume":"19","author":"A Al-Hourani","year":"2018","unstructured":"Al-Hourani A, Evans R J, Kandeepan S, et al. Stochastic geometry methods for modeling automotive radar interference. IEEE Trans Intell Transport Syst, 2018, 19: 333\u2013344","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR63","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/BF01158933","volume":"13","author":"W A Massey","year":"1993","unstructured":"Massey W A, Whitt W. Networks of infinite-server queues with nonstationary Poisson input. Queueing Syst, 1993, 13: 183\u2013250","journal-title":"Queueing Syst"},{"key":"3487_CR64","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1017\/S0269964800003612","volume":"8","author":"W A Massey","year":"1994","unstructured":"Massey W A, Whitt W. A stochastic model to capture space and time dynamics in wireless communication systems. Prob Eng Inf Sci, 1994, 8: 541\u2013569","journal-title":"Prob Eng Inf Sci"},{"key":"3487_CR65","doi-asserted-by":"crossref","unstructured":"Leung K K, Massey W A, Whitt W. Traffic models for wireless communication networks. In: Proceedings of INFOCOM\u201994 Conference on Computer Communications, 1994. 1029\u20131037","DOI":"10.1109\/INFCOM.1994.337587"},{"key":"3487_CR66","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1109\/TNET.2010.2057257","volume":"19","author":"I W H Ho","year":"2011","unstructured":"Ho I W H, Leung K K, Polak J W. Stochastic model and connectivity dynamics for VANETs in signalized road systems. IEEE ACM Trans Network, 2011, 19: 195\u2013208","journal-title":"IEEE ACM Trans Network"},{"key":"3487_CR67","doi-asserted-by":"publisher","first-page":"2440","DOI":"10.1109\/TVT.2007.912161","volume":"57","author":"M Khabazian","year":"2008","unstructured":"Khabazian M, Ali M. A performance modeling of connectivity in vehicular ad hoc networks. IEEE Trans Veh Technol, 2008, 57: 2440\u20132450","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR68","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.trc.2007.12.002","volume":"16","author":"S Ukkusuri","year":"2008","unstructured":"Ukkusuri S, Du L. Geometric connectivity of vehicular ad hoc networks: analytical characterization. Transport Res Part C-Emerging Tech, 2008, 16: 615\u2013634","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR69","doi-asserted-by":"publisher","first-page":"3341","DOI":"10.1109\/TVT.2008.2002957","volume":"57","author":"S Yousefi","year":"2008","unstructured":"Yousefi S, Altman E, El-Azouzi R, et al. Analytical model for connectivity in vehicular ad hoc networks. IEEE Trans Veh Technol, 2008, 57: 3341\u20133356","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR70","doi-asserted-by":"publisher","first-page":"3296","DOI":"10.1109\/TVT.2020.2965159","volume":"69","author":"X Zhang","year":"2020","unstructured":"Zhang X, Zhang J, Liu Z, et al. MDP-based task offloading for vehicular edge computing under certain and uncertain transition probabilities. IEEE Trans Veh Technol, 2020, 69: 3296\u20133309","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR71","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MCOM.2017.1600238CM","volume":"55","author":"H Menouar","year":"2017","unstructured":"Menouar H, Guvenc I, Akkaya K, et al. UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun Mag, 2017, 55: 22\u201328","journal-title":"IEEE Commun Mag"},{"key":"3487_CR72","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1109\/TITS.2011.2156406","volume":"12","author":"G Yan","year":"2011","unstructured":"Yan G, Olariu S. A probabilistic analysis of link duration in vehicular ad hoc networks. IEEE Trans Intell Transport Syst, 2011, 12: 1227\u20131236","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR73","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/JSAC.2013.SUP.0513038","volume":"31","author":"Y Jeong","year":"2013","unstructured":"Jeong Y, Chong J W, Shin H, et al. Intervehicle communication: Cox-Fox modeling. IEEE J Sel Areas Commun, 2013, 31: 418\u2013433","journal-title":"IEEE J Sel Areas Commun"},{"key":"3487_CR74","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1049\/iet-gtd.2014.0446","volume":"9","author":"R-C Leou","year":"2015","unstructured":"Leou R-C, Teng J-H, Su C-L. Modelling and verifying the load behaviour of electric vehicle charging stations based on field measurements. IET Gener Transm Distr, 2015, 9: 1112\u20131119","journal-title":"IET Gener Transm Distr"},{"key":"3487_CR75","doi-asserted-by":"crossref","unstructured":"Liu J, Cui E, Hu H, et al. Short-term forecasting of emerging on-demand ride services. In: Proceedings of the 4th International Conference on Transportation Information and Safety (ICTIS), 2017. 489\u2013495","DOI":"10.1109\/ICTIS.2017.8047810"},{"key":"3487_CR76","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1109\/TITS.2017.2688860","volume":"19","author":"J Guo","year":"2018","unstructured":"Guo J, Zhang Y, Chen X, et al. Spatial stochastic vehicle traffic modeling for VANETs. IEEE Trans Intell Transport Syst, 2018, 19: 416\u2013425","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR77","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1023\/A:1019172312328","volume":"7","author":"F Baccelli","year":"1997","unstructured":"Baccelli F, Klein M, Lebourges M, et al. Stochastic geometry and architecture of communication networks. Telecommun Syst, 1997, 7: 209\u2013227","journal-title":"Telecommun Syst"},{"key":"3487_CR78","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1109\/JSAC.2007.071005","volume":"25","author":"N Wisitpongphan","year":"2007","unstructured":"Wisitpongphan N, Bai F, Mudalige P, et al. Routing in sparse vehicular ad hoc wireless networks. IEEE J Sel Areas Commun, 2007, 25: 1538\u20131556","journal-title":"IEEE J Sel Areas Commun"},{"key":"3487_CR79","doi-asserted-by":"crossref","unstructured":"Golmohammadi P, Mokhtarian P, Safaei F, et al. An analytical model of network connectivity in vehicular ad hoc networks using spatial point processes. In: Proceedings of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, 2014. 1\u20136","DOI":"10.1109\/WoWMoM.2014.6918988"},{"key":"3487_CR80","unstructured":"Chetlur V V, Dhillon H S, Dettmann C P. Characterizing shortest paths in road systems modeled as Manhattan Poisson line processes. 2018. ArXiv:1811.11332"},{"key":"3487_CR81","doi-asserted-by":"crossref","unstructured":"Steinmetz E, Wildemeersch M, Wymeersch H. WiP abstract: reception probability model for vehicular ad-hoc networks in the vicinity of intersections. In: Proceedings of ACM\/IEEE 5th International Conference on Cyber-Physical Systems, Washington, 2014. 223\u2013223","DOI":"10.1109\/ICCPS.2014.6843736"},{"key":"3487_CR82","doi-asserted-by":"crossref","unstructured":"Steinmetz E, Hult R, de Campos G R, et al. Communication analysis for centralized intersection crossing coordination. In: Proceedings of the 11th International Symposium on Wireless Communications Systems (ISWCS), 2014. 813\u2013818","DOI":"10.1109\/ISWCS.2014.6933465"},{"key":"3487_CR83","doi-asserted-by":"crossref","unstructured":"Wymeersch E S, Wildemeersch M, Quek T Q S, et al. A stochastic geometry model for vehicular communication near intersections. In: Proceedings of 2015 IEEE Globecom Workshops (GC Wkshps), 2015. 1\u20136","DOI":"10.1109\/GLOCOMW.2015.7413975"},{"key":"3487_CR84","doi-asserted-by":"publisher","first-page":"2035","DOI":"10.1109\/JSYST.2020.3027883","volume":"15","author":"G Singh","year":"2020","unstructured":"Singh G, Srivastava A, Bohara V A. Stochastic geometry-based interference characterization for RF and VLC-based vehicular communication system. IEEE Syst J, 2020, 15: 2035\u20132045","journal-title":"IEEE Syst J"},{"key":"3487_CR85","doi-asserted-by":"publisher","first-page":"1448","DOI":"10.1109\/TITS.2015.2507939","volume":"17","author":"Z Tong","year":"2016","unstructured":"Tong Z, Lu H, Haenggi M, et al. A stochastic geometry approach to the modeling of DSRC for vehicular safety communication. IEEE Trans Intell Transport Syst, 2016, 17: 1448\u20131458","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR86","doi-asserted-by":"publisher","first-page":"8023","DOI":"10.1109\/TWC.2021.3089553","volume":"20","author":"J P Jeyaraj","year":"2021","unstructured":"Jeyaraj J P, Haenggi M, Sakr A H, et al. The transdimensional Poisson process for vehicular network analysis. IEEE Trans Wireless Commun, 2021, 20: 8023\u20138038","journal-title":"IEEE Trans Wireless Commun"},{"key":"3487_CR87","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1109\/TWC.2020.3023914","volume":"20","author":"J P Jeyaraj","year":"2021","unstructured":"Jeyaraj J P, Haenggi M. Cox models for vehicular networks: SIR performance and equivalence. IEEE Trans Wireless Commun, 2021, 20: 171\u2013185","journal-title":"IEEE Trans Wireless Commun"},{"key":"3487_CR88","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s11276-012-0518-0","volume":"19","author":"C H Lee","year":"2013","unstructured":"Lee C H, Shih C Y, Chen Y S. Stochastic geometry based models for modeling cellular networks in urban areas. Wireless Netw, 2013, 19: 1063\u20131072","journal-title":"Wireless Netw"},{"key":"3487_CR89","doi-asserted-by":"crossref","unstructured":"Ying Q, Zhao Z, Zhou Y, et al. Characterizing spatial patterns of base stations in cellular networks. In: Proceedings of IEEE\/CIC International Conference on Communications in China, 2014. 490\u2013495","DOI":"10.1109\/ICCChina.2014.7008327"},{"key":"3487_CR90","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1109\/TSMCC.2007.913917","volume":"38","author":"Z G Wang","year":"2008","unstructured":"Wang Z G, Liu L C, Zhou M C, et al. A position-based clustering technique for ad hoc intervehicle communication. IEEE Trans Syst Man Cybern C, 2008, 38: 201\u2013208","journal-title":"IEEE Trans Syst Man Cybern C"},{"key":"3487_CR91","doi-asserted-by":"publisher","first-page":"2121","DOI":"10.1109\/LWC.2020.3014585","volume":"9","author":"V V Chetlur","year":"2020","unstructured":"Chetlur V V, Dhillon H S. On the load distribution of vehicular users modeled by a Poisson line cox process. IEEE Wireless Commun Lett, 2020, 9: 2121\u20132125","journal-title":"IEEE Wireless Commun Lett"},{"key":"3487_CR92","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1111\/rssb.12096","volume":"77","author":"F Lavancier","year":"2015","unstructured":"Lavancier F, M\u00f8ller J, Rubak E. Determinantal point process models and statistical inference. J Royal Stat Soc B, 2015, 77: 853\u2013877","journal-title":"J Royal Stat Soc B"},{"key":"3487_CR93","doi-asserted-by":"publisher","first-page":"5800","DOI":"10.1109\/TWC.2013.100113.130220","volume":"12","author":"A Guo","year":"2013","unstructured":"Guo A, Haenggi M. Spatial stochastic models and metrics for the structure of base stations in cellular networks. IEEE Trans Wireless Commun., 2013, 12: 5800\u20135812","journal-title":"IEEE Trans Wireless Commun."},{"key":"3487_CR94","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1109\/TWC.2014.2332335","volume":"14","author":"N Deng","year":"2015","unstructured":"Deng N, Zhou W, Haenggi M. The Ginibre point process as a model for wireless networks with repulsion. IEEE Trans Wireless Commun, 2015, 14: 107\u2013121","journal-title":"IEEE Trans Wireless Commun"},{"key":"3487_CR95","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1098\/rspa.1955.0089","volume":"229","author":"M J Lighthill","year":"1955","unstructured":"Lighthill M J, Whitham G B. On kinematic waves II. a theory of traffic flow on long crowded roads. Proc R Soc Lond A, 1955, 229: 317\u2013345","journal-title":"Proc R Soc Lond A"},{"key":"3487_CR96","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1287\/opre.4.1.42","volume":"4","author":"P I Richards","year":"1956","unstructured":"Richards P I. Shock waves on the highway. Oper Res, 1956, 4: 42\u201351","journal-title":"Oper Res"},{"key":"3487_CR97","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/S0965-8564(01)00042-8","volume":"36","author":"G C K Wong","year":"2002","unstructured":"Wong G C K, Wong S C. A multi-class traffic flow model\u2014an extension of LWR model with heterogeneous drivers. Transport Res Part A-Policy Pract, 2002, 36: 827\u2013841","journal-title":"Transport Res Part A-Policy Pract"},{"key":"3487_CR98","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1109\/TITS.2011.2178837","volume":"13","author":"Y Yuan","year":"2012","unstructured":"Yuan Y, van Lint J W C, Wilson R E, et al. Real-time lagrangian traffic state estimator for freeways. IEEE Trans Intell Transport Syst, 2012, 13: 59\u201370","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR99","doi-asserted-by":"crossref","unstructured":"Chu K, Yang L, Saigal R, et al. Validation of stochastic traffic flow model with microscopic traffic simulation. In: Proceedings of IEEE International Conference on Automation Science and Engineering, 2011. 672\u2013677","DOI":"10.1109\/CASE.2011.6042479"},{"key":"3487_CR100","doi-asserted-by":"crossref","unstructured":"Chu K, Saigal R, Saitou K. Stochastic Lagrangian traffic flow modeling and real-time traffic prediction. In: Proceedings of IEEE International Conference on Automation Science and Engineering (CASE), 2016. 213\u2013218","DOI":"10.1109\/COASE.2016.7743383"},{"key":"3487_CR101","first-page":"51","volume":"28","author":"H J Payne","year":"1971","unstructured":"Payne H J. Models of Freeway Traffic and Control. La Jolla: Simulation Councils, Inc., 1971. 28: 51\u201361","journal-title":"La Jolla: Simulation Councils, Inc."},{"key":"3487_CR102","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1063\/1.3069011","volume":"28","author":"G B Whitham","year":"1975","unstructured":"Whitham G B, Fowler R G. Linear and nonlinear waves. Phys Today, 1975, 28: 55\u201356","journal-title":"Phys Today"},{"key":"3487_CR103","unstructured":"K\u00fchne R. Macroscopic freeway model for dense traffic-stop-start waves and incident detection. In: Proceedings of the 9th International Symposium of Transportation and Traffic Theory, 1984. 21\u201342"},{"key":"3487_CR104","doi-asserted-by":"publisher","first-page":"R2335","DOI":"10.1103\/PhysRevE.48.R2335","volume":"48","author":"B S Kerner","year":"1993","unstructured":"Kerner B S, Konh\u00e4user P. Cluster effect in initially homogeneous traffic flow. Phys Rev E, 1993, 48: R2335\u2013R2338","journal-title":"Phys Rev E"},{"key":"3487_CR105","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/0191-2615(93)90041-8","volume":"27","author":"P G Michalopoulos","year":"1993","unstructured":"Michalopoulos P G, Yi P, Lyrintzis A S. Continuum modelling of traffic dynamics for congested freeways. Transport Res Part B-Meth, 1993, 27: 315\u2013332","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR106","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1137\/S0036139997332099","volume":"60","author":"A Aw","year":"2000","unstructured":"Aw A, Rascle M. Resurrection of \u201csecond order\u201d models of traffic flow. SIAM J Appl Math, 2000, 60: 916\u2013938","journal-title":"SIAM J Appl Math"},{"key":"3487_CR107","doi-asserted-by":"crossref","unstructured":"Zhang L, Xu C, Yu L. Calibration of the Aw-Rascle traffic flow model via flow-density diagram data. In: Proceedings of Chinese Control Conference, 2016. 9234\u20139239","DOI":"10.1109\/ChiCC.2016.7554826"},{"key":"3487_CR108","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1016\/S0895-7177(02)80029-2","volume":"35","author":"R M Colombo","year":"2002","unstructured":"Colombo R M. A 2\u00d72 hyperbolic traffic flow model. Math Comput Model, 2002, 35: 683\u2013688","journal-title":"Math Comput Model"},{"key":"3487_CR109","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1016\/S0191-2615(00)00050-3","volume":"36","author":"H M Zhang","year":"2002","unstructured":"Zhang H M. A non-equilibrium traffic model devoid of gas-like behavior. Transport Res Part B-Meth, 2002, 36: 275\u2013290","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR110","unstructured":"Lebacque J P, Mammar S, Haj-Salem H. Generic second order traffic flow modelling. In: Proceedings of Transportation and Traffic Theory, 2007. 755\u2013776"},{"key":"3487_CR111","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1080\/03081067908717157","volume":"5","author":"W F Phillips","year":"1979","unstructured":"Phillips W F. A kinetic model for traffic flow with continuum implications. Transport Planning Tech, 1979, 5: 131\u2013138","journal-title":"Transport Planning Tech"},{"key":"3487_CR112","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.1103\/RevModPhys.73.1067","volume":"73","author":"D Helbing","year":"2001","unstructured":"Helbing D. Traffic and related self-driven many-particle systems. Rev Mod Phys, 2001, 73: 1067\u20131141","journal-title":"Rev Mod Phys"},{"key":"3487_CR113","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/S0191-2615(99)00047-8","volume":"35","author":"D Helbing","year":"2001","unstructured":"Helbing D, Hennecke A, Shvetsov V, et al. MASTER: macroscopic traffic simulation based on a GAS-kinetic, non-local traffic model. Transport Res Part B-Meth, 2001, 35: 183\u2013211","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR114","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/0191-2615(94)90002-7","volume":"28","author":"C F Daganzo","year":"1994","unstructured":"Daganzo C F. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transport Res Part B-Meth, 1994, 28: 269\u2013287","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR115","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/0191-2615(94)00022-R","volume":"29","author":"C F Daganzo","year":"1995","unstructured":"Daganzo C F. The cell transmission model, part II: network traffic. Transport Res Part B-Meth, 1995, 29: 79\u201393","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR116","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3141\/2085-09","volume":"2085","author":"W Y Szeto","year":"2008","unstructured":"Szeto W Y. Enhanced lagged cell-transmission model for dynamic traffic assignment. Transport Res Record, 2008, 2085: 76\u201385","journal-title":"Transport Res Record"},{"key":"3487_CR117","doi-asserted-by":"crossref","unstructured":"Munoz L, Sun X T, Horowitz R, et al. Traffic density estimation with the cell transmission model. In: Proceedings of the 2003 American Control Conference, 2003. 3750\u20133755","DOI":"10.1109\/ACC.2003.1240418"},{"key":"3487_CR118","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.trb.2010.09.006","volume":"45","author":"A Sumalee","year":"2011","unstructured":"Sumalee A, Zhong R X, Pan T L, et al. Stochastic cell transmission model (SCTM): a stochastic dynamic traffic model for traffic state surveillance and assignment. Transport Res Part B-Meth, 2011, 45: 507\u2013533","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR119","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.trc.2006.08.001","volume":"14","author":"G Gomes","year":"2006","unstructured":"Gomes G, Horowitz R. Optimal freeway ramp metering using the asymmetric cell transmission model. Transport Res Part C-Emerging Tech, 2006, 14: 244\u2013262","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR120","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/S0968-090X(02)00009-8","volume":"10","author":"B L Smith","year":"2002","unstructured":"Smith B L, Williams B M, Oswald R K. Comparison of parametric and nonparametric models for traffic flow forecasting. Transport Res Part C-Emerging Tech, 2002, 10: 303\u2013321","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR121","doi-asserted-by":"crossref","unstructured":"Smith B L, Demetsky M J. Short-term traffic flow prediction models\u2014a comparison of neural network and nonparametric regression approaches. In: Proceedings of IEEE International Conference on Systems, Man and Cybernetics, 1994. 1706\u20131709","DOI":"10.1109\/ICSMC.1994.400094"},{"key":"3487_CR122","doi-asserted-by":"publisher","first-page":"132","DOI":"10.3141\/1644-14","volume":"1644","author":"B M Williams","year":"1998","unstructured":"Williams B M, Durvasula P K, Brown D E. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transport Res Record, 1998, 1644: 132\u2013141","journal-title":"Transport Res Record"},{"key":"3487_CR123","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"B M Williams","year":"2003","unstructured":"Williams B M, Hoel L A. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J Transport Eng, 2003, 129: 664\u2013672","journal-title":"J Transport Eng"},{"key":"3487_CR124","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/S0968-090X(03)00004-4","volume":"11","author":"A Stathopoulos","year":"2003","unstructured":"Stathopoulos A, Karlaftis M G. A multivariate state space approach for urban traffic flow modeling and prediction. Transport Res Part C-Emerging Tech, 2003, 11: 121\u2013135","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR125","doi-asserted-by":"publisher","first-page":"6164","DOI":"10.1016\/j.eswa.2008.07.069","volume":"36","author":"M Castro-Neto","year":"2009","unstructured":"Castro-Neto M, Jeong Y S, Jeong M K, et al. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl, 2009, 36: 6164\u20136173","journal-title":"Expert Syst Appl"},{"key":"3487_CR126","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1109\/TITS.2008.2011693","volume":"10","author":"M-C Tan","year":"2009","unstructured":"Tan M-C, Wong S C, Xu J-M, et al. An aggregation approach to short-term traffic flow prediction. IEEE Trans Intell Transport Syst, 2009, 10: 60\u201369","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR127","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1061\/(ASCE)0733-947X(2005)131:10(771)","volume":"131","author":"X Jiang","year":"2005","unstructured":"Jiang X, Adeli H. Dynamic wavelet neural network model for traffic flow forecasting. J Transport Eng, 2005, 131: 771\u2013779","journal-title":"J Transport Eng"},{"key":"3487_CR128","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1061\/(ASCE)0733-947X(2006)132:2(114)","volume":"132","author":"W Z Zheng","year":"2006","unstructured":"Zheng W Z, Lee D H, Shi Q X. Short-term freeway traffic flow prediction: bayesian combined neural network approach. J Transport Eng, 2006, 132: 114\u2013121","journal-title":"J Transport Eng"},{"key":"3487_CR129","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1049\/iet-its.2011.0123","volume":"6","author":"H Chang","year":"2012","unstructured":"Chang H, Lee Y, Yoon B, et al. Dynamic near-term traffic flow prediction: system-oriented approach based on past experiences. IET Intell Transport Syst, 2012, 6: 292\u2013305","journal-title":"IET Intell Transport Syst"},{"key":"3487_CR130","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/TVT.2014.2319107","volume":"64","author":"N Akhtar","year":"2015","unstructured":"Akhtar N, Ergen S C, Ozkasap O. Vehicle mobility and communication channel models for realistic and efficient highway VANET simulation. IEEE Trans Veh Technol, 2015, 64: 248\u2013262","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR131","first-page":"865","volume":"16","author":"Y Lv","year":"2015","unstructured":"Lv Y, Duan Y, Kang W, et al. Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst, 2015, 16: 865\u2013873","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"3487_CR132","doi-asserted-by":"crossref","first-page":"2221","DOI":"10.1051\/jp2:1992262","volume":"2","author":"K Nagel","year":"1992","unstructured":"Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic. J de Physique I, 1992, 2: 2221\u20132229","journal-title":"J de Physique I"},{"key":"3487_CR133","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1143\/JPSJ.65.1868","volume":"65","author":"M Fukui","year":"1996","unstructured":"Fukui M, Ishibashi Y. Traffic flow in 1D cellular automaton model including cars moving with high speed. J Phys Soc Jpn, 1996, 65: 1868\u20131870","journal-title":"J Phys Soc Jpn"},{"key":"3487_CR134","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/S0378-4371(96)00314-7","volume":"235","author":"D Chowdhury","year":"1997","unstructured":"Chowdhury D, Wolf D E, Schreckenberg M. Particle hopping models for two-lane traffic with two kinds of vehicles: effects of lane-changing rules. Phys A-Stat Mech Appl, 1997, 235: 417\u2013439","journal-title":"Phys A-Stat Mech Appl"},{"key":"3487_CR135","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1016\/0378-4371(95)00442-4","volume":"231","author":"M Rickert","year":"1996","unstructured":"Rickert M, Nagel K, Schreckenberg M, et al. Two lane traffic simulations using cellular automata. Phys A-Stat Mech Appl, 1996, 231: 534\u2013550","journal-title":"Phys A-Stat Mech Appl"},{"key":"3487_CR136","doi-asserted-by":"publisher","first-page":"1425","DOI":"10.1103\/PhysRevE.58.1425","volume":"58","author":"K Nagel","year":"1998","unstructured":"Nagel K, Wolf D E, Wagner P, et al. Two-lane traffic rules for cellular automata: a systematic approach. Phys Rev E, 1998, 58: 1425\u20131437","journal-title":"Phys Rev E"},{"key":"3487_CR137","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.physa.2005.11.016","volume":"367","author":"X G Li","year":"2006","unstructured":"Li X G, Jia B, Gao Z Y, et al. A realistic two-lane cellular automata traffic model considering aggressive lane-changing behavior of fast vehicle. Phys A-Stat Mech Appl, 2006, 367: 479\u2013486","journal-title":"Phys A-Stat Mech Appl"},{"key":"3487_CR138","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/MITS.2016.2549979","volume":"8","author":"Q Chen","year":"2016","unstructured":"Chen Q, Wang Y. A cellular automata (CA) model for two-way vehicle flows on low-grade roads without hard separation. IEEE Intell Transport Syst Mag, 2016, 8: 43\u201353","journal-title":"IEEE Intell Transport Syst Mag"},{"key":"3487_CR139","doi-asserted-by":"publisher","first-page":"R6124","DOI":"10.1103\/PhysRevA.46.R6124","volume":"46","author":"O Biham","year":"1992","unstructured":"Biham O, Middleton A A, Levine D. Self-organization and a dynamical transition in traffic-flow models. Phys Rev A, 1992, 46: R6124\u2013R6127","journal-title":"Phys Rev A"},{"key":"3487_CR140","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/S0191-2615(98)00014-9","volume":"32","author":"H M Zhang","year":"1998","unstructured":"Zhang H M. A theory of nonequilibrium traffic flow. Transport Res Part B-Meth, 1998, 32: 485\u2013498","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR141","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1287\/opre.9.2.209","volume":"9","author":"G F Newell","year":"1961","unstructured":"Newell G F. Nonlinear effects in the dynamics of car following. Oper Res, 1961, 9: 209\u2013229","journal-title":"Oper Res"},{"key":"3487_CR142","volume-title":"Methods of Making Traffic Surveys Especially \u201cBefore and After\u201d Studies","author":"G Charlesworth","year":"1950","unstructured":"Charlesworth G. Methods of Making Traffic Surveys Especially \u201cBefore and After\u201d Studies. London: Institution of Highway Engineers, 1950"},{"key":"3487_CR143","volume-title":"Road Research and Its Bearing on Road Transport","author":"W H Glanville","year":"1953","unstructured":"Glanville W H. Road Research and Its Bearing on Road Transport. Houston: C. Baldwin Ltd., 1953"},{"key":"3487_CR144","first-page":"144","volume":"99","author":"W H Glanville","year":"1951","unstructured":"Glanville W H. Road safety and road research. J Royal Soc Arts, 1951, 99: 144\u2013192","journal-title":"J Royal Soc Arts"},{"key":"3487_CR145","first-page":"3996","volume":"173","author":"I Prigogine","year":"1971","unstructured":"Prigogine I, Herman R. Vehicles as particles: kinetic theory of vehicular traffic. Science, 1971, 173: 3996","journal-title":"Science"},{"key":"3487_CR146","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1016\/S0191-2615(98)00020-4","volume":"32","author":"P Nelson","year":"1998","unstructured":"Nelson P, Sopasakis A. The Prigogine-Herman kinetic model predicts widely scattered traffic flow data at high concentrations. Transpation Res Part B-Meth, 1998, 32: 589\u2013604","journal-title":"Transpation Res Part B-Meth"},{"key":"3487_CR147","doi-asserted-by":"publisher","first-page":"13887","DOI":"10.1109\/ACCESS.2020.2966531","volume":"8","author":"Z Pu","year":"2020","unstructured":"Pu Z, Jiao X, Yang C, et al. An adaptive stochastic model predictive control strategy for plug-in hybrid electric bus during vehicle-following scenario. IEEE Access, 2020, 8: 13887\u201313897","journal-title":"IEEE Access"},{"key":"3487_CR148","doi-asserted-by":"publisher","first-page":"102671","DOI":"10.1016\/j.trc.2020.102671","volume":"117","author":"Z Cui","year":"2020","unstructured":"Cui Z, Lin L, Pu Z, et al. Graph Markov network for traffic forecasting with missing data. Transport Res Part C-Emerging Tech, 2020, 117: 102671","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR149","unstructured":"Daganzo C F. The lagged cell-transmission model. In: Proceedings of the 14th International Symposium on Transportation and Traffic Theory, Jerusalem, 1999"},{"key":"3487_CR150","doi-asserted-by":"crossref","unstructured":"Xie B, Xu M, H\u00e4rri J, et al. A traffic light extension to cell transmission model for estimating urban traffic JAM. In: Proceedings of IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications, 2013. 2566\u20132570","DOI":"10.1109\/PIMRC.2013.6666579"},{"key":"3487_CR151","doi-asserted-by":"publisher","first-page":"10771","DOI":"10.1109\/ACCESS.2018.2794555","volume":"6","author":"P Shao","year":"2018","unstructured":"Shao P, Wang L, Qian W, et al. A distributed traffic control strategy based on cell-transmission model. IEEE Access, 2018, 6: 10771\u201310778","journal-title":"IEEE Access"},{"key":"3487_CR152","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1142\/S0218348X93000885","volume":"01","author":"M Takayasu","year":"1993","unstructured":"Takayasu M, Takayasu H. 1\/f noise in a traffic model. Fractals, 1993, 01: 860\u2013866","journal-title":"Fractals"},{"key":"3487_CR153","first-page":"2909","volume":"51","author":"K Nagel","year":"1995","unstructured":"Nagel K, Paczuski M. Emergent traffic jams. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics, 1995, 51: 2909\u20132918","journal-title":"Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics"},{"key":"3487_CR154","doi-asserted-by":"publisher","first-page":"066128","DOI":"10.1103\/PhysRevE.64.066128","volume":"64","author":"X Li","year":"2001","unstructured":"Li X, Wu Q, Jiang R. Cellular automaton model considering the velocity effect of a car on the successive car. Phys Rev E, 2001, 64: 066128","journal-title":"Phys Rev E"},{"key":"3487_CR155","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1049\/iet-its.2016.0275","volume":"12","author":"C Jin","year":"2018","unstructured":"Jin C, Knoop V L, Jiang R, et al. Calibration and validation of cellular automaton traffic flow model with empirical and experimental data. IET Intell Transport Syst, 2018, 12: 359\u2013365","journal-title":"IET Intell Transport Syst"},{"key":"3487_CR156","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s12544-015-0170-8","volume":"7","author":"S V Kumar","year":"2015","unstructured":"Kumar S V, Vanajakshi L. Short-term traffic flow prediction using seasonal ARIMA model with limited input data. Eur Transport Res Rev, 2015, 7: 21","journal-title":"Eur Transport Res Rev"},{"key":"3487_CR157","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.sbspro.2013.08.076","volume":"96","author":"L Zhang","year":"2013","unstructured":"Zhang L, Liu Q, Yang W, et al. An improved K-nearest neighbor model for short-term traffic flow prediction. Procedia-Soc Behavioral Sci, 2013, 96: 653\u2013662","journal-title":"Procedia-Soc Behavioral Sci"},{"key":"3487_CR158","doi-asserted-by":"crossref","unstructured":"Hong H, Huang W, Xing X, et al. Hybrid multi-metric K-nearest neighbor regression for traffic flow prediction. In: Proceedings of International Conference on Intelligent Transportation Systems, 2015. 2262\u20132267","DOI":"10.1109\/ITSC.2015.365"},{"key":"3487_CR159","unstructured":"Lin W-H. A Gaussian maximum likelihood formulation for short-term forecasting of traffic flow. In: Proceedings of IEEE Intelligent Transportation Systems, 2001. 150\u2013155"},{"key":"3487_CR160","unstructured":"Li Y, Yu R, Shahabi C, et al. Graph convolutional recurrent neural network: Data-driven traffic forecasting. 2017. ArXiv:1707.01926"},{"key":"3487_CR161","doi-asserted-by":"crossref","unstructured":"Yao H, Wu F, Ke J, et al. Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, 2018","DOI":"10.1609\/aaai.v32i1.11836"},{"key":"3487_CR162","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1049\/iet-its.2017.0006","volume":"12","author":"S Hao","year":"2018","unstructured":"Hao S, Yang L, Shi Y. Data-driven car-following model based on rough set theory. IET Intell Transport Syst, 2018, 12: 49\u201357","journal-title":"IET Intell Transport Syst"},{"key":"3487_CR163","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/TITS.2017.2706963","volume":"19","author":"X Wang","year":"2018","unstructured":"Wang X, Jiang R, Li L, et al. Capturing car-following behaviors by deep learning. IEEE Trans Intell Transport Syst, 2018, 19: 910\u2013920","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR164","doi-asserted-by":"publisher","first-page":"1289","DOI":"10.1109\/TITS.2016.2603007","volume":"18","author":"J Morton","year":"2017","unstructured":"Morton J, Wheeler T A, Kochenderfer M J. Analysis of recurrent neural networks for probabilistic modeling of driver behavior. IEEE Trans Intell Transport Syst, 2017, 18: 1289\u20131298","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR165","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1109\/TITS.2013.2285337","volume":"15","author":"Y Hou","year":"2014","unstructured":"Hou Y, Edara P, Sun C. Modeling mandatory lane changing using Bayes classifier and decision trees. IEEE Trans Intell Transport Syst, 2014, 15: 647\u2013655","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR166","doi-asserted-by":"crossref","unstructured":"Zheng G, Gu H, Chen Z. A short-term traffic flow prediction method based on asynchronous temporal and spatial correlation. In: Proceedings of 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), 2021. 4015\u20134021","DOI":"10.1109\/ITSC48978.2021.9564803"},{"key":"3487_CR167","doi-asserted-by":"publisher","first-page":"172203","DOI":"10.1007\/s11432-020-2961-8","volume":"64","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Zhang X L, Xu X, et al. Event-triggered shared lateral control for safe-maneuver of intelligent vehicles. Sci China Inf Sci, 2021, 64: 172203","journal-title":"Sci China Inf Sci"},{"key":"3487_CR168","doi-asserted-by":"crossref","unstructured":"Ye J, Zhao J, Ye K, et al. Multi-STGCnet: a graph convolution based spatial-temporal framework for subway passenger flow forecasting. In: Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN), 2020. 1\u20138","DOI":"10.1109\/IJCNN48605.2020.9207049"},{"key":"3487_CR169","unstructured":"Zhou X, Shen Y, Huang L. Revisiting flow information for traffic prediction. 2019. ArXiv:1906.00560"},{"key":"3487_CR170","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1109\/TITS.2019.2963722","volume":"22","author":"K Guo","year":"2020","unstructured":"Guo K, Hu Y, Qian Z, et al. Optimized graph convolution recurrent neural network for traffic prediction. IEEE Trans Intell Transport Syst, 2020, 22: 1138\u20131149","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR171","doi-asserted-by":"crossref","unstructured":"Chen W, Chen L, Xie Y, et al. Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 3529\u20133536","DOI":"10.1609\/aaai.v34i04.5758"},{"key":"3487_CR172","doi-asserted-by":"publisher","first-page":"172207","DOI":"10.1007\/s11432-020-3071-8","volume":"64","author":"H B Gao","year":"2021","unstructured":"Gao H B, Su H, Cai Y F, et al. Trajectory prediction of cyclist based on dynamic Bayesian network and long short-term memory model at unsignalized intersections. Sci China Inf Sci, 2021, 64: 172207","journal-title":"Sci China Inf Sci"},{"key":"3487_CR173","doi-asserted-by":"crossref","unstructured":"Pan Z, Wang Z, Wang W, et al. Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019. 2683\u20132691","DOI":"10.1145\/3357384.3357832"},{"key":"3487_CR174","doi-asserted-by":"publisher","first-page":"190205","DOI":"10.1007\/s11432-019-2792-9","volume":"63","author":"Y F Li","year":"2020","unstructured":"Li Y F, Ren C, Zhao H W, et al. Investigating long-term vehicle speed prediction based on GA-BP algorithms and the road-traffic environment. Sci China Inf Sci, 2020, 63: 190205","journal-title":"Sci China Inf Sci"},{"key":"3487_CR175","first-page":"653","volume":"16","author":"A Abadi","year":"2015","unstructured":"Abadi A, Rajabioun T, Ioannou P A. Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans Intell Transp Syst, 2015, 16: 653\u2013662","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"3487_CR176","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1109\/MCOM.2019.1800644","volume":"57","author":"Q Cui","year":"2019","unstructured":"Cui Q, Gong Z, Ni W, et al. Stochastic online learning for mobile edge computing: learning from changes. IEEE Commun Mag, 2019, 57: 63\u201369","journal-title":"IEEE Commun Mag"},{"key":"3487_CR177","doi-asserted-by":"publisher","first-page":"4648","DOI":"10.1109\/TITS.2020.3023446","volume":"22","author":"X Zhu","year":"2021","unstructured":"Zhu X, Luo Y, Liu A, et al. A deep learning-based mobile crowdsensing scheme by predicting vehicle mobility. IEEE Trans Intell Transp Syst, 2021, 22: 4648\u20134659","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"3487_CR178","unstructured":"Shi X, Chen Z, Hao W, et al. Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Proceedings of International Conference on Neural Information Processing Systems, 2015"},{"key":"3487_CR179","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, et al. Graph attention networks. 2017. ArXiv:1710.10903"},{"key":"3487_CR180","first-page":"49","volume":"20","author":"X Q Chen","year":"2020","unstructured":"Chen X Q, Zhou L X, Cao Z. Short-term network-wide traffic prediction based on graph convolutional network (in Chinese). J Transport Syst Eng Inf Tech, 2020, 20: 49\u201355","journal-title":"J Transport Syst Eng Inf Tech"},{"key":"3487_CR181","doi-asserted-by":"publisher","first-page":"4909","DOI":"10.1109\/TITS.2020.2983651","volume":"22","author":"X Shi","year":"2021","unstructured":"Shi X, Qi H, Shen Y, et al. A spatial-temporal attention approach for traffic prediction. IEEE Trans Intell Transport Syst, 2021, 22: 4909\u20134918","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR182","doi-asserted-by":"crossref","unstructured":"Zheng C, Fan X, Wang C, et al. GMAN: a graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2020. 1234\u20131241","DOI":"10.1609\/aaai.v34i01.5477"},{"key":"3487_CR183","doi-asserted-by":"publisher","first-page":"8746","DOI":"10.1109\/TVT.2017.2707076","volume":"66","author":"J Liu","year":"2017","unstructured":"Liu J, Jayakumar P, Stein J L, et al. Combined speed and steering control in high-speed autonomous ground vehicles for obstacle avoidance using model predictive control. IEEE Trans Veh Technol, 2017, 66: 8746\u20138763","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR184","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.1049\/iet-cta.2015.0437","volume":"9","author":"H Zhao","year":"2015","unstructured":"Zhao H, Ren B, Chen H, et al. Model predictive control allocation for stability improvement of four-wheel drive electric vehicles in critical driving condition. IET Control Theor Appl, 2015, 9: 2688\u20132696","journal-title":"IET Control Theor Appl"},{"key":"3487_CR185","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.cnsns.2016.05.016","volume":"42","author":"Z Li","year":"2017","unstructured":"Li Z, Xu X, Xu S, et al. A heterogeneous traffic flow model consisting of two types of vehicles with different sensitivities. Commun Nonlin Sci Numer Simul, 2017, 42: 132\u2013145","journal-title":"Commun Nonlin Sci Numer Simul"},{"key":"3487_CR186","doi-asserted-by":"publisher","first-page":"2856","DOI":"10.1109\/TITS.2017.2765694","volume":"19","author":"M Lindorfer","year":"2018","unstructured":"Lindorfer M, Mecklenbr\u00e4uker C F, Ostermayer G. Modeling the imperfect driver: incorporating human factors in a microscopic traffic model. IEEE Trans Intell Transport Syst, 2018, 19: 2856\u20132870","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR187","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1109\/TITS.2017.2759273","volume":"19","author":"J W Ro","year":"2018","unstructured":"Ro J W, Roop P S, Malik A, et al. A formal approach for modeling and simulation of human car-following behavior. IEEE Trans Intell Transport Syst, 2018, 19: 639\u2013648","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR188","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1049\/iet-its.2012.0111","volume":"8","author":"A Khodayari","year":"2014","unstructured":"Khodayari A, Ghaffari A, Kazemi R, et al. Improved adaptive neuro fuzzy inference system car-following behaviour model based on the driver-vehicle delay. IET Intell Transport Syst, 2014, 8: 323\u2013332","journal-title":"IET Intell Transport Syst"},{"key":"3487_CR189","doi-asserted-by":"crossref","unstructured":"Tejada F, Estevez C, Zacepins A, et al. Autoregressive dynamic mechanism for urban area microscopic traffic flow models. In: Proceedings of 2016 IEEE International Smart Cities Conference (ISC2), 2016. 1\u20135","DOI":"10.1109\/ISC2.2016.7580858"},{"key":"3487_CR190","doi-asserted-by":"publisher","first-page":"57497","DOI":"10.1109\/ACCESS.2018.2873942","volume":"6","author":"L Huang","year":"2018","unstructured":"Huang L, Guo H, Zhang R, et al. Capturing drivers\u2019 lane changing behaviors on operational level by data driven methods. IEEE Access, 2018, 6: 57497\u201357506","journal-title":"IEEE Access"},{"key":"3487_CR191","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TIV.2018.2843177","volume":"3","author":"K Liu","year":"2018","unstructured":"Liu K, Gong J, Kurt A, et al. Dynamic modeling and control of high-speed automated vehicles for lane change maneuver. IEEE Trans Intell Veh, 2018, 3: 329\u2013339","journal-title":"IEEE Trans Intell Veh"},{"key":"3487_CR192","doi-asserted-by":"publisher","first-page":"1903","DOI":"10.1109\/TVT.2004.836967","volume":"53","author":"P N Pathirana","year":"2004","unstructured":"Pathirana P N, Savkin A V, Jha S. Location estimation and trajectory prediction for cellular networks with mobile base stations. IEEE Trans Veh Technol, 2004, 53: 1903\u20131913","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR193","doi-asserted-by":"crossref","unstructured":"Houenou A, Bonnifait P, Cherfaoui V, et al. Vehicle trajectory prediction based on motion model and maneuver recognition. In: Proceedings of 2013 IEEE\/RSJ International Conference on Intelligent Robots and Systems, 2013. 4363\u20134369","DOI":"10.1109\/IROS.2013.6696982"},{"key":"3487_CR194","doi-asserted-by":"crossref","unstructured":"Oliva J A, Weihrauch C, Bertram T. Model-based remaining driving range prediction in electric vehicles by using particle filtering and Markov chains. In: Proceedings of World Electric Vehicle Symposium and Exhibition (EVS27), 2013. 1\u201310","DOI":"10.1109\/EVS.2013.6914989"},{"key":"3487_CR195","doi-asserted-by":"publisher","first-page":"25200","DOI":"10.1109\/ACCESS.2018.2829545","volume":"6","author":"X Wang","year":"2018","unstructured":"Wang X, Jiang X, Chen L, et al. KVLMM: a trajectory prediction method based on a variable-order Markov model with kernel smoothing. IEEE Access, 2018, 6: 25200\u201325208","journal-title":"IEEE Access"},{"key":"3487_CR196","first-page":"393","volume-title":"Learning social network embeddings for predicting information diffusion","author":"S Bourigault","year":"2014","unstructured":"Bourigault S, Lagnier C, Lamprier S, et al. Learning social network embeddings for predicting information diffusion. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining. New York: ACM, 2014. 393\u2013402"},{"key":"3487_CR197","unstructured":"de Br\u00e9bisson A, Simon E, Auvolat A, et al. Artificial neural networks applied to taxi destination prediction. 2015. ArXiv:1508.00021"},{"key":"3487_CR198","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1109\/COMST.2015.2410831","volume":"18","author":"D Jia","year":"2016","unstructured":"Jia D, Lu K, Wang J, et al. A survey on platoon-based vehicular cyber-physical systems. IEEE Commun Surv Tut, 2016, 18: 263\u2013284","journal-title":"IEEE Commun Surv Tut"},{"key":"3487_CR199","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1287\/opre.7.4.499","volume":"7","author":"D C Gazis","year":"1959","unstructured":"Gazis D C, Herman R, Potts R B. Car-following theory of steady-state traffic flow. Oper Res, 1959, 7: 499\u2013505","journal-title":"Oper Res"},{"key":"3487_CR200","doi-asserted-by":"publisher","first-page":"5981","DOI":"10.1109\/TVT.2019.2910324","volume":"68","author":"S C Hung","year":"2019","unstructured":"Hung S C, Zhang X, Festag A, et al. Vehicle-centric network association in heterogeneous vehicle-to-vehicle networks. IEEE Trans Veh Technol, 2019, 68: 5981\u20135996","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR201","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1109\/TITS.2008.928265","volume":"9","author":"C Wang","year":"2008","unstructured":"Wang C, Coifman B. The effect of lane-change maneuvers on a simplified car-following theory. IEEE Trans Intell Transport Syst, 2008, 9: 523\u2013535","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR202","doi-asserted-by":"crossref","unstructured":"Liang Z, Zheng G, Li J. Automatic parking path optimization based on Bezier curve fitting. In: Proceedings of 2012 IEEE International Conference on Automation and Logistics, 2012. 583\u2013587","DOI":"10.1109\/ICAL.2012.6308145"},{"key":"3487_CR203","doi-asserted-by":"crossref","unstructured":"Ammoun S, Nashashibi F. Real time trajectory prediction for collision risk estimation between vehicles. In: Proceedings of 2009 IEEE 5th International Conference on Intelligent Computer Communication and Processing, 2009. 417\u2013422","DOI":"10.1109\/ICCP.2009.5284727"},{"key":"3487_CR204","doi-asserted-by":"crossref","unstructured":"Berthelot A, Tamke A, Dang T, et al. Handling uncertainties in criticality assessment. In: Proceedings of 2011 IEEE Intelligent Vehicles Symposium (IV), 2011. 571\u2013576","DOI":"10.1109\/IVS.2011.5940483"},{"key":"3487_CR205","doi-asserted-by":"publisher","first-page":"1911","DOI":"10.1109\/TMC.2013.159","volume":"13","author":"Y Li","year":"2014","unstructured":"Li Y, Jin D, Wang Z, et al. A Markov jump process model for urban vehicular mobility: modeling and applications. IEEE Trans Mobile Comput, 2014, 13: 1911\u20131926","journal-title":"IEEE Trans Mobile Comput"},{"key":"3487_CR206","first-page":"91","volume-title":"Modeling mobility for vehicular ad-hoc networks","author":"A K Saha","year":"2004","unstructured":"Saha A K, Johnson D B. Modeling mobility for vehicular ad-hoc networks. In: Proceedings of the 1st ACM International Workshop on Vehicular Ad Hoc Networks. New York: ACM, 2004. 91\u201392"},{"key":"3487_CR207","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TVT.2003.808795","volume":"52","author":"P I Bratanov","year":"2003","unstructured":"Bratanov P I, Bonek E. Mobility model of vehicle-borne terminals in urban cellular systems. IEEE Trans Veh Technol, 2003, 52: 947\u2013952","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR208","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-32460-4","volume-title":"Traffic Flow Dynamics","author":"M Treiber","year":"2013","unstructured":"Treiber M, Kesting A. Traffic Flow Dynamics. Berlin: Springer, 2013"},{"key":"3487_CR209","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1109\/TITS.2014.2331453","volume":"16","author":"V Punzo","year":"2015","unstructured":"Punzo V, Montanino M, Ciuffo B. Do we really need to calibrate all the parameters? Variance-based sensitivity analysis to simplify microscopic traffic flow models. IEEE Trans Intell Transport Syst, 2015, 16: 184\u2013193","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR210","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1016\/S0968-090X(99)00020-0","volume":"7","author":"P Chakroborty","year":"1999","unstructured":"Chakroborty P, Kikuchi S. Evaluation of the general motors based car-following models and a proposed fuzzy inference model. Transport Res Part C-Emerging Tech, 1999, 7: 209\u2013235","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR211","volume-title":"Rough Sets: Theoretical Aspects of Reasoning About Data","author":"Z Pawlak","year":"1992","unstructured":"Pawlak Z. Rough Sets: Theoretical Aspects of Reasoning About Data. Norwell: Kluwer Academic Publishers, 1992"},{"key":"3487_CR212","doi-asserted-by":"publisher","first-page":"4369","DOI":"10.1109\/TIE.2011.2180271","volume":"59","author":"K Singh","year":"2012","unstructured":"Singh K, Li B. Estimation of traffic densities for multilane roadways using a Markov model approach. IEEE Trans Ind Electron, 2012, 59: 4369\u20134376","journal-title":"IEEE Trans Ind Electron"},{"key":"3487_CR213","doi-asserted-by":"publisher","first-page":"2706","DOI":"10.1109\/TMC.2013.66","volume":"13","author":"W Peng","year":"2014","unstructured":"Peng W, Dong G, Yang K, et al. A random road network model and its effects on topological characteristics of mobile delay-tolerant networks. IEEE Trans Mobile Comput, 2014, 13: 2706\u20132718","journal-title":"IEEE Trans Mobile Comput"},{"key":"3487_CR214","doi-asserted-by":"publisher","first-page":"534","DOI":"10.1038\/s41586-018-0095-1","volume":"557","author":"M M Vazifeh","year":"2018","unstructured":"Vazifeh M M, Santi P, Resta G, et al. Addressing the minimum fleet problem in on-demand urban mobility. Nature, 2018, 557: 534\u2013538","journal-title":"Nature"},{"key":"3487_CR215","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/s11067-018-9427-9","volume":"18","author":"S Marshall","year":"2018","unstructured":"Marshall S, Gil J, Kropf K, et al. Street network studies: from networks to models and their representations. Netw Spat Econ, 2018, 18: 735\u2013749","journal-title":"Netw Spat Econ"},{"key":"3487_CR216","first-page":"29","volume":"9","author":"S Marshall","year":"2015","unstructured":"Marshall S. Line structure representation for road network analysis. J Transport Land Use, 2015, 9: 29\u201364","journal-title":"J Transport Land Use"},{"key":"3487_CR217","doi-asserted-by":"publisher","first-page":"13290","DOI":"10.1073\/pnas.1403657111","volume":"111","author":"P Santi","year":"2014","unstructured":"Santi P, Resta G, Szell M, et al. Quantifying the benefits of vehicle pooling with shareability networks. Proc Natl Acad Sci USA, 2014, 111: 13290\u201313294","journal-title":"Proc Natl Acad Sci USA"},{"key":"3487_CR218","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1073\/pnas.1611675114","volume":"114","author":"J Alonso-Mora","year":"2017","unstructured":"Alonso-Mora J, Samaranayake S, Wallar A, et al. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc Natl Acad Sci USA, 2017, 114: 462\u2013467","journal-title":"Proc Natl Acad Sci USA"},{"key":"3487_CR219","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s11067-007-9021-z","volume":"10","author":"C Gloaguen","year":"2010","unstructured":"Gloaguen C, Fleischer F, Schmidt H, et al. Analysis of shortest paths and subscriber line lengths in telecommunication access networks. Netw Spat Econ, 2010, 10: 15\u201347","journal-title":"Netw Spat Econ"},{"key":"3487_CR220","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.1109\/JSAC.2009.090903","volume":"27","author":"F Voss","year":"2009","unstructured":"Voss F, Gloaguen C, Fleischer F, et al. Distributional properties of euclidean distances in wireless networks involving road systems. IEEE J Sel Areas Commun, 2009, 27: 1047\u20131055","journal-title":"IEEE J Sel Areas Commun"},{"key":"3487_CR221","doi-asserted-by":"publisher","first-page":"4517","DOI":"10.1109\/TVT.2016.2535210","volume":"66","author":"G P Gwon","year":"2017","unstructured":"Gwon G P, Hur W S, Kim S W, et al. Generation of a precise and efficient lane-level road map for intelligent vehicle systems. IEEE Trans Veh Technol, 2017, 66: 4517\u20134533","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR222","doi-asserted-by":"publisher","first-page":"2355","DOI":"10.1109\/TITS.2016.2521819","volume":"17","author":"C Guo","year":"2016","unstructured":"Guo C, Kidono K, Meguro J, et al. A low-cost solution for automatic lane-level map generation using conventional in-car sensors. IEEE Trans Intell Transport Syst, 2016, 17: 2355\u20132366","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR223","first-page":"48","volume":"34","author":"L Xia","year":"2018","unstructured":"Xia L, Li X, Li H. Efficient and reliable road modeling for digital maps based on cardinal spline. J Southeast Univ, 2018, 34: 48\u201353","journal-title":"J Southeast Univ"},{"key":"3487_CR224","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1109\/TSMC.2016.2586500","volume":"47","author":"L W Chen","year":"2017","unstructured":"Chen L W, Chang C C. Cooperative traffic control with green wave coordination for multiple intersections based on the internet of vehicles. IEEE Trans Syst Man Cybern Syst, 2017, 47: 1321\u20131335","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"3487_CR225","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/TITS.2008.928266","volume":"9","author":"R Wunderlich","year":"2008","unstructured":"Wunderlich R, Liu C, Elhanany I, et al. A novel signal-scheduling algorithm with quality-of-service provisioning for an isolated intersection. IEEE Trans Intell Transport Syst, 2008, 9: 536\u2013547","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR226","doi-asserted-by":"publisher","first-page":"1867","DOI":"10.1109\/TITS.2016.2616492","volume":"18","author":"K Zhang","year":"2017","unstructured":"Zhang K, Yang A, Su H, et al. Service-oriented cooperation models and mechanisms for heterogeneous driverless vehicles at continuous static critical sections. IEEE Trans Intell Transport Syst, 2017, 18: 1867\u20131881","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR227","doi-asserted-by":"crossref","unstructured":"Sha Z R, Huang M, Wu H B. A conceptual multi-level data model for road networks. In: Proceedings of the 5th International Conference on Intelligent Computation Technology and Automation, 2012. 712\u2013715","DOI":"10.1109\/ICICTA.2012.182"},{"key":"3487_CR228","doi-asserted-by":"crossref","unstructured":"Geng X, Li Y, Wang L, et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 3656\u20133663","DOI":"10.1609\/aaai.v33i01.33013656"},{"key":"3487_CR229","doi-asserted-by":"publisher","first-page":"6526","DOI":"10.1109\/TITS.2020.2993798","volume":"22","author":"N Davis","year":"2021","unstructured":"Davis N, Raina G, Jagannathan K. Grids versus graphs: partitioning space for improved taxi demand-supply forecasts. IEEE Trans Intell Transport Syst, 2021, 22: 6526\u20136535","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR230","doi-asserted-by":"crossref","unstructured":"Loose H, Franke U. B-spline-based road model for 3D lane recognition. In: Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, 2010. 91\u201398","DOI":"10.1109\/ITSC.2010.5624968"},{"key":"3487_CR231","doi-asserted-by":"crossref","unstructured":"Li X, Xia L, Song X, et al. Modeling the special intersection for enhanced digital map. In: Proceedings of IEEE Intelligent Vehicles Symposium (IV), 2018. 1490\u20131495","DOI":"10.1109\/IVS.2018.8500366"},{"key":"3487_CR232","doi-asserted-by":"publisher","first-page":"394","DOI":"10.1109\/TSG.2011.2159278","volume":"3","author":"S Bae","year":"2012","unstructured":"Bae S, Kwasinski A. Spatial and temporal model of electric vehicle charging demand. IEEE Trans Smart Grid, 2012, 3: 394\u2013403","journal-title":"IEEE Trans Smart Grid"},{"key":"3487_CR233","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1111\/j.1467-8667.2009.00606.x","volume":"24","author":"M W Ng","year":"2009","unstructured":"Ng M W, Lin D Y, Waller S T. Optimal long-term infrastructure maintenance planning accounting for traffic dynamics. Comput-Aided Civil Infrastruct Eng, 2009, 24: 459\u2013469","journal-title":"Comput-Aided Civil Infrastruct Eng"},{"key":"3487_CR234","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/S0965-8564(98)00049-4","volume":"33","author":"H K Lo","year":"1999","unstructured":"Lo H K. A novel traffic signal control formulation. Transport Res Part A-Policy Pract, 1999, 33: 433\u2013448","journal-title":"Transport Res Part A-Policy Pract"},{"key":"3487_CR235","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1142\/S0129183197000904","volume":"08","author":"J Esser","year":"1997","unstructured":"Esser J, Schreckenberg M. Microscopic simulation of urban traffic based on cellular automata. Int J Mod Phys C, 1997, 08: 1025\u20131036","journal-title":"Int J Mod Phys C"},{"key":"3487_CR236","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1109\/JIOT.2018.2872442","volume":"6","author":"Q Cui","year":"2019","unstructured":"Cui Q, Wang Y, Chen K C, et al. Big data analytics and network calculus enabling intelligent management of autonomous vehicles in a smart city. IEEE Internet Things J, 2019, 6: 2021\u20132034","journal-title":"IEEE Internet Things J"},{"key":"3487_CR237","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1109\/TSMC.2016.2582745","volume":"47","author":"K Dorling","year":"2017","unstructured":"Dorling K, Heinrichs J, Messier G G, et al. Vehicle routing problems for drone delivery. IEEE Trans Syst Man Cybern Syst, 2017, 47: 70\u201385","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"3487_CR238","doi-asserted-by":"publisher","first-page":"42868","DOI":"10.1038\/srep42868","volume":"7","author":"R Tachet","year":"2017","unstructured":"Tachet R, Sagarra O, Santi P, et al. Scaling law of urban ride sharing. Sci Rep, 2017, 7: 42868","journal-title":"Sci Rep"},{"key":"3487_CR239","doi-asserted-by":"publisher","first-page":"2473","DOI":"10.1109\/TCYB.2015.2478698","volume":"46","author":"Z Li","year":"2016","unstructured":"Li Z, Kolmanovsky I, Atkins E, et al. Road risk modeling and cloud-aided safety-based route planning. IEEE Trans Cybern, 2016, 46: 2473\u20132483","journal-title":"IEEE Trans Cybern"},{"key":"3487_CR240","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1049\/iet-its.2015.0168","volume":"10","author":"J Zhang","year":"2016","unstructured":"Zhang J, Feng Y, Shi F, et al. Vehicle routing in urban areas based on the Oil Consumption Weight-Dijkstra algorithm. IET Intell Transport Syst, 2016, 10: 495\u2013502","journal-title":"IET Intell Transport Syst"},{"key":"3487_CR241","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1049\/iet-its.2015.0027","volume":"9","author":"E Yao","year":"2015","unstructured":"Yao E, Lang Z, Yang Y, et al. Vehicle routing problem solution considering minimising fuel consumption. IET Intell Transport Syst, 2015, 9: 523\u2013529","journal-title":"IET Intell Transport Syst"},{"key":"3487_CR242","doi-asserted-by":"publisher","first-page":"1459","DOI":"10.1109\/TVT.2013.2241460","volume":"62","author":"K Pandit","year":"2013","unstructured":"Pandit K, Ghosal D, Zhang H M, et al. Adaptive traffic signal control with vehicular ad hoc networks. IEEE Trans Veh Technol, 2013, 62: 1459\u20131471","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR243","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/TITS.2015.2462843","volume":"17","author":"M Vajedi","year":"2016","unstructured":"Vajedi M, Azad N L. Ecological adaptive cruise controller for plug-in hybrid electric vehicles using nonlinear model predictive control. IEEE Trans Intell Transport Syst, 2016, 17: 113\u2013122","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR244","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/TIV.2015.2503342","volume":"1","author":"D Bevly","year":"2016","unstructured":"Bevly D, Cao X, Gordon M, et al. Lane change and merge maneuvers for connected and automated vehicles: a survey. IEEE Trans Intell Veh, 2016, 1: 105\u2013120","journal-title":"IEEE Trans Intell Veh"},{"key":"3487_CR245","doi-asserted-by":"publisher","first-page":"2373","DOI":"10.1109\/TITS.2015.2389527","volume":"16","author":"R Dang","year":"2015","unstructured":"Dang R, Wang J, Li S E, et al. Coordinated adaptive cruise control system with lane-change assistance. IEEE Trans Intell Transp Syst, 2015, 16: 2373\u20132383","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"3487_CR246","doi-asserted-by":"publisher","first-page":"910","DOI":"10.1109\/TVT.2005.844655","volume":"54","author":"J Mar","year":"2005","unstructured":"Mar J, Lin H T. The car-following and lane-changing collision prevention system based on the cascaded fuzzy inference system. IEEE Trans Veh Technol, 2005, 54: 910\u2013924","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR247","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1109\/TITS.2012.2187447","volume":"13","author":"G Xu","year":"2012","unstructured":"Xu G, Liu L, Ou Y, et al. Dynamic modeling of driver control strategy of lane-change behavior and trajectory planning for collision prediction. IEEE Trans Intell Transport Syst, 2012, 13: 1138\u20131155","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR248","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1109\/MITS.2017.2709782","volume":"9","author":"G Cesari","year":"2017","unstructured":"Cesari G, Schildbach G, Carvalho A, et al. Scenario model predictive control for lane change assistance and autonomous driving on highways. IEEE Intell Transport Syst Mag, 2017, 9: 23\u201335","journal-title":"IEEE Intell Transport Syst Mag"},{"key":"3487_CR249","doi-asserted-by":"publisher","first-page":"4422","DOI":"10.1109\/TVT.2014.2369522","volume":"64","author":"V A Butakov","year":"2015","unstructured":"Butakov V A, Ioannou P. Personalized driver\/vehicle lane change models for ADAS. IEEE Trans Veh Technol, 2015, 64: 4422\u20134431","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR250","doi-asserted-by":"publisher","first-page":"8182","DOI":"10.1109\/TWC.2016.2613078","volume":"15","author":"S Kwon","year":"2016","unstructured":"Kwon S, Kim Y, Shroff N B. Analysis of connectivity and capacity in 1-D vehicle-to-vehicle networks. IEEE Trans Wireless Commun, 2016, 15: 8182\u20138194","journal-title":"IEEE Trans Wireless Commun"},{"key":"3487_CR251","unstructured":"Pritesh P, Rudra D. Joint modeling of mobility and communication in a V2V network for congestion amelioration. In: Proceedings of the 16th International Conference on Computer Communications and Networks, 2017. 575\u2013582"},{"key":"3487_CR252","doi-asserted-by":"crossref","unstructured":"Li Y, Zhu Z, Kong D, et al. Learning heterogeneous spatial-temporal representation for bike-sharing demand prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2019. 1004\u20131011","DOI":"10.1609\/aaai.v33i01.33011004"},{"key":"3487_CR253","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.1109\/TITS.2019.2915525","volume":"20","author":"L Liu","year":"2019","unstructured":"Liu L, Qiu Z, Li G, et al. Contextualized spatial-temporal network for taxi origin-destination demand prediction. IEEE Trans Intell Transport Syst, 2019, 20: 3875\u20133887","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR254","doi-asserted-by":"crossref","unstructured":"Ye J, Sun L, Du B, et al. Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019. 305\u2013313","DOI":"10.1145\/3292500.3330887"},{"key":"3487_CR255","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1109\/MNET.2018.1800011","volume":"33","author":"K C Chen","year":"2019","unstructured":"Chen K C, Zhang T, Gitlin R D, et al. Ultra-low latency mobile networking. IEEE Network, 2019, 33: 181\u2013187","journal-title":"IEEE Network"},{"key":"3487_CR256","doi-asserted-by":"crossref","unstructured":"Lin C, Chen K, Wickramasuriya D, et al. Anticipatory mobility management by big data analytics for ultra-low latency mobile networking. In: Proceedings of IEEE International Conference on Communications (ICC), 2018. 1\u20137","DOI":"10.1109\/ICC.2018.8422231"},{"key":"3487_CR257","doi-asserted-by":"crossref","unstructured":"Xiao Y, Krunz M, Volos H, et al. Driving in the fog: latency measurement, modeling, and optimization of LTE-based fog computing for smart vehicles. In: Proceedings of Annual IEEE International Conference on Sensing, Communication, and Networking, 2019. 1\u20139","DOI":"10.1109\/SAHCN.2019.8824922"},{"key":"3487_CR258","doi-asserted-by":"crossref","unstructured":"Volos H, Bando T, Konishi K. ReLaDec: reliable latency decision algorithm for connected vehicle applications. In: Proceedings of IEEE Intelligent Vehicles Symposium, 2019. 1861\u20131868","DOI":"10.1109\/IVS.2019.8814083"},{"key":"3487_CR259","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1109\/TITS.2013.2246835","volume":"14","author":"S Sivaraman","year":"2013","unstructured":"Sivaraman S, Trivedi M M. Integrated lane and vehicle detection, localization, and tracking: a synergistic approach. IEEE Trans Intell Transport Syst, 2013, 14: 906\u2013917","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR260","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TITS.2013.2280766","volume":"15","author":"C G Keller","year":"2014","unstructured":"Keller C G, Gavrila D M. Will the pedestrian cross? A study on pedestrian path prediction. IEEE Trans Intell Transport Syst, 2014, 15: 494\u2013506","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR261","doi-asserted-by":"crossref","unstructured":"Lin I, Lin C, Hung H, et al. Autonomous vehicle as an intelligent transportation service in a smart city. In: Proceedings of IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017. 1\u20137","DOI":"10.1109\/VTCFall.2017.8288315"},{"key":"3487_CR262","doi-asserted-by":"publisher","first-page":"8176","DOI":"10.1109\/TVT.2020.2997712","volume":"69","author":"Y Wang","year":"2020","unstructured":"Wang Y, Zhou Z, Liu K, et al. Large-scale intelligent taxicab scheduling: a distributed and future-aware approach. IEEE Trans Veh Technol, 2020, 69: 8176\u20138191","journal-title":"IEEE Trans Veh Technol"},{"key":"3487_CR263","doi-asserted-by":"publisher","first-page":"2318","DOI":"10.1109\/TITS.2015.2409109","volume":"16","author":"A Mukhtar","year":"2015","unstructured":"Mukhtar A, Xia L, Tang T B. Vehicle detection techniques for collision avoidance systems: a review. IEEE Trans Intell Transport Syst, 2015, 16: 2318\u20132338","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR264","doi-asserted-by":"publisher","first-page":"190201","DOI":"10.1007\/s11432-019-2987-x","volume":"63","author":"J Liu","year":"2020","unstructured":"Liu J, Guo H Y, Song L H, et al. Driver-automation shared steering control for highly automated vehicles. Sci China Inf Sci, 2020, 63: 190201","journal-title":"Sci China Inf Sci"},{"key":"3487_CR265","doi-asserted-by":"publisher","first-page":"1593","DOI":"10.1109\/TITS.2017.2727224","volume":"19","author":"E Odat","year":"2018","unstructured":"Odat E, Shamma J S, Claudel C. Vehicle classification and speed estimation using combined passive infrared\/ultrasonic sensors. IEEE Trans Intell Transport Syst, 2018, 19: 1593\u20131606","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR266","doi-asserted-by":"publisher","first-page":"5075","DOI":"10.1109\/JSEN.2015.2432748","volume":"15","author":"R Hostettler","year":"2015","unstructured":"Hostettler R, Birk W, Nordenvaad M L. Joint vehicle trajectory and model parameter estimation using road side sensors. IEEE Sens J, 2015, 15: 5075\u20135086","journal-title":"IEEE Sens J"},{"key":"3487_CR267","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.1109\/TITS.2017.2741507","volume":"19","author":"W Balid","year":"2018","unstructured":"Balid W, Tafish H, Refai H H. Intelligent vehicle counting and classification sensor for real-time traffic surveillance. IEEE Trans Intell Transport Syst, 2018, 19: 1784\u20131794","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR268","doi-asserted-by":"publisher","first-page":"2749","DOI":"10.3390\/app10082749","volume":"10","author":"J Ni","year":"2020","unstructured":"Ni J, Chen Y, Chen Y, et al. A survey on theories and applications for self-driving cars based on deep learning methods. Appl Sci, 2020, 10: 2749","journal-title":"Appl Sci"},{"key":"3487_CR269","doi-asserted-by":"publisher","first-page":"107152","DOI":"10.1016\/j.patcog.2019.107152","volume":"101","author":"J Fu","year":"2020","unstructured":"Fu J, Liu J, Li Y, et al. Contextual deconvolution network for semantic segmentation. Pattern Recogn, 2020, 101: 107152","journal-title":"Pattern Recogn"},{"key":"3487_CR270","doi-asserted-by":"publisher","first-page":"105584","DOI":"10.1016\/j.knosys.2020.105584","volume":"194","author":"D Xiao","year":"2020","unstructured":"Xiao D, Yang X, Li J, et al. Attention deep neural network for lane marking detection. Knowledge-Based Syst, 2020, 194: 105584","journal-title":"Knowledge-Based Syst"},{"key":"3487_CR271","doi-asserted-by":"publisher","first-page":"11551","DOI":"10.1007\/s11042-019-08239-z","volume":"79","author":"H Xu","year":"2020","unstructured":"Xu H, Srivastava G. Automatic recognition algorithm of traffic signs based on convolution neural network. Multimed Tools Appl, 2020, 79: 11551\u201311565","journal-title":"Multimed Tools Appl"},{"key":"3487_CR272","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1109\/JPROC.2006.888388","volume":"95","author":"J C McCall","year":"2007","unstructured":"McCall J C, Trivedi M M. Driver behavior and situation aware brake assistance for intelligent vehicles. Proc IEEE, 2007, 95: 374\u2013387","journal-title":"Proc IEEE"},{"key":"3487_CR273","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.ejcon.2015.04.007","volume":"24","author":"A Carvalho","year":"2015","unstructured":"Carvalho A, Lef\u00e9vre S, Schildbach G, et al. Automated driving: the role of forecasts and uncertainty\u2014a control perspective. Eur J Control, 2015, 24: 14\u201332","journal-title":"Eur J Control"},{"key":"3487_CR274","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.cviu.2014.10.003","volume":"134","author":"E Ohn-Bar","year":"2015","unstructured":"Ohn-Bar E, Tawari A, Martin S, et al. On surveillance for safety critical events: in-vehicle video networks for predictive driver assistance systems. Comput Vision Image Underst, 2015, 134: 130\u2013140","journal-title":"Comput Vision Image Underst"},{"key":"3487_CR275","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1109\/TIV.2016.2571067","volume":"1","author":"E Ohn-Bar","year":"2016","unstructured":"Ohn-Bar E, Trivedi M M. Looking at humans in the age of self-driving and highly automated vehicles. IEEE Trans Intell Veh, 2016, 1: 90\u2013104","journal-title":"IEEE Trans Intell Veh"},{"key":"3487_CR276","doi-asserted-by":"crossref","unstructured":"Derbel O, Landry R. Driver behavior assessment based on the belief theory in the driver-vehicle-environment system. In: Proceedings of IEEE International Conference on Vehicular Electronics and Safety (ICVES), 2015. 7\u201312","DOI":"10.1109\/ICVES.2015.7396885"},{"key":"3487_CR277","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1109\/TITS.2014.2324414","volume":"16","author":"N Li","year":"2015","unstructured":"Li N, Busso C. Predicting perceived visual and cognitive distractions of drivers with multimodal features. IEEE Trans Intell Transport Syst, 2015, 16: 51\u201365","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR278","doi-asserted-by":"publisher","first-page":"190203","DOI":"10.1007\/s11432-019-2983-0","volume":"63","author":"H Y Huang","year":"2020","unstructured":"Huang H Y, Wang J Q, Fei C, et al. A probabilistic risk assessment framework considering lane-changing behavior interaction. Sci China Inf Sci, 2020, 63: 190203","journal-title":"Sci China Inf Sci"},{"key":"3487_CR279","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.trc.2017.01.007","volume":"76","author":"L Li","year":"2017","unstructured":"Li L, Chen X M. Vehicle headway modeling and its inferences in macroscopic\/microscopic traffic flow theory: a survey. Transport Res Part C-Emerging Tech, 2017, 76: 170\u2013188","journal-title":"Transport Res Part C-Emerging Tech"},{"key":"3487_CR280","doi-asserted-by":"publisher","first-page":"413","DOI":"10.1137\/060678415","volume":"68","author":"S Moutari","year":"2007","unstructured":"Moutari S, Rascle M. A hybrid lagrangian model based on the aw-rascle traffic flow model. SIAM J Appl Math, 2007, 68: 413\u2013436","journal-title":"SIAM J Appl Math"},{"key":"3487_CR281","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/S0191-2615(01)00010-8","volume":"36","author":"R Jiang","year":"2002","unstructured":"Jiang R, Wu Q S, Zhu Z J. A new continuum model for traffic flow and numerical tests. Transport Res Part B-Meth, 2002, 36: 405\u2013419","journal-title":"Transport Res Part B-Meth"},{"key":"3487_CR282","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1137\/S0036139997326946","volume":"59","author":"A Klar","year":"1998","unstructured":"Klar A, Wegener R. A hierarchy of models for multilane vehicular traffic I: modeling. SIAM J Appl Math, 1998, 59: 983\u20131001","journal-title":"SIAM J Appl Math"},{"key":"3487_CR283","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1137\/S0036139997326958","volume":"59","author":"A Klar","year":"1998","unstructured":"Klar A, Wegener R. A hierarchy of models for multilane vehicular traffic II: numerical investigations. SIAM J Appl Math, 1998, 59: 1002\u20131011","journal-title":"SIAM J Appl Math"},{"key":"3487_CR284","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1109\/TITS.2004.828170","volume":"5","author":"K Li","year":"2004","unstructured":"Li K, Ioannou P. Modeling of traffic flow of automated vehicles. IEEE Trans Intell Transport Syst, 2004, 5: 99\u2013113","journal-title":"IEEE Trans Intell Transport Syst"},{"key":"3487_CR285","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.mcm.2006.01.016","volume":"44","author":"P Goatin","year":"2006","unstructured":"Goatin P. The Aw-Rascle vehicular traffic flow model with phase transitions. Math Comput Model, 2006, 44: 287\u2013303","journal-title":"Math Comput Model"},{"key":"3487_CR286","doi-asserted-by":"crossref","unstructured":"Hoogendoorn S P, van Lint H, Knoop V. Dynamic first-order modeling of phase-transition probabilities. In: Proceedings of Traffic and Granular Flow\u201907. Berlin: Springer, 2009. 85\u201392","DOI":"10.1007\/978-3-540-77074-9_7"},{"key":"3487_CR287","first-page":"14","volume":"5","author":"A Khelifi","year":"2018","unstructured":"Khelifi A, Haj-Salem H, Lebacque J P, et al. Lagrangian generic second order traffic flow models for node. J Traffic Transport Eng, 2018, 5: 14\u201327","journal-title":"J Traffic Transport Eng"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-021-3487-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-021-3487-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-021-3487-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T04:16:12Z","timestamp":1728274572000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-021-3487-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,25]]},"references-count":287,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["3487"],"URL":"https:\/\/doi.org\/10.1007\/s11432-021-3487-x","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,25]]},"assertion":[{"value":"13 September 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 April 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"211301"}}