{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:28:58Z","timestamp":1775665738901,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["33217"],"award-info":[{"award-number":["33217"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. ITS Res."],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Traffic prediction is a task where the goal is to determine the number and type of vehicles, or some other traffic related metric, at certain time point. In addition to predicting the short-term evolution of traffic, prediction can be done for estimating traffic for distant future based on the trends found in historical traffic data, which is a critical component of traffic simulators being able to spawn realistic number of vehicles under prevailing situation. Such prediction system needs to be dependent on the characteristics of the situation and not the preceding traffic flow. This work presents a deep learning based prediction pipeline that uses a Long Short Term Memory (LSTM) network to map temporal, weather and traffic accident data accurately into traffic flow to predict traffic flow over multiple timesteps from various non-traffic inputs. Traffic data can then be produced based on independent data like weather forecasts and be used for other applications. As far as we know, no previous traffic predictor combines so many input variables to predict traffic flow with vehicle type information. To make the event based traffic accident dataset compatible with time series data, a novel preprocessing step based on power law decay phenomenon is added. Quantitative experiments show that the proposed preprocessing step and optimized hyperparameters improve the accuracy of the predictor on multiple metrics compared to a model without accident information. In two established statistical evaluation metrics, Mean Absolute Error and Mean Squared Error, the improvement was over <jats:inline-formula>\n              <jats:alternatives>\n                <jats:tex-math>$$20 \\%$$<\/jats:tex-math>\n                <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mn>20<\/mml:mn>\n                    <mml:mo>%<\/mml:mo>\n                  <\/mml:mrow>\n                <\/mml:math>\n              <\/jats:alternatives>\n            <\/jats:inline-formula> for certain vehicle types.<\/jats:p>","DOI":"10.1007\/s13177-024-00451-y","type":"journal-article","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T04:15:10Z","timestamp":1733544910000},"page":"354-371","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Long Short Term Memory Based Traffic Prediction Using Multi-Source Data"],"prefix":"10.1007","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8016-6833","authenticated-orcid":false,"given":"Matti","family":"Leinonen","sequence":"first","affiliation":[]},{"given":"Ahmed","family":"Al-Tachmeesschi","sequence":"additional","affiliation":[]},{"given":"Banu","family":"Turkmen","sequence":"additional","affiliation":[]},{"given":"Nahid","family":"Atashi","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Ruotsalainen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"451_CR1","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1016\/j.jtrangeo.2017.05.014","volume":"62","author":"B Moya-G\u00f3mez","year":"2017","unstructured":"Moya-G\u00f3mez, B., Garc\u00eda-Palomares, J.C.: The impacts of congestion on automobile accessibility. What happens in large european cities? J. Transport Geography 62, 148\u2013159 (2017). https:\/\/doi.org\/10.1016\/j.jtrangeo.2017.05.014","journal-title":"J. Transport Geography"},{"key":"451_CR2","unstructured":"Progress of EU Transport Sector Towards Its Environment and Climate Objectives. https:\/\/www.eea.europa.eu\/publications\/progress-of-eu-transport-sector-1 Accessed 2023-05-17"},{"key":"451_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1504\/IJCCPS.2021.113100","volume":"1","author":"F Qiao","year":"2021","unstructured":"Qiao, F., Liu, T., Sun, H., Guo, L., Chen, Y.: Modelling and simulation of urban traffic systems: present and future. Int. J. Cybern. Cyber-Phys. Syst. 1, 1 (2021). https:\/\/doi.org\/10.1504\/IJCCPS.2021.113100","journal-title":"Int. J. Cybern. Cyber-Phys. Syst."},{"issue":"3","key":"451_CR4","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.apr.2019.11.018","volume":"11","author":"JA Pinto","year":"2020","unstructured":"Pinto, J.A., Kumar, P., Alonso, M.F., Andre\u00e3o, W.L., Pedruzzi, R., dos Santos, F.S., Moreira, D.M., de Almeida Albuquerque, T.T.: Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research. Atmos. Pollut. Res. 11(3), 454\u2013468 (2020). https:\/\/doi.org\/10.1016\/j.apr.2019.11.018","journal-title":"Atmos. Pollut. Res."},{"key":"451_CR5","unstructured":"United Nations, D.o.E., Development, S.A.-S.: Transforming our world: the 2030 Agenda for Sustainable Development (2015). https:\/\/sdgs.un.org\/2030agenda Accessed 2023-05-17"},{"key":"451_CR6","doi-asserted-by":"publisher","unstructured":"Pi, Y., Duffield, N., Behzadan, A., Lomax, T.: Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks. Comput Urban Sci 2 (2022). https:\/\/doi.org\/10.1007\/s43762-021-00031-w","DOI":"10.1007\/s43762-021-00031-w"},{"key":"451_CR7","doi-asserted-by":"publisher","unstructured":"Kendall, A., Hawke, J., Janz, D., Mazur, P., Reda, D., Allen, J.-M., Lam, V.-D., Bewley, A., Shah, A.: Learning to drive in a day. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 8248-8254 (2019). https:\/\/doi.org\/10.1109\/ICRA.2019.8793742","DOI":"10.1109\/ICRA.2019.8793742"},{"issue":"04","key":"451_CR8","doi-asserted-by":"publisher","first-page":"3414","DOI":"10.1609\/aaai.v34i04.5744","volume":"34","author":"C Chen","year":"2020","unstructured":"Chen, C., Wei, H., Xu, N., Zheng, G., Yang, M., Xiong, Y., Xu, K., Li, Z.: Toward a thousand lights: Decentralized deep reinforcement learning for large-scale traffic signal control. Proc. AAAI Conf. Artif. Intell. 34(04), 3414\u20133421 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i04.5744","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"issue":"5","key":"451_CR9","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. Trans. Res. Part A: Policy Pract 32(5), 323\u2013329 (1998). https:\/\/doi.org\/10.1016\/S0965-8564(97)00048-7","journal-title":"Trans. Res. Part A: Policy Pract"},{"key":"451_CR10","doi-asserted-by":"publisher","unstructured":"Mohan, R., Ramadurai, G.: State-of-the art of macroscopic traffic flow modelling. Int. J. Advan. Eng. Sci. Appl. Math. 5 (2013). https:\/\/doi.org\/10.1007\/s12572-013-0087-1","DOI":"10.1007\/s12572-013-0087-1"},{"key":"451_CR11","doi-asserted-by":"publisher","unstructured":"Tan, S., Wong, K., Wang, S., Manivasagam, S., Ren, M., Urtasun, R.: Scenegen: learning to generate realistic traffic scenes. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 892-901. IEEE Computer Society, Los Alamitos, CA, USA (2021). https:\/\/doi.org\/10.1109\/CVPR46437.2021.00095","DOI":"10.1109\/CVPR46437.2021.00095"},{"key":"451_CR12","doi-asserted-by":"publisher","unstructured":"Yuan, H., Li, G.: A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Sci. Eng. 6 (2021). https:\/\/doi.org\/10.1007\/s41019-020-00151-z","DOI":"10.1007\/s41019-020-00151-z"},{"key":"451_CR13","doi-asserted-by":"publisher","unstructured":"Taylor, M., Bonsall, P., Young, W.: Understanding Traffic Systems: data Analysis and Presentation, 2nd edn. Ashgate Publishing Limited, United Kingdom (2000). https:\/\/doi.org\/10.4324\/9781315235370","DOI":"10.4324\/9781315235370"},{"issue":"2","key":"451_CR14","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1287\/trsc.2.2.107","volume":"2","author":"DJ Buckley","year":"1968","unstructured":"Buckley, D.J.: A Semi-Poisson Model of Traffic Flow. Transportation Science 2(2), 107\u2013133 (1968). https:\/\/doi.org\/10.1287\/trsc.2.2.107","journal-title":"Transportation Science"},{"key":"451_CR15","doi-asserted-by":"publisher","unstructured":"Lopez, P.A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Fl\u00f6tter\u00f6d, Y.-P., Hilbrich, R., L\u00fccken, L., Rummel, J., Wagner, P., Wiessner, E.: Microscopic traffic simulation using sumo. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2575-2582 (2018). https:\/\/doi.org\/10.1109\/ITSC.2018.8569938","DOI":"10.1109\/ITSC.2018.8569938"},{"issue":"2","key":"451_CR16","doi-asserted-by":"publisher","first-page":"1831","DOI":"10.32604\/cmc.2021.012231","volume":"68","author":"N Al-Nabhan","year":"2021","unstructured":"Al-Nabhan, N., AlDuhaim, M., AlHussan, S., Abdullah, H., AlHaid, M., AlDuhaishi, R.: Ksutraffic: a microscopic traffic simulator for traffic planning in smart cities. Comput. Mater. Continua 68(2), 1831\u20131845 (2021). https:\/\/doi.org\/10.32604\/cmc.2021.012231","journal-title":"Comput. Mater. Continua"},{"issue":"2","key":"451_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.trc.2004.07.006","volume":"12","author":"M Zhong","year":"2004","unstructured":"Zhong, M., Lingras, P., Sharma, S.: Estimation of missing traffic counts using factor, genetic, neural, and regression techniques. Trans. Res. Part C: Emerg. Technol. 12(2), 139\u2013166 (2004). https:\/\/doi.org\/10.1016\/j.trc.2004.07.006","journal-title":"Trans. Res. Part C: Emerg. Technol."},{"issue":"4","key":"451_CR18","doi-asserted-by":"publisher","first-page":"1679","DOI":"10.1109\/TITS.2012.2200474","volume":"13","author":"A Hofleitner","year":"2012","unstructured":"Hofleitner, A., Herring, R., Abbeel, P., Bayen, A.: Learning the dynamics of arterial traffic from probe data using a dynamic bayesian network. IEEE Trans. Intell. Transp. Syst. 13(4), 1679\u20131693 (2012). https:\/\/doi.org\/10.1109\/TITS.2012.2200474","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"451_CR19","doi-asserted-by":"publisher","unstructured":"La\u00f1a, I., Oregi, I., Del Ser, J.: Soft sensing methods for the generation of plausible traffic data in sensor-less locations. In: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pp. 3183-3189 (2021). https:\/\/doi.org\/10.1109\/ITSC48978.2021.9564670","DOI":"10.1109\/ITSC48978.2021.9564670"},{"key":"451_CR20","doi-asserted-by":"publisher","first-page":"186191","DOI":"10.1109\/ACCESS.2020.3029230","volume":"8","author":"C Wu","year":"2020","unstructured":"Wu, C., Chen, L., Wang, G., Chai, S., Jiang, H., Peng, J., Hong, Z.: Spatiotemporal scenario generation of traffic flow based on LSTM-GAN. IEEE Access 8, 186191\u2013186198 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3029230","journal-title":"IEEE Access"},{"key":"451_CR21","doi-asserted-by":"publisher","unstructured":"Lee, J., Hong, B., Lee, K., Jang, Y.-J.: A Prediction Model of Traffic Congestion Using Weather Data. In: 2015 IEEE International conference on data science and data intensive systems, Sydney, NSW, Australia, pp. 81-88 (2015). https:\/\/doi.org\/10.1109\/DSDIS.2015.96","DOI":"10.1109\/DSDIS.2015.96"},{"key":"451_CR22","doi-asserted-by":"publisher","first-page":"1685","DOI":"10.1007\/s11069-021-05112-x","volume":"111","author":"S AlKheder","year":"2022","unstructured":"AlKheder, S., AlOmair, A.: Urban traffic prediction using metrological data with fuzzy logic, long short-term memory (LSTM), and decision trees (DTs). Nat. Hazards 111, 1685\u20131719 (2022). https:\/\/doi.org\/10.1007\/s11069-021-05112-x","journal-title":"Nat. Hazards"},{"key":"451_CR23","doi-asserted-by":"publisher","unstructured":"Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K.K., Prabowo, A., Rahaman, M.S., Salim, F.D.: Generative adversarial networks for spatio-temporal data: A survey. ACM Trans. Intell. Syst. Technol. 13(2) (2022). https:\/\/doi.org\/10.1145\/3474838","DOI":"10.1145\/3474838"},{"key":"451_CR24","doi-asserted-by":"publisher","first-page":"57311","DOI":"10.1109\/ACCESS.2018.2873569","volume":"6","author":"F-H Tseng","year":"2018","unstructured":"Tseng, F.-H., Hsueh, J.-H., Tseng, C.-W., Yang, Y.-T., Chao, H.-C., Chou, L.-D.: Congestion prediction with big data for real-time highway traffic. IEEE Access 6, 57311\u201357323 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2873569","journal-title":"IEEE Access"},{"key":"451_CR25","doi-asserted-by":"publisher","unstructured":"Chrobok, R., Wahle, J., Schreckenberg, M.: Traffic forecast using simulations of large scale networks. In: ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pp. 434-439 (2001). https:\/\/doi.org\/10.1109\/ITSC.2001.948696","DOI":"10.1109\/ITSC.2001.948696"},{"key":"451_CR26","doi-asserted-by":"publisher","first-page":"51258","DOI":"10.1109\/ACCESS.2021.3069770","volume":"9","author":"SA Kashinath","year":"2021","unstructured":"Kashinath, S.A., Mostafa, S.A., Mustapha, A., Mahdin, H., Lim, D., Mahmoud, M.A., Mohammed, M.A., Al-Rimy, B.A.S., Fudzee, M.F.M., Yang, T.J.: Review of data fusion methods for real-time and multisensor traffic flow analysis. IEEE Access 9, 51258\u201351276 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3069770","journal-title":"IEEE Access"},{"issue":"3","key":"451_CR27","doi-asserted-by":"publisher","first-page":"1765","DOI":"10.1109\/TNSE.2022.3152983","volume":"9","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Zhang, R., Wang, C., Xiao, Z., Jiang, H.: Spatial-temporal convsequence learning with accident encoding for traffic flow prediction. IEEE Trans. Netw. Sci. Eng. 9(3), 1765\u20131775 (2022). https:\/\/doi.org\/10.1109\/TNSE.2022.3152983","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"451_CR28","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"451_CR29","doi-asserted-by":"publisher","first-page":"201814982","DOI":"10.1073\/pnas.1814982116","volume":"116","author":"L Zhang","year":"2019","unstructured":"Zhang, L., Zeng, G., Daqing, L., Huang, H.-J., Stanley, H., Havlin, S.: Scale-free resilience of real traffic jams. Proc. Natl. Acad. Sci. 116, 201814982 (2019). https:\/\/doi.org\/10.1073\/pnas.1814982116","journal-title":"Proc. Natl. Acad. Sci."},{"key":"451_CR30","first-page":"317","volume":"229","author":"MJ Lighthill","year":"1955","unstructured":"Lighthill, M.J., Whitham, G.B.: On kinematic waves ii. a theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London. Series A. Math. Phys. Sci. 229, 317\u2013345 (1955)","journal-title":"Math. Phys. Sci."},{"key":"451_CR31","doi-asserted-by":"crossref","unstructured":"Richards, P.I.: Shock waves on the highway. Oper. Res. 4(1), 42\u201351 (1956). Accessed 2022-05-23","DOI":"10.1287\/opre.4.1.42"},{"key":"451_CR32","doi-asserted-by":"publisher","unstructured":"Darbha, S., Rajagopal, K.: Aggregation of a class of large-scale, interconnected, nonlinear dynamical systems, pp. 487-494 (2000). https:\/\/doi.org\/10.1115\/IMECE2000-2339","DOI":"10.1115\/IMECE2000-2339"},{"key":"451_CR33","doi-asserted-by":"publisher","unstructured":"Alam, I., Farid, D.M., Rossetti, R.J.F: The Prediction of Traffic Flow with Regression Analysis. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds.) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol. 813. Springer, Singapore. (2019). https:\/\/doi.org\/10.1007\/978-981-13-1498-8_58","DOI":"10.1007\/978-981-13-1498-8_58"},{"issue":"6","key":"451_CR34","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1061\/(ASCE)0733-947X(2003)129:6(664)","volume":"129","author":"BM 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. Transp. Eng. 129(6), 664\u2013672 (2003)","journal-title":"J. Transp. Eng."},{"key":"451_CR35","doi-asserted-by":"publisher","unstructured":"Szeto, W., Ghosh, B., Basu, B., O\u2019Mahony, M.: Multivariate traffic forecasting technique using cell transmission model and SARIMA model. J. Trans. Eng.-asce - J TRANSP ENG-ASCE 135 (2009). https:\/\/doi.org\/10.1061\/(ASCE)0733-947X(2009)135:9(658)","DOI":"10.1061\/(ASCE)0733-947X(2009)135:9(658)"},{"key":"451_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12205-018-0429-4","volume":"22","author":"X Luo","year":"2018","unstructured":"Luo, X., Niu, L., Zhang, S.: An algorithm for traffic flow prediction based on improved sarima and ga. KSCE J. Civ. Eng. 22, 1\u20139 (2018). https:\/\/doi.org\/10.1007\/s12205-018-0429-4","journal-title":"KSCE J. Civ. Eng."},{"issue":"9","key":"451_CR37","doi-asserted-by":"publisher","first-page":"3848","DOI":"10.1109\/TITS.2019.2935152","volume":"21","author":"L Zhao","year":"2020","unstructured":"Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848\u20133858 (2020). https:\/\/doi.org\/10.1109\/TITS.2019.2935152","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"451_CR38","first-page":"4534","volume":"2015","author":"S Venugopalan","year":"2015","unstructured":"Venugopalan, S., Rohrbach, M., Donahue, J., Mooney, R.J., Darrell, T., Saenko, K.: Sequence to Sequence - Video to Text. IEEE International Conference on Computer Vision (ICCV) 2015, 4534\u20134542 (2015)","journal-title":"IEEE International Conference on Computer Vision (ICCV)"},{"key":"451_CR39","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27. Curran Associates, Inc., (2014). https:\/\/proceedings.neurips.cc\/paper\/2014\/file\/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf"},{"key":"451_CR40","doi-asserted-by":"crossref","unstructured":"Yin, X., Wu, G., Wei, J., Shen, Y., Qi, H., Yin, B.: Deep learning on traffic prediction: methods, analysis and future directions. IEEE Trans. Intell. Trans. Syst. (2021)","DOI":"10.1109\/TITS.2021.3054840"},{"key":"451_CR41","doi-asserted-by":"publisher","unstructured":"Huang, Z., Xia, J., Li, F., Li, Z., Li, Q.: A peak traffic congestion prediction method based on bus driving time. Entropy 21(7) (2019). https:\/\/doi.org\/10.3390\/e21070709","DOI":"10.3390\/e21070709"},{"key":"451_CR42","doi-asserted-by":"publisher","unstructured":"Yu, R., Li, Y., Shahabi, C., Demiryurek, U., Liu, Y.: Deep learning: A generic approach for extreme condition traffic forecasting. Proceedings of the 2017 SIAM International Conference on Data Mining (SDM), 777-7https:\/\/doi.org\/10.1137\/1.9781611974973.87","DOI":"10.1137\/1.9781611974973.87"},{"key":"451_CR43","doi-asserted-by":"publisher","unstructured":"Pan, B., Demiryurek, U., Shahabi, C., Gupta, C.: Forecasting spatiotemporal impact of traffic incidents on road networks. In: 2013 IEEE 13th international conference on data mining, pp. 587-596 (2013). https:\/\/doi.org\/10.1109\/ICDM.2013.44","DOI":"10.1109\/ICDM.2013.44"},{"key":"451_CR44","doi-asserted-by":"publisher","first-page":"364","DOI":"10.2478\/ttj-2018-0031","volume":"19","author":"M Savrasovs","year":"2018","unstructured":"Savrasovs, M., Pticina, I., Zemlyanikin, V., Karakikes, I.: Demand data modelling for microscopic traffic simulation. Trans. Telecommun. J. 19, 364\u2013371 (2018). https:\/\/doi.org\/10.2478\/ttj-2018-0031","journal-title":"Trans. Telecommun. J."},{"key":"451_CR45","doi-asserted-by":"publisher","unstructured":"Lens Shiang, E.P., Chien, W.-C., Lai, C.-F., Chao, H.-C.: Gated Recurrent Unit Network-based Cellular Trafile Prediction, In: 2020 International Conference on Information Networking (ICOIN), Barcelona, Spain, pp. 471-476 (2020). https:\/\/doi.org\/10.1109\/ICOIN48656.2020.9016439.","DOI":"10.1109\/ICOIN48656.2020.9016439."},{"key":"451_CR46","unstructured":"PyTorch. https:\/\/pytorch.org\/ Accessed 2023-05-17"},{"key":"451_CR47","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(56), 1929\u20131958 (2014). http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html"},{"issue":"5","key":"451_CR48","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1080\/00107510500052444","volume":"46","author":"M Newman","year":"2005","unstructured":"Newman, M.: Power laws, Pareto distributions and Zipf\u2019s law. Contemp. Phys. 46(5), 323\u2013351 (2005). https:\/\/doi.org\/10.1080\/00107510500052444","journal-title":"Contemp. Phys."},{"key":"451_CR49","doi-asserted-by":"publisher","unstructured":"Zeng, J., Qian, Y., Wang, B., Wang, T., Wei, X.: The impact of traffic crashes on urban network traffic flow. Sustainability 11(14) (2019). https:\/\/doi.org\/10.3390\/su11143956","DOI":"10.3390\/su11143956"},{"issue":"1","key":"451_CR50","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.ijtst.2018.06.005","volume":"8","author":"HJ Haule","year":"2019","unstructured":"Haule, H.J., Sando, T., Lentz, R., Chuan, C.-H., Alluri, P.: Evaluating the impact and clearance duration of freeway incidents. Int. J. Trans. Sci. Technol. 8(1), 13\u201324 (2019). https:\/\/doi.org\/10.1016\/j.ijtst.2018.06.005","journal-title":"Int. J. Trans. Sci. Technol."},{"key":"451_CR51","doi-asserted-by":"crossref","unstructured":"Virkar, Y., Clauset, A.: Power-law distributions in binned empirical data. Annals Appl. Stat. 8(1), 89\u2013119 (2014). Accessed 2022-11-21","DOI":"10.1214\/13-AOAS710"},{"issue":"4","key":"451_CR52","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1137\/070710111","volume":"51","author":"A Clauset","year":"2009","unstructured":"Clauset, A., Shalizi, C.R., Newman, M.E.J.: Power-law distributions in empirical data. SIAM Rev. 51(4), 661\u2013703 (2009). https:\/\/doi.org\/10.1137\/070710111","journal-title":"SIAM Rev."},{"key":"451_CR53","unstructured":"Traffic Measurement System Data. https:\/\/www.digitraffic.fi\/en\/road-traffic\/lam\/ Accessed 2023-17-05"},{"key":"451_CR54","doi-asserted-by":"publisher","unstructured":"Lepot, M., Aubin, J.-B., Clemens, F.H.L.R.: Interpolation in time series: An introductive overview of existing methods, their performance criteria and uncertainty assessment. Water 9(10) (2017). https:\/\/doi.org\/10.3390\/w9100796","DOI":"10.3390\/w9100796"},{"key":"451_CR55","doi-asserted-by":"publisher","unstructured":"H. Theil: Economic Forecasts and Policy. Assisted by J.S. Cramer, H. Moerman, A. Russchen. Contributions to Economic Analysis, nr XV. Amsterdam, North-Holland Publishing Company, 1958, XXXI p. 562 p., fl. 50-. Bulletin de l\u2019Institut de recherches \u00e9conomiques et sociales. 1959;25(2):169-169. https:\/\/doi.org\/10.1017\/S1373971900078951","DOI":"10.1017\/S1373971900078951"},{"issue":"8","key":"451_CR56","doi-asserted-by":"publisher","first-page":"10858","DOI":"10.1109\/TITS.2021.3096798","volume":"23","author":"C Furtlehner","year":"2022","unstructured":"Furtlehner, C., Lasgouttes, J.-M., Attanasi, A., Pezzulla, M., Gentile, G.: Short-Term Forecasting of Urban Traffic Using Spatio-Temporal Markov Field. IEEE Trans. Intell. Transp. Syst. 23(8), 10858\u201310867 (2022). https:\/\/doi.org\/10.1109\/TITS.2021.3096798","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"451_CR57","doi-asserted-by":"publisher","first-page":"01008","DOI":"10.1051\/matecconf\/201823101008","volume":"231","author":"E Ciszewska-Kulwi\u0144ska","year":"2018","unstructured":"Ciszewska-Kulwi\u0144ska, E., Romanowska, A., Kustra, W.: Analysing the impact of traffic incidents on express road traffic flow using freeval. MATEC Web Conf. 231, 01008 (2018). https:\/\/doi.org\/10.1051\/matecconf\/201823101008","journal-title":"MATEC Web Conf."},{"key":"451_CR58","doi-asserted-by":"publisher","unstructured":"Zhu, H.B., Lei, L., Dai, S.Q.: Two-lane traffic simulations with a blockage induced by an accident car. Physica A 388(14), 2903\u20132910 (2009). https:\/\/doi.org\/10.1016\/j.physa.2009.01.040","DOI":"10.1016\/j.physa.2009.01.040"}],"container-title":["International Journal of Intelligent Transportation Systems Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-024-00451-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13177-024-00451-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13177-024-00451-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,15]],"date-time":"2025-03-15T16:29:25Z","timestamp":1742056165000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13177-024-00451-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"references-count":58,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["451"],"URL":"https:\/\/doi.org\/10.1007\/s13177-024-00451-y","relation":{},"ISSN":["1348-8503","1868-8659"],"issn-type":[{"value":"1348-8503","type":"print"},{"value":"1868-8659","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,7]]},"assertion":[{"value":"6 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All authors consent to publish.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}