{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:10:35Z","timestamp":1770898235929,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Humanities and Social Science Fund of the Ministry of Education","award":["23YJAZH122"],"award-info":[{"award-number":["23YJAZH122"]}]},{"name":"Humanities and Social Science Fund of the Ministry of Education","award":["24XZB025"],"award-info":[{"award-number":["24XZB025"]}]},{"name":"Humanities and Social Science Fund of the Ministry of Education","award":["62104208"],"award-info":[{"award-number":["62104208"]}]},{"name":"Jiangsu Provincial Social Science City-School Cooperation Project","award":["23YJAZH122"],"award-info":[{"award-number":["23YJAZH122"]}]},{"name":"Jiangsu Provincial Social Science City-School Cooperation Project","award":["24XZB025"],"award-info":[{"award-number":["24XZB025"]}]},{"name":"Jiangsu Provincial Social Science City-School Cooperation Project","award":["62104208"],"award-info":[{"award-number":["62104208"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["23YJAZH122"],"award-info":[{"award-number":["23YJAZH122"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["24XZB025"],"award-info":[{"award-number":["24XZB025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62104208"],"award-info":[{"award-number":["62104208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the rapid development of the economy, urban road congestion has become more serious. The travel times for vehicles are becoming more uncontrollable, making it challenging to reach destinations on time. In order to find an optimal route and arrive at the destination with the shortest travel time, this paper proposes a dynamic shortest travel time path planning algorithm with an overtaking function (DSTTPP-OF) based on a vehicular ad hoc network (VANET) environment. Considering the uncertainty of driving vehicles, the target vehicle (vehicle for special tasks) is influenced by surrounding vehicles, leading to possible deadlock or congestion situations that extend travel time. Therefore, overtaking planning should be conducted through V2V communication, enabling surrounding vehicles to coordinate with the target vehicle to avoid deadlock and congestion through lane changing and overtaking. In the proposed DSTTPP-OF, vehicles may queue up at intersections, so we take into account the impact of traffic signals. We classify road segments into congested and non-congested sections, calculating travel times for each section separately. Subsequently, in front of each intersection, the improved Dijkstra algorithm is employed to find the shortest travel time path to the destination, and the overtaking function is used to prevent the target vehicle from entering a deadlocked state. The real-time traffic data essential for dynamic path planning were collected through a VANET of symmetrically deployed roadside units (RSUs) along the roadway. Finally, simulations were conducted using the SUMO simulator. Under different traffic flows, the proposed DSTTPP-OF demonstrates good performance; the target vehicle can travel smoothly without significant interruptions and experiences the fewest stops, thanks to the proposed algorithm. Compared to the shortest distance path planning (SDPP) algorithm, the travel time is reduced by approximately 36.9%, and the waiting time is reduced by about 83.2%. Compared to the dynamic minimum time path planning (DMTPP) algorithm, the travel time is reduced by around 18.2%, and the waiting time is reduced by approximately 65.6%.<\/jats:p>","DOI":"10.3390\/sym17030345","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T06:13:18Z","timestamp":1740463998000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Dynamic Shortest Travel Time Path Planning Algorithm with an Overtaking Function Based on VANET"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7830-2848","authenticated-orcid":false,"given":"Chunxiao","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Engineering, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Changhao","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Mu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Jiajun","family":"Shen","sequence":"additional","affiliation":[{"name":"College of Civil Science and Engineering, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Jiang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Fundamental Science and Engineering, Waseda University, Tokyo 169-8050, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"ref_1","unstructured":"(2025, January 13). National Bureau of Statistics of China, Available online: https:\/\/data.stats.gov.cn\/easyquery.htm?cn=C01."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1109\/TITS.2015.2498160","article-title":"Finding the Shortest Path in Stochastic Vehicle Routing: A Cardinality Minimization Approach","volume":"17","author":"Cao","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","first-page":"1491","article-title":"A Review on Multi-Channel MAC Mechanism in Vehicular Ad Hoc Networks","volume":"51","author":"Wu","year":"2018","journal-title":"Commun. Technol."},{"key":"ref_4","first-page":"3781","article-title":"Adapted Speed System in a Road Bend Situation in VANET Environment","volume":"74","author":"Benkirane","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_5","first-page":"178","article-title":"Matrix Solution for the Shortest Path Problem","volume":"48","author":"Xu","year":"2018","journal-title":"Math. Pract. Theory"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5635","DOI":"10.1109\/TVT.2018.2806979","article-title":"Real-Time Path Planning in Urban Area via VANET-Assisted Traffic Information Sharing","volume":"67","author":"Guo","year":"2018","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, L., Jiang, H., and Hua, L. (2019). Anti-congestion route planning scheme based on Dijkstra algorithm for automatic valet parking system. Appl. Sci., 9.","DOI":"10.3390\/app9235016"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1716","DOI":"10.1109\/TITS.2020.2972770","article-title":"Minimization of Fuel Consumption for Vehicle Trajectories","volume":"21","author":"Typaldos","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.ejor.2013.10.044","article-title":"Finding a minimum cost path between a pair of nodes in a time-varying road network with a congestion charge","volume":"236","author":"Wen","year":"2014","journal-title":"Eur. J. Oper. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"59695","DOI":"10.1109\/ACCESS.2018.2871843","article-title":"Optimal Route Algorithm Considering Traffic Light and Energy Consumption","volume":"6","author":"Hu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","first-page":"95","article-title":"Reducing Waiting Times at Charging Stations with Adaptive Electric Vehicle Route Planning","volume":"8","author":"Schoenberg","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3879","DOI":"10.1109\/TCYB.2016.2587673","article-title":"Road Disturbance Estimation and Cloud-Aided Comfort-Based Route Planning","volume":"47","author":"Li","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"190596","DOI":"10.1109\/ACCESS.2020.3030654","article-title":"Vehicle Routing for Dynamic Road Network Based on Travel Time Reliability","volume":"8","author":"Zhi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8037","DOI":"10.1109\/TITS.2021.3075221","article-title":"Urban Multiple Route Planning Model Using Dynamic Programming in Reinforcement Learning","volume":"23","author":"Peng","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/JSYST.2021.3066776","article-title":"An Efficient Context-Aware Vehicle Incidents Route Service Management for Intelligent Transport System","volume":"16","author":"Chavhan","year":"2022","journal-title":"IEEE Syst. J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1109\/TVT.2019.2905753","article-title":"A New Distributed Predictive Congestion Aware Re-Routing Algorithm for CO2 Emissions Reduction","volume":"68","author":"Santamaria","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108866","DOI":"10.1109\/ACCESS.2019.2933531","article-title":"An Energy-Efficient Dynamic Route Optimization Algorithm for Connected and Automated Vehicles Using Velocity-Space-Time Networks","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"14523","DOI":"10.1109\/TVT.2020.3043306","article-title":"SEARCH: An SDN-Enabled Approach for Vehicle Path-Planning","volume":"69","author":"Oubbati","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9003","DOI":"10.1109\/TVT.2023.3348140","article-title":"An Efficient and Accurate A-Star Algorithm for Autonomous Vehicle Path Planning","volume":"73","author":"Lin","year":"2024","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Kong, H., Zhang, Q., and Wang, C. (2023, January 27\u201329). Obstacle avoidance path planning for intelligent vehicles based on improved RRT algorithm. Proceedings of the 2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI), Changsha, China.","DOI":"10.1109\/CVCI59596.2023.10397105"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Jin, H., Jin, Z., and Kim, Y.G. (2024, January 7\u20139). Deep Reinforcement Learning-Based (DRLB) Optimization for Autonomous Driving Vehicle Path Planning. Proceedings of the 2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India.","DOI":"10.1109\/ICESC60852.2024.10689874"},{"key":"ref_22","first-page":"1093","article-title":"Learning-Based Path Planning and Predictive Control for Autonomous Vehicles with Low-Cost Positioning","volume":"8","author":"Qi","year":"2023","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1109\/TVT.2023.3314860","article-title":"Overtaking Path Planning for CAV Based on Improved Artificial Potential Field","volume":"73","author":"Ma","year":"2024","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_24","unstructured":"Li, S.E., Zheng, Y., Li, K., Wang, L., and Zhang, H. (2017). Platoon control of connected vehicles from a networked control perspective: Literature review, component modeling, and controller synthesis. IEEE Trans. Veh. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Shi, Y., Yu, H., Guo, Y., and Yuan, Z. (2021). A Collaborative Merging Strategy with Lane Changing in Multilane Freeway On-Ramp Area with V2X Network. Future Internet, 13.","DOI":"10.3390\/fi13050123"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1007\/978-981-16-5429-9_61","article-title":"Analysis of mixed vehicle traffic flow at signalized intersections based on the mixed traffic agent model of autonomous manual driving connected vehicles","volume":"775","author":"Ren","year":"2022","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1109\/LCOMM.2018.2835484","article-title":"Modeling of V2V communications for C-ITS safety applications: A CPS perspective","volume":"22","author":"Vinel","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Kamal, M.A.S., Taguchi, S., and Yoshimura, T. (July, January 28). Effcient Vehicle Driving on Multi-lane Roads Using Model Predictive Control under a Connected Vehicle Environment. Proceedings of the 2015 IEEE Intelligent Vehicles Symposium (IV), Seoul, Republic of Korea.","DOI":"10.1109\/IVS.2015.7225772"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Khan, U., Basaras, P., Schmidt-Thieme, L., Nanopoulos, A., and Katsaros, D. (2014, January 3\u20137). Analyzing Cooperative Lane Change Models for Connected Vehicles. Proceedings of the 2014 International Conference on Connected Vehicles and Expo (ICCVE), Vienna, Austria.","DOI":"10.1109\/ICCVE.2014.7297612"},{"key":"ref_30","first-page":"24","article-title":"Research on Dynamic Macroscopic Section Travel Time Model","volume":"28","author":"Li","year":"2004","journal-title":"J. Wuhan Univ. Technol."},{"key":"ref_31","first-page":"106","article-title":"Improved Dynamic Road Impedance Function Based on Traffic Wave Theory","volume":"33","author":"Song","year":"2014","journal-title":"J. Chongqing Jiaotong Univ. Nat. Sci."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ju, C., Luo, Q., and Yan, X. (2020, January 23\u201325). Path Planning Using an Improved A-star Algorithm. Proceedings of the 2020 11th International Conference on Prognostics and System Health Management (PHM-2020 Jinan), Jinan, China.","DOI":"10.1109\/PHM-Jinan48558.2020.00012"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1109\/TITS.2019.2917885","article-title":"Real-Time Speed Trajectory Planning for Minimum Fuel Consumption of a Ground Vehicle","volume":"21","author":"Kim","year":"2020","journal-title":"IEEE Tran. Intell.Transp. Syst."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:42:06Z","timestamp":1760028126000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/3\/345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,25]]},"references-count":33,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["sym17030345"],"URL":"https:\/\/doi.org\/10.3390\/sym17030345","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,25]]}}}