Real-time urban regional route planning model for connected vehicles based on V2X communication
Keywords:Intelligent transportation system, vehicle to X communication system(V2X), real-time route planning, connected vehicles(CV), dynamic traffic guidance
Advancement in the novel technology of connected vehicles has presented opportunities and challenges for smart urban transport and land use. To improve the capacity of urban transport and optimize land-use planning, a novel real-time regional route planning model based on vehicle to X communication (V2X) is presented in this paper. First, considering the traffic signal timing and phase information collected by V2X, road section resistance values are calculated dynamically based on real-time vehicular driving data. Second, according to the topology structure of the current regional road network, all predicted routes are listed based on the Dijkstra algorithm. Third, the predicted travel time of each alternative route is calculated, while the predicted route with the least travel time is selected as the optimal route. Finally, we design the test scenario with different traffic saturation levels and collect 150 sets of data to analyze the feasibility of the proposed method. The numerical results have shown that the average travel times calculated by the proposed optimal route are 8.97 seconds, 12.54 seconds, and 21.85 seconds, which are much shorter than the results of traditional navigation routes. This proposed model can be further applied to the whole urban traffic network and contribute to a greater transport and land-use efficiency in the future.
Adler, J. L., Satapathy, G., Manikonda, V., Bowles, B., & Blue, V. J. (2005). A multi-agent approach to cooperative traffic management and route guidance. Transportation Research Part B: Methodological, 39(4), 297–318. doi:10.1016/j.trb.2004.03.005
Backfrieder, C., Ostermayer, G., & Mecklenbräuker, C. F. (2016). Increased traffic flow through node-based bottleneck prediction and v2x communication. IEEE Transactions on Intelligent Transportation Systems, 18(2), 349–363. doi:10.1109/tits.2016.2573292
Cervero, R. (2013). Linking urban transport and land use in developing countries. Journal of Transport and Land Use, 6(1), 7–24. doi:10.5198/jtlu.v6i1.425
Chen, B. Y., Lam, W. H., Sumalee, A., Li, Q., Shao, H., & Fang, Z. (2013). Finding reliable shortest paths in road networks under uncertainty. Networks & Spatial Economics, 13(2), 123–148. doi:10.1007/s11067-012-9175-1
Ciesielski, K. C., Falcão, A. X., & Miranda, P. A. (2018). Path-value functions for which Dijkstra’s algorithm returns optimal mapping. Journal of Mathematical Imaging and Vision, 60(7), 1025–1036. doi:10.1007/s10851-018-0793-1
Faisal, A., Yigitcanlar, T., Kamruzzaman, M., & Currie, G. (2019). Understanding autonomous vehicles: A systematic literature review on capability, impact, planning and policy. Journal of Transport and Land Use, 12(1), 45–72. doi:10.5198/jtlu.2019.1405
Ford, A., Dawson, R., Blythe, P., & Barr, S. (2018). Land-use transport models for climate change mitigation and adaptation planning. Journal of Transport and Land Use, 11(1), 83–101. doi:10.5198/ jtlu.2018.1209
Galán-García, J. L., Aguilera-Venegas, G., Galán-García, M. Á., & Rodriguez-cielos, P. (2015). A new probabilistic extension of Dijkstra’s algorithm to simulate more realistic traffic flow in a smart city. Applied Mathematics and Computation, 267, 780–789. doi:10.1016/j.amc.2014.11.076
Huang, W., Yan, C., Wang, J., & Wang, W.(2017). A time-delay neural network for solving time-dependent shortest path problem. Neural Networks, 90, 21–28. doi:10.1016/j.neunet.2017.03.002
Huang, W., Zhang, Y., Shang, Z., & Yu, J.X. (2017). To meet or not to meet: Finding the shortest paths in road network. IEEE Transactions on Knowledge and Data Engineering, 30(4), 772–785. doi:10.1109/tkde.2017.2777851
Jabbarpour, M. R., Zarrabi, H., Khokhar, R. H., Shamshireband, S., & Choo, K. K. R. (2018). Applications of computational intelligence in vehicle traffic congestion problem: A survey. Soft Computing, 22(7), 2299–2320. doi:10.1007/s00500-017-2492-z
Li, L. R., Wang, L., Gao, Y. B., & He, C. (2013). A dynamic path planning model based on the optimal ant colony algorithm. Journal of Guangxi University: Nat Sci Ed, 38(2), 359–367.
Liu, G., Qiu, Z., Qu, H., & Ji, L. (2015). Computing k shortest paths using modified pulse-coupled neural network. Neurocomputing, 149, 1162–1176. doi:10.1016/j.neucom.2014.09.012
Liu, J., Wan, J., Wang, Q., Deng, P., Zhou, K., & Qiao, Y. (2016). A survey on position-based routing for vehicular ad hoc networks. Telecommunication Systems, 62(1), 15–30.
Paz, A., & Peeta, S. (2009). On-line calibration of behavior parameters for behavior-consistent route guidance. Transportation Research Part B: Methodological, 43(4), 403–421. doi:10.1016/j.trb.2008.07. 007
Qin, Q., Feng, M., Sun, J., & Sun, B. (2015, August). Prediction of road resistance based on historical/real-time information and road quality. In 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 1073–1077. doi:10.1109/fskd. 2015.7382091
Sen, S., Pillai, R., Joshi, S., & Rathi, A. K. (2001). A mean-variance model for route guidance in advanced traveler information systems. Transportation Science, 35(1), 37–49. doi:10.1287/trsc. 188.8.131.5241
Wang, P. W., Yu, H. B., Xiao, L., & Wang, L. (2017). Online traffic condition evaluation method for connected vehicles based on multisource data fusion. Journal of Sensors, 2017(11), 1–11. doi:10.1155/2017/7248189
Wang, Q., Huang, W., Liu, B., & Zhang, Y. (2018). An improved A∗ algorithm for path-planning of two-wheeled self-balancing vehicle. 13th IEEE Conference on Industrial Electronics and Applications (ICIEA), 841–846. doi:10.1109/iciea.2018.8397830
Wang, X., & Qiao, Q. (2010). Solving the shortest path routing problems by integrating a fast searching strategy into a hysteretic neural network with transient chaos. Sixth International Conference on Natural Computation, 2, 598–602. doi:10.1109/icnc.2010.5583418
Wang, Z., Chen, Y., Chen, N., & Han, W. (2016). A control strategy of urban expressway under CVIS. International Journal of Simulation — Systems, Science & Technology, 16(1), 30–34.
Wang, Z., Li, J., Fang, M., & Li, Y.(2015). A multimetric ant colony optimization algorithm for dynamic path planning in vehicular networks. International Journal of Distributed Sensor Networks, 11(10), 1–10. doi:10.1155/2015/271067
Wu, X., & Nie, Y. M. (2011). Modeling heterogeneous risk-taking behavior in route choice: A stochastic dominance approach. Transportation Research Part A Policy & Practice, 45(9), 896–915. doi:10.1016/j.sbspro.2011.04.523
Xing, T., & Zhou, X. (2011). Finding the most reliable path with and without link travel time correlation: A Lagrangian substitution based approach. Transportation Research Part B Methodological, 45(10), 1660–1679. doi:10.1016/j.trb.2011.06.004
Yigitcanlar, T., & Kamruzzaman, M. (2014). Investigating the interplay between transport, land use and the environment: A review of the literature. International Journal of Environmental Science and Technology, 11, 2121–2132. doi:10.1007/s13762-014-0691-z
Yu, F., Li, Y., & Wu, T. J. (2010). A temporal ant colony optimization approach to the shortest path problem in dynamic scale-free networks. Physica A: Statistical Mechanics and its Applications, 389(3), 629–636. doi:10.1016/j.physa.2009.10.005
Zhu, T., Song, Z., Wu, D., & Yu, J. (2016). A novel freeway traffic speed estimation model with massive cellular signaling data. International Journal of Web Services Research, 13(1), 69–87. doi:10.4018/jwsr.2016010105
How to Cite
Copyright (c) 2020 Pangwei Wang, Hui Deng, Juan Zhang, Mingfang Zhang
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-Noncommercial License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.