Real-time urban regional route planning model for connected vehicles based on V2X communication

Pangwei Wang

North China University of Technology

Hui Deng

North China University of Technology

Juan Zhang

University of Exeter

Mingfang Zhang

North China University of Technology

DOI: https://doi.org/10.5198/jtlu.2020.1598

Keywords: Intelligent transportation system, vehicle to X communication system(V2X), real-time route planning, connected vehicles(CV), dynamic traffic guidance


Abstract

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.


References

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. 35.1.37.10141

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