A Hybrid Control Path Planning Architecture Based on Traffic Equilibrium Assignment for Emergency
Source abstract
Abstract
Unconventional events exacerbate the imbalance between regional transportation demand and limited road network resources. Scientific and efficient path planning serves as the foundation for rapidly restoring equilibrium to the road network. In real large-scale road networks, especially during emergencies, it is usually difficult to obtain or predict accurate dynamic traffic network flows in real-time, which is used to support equilibrium path planning. Moreover, the traditional iterative methods cannot meet the real-time demand of emergency equilibrium path planning decision generation. To solve the above problems, this paper proposes a hybrid control architecture for path planning based on equilibrium traffic assignment theory. The architecture introduces the travelers’ real-time travel data and constructs a spatio-temporal neural network, which captures the evolution of traffic network loads. Adaptive multi-graph fusion technology is used to mix the background traffic flow data and the traveler’s real-time Origin–Destination (OD) data, to mine the dynamic correlation between the traffic state and the travelers’ travel. Based on the real-time prediction of dynamic network states, equilibrium mapping learning is carried out to pre-allocate potential travel demands and construct equilibrium traffic graphs based on system optimization traffic assignment. Finally, individual evacuation path strategies are generated online in a data-driven manner in real time to achieve improved resilience in the transportation system.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Traffic control and management · Traffic Prediction and Management Techniques · Transportation Planning and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
The Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Funding
National Key R & D Program of China · Key R & D Program of Shandong Province · National Natural Science of Foundation of China · Beijing Natural Science Foundation · Humanities and Social Sciences Foundation of the Ministry of Education
References (52)
Show source-provided references
1. Murray-Tuite, P.M. (2006, January 3–6). A comparison of transportation network resilience under simulated system optimum and user equilibrium conditions. Proceedings of the 2006 Winter Simulation Conference, Monterey, CA, USA.10.1109/WSC.2006.323240 ↗
2. Road paper. Some theoretical aspects of road traffic research
3. Studies in the Economics of Transportation
4. Some developments in equilibrium traffic assignment10.1016/0191-2615(80)90004-1 ↗
5. Dial, R. (2001, January 7–11). Equilibrium logit traffic assignment: Elementary theory and algorithms. Proceedings of the 80th Annual Meeting of the Transportation Research Board, Washington, DC, USA.10.1016/S0191-2615(01)00037-6 ↗
6. A network flow model for lane-based evacuation routing10.1016/S0965-8564(03)00007-7 ↗
7. Integrated evacuation network optimization and emergency vehicle assignment10.3141/2091-09 ↗
8. A bi-level model for planning signalized and uninterrupted flow intersections in an evacuation network10.1111/j.1467-8667.2012.00778.x ↗
9. Compromising system and user interests in shelter location and evacuation planning10.1016/j.trb.2014.11.010 ↗
10. A review on travel behaviour modelling in dynamic traffic simulation models for evacuations10.1007/s11116-011-9320-6 ↗
11. A predictive dynamic traffic assignment model in congested capacity-constrained road networks10.1016/S0191-2615(99)00045-4 ↗
12. Application of a simulation-based dynamic traffic assignment model10.1016/j.ejor.2006.07.054 ↗
13. Modeling evacuation of a transport system: Application of a multimodal mesoscopic dynamic traffic assignment model10.1109/TITS.2011.2143408 ↗
14. A stochastic dynamic traffic assignment model for emergency evacuations that considers background traffic10.1109/MITS.2021.3122432 ↗
15. Modeling evacuation risk using a stochastic process formulation of mesoscopic dynamic network loading
16. Optimization models for large scale network evacuation planning and management: A literature review
17. Foundations of dynamic traffic assignment: The past, the present and the future10.1023/A:1012827724856 ↗
18. An aggregate approach to model evacuee behavior for no-notice evacuation operations10.1007/s11116-012-9440-7 ↗
19. Location planning for transit-based evacuation under the risk of service disruptions10.1016/j.trb.2013.03.002 ↗
20. A two-stage robustness approach to evacuation planning with buses10.1016/j.trb.2015.04.008 ↗
21. Dynamic network traffic assignment and simulation methodology for advanced system management applications10.1023/A:1012831808926 ↗
22. Ben-Akiva, M., Bierlaire, M., Koutsopoulos, H.N., and Mishalani, R. (2002). Transportation and Network Analysis: Current Trends: Miscellanea in Honor of Michael Florian, Springer.
23. Calibration and application of a simulation-based dynamic traffic assignment model10.3141/1876-11 ↗
24. Mao, X., Yuan, C., Gan, J., and Zhou, J. (2019). Optimal evacuation strategy for parking lots considering the dynamic background traffic flows. Int. J. Environ. Res. Public Health, 16.10.3390/ijerph16122194 ↗
25. Chiabaut, N., Lopez, C., and Leclercq, L. (2016, January 10–14). Evaluation of the performance of an urban freight system using a multi-use-lane arterial. Proceedings of the 95th Meeting of the Transportation Research Board, Washington, DC, USA.
26. Toqué, F., Côme, E., El Mahrsi, M.K., and Oukhellou, L. (2016, January 1–4). Forecasting dynamic public transport origin-destination matrices with long-short term memory recurrent neural networks. Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil.10.1109/ITSC.2016.7795689 ↗
27. Macroscopic fundamental diagrams for freeway networks: Theory and observation10.3141/2260-02 ↗
28. An excess-demand dynamic traffic assignment approach for inferring origin-destination trip matrices10.1007/s11067-014-9277-z ↗
29. Shi, H., Yao, Q., Guo, Q., Li, Y., Zhang, L., Ye, J., Li, Y., and Liu, Y. (2020, January 20–24). Predicting origin-destination flow via multi-perspective graph convolutional network. Proceedings of the 2020 IEEE 36th International Conference on Data Engineering (ICDE), Dallas, TX, USA.10.1109/ICDE48307.2020.00178 ↗
30. Contextualized spatial–temporal network for taxi origin-destination demand prediction10.1109/TITS.2019.2915525 ↗
31. Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions10.1109/TITS.2019.2924971 ↗
32. Wang, Y., Yin, H., Chen, H., Wo, T., Xu, J., and Zheng, K. (2019, January 4–8). Origin-destination matrix prediction via graph convolution: A new perspective of passenger demand modeling. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA.10.1145/3292500.3330877 ↗
33. Zhang, J., Zheng, Y., and Qi, D. (2017, January 4–9). Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.10.1609/aaai.v31i1.10735 ↗
34. Tian, Y., and Pan, L. (2015, January 19–21). Predicting short-term traffic flow by long short-term memory recurrent neural network. Proceedings of the 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, China.10.1109/SmartCity.2015.63 ↗
35. Fu, R., Zhang, Z., and Li, L. (2016, January 11–13). Using LSTM and GRU neural network methods for traffic flow prediction. Proceedings of the 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China.10.1109/YAC.2016.7804912 ↗
36. A comprehensive survey on graph neural networks10.1109/TNNLS.2020.2978386 ↗
37. Li, Y., Yu, R., Shahabi, C., and Liu, Y. (2017). Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv.
38. Yu, B., Yin, H., and Zhu, Z. (2017). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv.10.24963/ijcai.2018/505 ↗
39. Forecasting road traffic speeds by considering area-wide spatio-temporal dependencies based on a graph convolutional neural network (GCN)10.1016/j.trc.2020.02.013 ↗
40. T-gcn: A temporal graph convolutional network for traffic prediction10.1109/TITS.2019.2935152 ↗
41. Bai, J., Zhu, J., Song, Y., Zhao, L., Hou, Z., Du, R., and Li, H. (2021). A3t-gcn: Attention temporal graph convolutional network for traffic forecasting. ISPRS Int. J.-Geo-Inf., 10.10.3390/ijgi10070485 ↗
42. Fang, S., Zhang, Q., Meng, G., Xiang, S., and Pan, C. (2019, January 10–16). GSTNet: Global Spatial-Temporal Network for Traffic Flow Prediction. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19), Macao, China.10.24963/ijcai.2019/317 ↗
43. A path-based user-equilibrium traffic assignment algorithm that obviates path storage and enumeration10.1016/j.trb.2006.02.008 ↗
44. Multiscale dynamical embeddings of complex networks10.1103/PhysRevE.99.062308 ↗
45. Kipf, T.N., and Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv.
46. Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J. (2018, January 3–8). Hierarchical graph representation learning with differentiable pooling. Proceedings of the Advances in Neural Information Processing Systems, Montreal, Canada.
47. Geisberger, R., Sanders, P., Schultes, D., and Delling, D. (June, January 30). Contraction hierarchies: Faster and simpler hierarchical routing in road networks. Proceedings of the Experimental Algorithms: 7th International Workshop, WEA 2008, Provincetown, MA, USA.
48. Guo, S., Lin, Y., Feng, N., Song, C., and Wan, H. (February, January 27). Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
49. Neural network meets DCN: Traffic-driven topology adaptation with deep learning10.1145/3224421 ↗
50. Wu, Z., Pan, S., Long, G., Jiang, J., and Zhang, C. (2019). Graph wavenet for deep spatial-temporal graph modeling. arXiv.10.24963/ijcai.2019/264 ↗
51. Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.i., and Jegelka, S. (2018, January 10–15). Representation learning on graphs with jumping knowledge networks. Proceedings of the 35th International Conference on Machine Learning, PMLR, Stockholm, Sweden.
52. Li, F., and Wang, S. (2005, January 6–8). Determining route traffic flows for traffic assignment problem with Frank-Wolfe algorithm. Proceedings of the IEEE Proceedings, Intelligent Vehicles Symposium, Las Vegas, NV, USA.