Electrical and Electronic Engineering; General Computer Science; Control and Systems Engineering; Intelligent Transportation Systems; Artificial Intelligence; Traffic Management Systems; PEMAP
Abstract :
[en] In recent years, transportation systems and communities have faced major disruptions, such as heavy traffic, accidents, natural disasters, and malicious acts. Managing these disruptions and their impact on transportation systems is crucial. This paper proposes PEMAP, Post-Event MAnagement of transPortation systems, an intelligence-based framework for redirecting traffic after disturbances. PEMAP aims to ensure the swift recovery of network services despite various disruptions, benefiting transportation systems and communities. It is composed of four phases: indices selection and evaluation, criticality index calculation, diversion route decision making, and decision application. Relevant indices are evaluated and calculated into one criticality index using microscopic simulation. An intelligent model for vehicular re-routing, considering the criticality index, is then introduced. In the last phase, Vehicular ad-hoc networks (VANETs) is used to facilitate communication and implementation of rerouting decisions. The proposed implementation uses the Veins framework along with Python and C++ programming languages.
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Bibliography
Redzuan, A., Zakaria, R., Anuar, A., Aminudin, E., Yusof, N., Road network vulnerability based on diversion routes to reconnect disrupted road segments. Sustainability, 14, 2022, 2244.
Ahmed, S., Dey, K., Resilience modeling concepts in transportation systems a comprehensive review based on mode, and modeling techniques. J Infrastruct Preserv Resil, 1, 2020.
Jenelius, E., Petersen, T., Mattsson, L.-G., Road network vulnerability: Identifying important links and exposed regions. Transp Res A, 20, 2006.
Feng, Y., Wang, H., Chang, C., Lu, H., Intrinsic correlation with betweenness centrality and distribution of shortest paths. Mathematics, 10(14), 2022.
Akbarzadeh, M., Memarmontazerin, S., Derrible, S., Salehi, F., The role of travel demand and network centrality on the connectivity and resilience of an urban street system. Transportation, 46, 2019.
Gauthier, P., Furno, A., El Faouzi, N.-E., Road network resilience: How to identify critical links subject to day-to-day disruptions. Transp Res Rec: J Transp Res Board, 2672, 2018, 036119811879211.
el rashidy, R., Grant-Muller, S., An assessment method for highway network vulnerability. J Transp Geogr 34 (2014), 34–43.
Li, F., Jia, H., Luo, Q., Li, Y., Yang, L., Identification of critical links in a large-scale road network considering the traffic flow betweenness index. PLOS ONE, 15, 2020, e0227474.
Jenelius, E., Redundancy importance: Links as rerouting alternatives during road network disruptions. Procedia Eng 3 (2010), 129–137.
Scott, D., Novak, D., Aultman-Hall, L., Guo, F., Network Robustness Index: A new method for identifying critical links and evaluating the performance of transportation networks. J Transp Geogr 14 (2006), 215–227.
Sullivan, J., Novak, D., Aultman-Hall, L., Scott, D., Identifying critical road segments and measuring system-wide robustness in transportation networks with isolating links: A link-based capacity-reduction approach. Transp Res A 44:5 (2010), 323–336.
Elsafdi, O.H., Development of inherent and dynamic resilience in traffic networks: Microscopic simulation, dynamic stochastic assignment and Bayesian decision models. (Ph.D. thesis), 2020, 280, 10.22215/etd/2020-14031.
Tian, Y., Liu, X., Li, Z., Tang, S., Shang, C., Wei, L., Identification of critical links in urban road network considering cascading failures. Math Probl Eng, 2021, 2021.
Lee, Y.-H., Kim, Y.-C., Seo, H., Selecting disaster waste transportation routes to reduce overlapping of transportation routes after floods. Sustainability, 14, 2022, 2866.
Khan, M., Popa, I., Zeitouni, K., Borcea, C., Proactive vehicle re-routing strategies for congestion avoidance. Proceedings - IEEE international conference on distributed computing in sensor systems, DCOSS 2012, 2012.
Codeca L, Härri J. Monaco SUMO Traffic (MoST) Scenario: A 3D Mobility Scenario for Cooperative ITS. In: SUMO 2018, SUMO user conference, simulating autonomous and intermodal transport systems, May 14-16, 2018, Berlin, Germany. Berlin, GERMANY; 2018.
Wang, S., Djahel, S., Zhang, Z., Mcmanis, J., Next road rerouting: A multiagent system for mitigating unexpected urban traffic congestion. IEEE Trans Intell Transp Syst 17 (2016), 1–12.
Zhou, T., Deep learning models for route planning in road networks. (Ph.D. thesis), 2018, 10.13140/RG.2.2.30527.56486.
Jayapal, C., Realtime congestion avoidance using VANET. 2022.
Toulni, H., Nsiri, B., Boulmalf, M., Bakhouya, M., Tayeb, S., An approach to avoid traffic congestion using VANET. International conference on next generation networks and services, NGNS, 2014, 154–159, 10.1109/NGNS.2014.6990245.
Wang, M., Shan, H., Lu, R., Zhang, R., Shen, X., Bai, F., Real-time path planning based on hybrid-VANET-enhanced transportation system. IEEE Trans Veh Technol 64 (2015), 1664–1678.
Santamaria, A.F., Tropea, M., Fazio, P., De Rango, F., Managing emergency situations in VANET through heterogeneous technologies cooperation. Sensors, 18, 2018, 1461.
Knorr, F., Baselt, D., Schreckenberg, M., Mauve, M., Reducing traffic jams via VANETs. IEEE Trans Veh Technol 61 (2012), 3490–3498.
Henry E, Furno A, El Faouzi N-E. A Graph-based Approach with Simulated Traffic Dynamics for the Analysis of Transportation Resilience in Smart Cities. In: TRB 2019, 98th annual meeting transportation research board. Washigton, D.C., United States; 2019, p. 21p, URL: https://hal.archives-ouvertes.fr/hal-03258248, TRB 2019, 98th Annual Meeting Transportation Research Board, Washigton, D.C., ETATS-UNIS, 13-/01/2019 - 17/01/2019.
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