A Conceptual Framework for Developing the Smart Routing Recommendation Model on Flexible Public Transport (FPT)
Article Sidebar
Main Article Content
Abstract
A flexible public transportation system has the flexibility in the routing to pick up and drop off passengers with no fixed routes. Maximizing the efficiency of the transportation system requires the flexible routing that can direct a vehicle in the system to pick up passengers on request and re-routing the vehicles in the system in real time. This article discusses the model for the flexible vehicle routing for a flexible public transportation system and the application of artificial intelligence for forecasting the passenger demand. An artificial intelligence model, Long-Short-Term Memory (LSTM) is proposed for the forecast of the passenger demands and their locations. The flexible vehicle routing with LSTM will help improve the utilization of vehicles and the service level for the passengers. They can get picked up and drop-offs with the minimized time within the time-limit constraints. In addition, the model can be further developed into an algorithm, which can be implemented with information technologies to better manage a flexible public transportation system.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
References
Agra, A., Christiansen, M., Figueiredo, R., Hvattum, L. M., Poss, M., & Requejo, C. (2013). The robust vehicle routing problem with time windows. Computers and Operations Research, 40(3), 856–866.
Aiko, S., Thaithatkul, P., & Asakura, Y. (2018). Incorporating User Preference into Optimal Vehicle Routing Problem of Integrated Sharing Transport System. Asian Transport Studies, 5(1), 98-116.
Baños, R., Ortega, J., Gil, C., Fernández, A., & de Toro, F. (2013). A Simulated Annealing-based parallel multi-objective approach to vehicle routing problems with time windows. Expert Systems with Applications, 40(5), 1696–1707.
Blum, C., & Roli, A. (2003). Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Survey, 35(3), 268-308.
Crevier, B., Cordeau, J. F., & Laporte, G. (2007). The multi-depot vehicle routing problem with inter-depot routes. European Journal of Operational Research, 176(2), 756–773.
Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem, Management Science, 6(1), 80-91.
Figliozzi, M. A. (2010). An iterative route construction and improvement algorithm for the vehicle routing problem with soft time windows. Transportation Research Part C: Emerging Technologies, 18(5), 668–679.
Gendreau, M., Laporte, G., & Séguin, R. (1996). A Tabu Search Heuristic for the Vehicle Routing Problem with Stochastic Demands and Customers. Operations Research, 44(3), 423-528.
Gendreau, M., Laporte, G., & Séguin, R. (1995). An Exact Algorithm for the Vehicle Routing Problem with Stochastic Demands and Customers. Transportation Science, 29(2), 143-155.
Giuffrida, N., Fajardo-Calderin, J., Masegosa, A. D., Werner, F., Steudter, M., & Pilla, F. (2022). Optimization and Machine Learning Applied to Last-Mile Logistics: A Review. Sustainability, 14(9), 1-16.
Ho, W., Ho, G. T. S., Ji, P., & Lau, H. C. W. (2008). A hybrid genetic algorithm for the multi-depot vehicle routing problem. Engineering Applications of Artificial Intelligence, 21(4), 548–557.
Hulagu, S., & Celikoglu, H. B. (2022). An Electric Vehicle Routing Problem with Intermediate Nodes for Shuttle Fleets. IEEE Transactions on Intelligent Transportation Systems, 23(2), 1223–1235.
Li, B., Krushinsky, D., Reijers, H. A., & van Woensel, T. (2014). The Share-A-Ride Problem: People and parcels sharing taxis. European Journal of Operational Research, 238(1), 31–40.
Li, J., Li, Y., & Pardalos, P. M. (2016). Multi-depot vehicle routing problem with time windows under shared depot resources. Journal of Combinatorial Optimization, 31(2), 515–532.
Lin, Y., Li, W., Qiu, F., & Xu, H. (2012). Research on Optimization of Vehicle Routing Problem for Ride-sharing Taxi. Procedia - Social and Behavioral Sciences, 43(1), 494–502.
Liyanage, S., Abduljabbar, R., Dia, H., & Tsai, P. W. (2022). AI-based neural network models for bus
passenger demand forecasting using smart card data. Journal of Urban Management, 11(3), 365–380.
Ma, B., Hu, D., Chen, X., Wang, Y., & Wu, X. (2021). The vehicle routing problem with speed optimization for shared autonomous electric vehicles service. Computers and Industrial Engineering, 161(1), 1-17.
Petit, A. and Ouyang, Y. (2022). Design of heterogeneous flexible-route public transportation networks under low demand. Transportation Research Part C: Emerging Technologies, 138(1), 1–22.
Polacek, M., Hartl, R. F., Doerner, K., & Reimann, M. (2004). A Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows. Journal of Heuristics, 10(1), 613–627.
Talbi, E. G. (2009). Metaheuristics From Design to Implementation. Canada: John Wiley and Sons.
Taş, D., Dellaert, N., van Woensel, T., & de Kok, T. (2013). Vehicle routing problem with stochastic travel times including soft time windows and service costs. Computers and Operations Research, 40(1), 214–224.
Tholen, M., Beirigo, B. A., Jovanova, J., & Schulte, F. (2021). The Share-A-Ride Problem with Integrated Routing and Design Decisions: The Case of Mixed-Purpose Shared Autonomous Vehicles. Computational Logistics: Proceedings of the 12th International Conference (pp. 347-361). Enschede, The Netherlands : Springer.
Vidal, T., Crainic, T. G., Gendreau, M., & Prins, C. (2013). A hybrid genetic algorithm with adaptive diversity management for a large class of vehicle routing problems with time-windows. Computers and Operations Research. 40(1), 475–489.
Xiao, W., and Xu, H. (2022). A novel bus scheduling model based on passenger flow and bus
travel time prediction using the improved cuckoo search algorithm. 2022 International
Conference on Big Data, Information and Computer Network (BDICN). China : Sanya .
Zhang, C., Zhu, F., Wang, X., Sun L., Tang H., and Lv, Y. (2022). Taxi Demand Prediction Using Parallel Multi-Task Learning Model. IEEE Transactions on Intelligent Transportation
Systems, 23(2), 794-803.