Integrated optimization of multi‐vehicle‐type timetabling and scheduling to accommodate periodic passenger flow

Abstract

A method was developed for integrating the process of multitype bus timetable generation and chains of trips formed by considering multiperiod passenger flow characteristics. First, the cumulative passenger demand curve was fitted and combined with the vehicle trajectory. Second, according to the results, the combined departure interval for multiple vehicle types was determined, and a one‐way possible timetable set was established. Third, considering the departure time window, the upward and downward possible timetable sets were connected. A bi‐objective mixed integer nonlinear programming model based on a spatiotemporal network was developed through two‐way matching. The objective of the model was to minimize the generalized fleet cost (a problem solved by the continuous deficit function of multiple vehicle types) and passenger waiting time. For two‐way matching, the computational complexity was reduced according to the postroot jump traversal rule, and the 𝑘‐shortest path algorithm was used to solve the bi‐objective Pareto‐optimal solution set. Finally, the Harbin No. 96 bus line was used as an example to validate the proposed model and algorithm. The optimization reduced the bus purchase cost by 51.28%, energy loss cost by 31.78%, and passenger waiting time by 37.07%, indicating that the proposed model can significantly reduce the costs for bus companies and passengers.

Joint design of timetabling and capacity for mixed transit system with autonomous and human-driven buses considering passengers’ acceptance

“…There is a rich literature on bus timetable (Ali et al, 2003;Gavish & Shlifer, 1979;Liu et al, 2007;Zuo et al, 2015;Tang et al, 2022;Hu et al, 2023;Huang et ai., 2023). This section provides a review of the most recent relevant literature, divided into two subsections on HBs and ABs.…”

Optimizing Multi-Vehicle Demand-Responsive Bus Dispatching: A Real-Time Reservation-Based Approach

“…Under such conditions, the formulated vehicle routes are also consistent, so it is called a static multi-vehicle scheduling problem. Hu [28] established a two-stage model for the multi-model vehicle scheduling problem including dynamic demand and used the hybrid quantum evolutionary algorithm to solve the model, which met the real-time requirements of vehicle scheduling.…”

Integrated optimization of timetable, bus formation, and vehicle scheduling in autonomous modular public transport systems

“…Carosi et al (2019) developed a multicommodity-flow model to optimize both timetable and vehicle scheduling for a complex bidirectional bus line with multiple patterns in one direction, corresponding to different terminal choices. Hu et al (2023) introduced a method for integrating the generation of multitype bus timetables and the formation of chains of trips, considering the multiperiod characteristics of passenger flow on a bidirectional bus line. These studies have explored the advantages of integration problem, utilizing flexible scheduling tactics such as multiple trip patterns and vehicle types.…”