Shuttle Bus Rerouting and Rescheduling Problem considering Daily Demand Fluctuation

Abstract

In an urban shuttle system, shuttle buses need to pick up passengers waiting at predetermined stops according to their planned schedules (routes and timetables). However, in practice, passenger demand is unstable and has fluctuations, which means that passenger demand at a specific stop is likely to increase or decrease, causing low service quality, long passenger waiting times, and imbalanced utilization of bus capacity. Therefore, we introduce the shuttle bus rerouting and rescheduling strategy, based on which the operator can change the visited stops and arrival times of the shuttle buses and can operate backup buses to handle the passenger demand fluctuations. A three-dimensional space-time-state network is formulated to depict shuttle routes, timetables, and passenger-loading states, and the proposed problem can be formulated as a multicommodity network-flow optimization problem. To solve the model efficiently, we adopt the alternating direction method of multipliers (ADMM) decomposition method to decompose the original problem into several single shuttle routing subproblems. We test the model and algorithm in the 9-node network with three stops, and a larger scale Chicago sketch network is also adopted to demonstrate the effectiveness and efficiency of the proposed model and algorithm. The rerouting and rescheduling results for the Chicago case represent a 5.7% improvement relative to the results with the planned schedules.

Mathematical Modeling of Dynamic Supply Chains Subject to Demand Fluctuations

“…Transportation [29] Introduces the shuttle bus rerouting and rescheduling strategy, where the operator can change the visited stops and can operate backup buses to handle the passenger demand fluctuations.…”

Shuttle Bus Timetable Adjustment in Response to Behind-Schedule Commuter Railway Disturbance

“…Constraint (13) ensures that the waiting time of passenger u is non-negative, i.e., if passengers want to get on the bus, they must arrive at origin station earlier than the departure time of trip n at origin station. Constraint (14) expresses the relationship between the bus dwell time and the number of alighting passengers, as well as door opening and closing times. Constraint (15) validates that the passenger flow of each link is non-negative.…”

“…Likewise, shuttle bus services are deprived of the opportunity of feeding into well-connected and timely transfers. Therefore, timely adjustment of the shuttle bus schedule necessarily entails responding to behind-schedule commuter railway trains [14]; however, this involves taking the uncertainty of the nature of the disturbance into account. The shuttle bus is inclined, by rational programming, to propose multiple dimensional adjustments including route selections, stop locations, and timetable in order to handle different delay ranges.…”