Real-Time Bus-Holding Control Strategy to Reduce Passenger Waiting Time

Abstract

This paper presents research on a real-time bus-holding control strategy that minimizes passenger waiting time. This bus-holding strategy forces buses to hold at stations for a while after a passenger exchange is finished. A mathematical model is proposed to determine the optimal holding time. Both onboard and on-station passenger waiting times have been taken into account. Given the real-time nature of the problem, a heuristic method based on gradient descent algorithms was developed. The proposed control strategy was evaluated by using data derived from a shuttle bus rapid transit (BRT) line in the city of Mashhad, Iran. The BRT line was simulated and calibrated by available empirical and real-time data from the automatic vehicle location and automatic passenger counting systems. The results indicate that the proposed bus-holding strategy reduces total passenger waiting time by 8.65%.

Improving Bus Operations through Integrated Dynamic Holding Control and Schedule Optimization

“…Control stops are designated every 3 stops. Therefore, we have = 1 ( = 3, 6,9,12), and = 0 for the other stops.…”

“…Furthermore, var( ,1 ) = 0 since ,1 is a deterministic value. Equation (12) can be rewritten as follows:…”

“…Hall et al [10], Delgado et al [11] take transfer time into account when measuring the waiting time. Asgharzadeh & Shafahi [12] minimize passengers' total travel time which consist of passengers' ordinary waiting time, extra waiting time when the bus is at full capacity, and the waiting time of onboard passengers.…”

Combining ITS and optimization in public transportation planning: state of the art and future research paths

“…In a different approach, Yu and Yang [91] used support vector machine regression to more accurately predict vehicle departure times per stop and subsequently employed GA optimization to determine the optimal holding time. A few studies directly exploited real-time APC/ AFC data to model passenger flows in holding control optimization attempting to minimize travel times and delays due to holding [80,[92][93][94].…”

“…Finally, control strategies are almost exclusively verified using simulation, yet the implementation of a real-time holding method involves technical challenges that can be overlooked in a simulation environment [96]. Although it can be hard to convince agencies to allow experimentation [94], such experiments lead to valuable conclusions and advance both research and practice.…”