A Slack Departure Strategy for Demand Responsive Transit Based on Bounded Rationality

Abstract

Demand responsive transit (DRT) is emerging as one of the most potential travel modes to satisfy flexible travel demands. Nevertheless, how to attract more passengers has become a critical problem in the success of DRT projects. It is necessary to take into account the psychological factors impacting passengers’ choices. The study proposes a slack departure strategy considering boundedly rational passengers, which introduces passengers’ decision-making psychology into the optimization process. The strategy can adjust the departure time of passengers to adjacent time windows. The discount-incentive mechanism is presented to attract passengers to accept the changes while maintaining the quality of service. On this basis, the theory of bounded rationality is applied to describe the decision-making process of passengers. We construct a multiobjective programming model to analyze the operator-passenger interactive effect. To address the multiobjective problem, a two-phase heuristic algorithm is established to get the Pareto solution for the model. A numerical experiment is carried out on the Sioux Falls network. The case study of Beijing is discussed to evaluate the effectiveness of the strategy. The results indicate that the slack departure strategy can significantly benefit both the operators and passengers. The operating profit substantially increases by up to 63%. Meanwhile, the passenger’s general travel cost declines by 12%. The optimal discount rate of the incentive mechanism is 20%. Therefore, the study contributes to comprehending the passenger’s decision-making psychology and providing a new optimization strategy for the operator of DRT.

Towards a more flexible demand responsive transit service with compensation mechanism considering boundedly rational passengers

“…Penalty cost of passengers : The penalty cost can generally be separated into two terms: the deviation $$P{C_p}$$ between the desired pick‐up time and the actual pick‐up time, the deviation $$D{C_p}$$ between the desired delivery time and the actual delivery time. To understand the psychological effect of the deviation, the prospect theory [46, 54, 55] is incorporated to describe penalty cost.…”

Modeling the Dynamic Choice of Travel Locations With the Spatial-Temporal Bounded Rationality

“…Ge and Zhou [28] put forth the bounded rational routing model, known as DUE (dynamic user equilibrium), which allows for the endogenous determination of undifferentiated intervals. H. Wang et al [29] used bounded rationality (BR) to improve Demand Responsive Transit. Nonetheless, a definitive solution has yet to be provided.…”

An Optimization Model for the Demand-Responsive Transit With Non-Fixed Stops and Multi-Vehicle Type

“…Ayadi et al [8][9], Jin et al [10] and Guan et al [11] solved DRT scheduling problem by introducing penalty function into the proposed model, aiming at maximizing benefits of both enterprises and passengers, which could significantly improve passengers' travel satisfaction. A model aiming at maximizing operator profits, maximizing the number of boarding passengers and minimizing total travel costs of passengers is established to optimize the vehicle route [12][13][14] [15]. Above models only take into account the benefits of enterprises and passengers, but regardless of the social benefits, especially environmental factors.…”