Vehicle Routing and Scheduling of Flex-Route Transit under a Dynamic Operating Environment

Abstract

To improve the reliability, responsiveness, and productivity of the flex-route transit service, this paper investigates the vehicle scheduling and routing problem under a dynamic operating environment. First, we discuss the new operating polices after the introduction of intelligent transportation systems (ITSs), including automatic vehicle location (AVL) system, mobile data terminal (MDT), and computer-aided dispatch (CAD) system. Second, a mixed integer programming (MIP) formulation is employed to solve the offline routing problem. Third, an online scheduling scheme is presented to tackle different dynamic events, such as dynamic requests, travel time fluctuations, cancellations of requests, and customer no-shows. Finally, simulation experiments based on a real-life flex-route transit service are conducted to assess the influence of different dynamic events. The results demonstrate that the proposed scheduling scheme is reliable for coping with various dynamic events, and our findings can be used to guide the policy making of flex-route transit services.

An optimized two‐phase demand‐responsive transit scheduling model considering dynamic demand

“…However, the situation with dynamic demands in DRT is much more common in daily life [16,17]. Zheng et al [18] and Sun et al [19] studied the DRT vehicle scheduling problem under dynamic scheduling and established a scheduling model considering the benefits for both passengers and operators. The results show that it can significantly improve vehicle occupancy as well as passenger satisfaction.…”

“…Zheng et al. [18] and Sun et al. [19] studied the DRT vehicle scheduling problem under dynamic scheduling and established a scheduling model considering the benefits for both passengers and operators.…”

The Multi-Type Demands Oriented Framework for Flex-Route Transit Design

“…In the early years, scholars [20][21][22][23] provided fixed service and dial-a-ride service for common passengers and special demands, respectively. Later, many studies [24][25][26][27][28][29][30][31][32] designed flex-route transit to serve static-station-based demands and dynamic-stationrelated demands. And bus operators successfully applied it in rural areas in the USA [33].…”

“…Differently, Flex-route Transit needs to distinguish static stations beforehand, for serving stable demands fixedly. Most of the relevant studies [24][25][26][27][28][29][30][31][32] relied on a single feature, and we consider their methods as different variants of the single-feature classification method. For example, Qiu et al [24] calculated the passenger flow of each station and manually chose the large-flow stations as static stations.…”

“…Similarly, the base routes in flex-route transit are planned with all static stations visited. Most scholars [24][25][26][27][28][29][30] only set one shortest route, while Qiu et al [31] generated two base routes for round-trip demands. Furthermore, Han and Fu [32] proposed a multi-route planning model with consideration of high-frequency and multi-direction demands, but these base routes suffered from route-length problems.…”