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.
Highlights
Public transit systems play an indispensable role in people’s daily life
For a fixed number of total requests received per trip, we vary the degree of dynamism (DOD) from 0% to 100% to generate different numbers of static and dynamic requests. e simulation results in Table 2 illustrate that with the increasing DOD, the riding time R decreases from 15.76 min to 15.60 min and stabilizes after DOD 75%. is trend is mainly caused by the decreased number of accepted passengers and the fewer detours that are taken by the vehicles
The waiting time A increases dramatically with the increasing DOD. is is because in scenarios with a high DOD, the dynamic requests may be frequently inserted before the prebooking requests or even before some dynamic requests that have already received the scheduled pick-up time. is unexpected delay inevitably deteriorates the reliability of the service and degrades the service level of flex-route transit systems
Summary
Public transit systems play an indispensable role in people’s daily life. High volume transit modes such as subway and conventional bus lines can provide convenient, rapid, and efficient service in high-density urban areas. Similar to the conventional fixed-route service, flex-route transit has a base route with several mandatory checkpoints located in high-density demand zones These checkpoints are assigned with fixed departure times to serve regular customers and are synchronized with other public transportation lines. E predetermined schedule is likely to be modified in real time due to dynamic events such as travel time fluctuations [12,13,14], cancellations of requests, customer no-shows [15, 16], and dynamic requests [17, 18] All these unpredictable events severely influence the on-time performance of the flex-route transit and inevitably degrade the system reliability [19].
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