Scheduling strategy for transit routes with modular autonomous vehicles

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scopus – master journals – JCR

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۴٫۲۷۶ در سال ۲۰۲۰

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۲۶ در سال ۲۰۲۱

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Scheduling strategy for transit routes with modular autonomous vehicles

فهرست مطالب مقاله:


The  Modular Autonomous Vehicle (MAV) systems allow a vehicle module to join  onto and       ۲۳ detach from other modules to dynamically adjust vehicle capacity. It potentially renders       ۲۴ transit  agencies more flexibility to  deal with  the  temporal  fluctuations of  passenger       ۲۵ demand. In this work, we  propose a strategy for  flexible MAV scheduling on  transit routes       ۲۶ to meet the time-varying passenger demand. The  proposed strategy is formulated as  a bi-          ۲۷ objective optimization model considering both the utilization of vehicles and service qual-        ۲۸ ity.  The  model determines the scheduled departure times from the terminals, the length of          ۲۹

MAV for  each scheduled trip, and the assignment of modules to all scheduled trips, simul-        ۳۰ taneously. The e-constraint method is adopted to solve the developed model and the fuzzy       ۳۱ satisfying approach is  employed to select the best possible solution. We  implement the       ۳۲ proposed strategy in  a real-world case study in  comparison with a traditional strategy to       ۳۳

demonstrate the effectiveness of the proposed strategy. The results show that the proposed       ۳۴ strategy can  remarkably improve the utilization of vehicles and also make passengers more        ۳۵ convenient. Specifically, it leads to an  ۸۴٫۹% reduction in the total empty seat, as well as a          ۳۶

۱۲٫۶۲%  reduction in  the total passenger waiting time.                                   

بخشی از متن مقاله:
  1. Introduction

44                Urban bus  systems often adopt larger bus  vehicles that can  potentially transport more passengers on  each run to  effi-

45         ciently utilize the road resources. However, it is challenging to schedule large buses on  a transit route with great temporal

46         fluctuations of travel demand, especially in the off-peak periods. Transit agencies have to make a trade-off between opera-

47          tion efficiency, e.g.  utilization of vehicles, and passengers’ experience such as  the waiting time. For instance, dispatching

48         buses with high frequencies during the off-peak periods reduces passengers’ waiting time, but also  results in  low  vehicle

49          occupancy, leading to inefficient bus  operation and the waste of energy (Potter, 2003). Thus, transit agencies tend to dispatch

50         buses in the off-peak periods with low  frequencies that inevitably increase the waiting time of passengers, making the tran-

51        sit  systems less  attractive, and in turn, reducing the patronage.

52               The Modular Autonomous Vehicle (MAV) system provides new opportunities to better match the supply of transit vehi-

53        cles  with the time-varying demand of passengers. The  concept of the MAV is illustrated in  Fig. 1(a).  The  MAV system has

54         been tested in  Dubai, UAE, and is expected to  be  implemented in  the near future (Tarek, 2018). In the MAV system, each

module can  drive autonomously on  roads, join  onto and detach from other modules when in motion. The  coupled vehicle

56         modules operate as a single modular bus  (Zhang et al., 2020), allowing passengers to walk from one  module to another seam-

57         lessly to balance the occupancies among modules. As in Fig. 1(b),  The MAV system is able  to adjust the capacity of vehicles

58         dynamically that renders transit agencies more flexibility to  deal with the temporal fluctuations of  travel demand. For

59         instance, the MAVs could be coupled with a various number of modules flexibly to provide proper vehicle capacities during

60         peak and off-peak periods, without a dramatic change in service frequency.

61               A scheduling tool  that takes advantage of the modularity features is essential to better unleash the potential of the MAV

62         system. Thus, in this work, we propose a flexible scheduling strategy for the MAVs on a single transit route to meet the time-

63         varying demand of passengers. The proposed strategy is adaptive to  passenger demand, by adjusting the scheduled depar-

64         ture time of the MAV trips from the terminals and the length of the MAV (i.e., number of coupled modules) for each sched-

65         uled trip. An optimization model is developed to efficiently operate the MAVs on the transit routes. Specifically, we develop a

66         bi-objective model to  optimize the proposed strategy with the objectives that balance the operational priorities of transit

67         agencies (i.e. vehicle utilization) and the convenience of passengers such as the waiting time. In addition, by jointly consid-

68         ering the timetabling and vehicle module assignment, the scheduled departure time from the terminals, length of MAV for

69         each scheduled trip, and the assignment of the modules to scheduled trips can  be determined simultaneously. Then,  we  also

70         offer  an effective algorithm to solve the developed model and to select the best possible solution. Finally, the effectiveness of

71         the proposed strategy is demonstrated by comparing it with a traditional strategy in a real-world case  study.

72               This paper is organized as follows. In Section 2, relevant past works in current literature are  reviewed. Section 3 proposes

73         the model that optimizes the MAV operation strategy. In Section 4, we  evaluate the proposed strategy based on a real-world

74        case  study. Section 5 concludes the paper and some possible directions for future research are  offered.

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