Scheduling strategy for transit routes with modular autonomous vehicles
|نوع نگارش مقاله||
scopus – master journals – JCR
۴٫۲۷۶ در سال ۲۰۲۰
۲۶ در سال ۲۰۲۱
۰٫۹۰۱ در سال ۲۰۲۰
|شاخص Quartile (چارک)||
Q1 در سال ۲۰۲۰
خرید محصول توسط کلیه کارت های شتاب امکان پذیر است و بلافاصله پس از خرید، لینک دانلود محصول در اختیار شما قرار خواهد گرفت و هر گونه فروش در سایت های دیگر قابل پیگیری خواهد بود.
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:|
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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