Abstract
The introduction of customized bus (CB) service intends to expand and elevate existing transit service, which offers an efficient and sustainable alternative to serve commuters. A probabilistic model is proposed to optimize CB service with mixed vehicle sizes in an urban setting considering stochastic bus arrival time and spatiotemporal demand, which minimizes total cost subject to bus capacity and time window constraints. The studied optimization problem is combinatorial with many decision variables including vehicle assignment, bus routes, timetables, and fleet size. A heuristic algorithm is developed, which integrates a hybrid genetic algorithm (HGA) and adaptive destroy-and-repair (ADAR) method. The efficiency of HGA-ADAR is demonstrated through numerical comparisons to the solutions obtained by LINGO and HGA. Numerical instances are carried out, and the results suggested that the probabilistic model considering stochastic bus arrival time is valuable and can dramatically reduce the total cost and early and late arrival penalties. A case study is conducted in which the proposed model is applied to optimize a real-world CB service in Xi’an, China. The relationship between decision variables and model parameters is explored. The impacts of time window and variance of bus arrival time, which significantly affect service reliability, are analysed.
Highlights
Public transportation agencies are experimenting with ondemand and shared technologies to augment traditional fixed-route bus services, such as flexible-route bus services [1], demand responsive transit (DRT) [2], ridesharing, microtransit [3], and customized bus (CB) service [4]. ese new, flexible transit solutions have tremendous potential to expand agencies’ service areas, attract new riders, fill transportation gaps, and provide more effective and sustainable ways to reach low-density communities and other traditionally hard-to-serve situations.As an emerging transportation mode, CB is similar to microtransit, which offers another option between the pricey convenience of taxis and slow, cheaper public transit
To demonstrate the performance of the probabilistic model, routing and scheduling for various sizes of networks with different PD pairs are optimized considering deterministic and stochastic bus arrival time. e minimized costs summarized in Table 6 are determined based on ten simulation runs. e results show that minimized total cost reduced by 4.5–13.3% for networks with various PD pairs if stochastic bus arrival time is considered, in which early and late arrival penalties reduced by 21.5–63.7%, user cost decreased by 0–10.2%, and operator cost increased by 0–17% because of increased fleet size
We propose a probabilistic model for optimizing CB service considering stochastic bus arrival time and some practical conditions, including time window and capacity in mixed bus fleet
Summary
Public transportation agencies are experimenting with ondemand and shared technologies to augment traditional fixed-route bus services, such as flexible-route bus services [1], demand responsive transit (DRT) [2], ridesharing, microtransit [3], and customized bus (CB) service [4]. ese new, flexible transit solutions have tremendous potential to expand agencies’ service areas, attract new riders, fill transportation gaps, and provide more effective and sustainable ways to reach low-density communities and other traditionally hard-to-serve situations. Is paper aims to optimize the CB service with a mixed bus fleet to minimize total cost, considering stochastic bus arrival time, which is known to be an NP-hard problem. 2. Literature Review e discussion of the literature review covers previous studies in transit planning with deterministic bus arrival time, vehicle routing problem with stochastic travel time, bus arrival time distribution, and feasible solution algorithms. Guo et al [13] proposed a model for optimizing passenger assignment and routing that minimized the total cost, and later they enhanced the model by considering time window [14] and time-dependent bus arrival time and path flexibility between stops [15]. Tas et al [34] developed a model for optimizing an SVRPSTT with soft time windows considering bus arrival time following a gamma distribution, which minimized the sum of transportation and service costs. None of the previous research has covered all the above issues. erefore, this study will fill the gap in comprehensive optimization for CB service and provide a significant basis for new areas of research to explore in the future
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