Vehicle scheduling based on variable trip times with expected on‐time performance

Shen, Yindong; Xu, Jie; Wu, X.

doi:10.1111/itor.12321

Cited by 5 publications

(5 citation statements)

References 31 publications

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“…Wang & Shen, 2007; S. Wang et al, 2018), the multiple depot VSPs (Hadjar et al, 2006), vehicle scheduling with multi-vehicle types (Ceder, 2011), vehicle and crew scheduling problem (Amberg et al, 2019;Kliewer et al, 2012), integrated approach to timetabling and vehicle scheduling (Ibarra-Rojas et al, 2014;Schmid & Ehmke, 2015), dynamic control method (Bie et al, 2020;Khan et al, 2019;M. Li et al, 2011;Xie & Jiang, 2016), reliability of trip times (Liu et al, 2013;Naumann et al, 2011;Shen et al, 2017), and other intelligent transportation systems (Adeli & Ghosh-Dastidar, 2004;Adeli & Jiang, 2009;Gao et al, 2020Gao et al, , 2021Ghosh-Dastidar & Adeli, 2006;X. Jiang & Adeli, 2003; S. Wang, Wei, et al, 2019;Xu et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Bie

Wang

et al. 2021

Computer aided Civil Eng

113

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This paper develops a vehicle scheduling method for the electric bus (EB) route considering stochastic volatilities in trip travel time and energy consumption.First, a model for estimating the trip energy consumption is proposed based on field-collected data, and the probability distribution function of trip energy consumption considering the stochastic volatility is determined. Second, we propose the charging strategy to recharge buses during their idle times. The impacts of stochastic volatilities on the departure time, the idle time, the battery state of charge, and the energy consumption of each trip are analyzed. Third, an optimization model is built with the objectives of minimizing the expectation of delays in trip departure times, the summation of energy consumption expectations, and bus procurement costs. Finally, a real bus route is taken as an example to validate the proposed method. Results show that reasonable idle times can be generated by optimizing the scheduling plan, and it is helpful to stop the accumulation of stochastic volatilities. Collaboratively optimizing vehicle scheduling and charging plans can reduce the EB fleet and delay times while meeting the route operation needs.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Bie

Wang

et al. 2021

Computer aided Civil Eng

113

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“…Since the bus departs from the depot and finally returns to the same place, the purchasing cost of the outgoing bus is only counted once. Consistent with most existing studies [22,23,36], the bus purchasing cost is included in the cost of a pullout arc. For a pull-in arc, j is the destination depot; thus, the energy consumption is just the energy consumed by the distance from trip i to j .…”

Section: Objective Functionmentioning

confidence: 99%

“…[21] designed a probabilistic model to minimize total costs and maximize on‐time performance. By comparing and analysing the on‐time performance of buses in the case of different trip times, they also found out that it was more intuitive and realistic for schedulers to design variable trip times than fixed‐trip times [22].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Assuming that trip time follows a probability distribution, Shen et al [21] designed a probabilistic model to minimize total costs and maximize on-time performance. By comparing and analysing the on-time performance of buses in the case of different trip times, they also found out that it was more intuitive and realistic for schedulers to design variable trip times than fixed-trip times [22]. While these studies provide valuable insights into bus route planning and operations scheduling, they usually research single-fleet operating environments and ignore the mixed operating conditions of CBs and EBs.…”

Section: Literature Reviewmentioning

confidence: 99%

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Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework

Duan

Guan

et al. 2021

IET Intelligent Trans Sys

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Bus scheduling plays a significant role in public transportation and supports the sustainable development of transportation systems. Challenges are beginning to appear with the newly emerging electric buses (EBs), as scheduling changes due to fleet composition make traditional fixed timetables no longer able to satisfy operational needs. Moreover, the fixed-trip time hypothesis has been inappropriate for large cities due to the variety of urban traffic statuses. This paper proposes an optimal framework for reforming the mixed operation schedule for electric buses and traditional fuel buses under stochastic trip times. Based on the primary grouping genetic algorithm (GGA), a straightforward framework with a Monte Carlo simulation is presented to optimize the scheduling scheme. Case studies based on the operating environment and service trips of real bus lines in Beijing are conducted to verify the effectiveness of the proposed model by considering both the composition of fleet types and time stochasticity. Additionally, the impacts of stochasticity, fleet composition, government subsidies and cost factors on operational costs are investigated. Considering stochastic trip times, the achieved scheduling strategies can provide the optimal proportion of electric and traditional fuel buses and make a crucial impact on operational costs. INTRODUCTIONRecently, considering climate change and health impacts, air quality has attracted more attention worldwide [1]. Therefore, reducing the use of fossil fuel is a commonly consentaneous measure [2,3]. In public transportation, most conventional bus types (such as heavy fuel diesel buses) make significant contributions to air pollution and greenhouse gases due to high daily mileages [4,5]. The introduction of electrified transportation solutions is part of a wide range of policy options worldwide [6,7]. Compared with traditional fuel buses (CBs), electric buses (EBs) have considerable inherent advantages such as zero exhaust pipe emissions, lower energy costs, high comfortability, and low noise emissions [8,9]. Therefore, the transformation of the public transport fleet from conventional fuel buses to electric or alternative energy buses can significantly reduce exhaust pipe emissions and improve air quality. At present, EBs have been introduced into the market and operated in many countries (e.g. China, Norway, Sweden, and Germany). However, high ownership costs and limitations on driving range and charging speed make it hard for EBs to com-This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Still, few studies on operational decisions have exploited AVL data, while so far, the only problem addressed has been the generation of optimal vehicle schedules. The associated Vehicle Scheduling Problem (VSP) is that of the optimal allocation of vehicles to trips, based on precompiled timetables, yet in the presence of AVL data, operators can devise more robust vehicle schedules based on observed trip times [71][72][73][74]. Indeed, AVL data have allowed for extracting periods of homogeneous running time [72] and trip time probability distributions [73,74] to determine reliable vehicle schedules that enhance service reliability;…”

Section: Operational Levelmentioning

confidence: 99%

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Iliopoulou

Κεπαπτσόγλου

2019

Eur. Transp. Res. Rev.

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Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.

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Vehicle scheduling based on variable trip times with expected on‐time performance

Cited by 5 publications

References 31 publications

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Optimization of electric bus scheduling considering stochastic volatilities in trip travel time and energy consumption

Reforming mixed operation schedule for electric buses and traditional fuel buses by an optimal framework

Combining ITS and optimization in public transportation planning: state of the art and future research paths

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