Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis

Gao, Kun; Yang, Ying; Li, Aoyong; Li, Junhong; Yu, Bo

doi:10.1016/j.tra.2020.12.009

Cited by 35 publications

(19 citation statements)

References 29 publications

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“…Therefore, the variation range of SOC is set as SOC min = 20% and SOC max = 80%. Equation (23) illustrates a good linear relationship between SOC and charging times in this range.…”

Section: Description Of Charging and Discharging Process Of Ebsmentioning

confidence: 85%

“…The timetabling design and vehicle scheduling with EBs of all-stop scheduling take recharging and driving range into consideration [22][23][24][25]. Teng et al (2019) introduced a multi-objective particle swarm optimization algorithm to optimize the single-line bus timetabling and VSP that smoothes the headway and minimizes the number of vehicles and charging cost [26].…”

Section: Related Workmentioning

confidence: 99%

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Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

2021

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The emission of greenhouse gases from public transportation has aroused extensive public attention in recent years. Electric buses have the advantage of zero emission, which could prevent the further deterioration of environmental problems. Since 2018, the number of electric buses has exceeded that of traditional buses. Thus, it is an inevitable trend for the sustainable development of the automobile industry to replace traditional fuel buses, and developing electric buses is an important measure to relieve traffic congestion. Furthermore, the bus scheduling has a significant impact on passenger travel times and operating costs. It is common that passenger demand at different stops is uneven in a public transportation system. Since applying all-stop scheduling only cannot match the passenger demand of some stops with bus resources, this paper proposes an integrated all-stop and short-turning service for electric buses, reducing the influence of uneven ridership on load factor to enhance transit attractiveness. Simultaneously, considering the time-of-use pricing strategy used by the power sector, the combinational charging strategy of daytime and overnight is proposed to reduce electricity costs. Finally, the branch-and-price algorithm is adopted to solve this problem. Compared with all-stop scheduling, the results demonstrate a reduction of 13.5% in total time cost under the combinational scheduling.

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“…Therefore, the variation range of SOC is set as SOC min = 20% and SOC max = 80%. Equation (23) illustrates a good linear relationship between SOC and charging times in this range.…”

Section: Description Of Charging and Discharging Process Of Ebsmentioning

confidence: 85%

Section: Related Workmentioning

confidence: 99%

Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

2021

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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

112

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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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“…Some studies on electric vehicles also help estimate the energy consumption of EBs [30][31][32][33][34][35]. Wang et al (2018) generated accurate driving patterns by a Markov chain Monte Carlo simulation.…”

Section: Literature Reviewsmentioning

confidence: 99%

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

et al. 2021

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Electric buses have many significant advantages, such as zero emissions and low noise and energy consumption, making them play an important role in saving the operation cost of bus companies and reducing urban traffic pollution emissions. Therefore, in recent years, many cities in the world dedicate to promoting the electrification of public transport vehicles. Whereas due to the limitation of on-board battery capacity, the driving range of electric buses is relatively short. The accurate estimation of energy consumption on the electric bus routes is the premise of conducting bus scheduling and optimizing the layout of charging facilities. This study collected the actual operation data of three electric bus routes in Meihekou City, China, and established the support vector machine regression (SVR) model by taking the state of charge (SOC), trip travel time, mean environment temperature and air-conditioning operation time as the independent variables; while the energy consumptions of the route operations served as the dependent variables. Furthermore, the grey wolf optimization (GWO) algorithm was adopted to select the optimal parameters of the proposed model. Finally, a support vector machine regression model based on the grey wolf optimization algorithm (GWO-SVR) is proposed. Three real bus lines were taken as examples to validate the model. The results show that the mean average percentage error is 14.47% and the mean average error is 0.7776. In addition, the estimation accuracy and training time of the proposed model are superior to the genetic algorithm-back propagation neural network model and grid-search support vector machine regression model.

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Quantifying economic benefits from free-floating bike-sharing systems: A trip-level inference approach and city-scale analysis

Cited by 35 publications

References 29 publications

Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

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

Energy Consumption Estimation of the Electric Bus Based on Grey Wolf Optimization Algorithm and Support Vector Machine Regression

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