Showing papers in "Transportation Letters: The International Journal of Transportation Research in 2022"

Exploring influence mechanism of bikesharing on the use of public transportation — a case of Shanghai

Abstract: ABSTRACT Given the vital role of public transportation in major cities, understanding the influence mechanism of bikesharing use on public transportation is necessary. In this study, we adopt the propensity score matching method to analyze the influence mechanism of bikesharing on the use of public transportation based on a data set in Shanghai. We find no significant influence of bikesharing on the use-frequency of public transportation, but a significant influence on the use-duration of public transportation. A grouping model is established based on gender, physical condition, private bike ownership, private car ownership, private electric bike ownership, and educational background. It is revealed that the use of bikesharing by the group with a private bike or a private electric bike, the group without a bachelor’s degree, the group in physical condition under sub-healthy may increase the use-duration of public transportation.

Investigating the role of green transport, environmental taxes and expenditures in mitigating the transport CO2 emissions

Abstract: ABSTRACT Transportation sector is considered a major contributor to the release of the carbon emissions in the atmosphere. The present research explores the effect of traffic, environmental taxes and expenditures on transport-related carbon emissions. We apply a cross-sectional autoregressive distributed lags estimator for short- and long-run estimates by using panel data for 35 OECD countries. We demonstrate traffic increase transport-related carbon emissions by 14.65% on average. Transport-related carbon emissions will rise by 1.5% over the near term as a result of the combined effect rail and road-vehicles, and energy consumption. Environmental expenditures and green transportation, on the other hand, will cut transportation emissions by 21.7% and 45.20% in the short and long runs, respectively. Furthermore, the findings reveal an inverted u-shaped link between transportation-related carbon emissions and consumption. Based on real-world evidence, this study advises that some countries reduce traffic while simultaneously increasing spending on the development of environmentally friendly transportation options.

Examining the persistence of telecommuting after the COVID-19 pandemic

Abstract: ABSTRACT This study focuses on the long-term impacts of COVID-19 on telecommuting behavior. We seek to study the future of telecommuting, in the post-pandemic era, by capturing the evolution of observed behavior during the COVID-19 pandemic. To do so, we implemented a comprehensive multi-wave nationwide panel survey (the Future Survey) in the U.S. throughout 2020 and 2021. A panel Generalized Structural Equation Model (GSEM) was used to investigate the effects of two perceptual factors on telecommuting behavior: (1) perceived risk of COVID-19; and (2) perceived telecommuting productivity. The findings of this study reveal significant and positive impacts of productivity and COVID-risk perception on telecommuting behavior. Moreover, the findings indicate a potential shift in preferences toward telecommuting in the post-pandemic era for millennials, employees with long commute times, high-income, and highly educated employees. Overall, a potential increase in telecommuting frequency is expected in the post-pandemic era, with differences across socio-economic groups.

An empirical study on characteristics of supply in e-hailing markets: a clustering approach

Abstract: E-hailing services have disrupted how, when, and where people travel in cities. This paper characterizes the attributes of the supply of e-hailing markets that is reflective of the labor characteristics of the drivers (contractors). Based on a clustering analysis of the observed behavior of an e-hailing company’s drivers over a month, the analysis identifies three major groups of drivers: (i) part-time drivers working flexible hours, (ii) part-time drivers working in the evenings, and (iii) full-time drivers. The clustering results of the e-hailing market supply is verified to have consistent characteristics over different days. The results of the clustering method are demonstrated to be effective for prediction of supply.

Evaluation of driver Acceleration/Deceleration behavior at signalized intersections using vehicle trajectory data

Abstract: ABSTRACT Vehicle Acceleration/Deceleration (A/D) characteristics are fundamental measures often used in designing intersection geometrics, A/D lanes, analyzing vehicle fuel consumption and emission, and traffic simulation modeling. The flow scenario at a signal-controlled intersection involves stop-and-go conditions with variability of A/D characteristics. This study attempts to analyze the vehicle’s A/D behavior at an urban signalized intersection under weak lane disciplined mixed traffic stream using vehicle trajectory data. The result shows that vehicles like car and motorized two-wheeler are associated with high acceleration behavior at lower speeds and need less time and distance to accelerate. Also, these vehicles can decelerate at a higher rate at a lower speed during the stop or clearing of the intersection. The statistical results show that a single and multi-regime polynomial model is more suitable to represent the behavior of A/D characteristics in terms of the approach speed of vehicles.

Optimal allocation of shared parking spaces for hospital parkers considering parking choice behavior under bounded rationality

Abstract: ABSTRACT Lack of parking spaces at downtown hospitals causes urban problems like patient access delays and additional carbon emissions. This paper proposes a cumulative prospect theory-based shared parking space allocation model (CPT-SPSA model) to alleviate the parking difficulties. The CPT-SPSA model aims to maximize the profit of the shared parking platform under time window constraints considering the parking choice behavior of hospital parkers. To determine the parking choice behavior, cumulative prospect theory (CPT) is utilized to model parkers’ evaluation of parking lots considering individual heterogeneity. Numerical experiments indicate that the profit of the platform and the utilization rate of the shared parking spaces are improved significantly and more successful matches of shared parking supply and demand are achieved under the CPT-SPSA model. Those with different parking purposes vary in acceptance of each parking lot. The platform can earn more profit by reasonably adjusting the parking fees of the shared parking spaces.

The policy effect on automobile industry considering the relationship between technology,market and production: the dual-credit policy as an example

Abstract: Comprehensively understanding how the policy influences the sustainability transition of the automotive industry is a critical focus in recent years. This paper focuses on the dual-credit policy, an underexplored but critical policy in China, and examines how it would influence the transition of automotive industry. To achieve this goal, an SD model is developed to simulate the effect of policy parameters on the policy objectives by considering the interaction of production, technology and market. The findings reveal that this policy can in general promote the sustainable development of the automotive industry by greatly decreasing its overall energy consumption. However, its impact on the market share of new energy vehicles is limited. Based on the results, some policy suggestions are proposed so as to improve the effectiveness of the dual-credit policy.

Research on carbon emission measurement of Shanghai expressway under the vision of peaking carbon emissions

Abstract: Generally, the carbon emissions of road transportation have substantial negative effects on biological ecosystem and sustainable development. Thus, to achieve the strategy of peaking carbon emissions before 2030, research on measuring vehicle emissions in large metropolitan road networks is indispensable. With that in mind, a carbon emission macroscopic fundamental diagram model (CE-MFD) is presented to assess network-wide road emissions, and an indicator, carbon emission intensity (CEI), is quantified to capture the influence of traffic dynamics on emissions. Further, the results of CE-MFD show a positive correlation between road congestion and emissions. Based on the CEI indicator, the corresponding network-wide traffic controls are proposed for effectively reducing the emissions. In addition, the effects of electric vehicle (EV) penetration are explored. This study suggests that by 2030, even if the traffic becomes more congested, the CE-MFD of the urban expressway network will reach a peak with an EV penetration of 73%.

Characterizing lane changing behavior and identifying extreme lane changing traits

Abstract: ABSTRACT This study characterizes lane changing behavior of drivers under differing congestion levels and identifies extreme lane changing traits using high-resolution trajectory data. Total lane change frequency exhibited a reciprocal relationship with congestion level, but the distribution of lane change per vehicle remained unchanged as congestion increased. On average, the speed of trajectories increased by 5.4 ft/s after changing a lane. However, this gain significantly diminished as congestion worsened. Further, the average speed of lane changing vehicles was 3.9 ft/s higher than those that executed no lane changes. Two metrics were employed to identify extreme lane changing behavior: critical time-to-line-crossing (TLCc) and lane changes per unit distance. The lowest 1% TLCc varied between 0.71–1.57 seconds. The highest 1% of lane change rates for all lane changing vehicles was 2.5 lane changes per 1,000 ft traveled. Interestingly, no drivers in thisdataset had both excessive lane changes and lane changes with low TLCc.

Applying the integrated model to understanding online buyers’ intention to adopt delivery drones in Iran

Abstract: ABSTRACT This study is aimed at determining the factors influential in the acceptance of delivery drones as a new way for last-mile delivery in the future. In this regard, the study model was proposed using the UTAUT2 as the base model and adding the factors of psychological characteristics, perceived risk, and need for human interaction to it. The information about 357 Iranian buyers was collected for the PLS-SEM by designing an online questionnaire. The results indicated that all variables of UTAUT2, except for performance expectancy, had a positive and significant effect on the intention of buyers to use delivery drones. Moreover, the personal norm positively affected the users’ intention, while the need for human interaction and perceived risk had adverse effects on it. The research findings indicated that innovativeness had a significant positive effect on effort expectancy and compatibility while having an insignificant effect on performance expectancy.

Emerging trends and influential outsiders of transportation science

Abstract: ABSTRACT Fifty years of evolution of transportation research is revisited based on bibliometric indicators of nearly 50,000 articles, the collective publication of all transportation journals. A multitude of objective indicators all consistently determined four major divisions in the field: (i) network analysis and traffic flow, (ii) economics of transportation and logistics, (iii) travel behaviour, and (iv) road safety. Trending themes of research within the abovementioned divisions respectively are: (i) macroscopic fundamental diagram and public transport network design, (ii) nil (no distinct trending topic), (iii) land-use, active transportation, residential self-selection, travel experience/satisfaction, social exclusion and transport/spatial equity, and (iv) statistical modelling of road accidents. Furthermore, clusters of research related to topics of (a) shared mobility, (b) electric mobility, and (c) autonomous mobility constitute trending topics that are each a cross between multiple divisions of the field. These outcomes document major directions to which the transportation research is headed. Additional outcome is determination of influential outsiders, seminal articles published by non-transportation journals that have proven instrumental in the development of transportation science.

Estimating sustainable transport efficiency and socioeconomic factors: application of non-parametric approach

Abstract: Sustainable transport has received substantial attention globally. Evidently, transportation is a major contributor to global carbon emission, which causes the degradation of environmental quality and safety. Economic-oriented activities are performed in terms of inclination aspect. Thus, this paper aims to analyze whether and how environmental pollution and fatality, economic-oriented transport, and socioeconomic factors affect transport efficiency. For this purpose, panel data from 2000 to 2020 for 35 OECD countries are used. First, data envelopment analysis slack-based measure and windows analysis are employed to measure transport efficiency by using fourteen inputs/output variables. Second, econometric analysis demonstrates the relationship between socioeconomic factors and sustainable transport efficiency. Outcomes suggest that 30.2% increase in sustainable transport efficiency is due to a 1% increase in transport–household expenditures. OECD countries significantly performed with high economic efficiency scores for the examined period. Econometric analysis indicates that socioeconomic factors have a remarkable impact on sustainable transport efficiency.

Optimal allocation and adjustment mechanism of shared parking slots considering combined parking resources

Abstract: This paper establishes a rolling shared parking allocation model to optimize the matching of supply and demand in parking-dense districts by maximizing platform revenue and minimizing parking users’ travel costs. An adjustment mechanism that dynamically adjusts the allocated parking slots is developed to be embedded in the model. Considering multi-candidate adjacent parking lots, combined sharing and individual sharing patterns are compared to verify the benefits of resource combination. Numerical experiments indicate that the proposed model works well for changing parking demands and achieves more successful matches than the first-book-first-serve model. The combination of adjacent parking resources improves the platform revenue, the parking slot utilization, and the acceptance rate of parking requests, as well as reduces the average walking distance. The direct parking revenue of the parking lot with fewer slots is remarkably increased under combined sharing. The findings provide managerial insights for local collaborative shared parking.

Home health care and dialysis routing with electric vehicles and private and public charging stations

Abstract: ABSTRACT This paper studies a joint multi-depot home health care and dialysis problem of routing and scheduling decisions of health specialists. The fleet consists of electric vehicles, which use both public and private charging stations. We formulate the problem as a mixed integer linear programming model. We describe a hybrid adaptive large neighborhood search (ALNS) algorithm, which integrates construction heuristic to generate initial solution and local search procedure based on variable neighborhood descent. The hybrid ALNS successfully combines existing heuristic mechanisms and introduces several new problem-specific procedures to effectively handle the complex structure of the problem. We conduct experiments on realistic benchmark instances to investigate various problem specifications, such as constructed teams, usage rate of fast and super-fast charging technologies, and public and private charging options. We analyze the performance of the hybrid ALNS and its mechanisms. The algorithm obtained good quality results on the complex optimization problem.

Digitally transferable ownership of mobility-as-a-service systems using blockchain and smart contracts

Abstract: ABSTRACT Mobility-as-a-service (MaaS) has promised to integrate multiple service providers to deliver a multimodal mobility service to commuters through a seamless digital platform. An efficient digital network architecture is imperative to achieve secure and reliable interactions among the involved parties. From the service provider’s perspective, by defining the mobility ownership for customers, MaaScan bring a personalized transport system to complement or even fully replace the concept of private vehicle ownership. This paper paves the initial steps to develop a distributed architecture to realize a MaaS digital network using blockchain technology. Therefore, a comprehensive blockchain-enabled MaaS platform with related industries is illustrated in this paper. To do so, first, we define crypto-tickets as means of ownership of the service and devise a straightforward smart contract to be executed in the blockchain network, which allows the customers to exchange the service ownership in a privacy-preserved manner. Then, we evaluate our blockchain-based ownership scheme’s efficacy against the traditional membership plans using a simplified simulation instance in MATLAB. Finally, we discuss the blockchain potentials in personalizing the ownership of the service, congestion management, and data trading between various stakeholder of a comprehensive MaaS platform.

Analysis of injury severity in rear-end crashes on an expressway involving different types of vehicles using random-parameters logit models with heterogeneity in means and variances

Abstract: To examine the difference in contributing factors of rear-end crashes of different injury severity involving different types of vehicles, this paper proposed random-parameters multinomial logit models with heterogeneity in means and variances. A three-year (2017–2019) rear-end crash data collected from Beijing-Shanghai Highways in China was used to calibrate the models. The rear-end crashes were classified as five types (Car-Car, Car-Truck, Truck-Truck, Truck-Car, Others). With two possible injury severity outcomes of medium/severe injury and light injury, a wide range of possible variables including crash, traffic, speed, geometric, and sight characteristics were considered in this study. Likelihood ratio tests revealed the rationality of adopting merged models using the data across three-year periods. Remarkably significant differences were shown in crashes involving different types of vehicles. The results accounting for the possible heterogeneity could be of value to roadway designers and traffic management departments seeking to promote highway safety and raise accurate safety countermeasures.

Multi-objective optimization for two-echelon joint delivery location routing problem considering carbon emission under online shopping

Abstract: A sharp increasing number of online shopping parcels and scattered customers lead each express company to invest a lot logistics resources, but its utilization rate is low. Each express company faces the challenge of improving customer satisfaction and market competitiveness while ensuring the total costs are under control. This paper develops a novel joint delivery (JD) model to reduce operation costs and carbon emission via strengthening horizontal cooperation and resource sharing among express companies. Furthermore, it designs a multi-depot two-echelon joint delivery location routing problem (MD-2E-JDLRP) mathematic model for JD considering multi-objectives and proposes a hybrid heuristic algorithm to solve the MD-2E-JDLRP. The performance of this algorithm was tested by benchmarks and a case study. Finally, a case study is conducted to verify the effectiveness of the MD-2E-JDLRP model. The obtained results show the JD model can effectively reduce costs and carbon emissions while ensuring higher customer satisfaction.

Detection and analysis of transfer time in urban rail transit system using WIFI data

Abstract: This study presents a comprehensive framework for estimating passengers' transfer times and extracting their distribution and related transfer routes using WIFI probe data. The departure time of preceding station, arrival time of subsequent station, and train running time are selected to obtain transfer times. Then, the collected data is analyzed using kernel density estimation to obtain candidate distribution. Gaussian mixture models are adopted to extract the distribution of each possible transfer route at both peak hours and off-peak hours. This method is tested at two transfer stations of Xi’an metro system with the comparison of results from automatic fare collection data and manual sampling survey data. The results indicate that the proposed approach can collect the transfer time with a sampling ratio greater than 30% and a deviation less than 5%. The route choice behaviors and distribution of transfer time under various conditions can be identified using the proposed methods.

Innovations impacting the future of transportation: an overview of connected, automated, shared, and electric technologies

Abstract: ABSTRACT This paper overviews emerging technology development by emphasizing Connected, Automated, Shared, and Electric (CASE) technologies. Existing literature on 15 CASE technologies is reviewed, and adds to the literature by consolidating important predictions of CASE-related technologies, services, and policies. Connected and automated technologies have not yet matured, and there remain many gaps in its abilities. Predictions include that shared mobility will have a large market share, electric technologies will replace internal combustion engines, and CASE-related services and policies will be used in more cities. A survey with 1,036 respondents conducted in April 2021 helps gauge how Americans make mode choices under various scenarios. Data analysis indicates that safety issues are the main factor that hinder people’s willingness to use CASE-related technologies, and the low cost encourages more users to choose shared mobility. Challenges that remain, and their associated impacts, are important to consider.

Research on carbon emissions of public bikes based on the life cycle theory

Abstract: ABSTRACT As a green travel mode, public bikes have positive implications in terms of reducing emissions. Based on the life cycle theory, we calculate the carbon emissions generated and reduced over the entire life cycle of public bikes.The calculation shows that when the average daily turnover rate of public bikes is 1.874 times/ bike, the average daily travel distance is 2.150 km, and the damage rate increases by 2.5% per month, each public bike needs approximately 7 months to reach the carbon balance. After the carbon emission balance is reached, the use of public bikes causes a net reduction in carbon emissions. However, the carbon emissions once again exceed the emission reductions after approximately 29 months of using public bikes. Furthermore, the carbon balance of the 186 stations used in Wenling City is studied.Based on the conclusions obtained, some policy recommendations are made to help public bikes achieve real emissions reduction.

Vehicle trajectory data extraction from the horizontal curves of mountainous roads

Abstract: Trajectory data is essential for understanding the driver-vehicle-road interaction which is crucial for safety and operational assessment. The video image-processing technique is useful to collect such data on the entire traffic stream. Existing techniques assume that the road is planar which limits the application of the video-based traffic data collection to the plain terrains. With the objective to collect trajectory data from mountainous terrain, the present study proposes an approach to convert the pixel coordinates into real-world coordinates. The hypothesis was that a non-planar scene could be converted into a sequence of piece-wise planar scenes with a separate projection matrix for each planar region. The analysis shows that the proposed method could effectively transform the pixel coordinates into the real-world coordinates. Comparison of the estimated and observed speed/path profiles indicate the adequacy of the proposed method in collecting video-based trajectory data from mountainous terrain.

Optimization of train timetables in high-speed railway corridors considering passenger departure time and seat-class preferences

Abstract: ABSTRACT There are a number of trains running in high-speed railway (HSR) corridors throughout the day, which results in diverse passenger travel choices. Passengers can select their preferred train according to their favorite departure time and seat class based on a fixed train timetable, that is, the selection differences from departure time to seat classes are dramatic. The train timetable determines the distribution of passenger flow on trains, and the requirement distribution in turn affects the train timetable. To address this game relationship, this paper develops a method to optimize uneven running train timetables with a given total number of trains considering passenger departure time and seat-class preferences. We analyze the impact of departure time and seat class on passenger choice behaviors and build a time-space-seat 3-dimensional network to account for these choices. Then, the impedance function of each type of arc in the network is designed. In addition, a bi-level programming model is constructed to optimize the train timetable in the HSR corridor; the upper-level model calculates train departure time, arrival time and dwelling time at each station and determines the assignment of car lists with different seat classes for each train. The lower-level model is used to distribute the passenger flow to each train. Combined with the user equilibrium principle, a complex genetic algorithm embedded with the Frank–Wolfe method is designed to reasonably distribute passenger flows to each train. Finally, we take the Lanzhou-Xi’an HSR as an example to test both the model and the algorithm. The results show that the optimal train timetable can meet the requirements of both seat class and departure time with appropriate solution time limitations.

Adaptive traffic signal control for developing countries using fused parameters derived from crowd-source data

Abstract: ABSTRACT Advancement of mobile technologies has enabled economical collection, storage, processing, and sharing of traffic data. These data are made accessible to intended users through various application program interfaces (API) and can be used to recognize and mitigate congestion in real time. In this paper, quantitative (time of arrival) and qualitative (color-coded congestion levels) data were acquired from the Google traffic APIs. New parameters that reflect heterogeneous traffic conditions were defined and utilized for real-time control of traffic signals while maintaining the green-to-red time ratio. The proposed method utilizes a congestion-avoiding principle commonly used in computer networking. Adaptive congestion levels were observed on three different intersections of Delhi (India), in peak hours. It showed good variation, hence sensitive for the control algorithm to act efficiently. Also, simulation study establishes that proposed control algorithm decreases waiting time and congestion. The proposed method provides an economical alternative to expensive sensing and tracking technologies.

Modeling hybrid cars adoption using an extended version of the theory of planned behavior

Abstract: Malaysian environmental issues produced by conventional cars have long been a subject of concern, and must be addressed by consumers’ adoption of hybrid cars. As a result, the purpose of this study is to identify significant factors influencing consumers’ intentions to purchase hybrid cars in Malaysia. TPB was employed as a base theory, and two supporting theories, self-identity and self-congruity, were incorporated to aid in the development of the study’s framework. An online survey was developed and distributed throughout Malaysia via social media platforms, and the results were analyzed using PLS-SEM. It was revealed that consumers’ high price sensitivity weakens the positive relationship between perceived behavioral control and purchase intention toward hybrid cars.

Methods to enhance the quality of bi-level origin–destination matrix adjustment process

Abstract: ABSTRACT The quality of Origin–Destination matrix (OD) estimation depends on number of factors including the selection of appropriate upper-level function of bi-level formulation, constraints to the OD flows, and suitable solution algorithm. Addressing these aspects, the study first explored different upper-level formulations using two types of traffic information: traffic counts and sub-path flows. Second, it investigated the effects of OD constraints on the quality of solution. Third, it proposed modified genetic algorithm (MGA) to address the computational limitations of traditional genetic algorithm (GA). The study findings were as follows: a) Using symmetric mean absolute percentage error (SMAPE) to match traffic counts showed greater improvements in the OD quality; b) The estimates improved as more number of OD pairs were known to have a-priori knowledge about their flows with higher confidence levels; c) The MGA approach outperformed GA in terms of computational efficiency, and gradient descent (GD) in terms of solution quality.

A generalized Benders decomposition approach for the mean-standard deviation shortest path problem

Abstract: This paper concentrates on the mean-standard deviation shortest path problem, which is an important extension of traditional shortest path problem. Due to the standard deviation term, the general formulation of this problem is nonlinear and concave. We transform this formulation into a mixed-integer conic quadratic program and develop a generalized Benders decomposition approach. The Benders master problem is a continuous conic quadratic program about travel time mean and standard deviation. The subproblem is a least expected travel time path problem with the variance limit. At each iteration, the subproblem generates a generalized Benders optimality cut for the relaxed Benders master problem. The relaxed Benders master problem provides an ascending lower bound and the subproblem produces a feasible solution to update the upper bound. In the numerical experiments, all instances in four transportation networks are solved optimally. This paper provides a novel solving scheme for the mean-standard deviation shortest path problem.

On the path to mobility as a service: a MaaS-checklist for assessing existing MaaS-like schemes

Abstract: ABSTRACT Mobility as a Service (MaaS) scientific literature base is rapidly growing, but an agreement on what it intrinsically means has not been reached, making fruitful conversations related to MaaS difficult. This study analyzes a five-year-old literature based on MaaS (90 peer-reviewed journal articles) and extracts from it, the key or common elements in all MaaS definitions, calling them the ‘MaaS-checklist’: a) app-related functions or features, b) mobility services, and c) mobility packages. Based upon the MaaS-checklist, we then analyze 21 MaaS-like schemes around the world. Our results show that even though there are many schemes claiming to be MaaS, few of them cover the main elements that intrinsically define MaaS. Our findings show that while most schemes cover app-related functions or features, few of them offer mobility packages, which yields that the path to MaaS may not be hindered by technology development but rather by governance issues on MaaS.

Demand-driven train timetabling for air and intercity high-speed rail synchronization service

Abstract: ABSTRACT Synchronizing air and intercity high-speed railway (A-IHSR) services has recently been developing vigorously. Train timetables are critical for improving A-IHSR synchronization. This paper proposes a method to optimize intercity high-speed railway (IHSR) timetables based on the dynamic passenger demand of the A-IHSR. An integer non-linear programming (INLP) model is formulated to minimize the passenger waiting time, in which rigorous train capacity, oversaturation situation, and skip-stop are also taken into consideration. We also extend the train timetabling model by considering the synchronization events of flight-train and the train circulation plan. Then, this model is reformulated as a relaxed non-linear programming (NLP) model. . A multimodal nomad algorithm (MNA) is proposed to solve it. The train circulation plan solves by the Gurobi solver. A numerical experiment based on data of the operation of the Jinnan–Binzhou HSR is implemented to demonstrate the efficiency and performance of our proposed model and algorithm.

Charging infrastructure planning for ride-sourcing electric vehicles considering drivers’ value of time

Abstract: ABSTRACT A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.