Rapid charging provision,multiplicity and battery electric vehicle (BEV) mobility in the UK

This paper provides significant new insights into the spatial heterogeneity of public rapid charging provision for Battery Electric Vehicles (BEVs) in the UK. In particular, the paper makes three new and original contributions to our understanding of BEV mobility and the importance of rapid chargers and the multiplicity of rapid charging provision therein. First, the paper sets out a new conceptual framework which captures the different dimensions of BEV mobility. Second, the paper highlights the importance of multiplicity, recognising the important distinction between i) ‘on-site’ multiplicity; and ii) spatial multiplicity in rapid charging provision. Both have received relatively little attention to date yet are highlighted as being fundamental in shaping an individual's ability to undertake extended journeys by a BEV. Third, the paper highlights how the development of charge point multiplicity is currently largely restricted to large urban areas and strategic road networks with current policies focusing on enhancing BEV infrastructure along the TEN-T Trans-European Comprehensive Transport Network. In considering spatial multiplicity in relation to the “reserve range” of BEVs, the paper identifies spatial variations in road network connectivity. Limited interconnectivity in rural areas - and which is particularly apparent for lower range BEVs - reflects an emerging market failure with the potential to undermine efforts to secure a “just transition” to BEVs     

The demand for cycle sharing: Examining the links between weather conditions,air quality levels,and cycling demand for regular and casual users

This paper examines temporal variation in the demand for cycling to understand how environmental conditions may promote or hinder active travel. The role of environmental conditions is considered in terms of the prevailing weather as well as the concentration levelof local air pollutants. Using data derived from the London Bicycle Sharing Scheme, a set of autoregressive distributed lag models are specified to explore these relationships. The models distinguish casual cyclists from regular cyclists to allow the analysis to consider the demand profiles of these two market segments separately rather than jointly. The analysis makes use of an open science approach, with the data inspected, the models applied, and the results derived being made freely available to interested parties through an online repository.The results of the models indicate that the demand of casual cyclists is more strongly linked to concurrent weather condition as compared to the demand of regular cyclists, though regular cyclists seem to be more inclined to delay trips to avoid inclement weather. The associations between cycling demand and air quality levels is mixed, with high concentrations of ozone linked with lower levels of demand from regular cyclists while high concentrations of particulate matter 10 are positively related to both regular and casual cycling demand. The findings of this paper could provide benefits to bicycle sharing system managers such as in planning the schedule of maintenance work as well as highlighting the need to inform cyclists about the actions they can take to reduce their exposure to local air pollutants.     

Conditions for electric vehicle taxi: A case study in the Greater Stockholm region

This study investigates the usability of electric vehicles (EVs) in a taxi company in Greater Stockholm, Sweden. By investigating cost and revenue data of both electric and conventional taxi vehicles, as well as by interviewing taxi drivers and carriers, an assessment has been made of the financial and operational implications of using EVs in a company’s taxi fleet. Both the drivers’ and the carriers’ perspectives have been examined. The main findings are that the investigated e-taxis have a similar or lower Total Cost of Ownership and slightly higher profitability than the investigated conventional taxis. For taxi drivers, using e-taxis implies more advanced planning and revenue service time being sacrificed for charging. However, certain customers’ preferences for EVs, as well as benefits such as corporate clients favoring e-taxis and a zero emission priority queuing system at Stockholm’s main international airport (partly) compensate for time devoted to charging. In order to facilitate increased use of e-taxis, more fast charging facilities should become available at strategic locations. Besides that, there are signs that carriers’ lack of information about the opportunities and consequences of shifting towards e-taxis hamper a wider deployment of e-taxis.     

A review of spatial localization methodologies for the electric vehicle charging infrastructure

With view to the high share of the transport sector in total energy consumption, e-mobility should play an important role within the transition of the energy systems. Policymakers in several countries consider electric vehicles (EV) as an alternative to fossil-fueled vehicles. In order to allow for the development of EV, the charging infrastructure has to be set up at locations with high charging potential, identified by means of various criteria such as demand density or trip length. Many methodologies for locating charging stations (CS) have been developed in the last few years. First, this paper presents a broad overview of publications in the domain of CS localization. A classification scheme is proposed regarding modeling theory and empirical application; further on, models are analyzed, distinguishing between users, route or destination centricity of the approaches and outcomes. In a second step, studies in the field of explicit spatial location planning are reviewed in more detail, that is, in terms of their target criteria and the specialization of underlying analytical processes. One divergence of these approaches lies in the varying level of spatial planning, which could be crucial depending on the planning requirements. It is striking that almost all CS locating concepts are proposed for urban areas. Other constraints, such as the lack of extensive empirical EV traffic data for a better understanding of the driving behavior, are identified. This paper provides an overview of the CS models, a classification approach especially considering the problem’s spatial dimension, and derives perspectives for further research.     

Optimization models for placement of an energy-aware electric vehicle charging infrastructure

This paper addresses the problem of optimally placing charging stations in urban areas. Two optimization criteria are used: maximizing the number of reachable households and minimizing overall e-transportation energy cost. The decision making models used for both cases are mixed integer programming with linear and nonlinear energy-aware constraints. A multi-objective optimization model that handles both criteria (number of reachable households and transportation energy) simultaneously is also presented. A number of simulation results are provided for two different cities in order to illustrate the proposed methods. Among other insights, these results show that the multi-objective optimization provides improved placement results.     

Bi-level optimization approach for configuring population and employment distributions with minimized vehicle travel demand

Compact development aims to lead to dense and mixed distributions of population and employment in land use planning which may result in reduced vehicle miles traveled (VMT). However, determining an optimum or desirable configuration of population and employment distribution to achieve the compact development goal remains challenging due to numerous competitive development alternatives or possible exacerbated traffic congestion as a result of over-intensification of urban land use. To address this challenge, a systematic approach is proposed with a bi-level optimization model aiming to find out an efficient population and employment map for vehicle travel reduction. The upper level of the model is formulated to minimize VMT and vehicle hours traveled (VHT) varying with possible changes in population and employment densities. The lower level is based on a tour-based travel demand model to mechanistically represent the response of travel choices to those changes. The programming is solved by a Genetic Algorithm. The proposed method is demonstrated through a case study of Hamilton County, Ohio, U.S. The results indicate that a more compact urban form reduces VMT; however, it may cause longer VHT depending upon the density. To avoid urban overconcentration and reduce both VMT and VHT simultaneously, a compact urban development configuration with a population density < 5289 persons/mi² and the employment density < 3282 jobs/mi² is recommended for the area. As a negative relationship demonstrated between transit headway reduction and vehicle travel demand, strategies for improving transit provision are helpful to reducing vehicle travels in compact development scenarios.     

Attracting travellers to the high-speed train: a methodology for comparing potential demand between stations

The High-Speed Railways (HSR) which are currently operative in 12 countries throughout the world include a wide range of stations with a highly varied capacity to attract travellers. Although there are various reasons for these differences, we have focused on the ones that can be quantified for further comparison. The main purpose of this study was twofold. First of all, we sought to identify stations with a low capacity to attract travellers and to explain the reasons for their concentration in certain countries rather than others. Secondly, we looked for a simple, but widely acceptable, method with which to calculate the capacity to attract users to HSR stations. This would help to establish a better way of allocating financial resources for public investment. This approach enabled us to identify those stations that had little or no potential at all and which had been established for essentially political reasons in countries where the concept of territorial cohesion had been applied in an inappropriate way. This led us to a number of observations relating to the railway policies undertaken in the countries studied and to underline the need for greater international coordination in areas like Europe and for better justified HSR policies. The calculations presented in this article use data relating to the urban populations which live in the vicinity of the world’s HSR stations, the distance from each of these stations to the nearest urban centres and the level of the corresponding regional GDP. All of this information has been used to build a GIS to facilitate analysis and pave the way towards a general comparative approach in which stations would be classified according to their capacity to attract travellers. Such an approach could also be used in areas in which lines are currently under construction and/or where future projects – such as the TEN-T in Europe – have already been approved but not yet executed. Our approach will make it easier to obtain data to show the potential success of, and need for, a HSR connexion as a preliminary step within the decision making process. This constitutes a significant contribution to the debate about the need to assess political decisions concerning HSR investments. It also highlights the need to encourage improved intermodality around railway stations and particularly around those located in non-metropolitan areas.     

Exploring the choice of battery electric vehicles in city logistics: A conjoint-based choice analysis

Adoption of electric vehicles by transport companies remains limited although major European cities should reach CO₂-free city logistics by 2030. This paper explores therefore the vehicle choice behaviour of transport companies through a conjoint-based choice analysis.The results showed that the benefits of battery electric vehicles are less valued than their disadvantages. However, a majority of respondents agrees that authorities should encourage the use of battery electric vehicles. Based on the preferences of transporters, we conclude that the most important measures are to develop a larger charging infrastructure and implement financial incentives through subsidies or tax exemption.     

A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London

This paper investigates the spatial demand for bikesharing through the application of a series of trip generation models for the London Bicycle Sharing Scheme (LBSS). The production of trips from and the arrival of trips at scheme stations are evaluated in reference to how they connect with features of the built environment, demographics of the resident and workplace populations, and attributes of the scheme's structure. A spatial econometrics approach is taken to specify the models, with four different time windows considered throughout the day for all trips taken during 2016. The built environment features show a consistent pattern of results in the model, indicating that proximity to cycling infrastructure, rail stations, parks, university facilities, as well as the density of shops and conventional roads in the vicinity of stations is linked with trip generation rates. The presence of males and Caucasians are associated with higher station demand, aligning with other work on the introduction of new mobility solutions elsewhere, though we do find that greater distances to work tend to depress use. Trip generation is also reduced at the minority of stations located south of the River Thames, indicating that the presence of natural barriers can affect the operation of schemes. The results carry implications for scheme integration in other cities.     

Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.     

Strategic policies and demonstration program of electric vehicle in China

Recent Chinese economic and energy policies recognize the transportation sector as a key element in the nation's effort to meet its energy and air quality goals. The development of alternative fuel vehicle (AFV) has been considered as a particularly promising strategy. AFV-related policies can be traced back to the eighth Five-Year Plan period (i.e., 1991-1995). All the work during the last twenty years has cumulatively prompted the transition of AFV development from policy-making to actual implementation and from research and development (R&D) to mass production. The year of 2009 is significant for the AFV industry in China in that the central government announced the “Plan on Shaping and Revitalizing the Auto Industry”. This Plan launched a demonstration program of electric vehicle (EV) deployment in 13 Chinese cities and set the national goal of manufacturing 0.5 million AFVs in three years. To better understand the current status, problems and uncertainties existed in the EV deployment in China, this paper reviewed the relevant policies and reported a survey with the pilot cities during the summer of 2009. Based on the survey findings, this paper developed a number of recommendations to help address the issues found in the demonstration program to date.     

Factors of electric vehicle adoption: A comparison of conventional and electric car users based on an extended theory of planned behavior

Increasing the share of battery electric vehicles (BEV) in the total car fleet is regarded as a promising way to reduce local car emissions. Based on online surveys in Denmark and Sweden, this study compares BEV users' (n = 673) and conventional vehicle (CV) users' (n = 1794) socio-demographic profiles, attitudinal profiles, and mobility patterns. In line with previous research, BEV users are typically male, highly educated, have high incomes, and often more than one car in their household. Additionally, BEV users perceive less functional barriers toward BEV use and have more positive attitudes and norms than CV users. The different profiles of these user groups suggest a separate analysis of potential factors of BEV adoption in both groups. In regression analyses, CV and BEV users' intention to use/purchase a BEV is modeled based on factors of the Theory of Planned Behavior extended by personal norm, perceived mobility necessities, and BEV experience. For CV users, symbolic attitudes related to BEVs are the most important factor of intention, while perceived functional barriers in terms of driving range are most relevant for BEV users' intention. How BEV users cope with trips of longer distance seems of particular relevance. In multiple car households, we found the percentage of actual BEV usage related to the type of other cars in the household, perceived functional barriers of BEVs as well as (successful) behavioral adaption to longer trips by BEVs. Based on the results, we discuss ways to increase BEV adoption for current users and non-users.     

Prospects for shared electric velomobility: Profiling potential adopters at a multi-campus university

Electric velomobility (e-velomobility) encompasses human movement using electric-assisted bicycles (pedelecs, or e-bikes), and the associated practices, systems and technologies. It is emerging as an active mode in developed economies. Electric bicycle sharing (EBS) schemes can attain higher per-vehicle use time and provide more equitable access than personal ownership. University campuses are ideal testing beds for such systems as young and lower-income groups are present there. The goal of this study is to understand the segmentation of the market for a hypothetical electric bicycle sharing scheme located in a multi-campus university. A cross-sectional survey was conducted at a multi-campus university in South East Queensland, Australia. Motives, reasons, and intention of students and staff for potential future use of a potential campus-based EBS scheme were revealed. Three distinctive potential user groups with varied modal, socio-demographic, and psychological characteristics emerged in the clustering analysis, namely: “multimodal enthusiasts” (28%), “car-loving pragmatics” (46%), and “car-loving skeptics” (26%). We identify the key market segments and potential adopters' demographics (residential location, country of origin, income, and academic major). Our results indicate that respondents who are more multimodal, especially those cycling often and with shared mobility experiences, are more positive about using e-bike sharing. Largely mono-modal car users tend to be more negative toward the scheme. International students also tend to be more positive. The individual preferences and attitudes toward campus-based shared e-velomobility, as revealed in this paper, provide important insights for planners, policymakers and sharing operators seeking to launch or improve uptake of such schemes.     

Incorporating the impact of spatio-temporal interactions on bicycle sharing system demand: A case study of New York CitiBike system

Recent success of bicycle-sharing systems (BSS) have led to their growth around the world. Not surprisingly, there is increased research towards better understanding of the contributing factors for BSS demand. However, these research efforts have neglected to adequately consider spatial and temporal interaction of BSS station's demand (arrivals and departures). It is possible that bicycle arrival and departure rates of one BSS station are potentially inter connected with bicycle flow rates for neighboring stations. It is also plausible that the arrival and departure rates at one time period are influenced by the arrival and departure rates of earlier time periods for that station and neighboring stations. Neglecting the presence of such effects, when they are actually present will result in biased model estimates. The major objective of this study is to accommodate for spatial and temporal effects (observed and unobserved) for modelling bicycle demand employing data from New York City's bicycle-sharing system (CitiBike). Towards this end, spatial error and spatial lag models that accommodate for the influence of spatial and temporal interactions are estimated. The exogenous variables for these models are drawn from BSS infrastructure, transportation network infrastructure, land use, point of interests, and meteorological and temporal attributes. The results provide strong evidence for the presence of spatial and temporal dependency for BSS station's arrival and departure rates. A hold out sample validation exercise further emphasizes the improved accuracy of the models with spatial and temporal interactions.     

A vehicle routing cost evaluation algorithm for the strategic analysis of radial distribution networks

This article presents a new algorithm, based on bin packing algorithms, that determines vehicle routing costs on networks with certain geographic characteristics. Requiring very little data and computation time, the algorithm is used to test the impact of adding flexibility to a retailer’s Canadian distribution operations. It is shown that it is possible to save 5–10% of vehicle routing costs by modifying shipping schedules.     

旅客出行方式选择的经济学分析

从供给和需求两个方面对旅客出行方式的选择进行分析。首先通过铁路与公路、航空等运输方式的比较 ,分析了运价和旅行时间对出行方式选择的影响。其次 ,从需求属性对出行方式的影响中讨论了收入水平、出行目的以及出行时间价值等3个影响因素。     

Measuring the potential implications of introducing a cap and share scheme in Ireland to reduce green house gas emissions

This paper examines some of the potential impacts of introducing a cap and share scheme in Ireland. Under such a scheme a cap or limit is placed on national CO₂ emissions and individuals are allocated an annual CO₂ allowance. The research presented in this paper focuses on travel-to-work trips specifically. CO₂ emissions for these annual work trips are calculated and a cap is determined based on these results. Cap levels are set based on average emissions and a 20% reduction in average emissions as per Ireland's reduction targets. A national and Dublin only cap are examined and the results are presented as a means of comparison. Binary logistic models are used to determine the socio-economic characteristics of individuals who fall above and below the cap. The results demonstrate the importance of car ownership, journey distance, mode choice and household composition in determining whether a commuter is above or below the cap. Many commuters who fall above the cap are likely drive to work over long distances, have dependent children in their household and own more than one car.     

How uncertain is the future of electric vehicle market: Results from Monte Carlo simulations using a nested logit model

Plug-in electric vehicles (PEVs) are widely regarded as an important component of the technology portfolio designed to accomplish policy goals in sustainability and energy security. However, the market acceptance of PEVs in the future remains largely uncertain from today's perspective. By integrating a consumer choice model based on nested multinomial logit and Monte Carlo simulation, this study analyzes the uncertainty of PEV market penetration using Monte Carlo simulation. Results suggest that the future market for PEVs is highly uncertain and there is a substantial risk of low penetration in the early and midterm market. Top factors contributing to market share variability are price sensitivities, energy cost, range limitation, and charging availability. The results also illustrate the potential effect of public policies in promoting PEVs through investment in battery technology and infrastructure deployment. Continued improvement of battery technologies and deployment of charging infrastructure alone do not necessarily reduce the spread of market share distributions, but may shift distributions toward right, i.e., increase the probability of having great market success.     

Shared autonomous vehicles and their potential impacts on household vehicle ownership: An exploratory empirical assessment

Emerging transportation technologies have the potential to significantly reshape the transportation systems and household vehicle ownership. Key among these transportation technologies are the autonomous vehicles, particularly when introduced in shared vehicle fleets. In this paper, we focus on the potential impact that fleets of shared autonomous vehicles might have on household vehicle ownership. To obtain initial insights into this issue, we asked a sample of university personnel and members of the American Automobile Association as to how likely they would consider relinquishing one of their household's personal vehicles if shared autonomous vehicles were available (thus reducing their household vehicle ownership level by one). For single-vehicle households, this would be relinquishing their only vehicle, and for multivehicle households (households owning two or more vehicles) this would be relinquishing just one of their vehicles. Possible responses to the question about relinquishing a household vehicle if shared autonomous vehicles are present are: extremely unlikely, unlikely, unsure, likely, and extremely likely. To determine the factors that influence this response, random parameters ordered probit models are estimated to account for the likelihood that considerable unobserved heterogeneity is likely to be present in the data. The findings show that a wide range of socioeconomic factors affects people's likelihood of vehicle relinquishment in the presence of shared autonomous vehicles. Key among these are gender effects, generational elements, commuting patterns, and respondents' vehicle crash history and experiences. While people's opinions of shared autonomous vehicles are evolving with the continual introduction of new autonomous vehicle technologies and shifting travel behavior, the results of this study provide important initial insights into the likely effects of shared autonomous vehicles on household vehicle ownership.