Minimum cost path problem for Plug-in Hybrid Electric Vehicles

We introduce a practically important and theoretically challenging problem: finding the minimum cost path for PHEVs in a road network with refueling and charging stations. We show that this problem is NP-complete and present a mixed integer quadratically constrained formulation, a discrete approximation dynamic programming heuristic, and a shortest path heuristic as solution methodologies. Practical applications of the problem in transportation and logistics, considering specifically the long-distance trips, are discussed in detail. Through extensive computational experiments, significant insights are provided. In addition to the charging infrastructure availability, a driver’s stopping tolerance arises as another critical factor affecting the transportation costs.     

Infrastructure development for alternative fuel vehicles on a highway road system

A new mathematical model for positioning alternative fuel (AF) refueling stations on directed-transportation networks with the objective of maximizing the coverage of path flow volume is proposed. This model is especially designed for developing an AF infrastructure on toll roads and other highways, where vehicles do not need to exit the road network for refueling, some candidate station locations are not located at interchanges, and some stations may only service vehicles on one driving direction. The proposed model is applied to the Pennsylvania Turnpike System using the 2011 truck traffic data and considering different vehicle driving ranges.     

Comparing the p-median and flow-refueling models for locating alternative-fuel stations

The p-median and flow-refueling models are two of the more popular models for optimal location of alternative-fuel stations. The p-median model, one of the most widely used location models of any kind, locates p facilities and allocates demand nodes to them to minimize total weighted distance traveled. In comparison, the flow-refueling location model (FRLM) is a path-based demand model that locates p stations to maximize the number of trips on their shortest paths that can be refueled. For a path to be considered refuelable, one or more stations must be located on the path in a way that allows the round trip to be completed without running out of fuel, given the vehicle driving range. In this paper, we analyze how well the facilities located by each model perform on the other’s objective function on road networks in Florida. While each objective function degrades somewhat when facilities are located by the other model, the stations located by the flow-refueling model generally do better on the p-median objective than the stations located by the p-median model do on the flow-refueling objective. This difference between the two models is even more pronounced at the state scale than at the metropolitan scale. In addition, the optimal locations for the FRLM tend to be more much more stable as p increases than those located by the p-median model.     

An optimal approach for a set covering version of the refueling-station location problem and its application to a diffusion model

This article deals with the refueling-station location problem for alternative fuel vehicles in a traffic network. Alternative fuel vehicles can be characterized by the vehicle range that limits the travelable distance with fuel at full capacity. I propose an efficient formulation of the refueling-station location problem using an optimal property and prove that the problem is NP(Non-deterministic Polynomial)-complete in the strong sense. I consider a special case of the refueling-station location problem in which the construction costs are equal for all nodes. In this case, the problem is to determine refueling station locations to minimize the total number of stations, while making the possible multiple predetermined origin–destination round-trips. I propose an optimal algorithm applicable when no refueling stations currently exist in a traffic network and a dynamic programming based algorithm applicable when a set of refueling stations already exists. I apply the algorithms to a traffic network to study the diffusion of refueling stations and predict the speed and range of station establishment. The computational experiments show that the speed of diffusion depends on the vehicle range and the sequence of the origin–destination demands considered in the diffusion process.     

Refueling-station location problem under uncertainty

In this paper, we introduce a two-stage stochastic refueling station location model, where the first stage locates permanent stations and the second locates portable stations. The portable alternative fuel stations are an innovative feature in transportation network. The models are applied to an intercity network for Arizona. Computational results show that the permanent stations locate in and around heavily populated nodes. In addition, the results obtained for the portable stations can be utilized to set up permanent stations when the investor intends to increase the number of such stations. The computational results of the exact and greedy approach are reported.     

Examining individuals preferences for hybrid electric and alternatively fuelled vehicles

This paper examines individuals motivations when purchasing vehicles, focusing upon what factors would encourage individuals to purchase hybrid electrical vehicle (HEV) or alternatively fuelled vehicle (AFV). AFVs in this paper refer to any cars run on alternatives to petrol and diesel. This research attempts to ascertain whether reductions in fuel costs, vehicle registration tax (VRT), or green house gas emissions would encourage individuals to purchase a HEV or an AFV instead of a conventional vehicle. VRT is an Irish tax that is levied on the purchase of new vehicles. One of the motivations to conduct this research was to examine a new car tax and VRT scheme introduced by the Irish government in 2008. This new policy rewards the purchase of environmentally friendly cars, with lower VRT and car tax rates. To understand individuals’ perceptions of these new taxes a survey was sent to recent customers of a car company in Ireland. The survey asked respondents about their recently purchased vehicle and how important they considered vehicle attributes such as environmental performance, fuel cost, and safety, before making their car purchase. The survey also contained a number of stated preference experiments that were designed to ascertain what factors influence individuals’ decisions when purchasing their new car. The results showed that respondents did not rate green house gas emissions or VRT as crucial attributes when purchasing a new vehicle. The vehicle attributes that respondents rated most highly were reliability, automobile safety, fuel costs, and the cost price. The majority of respondents agreed that HEVs and AFVs are better for the environment, cheaper to run than conventional vehicles and would be the vehicle of choice in ten years time.     

Can public slow charging accelerate plug-in electric vehicle sales? A simulation of charging infrastructure usage and its impact on plug-in electric vehicle sales for Germany

Alternative fuel vehicles face the lack of refueling infrastructure as one obstacle to market diffusion and potential operators of refueling stations await significant market shares before constructing a dense refueling network. The resulting lock-in effect or chicken-egg-problem has scarcely been analyzed for plug-in electric vehicles (PEVs) up to now. The research question of this article is How much public charging infrastructure for PEVs is needed and is there mutual interaction in the diffusion of public charging infrastructure and electric vehicles?

Here, we present an agent-based market diffusion model for PEVs and their charging infrastructure that is based on a large number of individual driving profiles for private and commercial car holders in Germany. Our results demonstrate the possibility of a market diffusion in Germany without any slow public charging infrastructure until 2030. Although a charging point at home is obligatory for early adopters, the second-best option for an infrastructure set-up is at work where the majority of vehicles is parked for a long time during the day, the installation is not costly and users profit more than from public facilities. Public slow charging facilities do not increase PEV market shares and they need to be subsidized for a long time.     

Location optimisation method for fast-charging stations along national roads

Limited range of electric vehicles is still a huge barrier compared to conventional vehicles. A well-established charging station network, which is derived from users' charging demand, facilitates the spread of electric vehicles and lessens the range anxiety. Several methods have been developed for locating fast-charging stations along national roads in Europe according to the given objective function. In this paper, an arc-based location optimisation method realized by using a geographic information system and greedy algorithm is presented. An ‘oil stain’ deployment strategy is used to achieve even coverage with the minimum number of fast-charging stations along the roads. Several demographic, neighbourhood, and transport-related attributes, as well as the available services that influence the utilization of a fast-charging station, have been identified and their effects have been revealed in a systematic approach. The developed multi-criteria decision-making method has been applied to evaluate the rest areas along motorways and main roads and to propose deployment locations for fast-charging stations. The method was applied for Hungary as a case study and validated using real origin-destination (O-D) data. By the application of the locating method, the user can specify a network character by geographic parameters. The method can be especially beneficial if the O-D flows are unknown. Furthermore, the even distribution of the stations contributes to the high utilization of the fast-charging stations.     

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.     

Locating passenger vehicle refueling stations

The study follows the concept of set cover and vehicle refueling logics to propose a hybrid model with dual objectives, using a mixed integer programming method, to economically site refueling stations to simultaneously serve intercity and intra-city travel. The model can be applied to plan a network of refueling stations for the emerging and/or monopolistic automotive market of alternative fuel vehicles. From a real-life case study, the factors of vehicle range and coverage distance are identified as playing important roles in any solution. Based on the non-inferior solutions, decision makers can thus better formulate viable station-deployment plans.     

Locating road-vehicle refueling stations

This study follows the concept of set cover for proposing a refueling-station-location model using a mixed integer programming method, based on vehicle-routing logics. Its solution uses only the easy-obtain data of the origin-destination distance matrix. A case study that focuses on the siting of refueling stations for achieving multiple origin-destination intercity travel via electric vehicles on Taiwan demonstrates the applicability of the model. Sensitivity analysis shows that greater vehicle range will result in a lower number of refueling stations that need to be sited. Range is crucial in reducing the facility-location costs, and therefore is an important issue in the development of alternative-fuel-vehicle technology.     

Time use, travel behavior, and the rural-urban continuum: results from the Halifax STAR project

This paper considers variations in time-related aspects of travel behavior along the urban-rural continuum, using the four categories of inner city, suburbs, inner commuter belt (ICB), and outer commuter belt (OCB). It employs geo-coded and GPS-validated data from the STAR survey conducted in the county-sized regional municipality of Halifax, Nova Scotia. Many significant inter-zonal differences are identified, and most travel variables are characterized by progressive urban-to-rural gradients, with large differences between inner-city and outer-commuter values. A clear break between city and country is seldom evident, however. Inner-city residents make most trips, but have trips of shortest duration, and spend least time in travel. Residents of the commuter belts spend most time in travel, and have trips of longest duration. While long trips and much driving were expected in commuter zones, there are significantly fewer trips in the OCB, which we attribute to lack of need, lack of opportunities, and adjustments in discretionary behavior.     

Is e-shopping likely to reduce shopping trips for car owners? A propensity score matching analysis

Reducing car use is commonly considered as a potential strategy to reduce transport-related problems such as traffic congestion and air pollution. The increasing use of online shopping may potentially replace shopping trips, thus possibly reducing car use. However, car owners – compared to non-car owners – can more easily visit physical stores and transport goods. Therefore, it can be assumed that online shopping is less likely to reduce shopping trips for car owners. Using 653 structured face-to-face interviews in Chengdu (China) in 2016, an empirical study is conducted. The results show that 44.0% of respondents indicated a decrease in shopping trip frequency after they started to purchase online, while only 14.4% indicated an increase in the frequency. This confirms that online shopping tends to be a substitute for shopping trips. Applying a propensity score matching approach, this paper further compares the likelihoods of changes in shopping trips caused by online buying between car owners and non-car owners, while considering sociodemographic factors, internet experiences, spatial attributes, and online shopping attitudes as covariates. The results indicate that – due to online buying – shopping trip frequency is less likely to decrease for car owners compared to non-car owners, while there is no significant difference in the likelihood of increasing shopping trip frequency between owners and non-owners. These findings imply that online shopping may not effectively reduce driving, thus unlikely being a valid solution for transportation problems resulting from the increasing use of cars.     

Disentangling the behavioural side of the first and last mile problem: the role of modality style and the built environment

The first and last mile (FLM) problem, namely the poor connection between trip origins or destination and public transport stations, is a significant obstacle to sustainable transportation as it is likely to encourage the use of cars for FLM travel, if not for the entire trip. This study examines the role of modality style and built environment in FLM mode choice behaviour, in order to identify the key features that might invoke a travel mode shift from cars to more sustainable travel options for both mandatory and discretionary trips. More specifically, this study draws on disaggregate data from the South East Queensland household travel survey and presents a latent class choice model to unravel modality style groups. Results reveal two distinct individual-level modality style groups: (1) driving and walking oriented; (2) multimodal travellers. Individuals in the second modality style group were found to be relatively inelastic to FLM travel time for mandatory trips, while individuals in the first group were largely unaffected by built environment characteristics and highly habitual in their mode choice behaviour for both mandatory and discretionary trips. Home residence environments with high road intersection density and public transport accessibility, and home residence environments with diverse land use mix, respectively encourage individuals within the second modality style to walk for mandatory trips, and discretionary trips. To this end, when place-based policies seek to change certain built environment features, individuals in the second modality style are more likely to shift their preference from cars to more sustainable modes. Finally, our findings have practical planning implications in targeting mode shift through highlighting the importance of considering the intersection of individual modality style in a given locale and mode choice behaviour. More specifically, our findings advocate for place-based policies that seek to target particular locales with the certain modality style deemed to be more predisposed to adopting a mode shift.     

Routing and refueling plans to minimize travel time in alternative-fuel vehicles

A driver who drives an alternative-fuel vehicle (AFV) from an origin point to a destination point needs to consider how to get there (i.e., the routing problem), when to stop, and how and when to refuel (i.e., the refueling plan). In this study, models and algorithms are proposed that optimize a one-way-trip path such that the total travel time from the origin to the destination is minimized. The travel time consists of the setup time, the refueling time and the driving time. The setup time includes waiting for the AFV to be served at a refueling station and the preparation time of charging the machine. We categorized the problems into two types: (1) the refueling plan problem when the routing decision is given and (2) an integrated problem of routing and refueling. Another axis of categorization is when (1) setup time and refueling times are site-independent and (2) parameters are site-dependent. We propose optimal algorithms for site-independent problems and the integrated problem of routing and refueling planning with site-independent parameters. We also conduct experiments and sensitivity analyses for the site-dependent integrated problems of routing and refueling.     

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.     

Longitudinal macro-analysis of car-use changes resulting from a TOD-type project: The case of Metro do Porto (Portugal)

Transit-oriented development has been widely studied in recent years as a means to reduce car trips and promote sustainable transport modes. However, longitudinal studies on the matter are still rare. This paper contributes to longitudinal research of Transit-Oriented Development (TOD) effects on travel behavior by analyzing the evolution of the number of car trips after the implementation of a light-rail metro system in the Porto region (Portugal). As Metro do Porto is a large infrastructure project (a metro network of 67 km), we relied on a macro-analysis performed at the civil parish level. Changes in the number of car trips are evaluated using a difference-in-differences model, extended to a spatial model to account for the metro's spillover effects. These effects became obvious as metro ridership is reported not only in the directly metro-served parishes but also in adjacent non-served parishes. The results highlight the importance of the metro system in reducing the number of car trips, and this effect is visible not only in metro-served parishes but also in the neighboring ones, which are not directly served by the new transport system. Furthermore, we compare the performance of parishes predominantly served with TOD stations to those with transit-adjacent (TAD) and park-and-ride (P&R) stations. We conclude that both station types can reduce the number of car trips, yet only TOD parishes generated significant spillover effects. The importance of other potentially influential factors like building density or socio-economic characteristics is also discussed.     

The impacts of the built environment on bicycle-metro transfer trips: A new method to delineate metro catchment area based on people's actual cycling space

Bicycle-metro integration is an efficient method of solving the “last mile” issue around metro stations. Built environment is believed to have a significant effect on cycling behavior. However, transfer cycling around metro stations, as a specific type of cycling behavior, has often been overlooked in transport research. In addition, static contextual units such as circular or street-network buffers are typically used to delineate metro catchment areas of transfer cycling trips. These methods are inaccurate to represent the actual geographic contexts of cycling trips, according to the uncertain geographic context problem (UGCoP). Thus, in this study, bicycle-metro catchment areas are delineated based on aggregating the end points of over three million transfer cycling trips. The impact of the built environment on transfer cycling behavior is also explored.First, we find that the aggregate-points buffer outperforms traditional static buffers in predicting transfer cycling trips. Second, we also identify a high level of spatial heterogeneity in catchment area and transfer cycling density between urban and suburban areas. Third, residential and working population density, bus stop density, and metro stations accessibility have a significant effect on bicycle-metro transfer cycling.     

Measuring the vulnerability of public transport networks

In the last years, studies on the vulnerability of public transport networks attract a growing attention because of the repercussions that incidents can have on the day-to-day functioning of a city. The aim of this paper is to develop a methodology for measuring public transport network vulnerability taking the Madrid Metro system as an example. The consequences of a disruptions of riding times or the number of missed trips are analysed for each of the network links with a full scan approach implemented in GIS (Geographic Information Systems). Using real trips distribution, each link in the network is measured for criticality, from which the vulnerability of lines and stations can be calculated. The proposed methodology also makes it possible to analyse the role of circular lines in network vulnerability and to obtain a worst-case scenario for the successive disruption of links by simulating a targeted attack on the network. Results show the presence of critical links in the southern part of the network, where line density is low and ridership high. They also highlight the importance of the circular line as an element of network robustness.     

Analysis of the potential demand for battery electric vehicle sharing: Mode share and spatiotemporal distribution

Carsharing is considered one of the solutions to urban transport problems. As a new mode in the urban transport system in China, there are still initial questions of how carsharing will perform and what the impacts will be. Accordingly, this study considers battery electric vehicle sharing and investigates its potential demand, with Beijing as the case study. A nested logit model is established and calibrated to analyze mode choice behavior. Further, real trip data is used to estimate the potential demand for battery electric vehicle sharing. In addition, the temporal and spatial distribution of potential demand, the impact of battery electric vehicle sharing on the mode split, and the impact of pricing strategies are analyzed. The results show that an optimistic mode split of battery electric vehicle sharing is 4.23% when the average distance between travelers and stations is 0.5 km. The main source of potential demand is public transport. However, the substitution effect of battery electric vehicle sharing for private vehicles is weak. The potential trips are concentrated in the morning peak period, mainly starting in residential or integrative areas, and ending in commercial areas or green spaces. Commuting and long-distance trips are more sensitive to decreases in price, such that they are more likely to be completed as battery electric vehicle sharing trips. This price decrease could also increase the potential trip ratio during the evening peak period. These findings are useful to governments and operators for implementing policies such as station planning, relocation, and pricing strategies.