Consumer valuation of changes in driving range: A meta-analysis

We perform a meta-analysis of studies investigating consumer preferences for electric and other alternative fuel vehicles (AFVs) to provide insights into the way driving range is traded off for capital costs. We find that consumers are willing to pay, on average, between 66 and 75 US$ for a 1-mile increase in driving range. Ceteris paribus, 100-mile-range cars have to be priced about 60% less than their conventional counterparts to become competitive. In line with intuition, but in contrast to most specifications employed in primary studies, we find that consumers’ marginal willingness to pay (WTP) decreases at a diminishing rate with increases in driving range. The variation in the WTP and compensating variation estimates among examined studies can be attributed to differences in the levels of driving range considered, in other elements of the study design and in the country of study. Our findings support stated preference literature’s conclusion that short driving range has been a major limitation to the large-scale adoption of battery electric vehicles (BEVs) and other AFVs, and that technological developments permitting longer driving ranges will, to some extent, facilitate their market penetration. We further propose that consumer valuation of driving range should not be examined in isolation from other attributes related to refuelling activities, such as refuelling duration and the coverage of refuelling infrastructure.     

Incorporating refuelling behaviour and drivers’ preferences in the design of alternative fuels infrastructure in a city

The purpose of this article is to present an optimization model to plan the deployment strategy for hydrogen refuelling stations in a city when Origin–Destination (OD) data are not available. This model considers two objectives: to maximize the traffic covered by the selected hydrogen refuelling stations and minimize the average distance of the city’s inhabitants to the nearest hydrogen refuelling station. As OD data are assumed to be unavailable, the clustering of stations in the highest traffic zones is prevented by a new constraint that takes into account information on the distribution of existing conventional refuelling stations. This model is applied to Seville, a city in Southern Spain of about 140 km² with a population of around 700,000. This application uses the results of a survey of more than 200 Sevillian drivers on their current refuelling tendencies, their willingness to use alternative fuel vehicles and their minimum requirements (regarding maximum distance to be travelled to refuel and number of stations in the city) when establishing a network of alternative refuelling stations.     

Innovative alternatives take action – Investigating determinants of alternative fuel vehicle adoption

Alternative fuel vehicles (AFVs) as environmentally friendly alternatives to conventional internal combustion engines have gained increasing attention in general public. While empirical studies have begun to explore product-specific factors that drive consumer adoption of AFVs, an integrative framework of a comprehensive set of AFV adoption factors and its theoretical foundation as well as empirical validation is still missing. By drawing on theory of innovation adoption and theory of reasoned action we show that consumers’ perceptions of AFV attributes lead to a general attitude formation towards AFV. In conjunction with consumers’ subjective and personal norm, this in turn determines AFV adoption behavior. Concerning AFV attributes, compatibility, design, and relative advantage of AFVs exhibit the strongest influence on consumers’ attitude formation toward AFV. We derive implications for future research and policy makers. The latter include suggestions on how to develop and communicate AFV in order to stimulate AFV adoption.     

A hedonic test of the effects of the Alternative Motor Fuels Act

Under the Alternative Motor Fuels Act (AMFA), vehicles that run on ethanol, methanol, or natural gas get extra credits in the calculation of Corporate Average Fuel Economy (CAFE). This paper uses hedonic techniques to examine the effect of production of alternative-fuel vehicles (AFVs) on the implicit price of fuel economy. This study finds that, after AFVs came to market, the marginal value of fuel economy from companies producing them decreased. This finding suggests that manufacturers who produced AFVs were willing to offer a lower price for fuel economy, because automakers had an additional way to achieve fuel economy standards beyond improving the fuel efficiency of conventional cars. These findings bolster the argument that a major role of the AMFA credit for AFVs is to allow automakers to increase their production of fuel-inefficient vehicles.     

Introducing alternative fuel vehicles in Hong Kong: views from the public light bus industry

Hong Kong was the first place in the world to implement a trial scheme to convert all public light buses (PLBs) on the road from diesel to alternative fuel vehicles (AFVs). The scheme, however, did not receive much support from PLB operators. At present, there is a rich literature on households’ demand for AFVs (especially in the USA). However, there have not been many studies about the demand for commercial AFVs in the business and public transport sectors. Since light buses running on alternative fuels are not widely available in the Hong Kong market, a stated preference (SP) survey was conducted to solicit the preferences of PLB operators on eight commercial vehicle attributes and seven forms of government support. The SP data are analyzed by multinomial logit (MNL) models. Detailed analyses on market segmentation and price elasticities follow. The results are of theoretical and practical significance.     

Optimization of refuelling truck fleets at an airport

This paper considers the process of aircraft refuelling using refuelling trucks at an airport. The problem which arises is to determine the minimum number of refuelling trucks and their routes for the given demand (schedule and fuel quantities). The constraint is that there is no aircraft departure delay due to the refuelling process. The optimum solution is found here using the branch-and-bound technique. This solution is compared with results obtained on the basis of random assignment and a few conclusions are made. The paper also presents a model application example based on real life data from a medium-sized airport.     

The role of alternative fuel vehicles: Using behavioral and sensor data to model hierarchies in travel

Greater adoption and use of alternative fuel vehicles (AFVs) can be environmentally beneficial and reduce dependence on gasoline. The use of AFVs vis-à-vis conventional gasoline vehicles is not well understood, especially when it comes to travel choices and short-term driving decisions. Using data that contains a sufficiently large number of early AFV adopters (who have overcome obstacles to adoption), this study explores differences in use of AFVs and conventional gasoline vehicles (and hybrid vehicles). The study analyzes large-scale behavioral data integrated with sensor data from global positioning system devices, representing advances in large-scale data analytics. Specifically, it makes sense of data containing 54,043,889 s of speed observations, and 65,652 trips made by 2908 drivers in 5 regions of California. The study answers important research questions about AFV use patterns (e.g., trip frequency and daily vehicle miles traveled) and driving practices. Driving volatility, as one measure of driving practice, is used as a key metric in this study to capture acceleration, and vehicular jerk decisions that exceed certain thresholds during a trip. The results show that AFVs cannot be viewed as monolithic; there are important differences within AFV use, i.e., between plug-in hybrids, battery electric, or compressed natural gas vehicles. Multi-level models are particularly appropriate for analysis, given that the data are nested, i.e., multiple trips are made by different drivers who reside in various regions. Using such models, the study also found that driving volatility varies significantly between trips, driver groups, and regions in California. Some alternative fuel vehicles are associated with calmer driving compared with conventional vehicles. The implications of the results for safety, informed consumer choices and large-scale data analytics are discussed.     

Willingness-to-pay for alternative fuel vehicle characteristics: A stated choice study for Germany

In the light of European energy efficiency and clean air regulations, as well as an ambitious electric mobility goal of the German government, we examine consumer preferences for alternative fuel vehicles (AFVs) based on a Germany-wide discrete choice experiment among 711 potential car buyers. We estimate consumers’ willingness-to-pay and compensating variation (CV) for improvements in vehicle attributes, also taking taste differences in the population into account by applying a latent class model with 6 distinct consumer segments. Our results indicate that about 1/3 of the consumers are oriented towards at least one AFV option, with almost half of them being AFV-affine, showing a high probability of choosing AFVs despite their current shortcomings. Our results suggest that German car buyers’ willingness-to-pay for improvements of the various vehicle attributes varies considerably across consumer groups and that the vehicle features have to meet some minimum requirements for considering AFVs. The CV values show that decision-makers in the administration and industry should focus on the most promising consumer group of ‘AFV aficionados’ and their needs. It also shows that some vehicle attribute improvements could increase the demand for AFVs cost-effectively, and that consumers would accept surcharges for some vehicle attributes at a level which could enable their private provision and economic operation (e.g. fast-charging infrastructure). Improvement of other attributes will need governmental subsidies to compensate for insufficient consumer valuation (e.g. battery capacity).     

The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives

Gaining a better understanding of the factors that affect the likelihood of a vehicle crash has been an area of research focus for many decades. However, in the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes – the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period. This paper provides a detailed review of the key issues associated with crash-frequency data as well as the strengths and weaknesses of the various methodological approaches that researchers have used to address these problems. While the steady march of methodological innovation (including recent applications of random parameter and finite mixture models) has substantially improved our understanding of the factors that affect crash-frequencies, it is the prospect of combining evolving methodologies with far more detailed vehicle crash data that holds the greatest promise for the future.     

Tour-based mode choice modelling as the core of an activity-based travel demand modelling framework: a review of state-of-the-art

ABSTRACT

The paper presents a critical review of the methodological approaches used in tour-based mode choice models within the activity-based modelling frameworks. Various components of the activity-based models, such as activity type choice, activity location choice, and activity duration have already matured significantly. However, the mode choice component is often simplified in many ways. Both trip-based and tour-based approaches are used in many cases. However, the tour-based approach is considered to be the most relevant to the activity-based modelling framework. This paper presents a synthesis of the strengths and weaknesses of existing tour-based mode choice models. The previous studies on tour-based mode choice models are grouped into seven categories, ranging from simplified main tour mode to complex dynamic discrete choice models. Besides, challenges with data-hungry models, simulation-based models and static models are discussed elaborately. In conclusion, it proposes a few methodological suggestions for researchers and practitioners for finding an appropriate mode choice modelling framework for activity-based models. In addition, the paper also provides a guideline on how to incorporate automated vehicles and Mobility-as-a-Service within the framework of tour-based mode choice models.     

Alternative Modeling Approaches Used for Examining Automobile Ownership: A Comprehensive Review

Abstract

Household vehicle ownership, and the associated dimensions including fleet size, vehicle type and usage, has been one of the most researched transport topics. This paper endeavors to provide a critical overview of the wide-ranging methodological approaches employed in vehicle ownership modeling depending on the ownership representation over the past two decades. The studies in the existing literature based on the vehicle ownership representation are classified as: exogenous static, exogenous dynamic, endogenous static and endogenous dynamic models. The methodological approaches applied range from simple linear regressions to complex econometrics formulations taking into account a rich set of covariates. In spite of the steady advancement and impressive evolution in terms of methodological approaches to examine the decision process, we identify complex issues that pose a formidable challenge to address the evolution of vehicle ownership in the coming years. Specifically, we discuss challenges with data availability and methodological framework selection. In light of these discussions, we provide a decision matrix for aiding researchers/practitioners in determining appropriate model frameworks for conducting vehicle ownership analysis.     

Methodologies for exploring the link between urban form and travel behavior

Communities are increasingly looking to urban design and the concept of the New Urbanism as an effective strategy for reducing automobile dependence in suburban areas. While the available empirical evidence suggests that automobile travel is lower in traditional-style neighborhoods, it provides limited insights as to how and why, largely because the research methodologies used have been insufficent for the task. Most of the studies addressing this question fall into three categories: simulation studies, aggregate analyses, and disaggregate analyses. Two other approaches offer greater promise for understanding the relationship between urban form and travel behavior: choice models and activity-based analyses. This paper reviews alternative approaches for exploring the link between urban form and travel behavior, outlines issues and complexities that this research must address, and, finally, suggests that the focus of this research should shift from the search for strategies to change behavior to a search for strategies to provide choices.     

Optimal development of alternative fuel station networks considering node capacity restrictions

A potential solution to reduce greenhouse gas (GHG) emissions in the transport sector is the use of alternative fuel vehicles (AFV). As global GHG emission standards have been in place for passenger cars for several years, infrastructure modelling for new AFV is an established topic. However, as the regulatory focus shifts towards heavy-duty vehicles (HDV), the market diffusion of AFV-HDV will increase as will planning the relevant AFV infrastructure for HDV. Existing modelling approaches need to be adapted, because the energy demand per individual refill increases significantly for HDV and there are regulatory as well as technical limitations for alternative fuel station (AFS) capacities at the same time. While the current research takes capacity restrictions for single stations into account, capacity limits for locations (i.e. nodes) – the places where refuelling stations are built such as highway entries, exits or intersections – are not yet considered. We extend existing models in this respect and introduce an optimal development for AFS considering (station) location capacity restrictions. The proposed method is applied to a case study of a potential fuel cell heavy-duty vehicle AFS network. We find that the location capacity limit has a major impact on the number of stations required, station utilization and station portfolio variety.     

A choice experiment on alternative fuel vehicle preferences of private car owners in the Netherlands

This paper presents results of an online stated choice experiment on preferences of Dutch private car owners for alternative fuel vehicles (AFVs) and their characteristics. Results show that negative preferences for alternative fuel vehicles are large, especially for the electric and fuel cell car, mostly as a result of their limited driving range and considerable refueling times. Preference for AFVs increases considerably with improvements on driving range, refueling time and fuel availability. Negative AFV preferences remain, however, also with substantial improvements in AFV characteristics; the remaining willingness to accept is on average € 10,000–€ 20,000 per AFV. Results from a mixed logit model show that consumer preferences for AFVs and AFV characteristics are heterogeneous to a large extent, in particular for the electric car, additional detour time and fuel time for the electric and fuel cell car. An interaction model reveals that annual mileage is by far the most important factor that determines heterogeneity in preferences for the electric and fuel cell car. When annual mileage increases, the preference for electric and fuel cell cars decreases substantially, whilst the willingness to pay for driving range increases substantially. Other variables such as using the car for holidays abroad and the daily commute also appear to be relevant for car choice.     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.     

Consumer intentions for alternative fuelled and autonomous vehicles: A segmentation analysis across six countries

Rapid advances in the development of autonomous and alternative-fuel vehicles (AFVs) are likely to transform the future of mobility and could bring benefits such as improved road safety and lower emissions. Achieving these potential benefits requires widespread consumer support for these disruptive technologies. To date, research to explore consumer perceptions of transport innovations has tended to consider them in isolation (e.g., driverless cars, electric vehicles). The current paper examines the predictors of consumer interest in and willing to pay for both AFVs and autonomous vehicles through a choice experiment conducted in six diverse markets: Germany, India, Japan, Sweden, UK and US. Using Latent Class Discrete Choice Models, we observe significant heterogeneity both within and across the country samples. For example, while Japanese consumers are generally willing to pay for autonomous vehicles, in most European countries, consumers need to be compensated for automation. Within countries, though, we found some segments – typically, those with a university degree, and self-identifying as having a pro-environmental identity and as being innovators– are more in favour of automation. Significantly, we also found that support for autonomous vehicles is associated with support for AFVs, perhaps, due to common demographic or socio-psychological predictors of both types of innovative technology. These findings are valuable for policymakers and the automotive industry in identifying potential early adopters, as well as consumer segments or cultures less convinced to adopt these innovative transport technologies.     

Valuing Freight Transport Time using Transport Demand Modelling: A Bibliographical Review

The value of time for freight transport is of major importance in infrastructure-related cost–benefit analysis and yet its study has been largely neglected when compared with its passenger counterpart. In fact, one of the attributes that could decide the profitability of a project is how much can be saved if freight vehicles use new infrastructure. Despite being the primary benefit of most investments in transport infrastructure, researchers have not yet reached agreement over either the size or the nature of the values of time that should be used when evaluating projects. This article provides a review of the estimation of freight value of time through transport demand modelling and extant empirical evidence on this topic. Similarly, the bibliographic review of studies undertaken has allowed us to pinpoint the most critical issues when modelling freight transport demand and the position of various research teams regarding these aspects. Such issues include identifying the decision-maker, heterogeneity in the transport flows and transport attributes considered by decision-makers.     

Evidence of land use impacts of rapid transit systems

This paper draws from the findings of published empirical studies and observations of the impacts of rapid transit systems on urban development. Analysis is based on comparisons of impact findings by different researchers and for different cities. An initial set of key issues is proposed, against which available information is arrayed and compared. It is concluded that rapid transit can have substantial growth-focusing impacts, but only if other supporting factors are present.     

Space,money, life-stage,and the allocation of time

Demographic, socioeconomic, seasonal, and scheduling factors affect the allocation of time to various activities. This paper examines those variables through exploration of the 1990 Nationwide Personal Transportation Survey, which has been inverted to track activity duration. Two key issues are considered. First, how much can activity duration and frequency explain travel duration? The analysis shows activity duration has positive and significant effects on travel duration, supporting recent arguments in favor of activity based models. Second, which recent trend is the main culprit in the rise in travel: suburbanization, rising personal incomes, or female labor force participation? This paper examines the share of time within a 24-hour budget allocated to several primary activities: home, work, shop, and other. The data suggest that income and location have modest effects on time allocation compared with the loss of discretionary time due to working.

     

The San Francisco Travel Quality Study: tracking trials and tribulations of a transit taker

In helping understand the dynamics of travel choice behavior and traveler satisfaction over time, multi-day panel data is invaluable (McFadden in Am Econ Rev 91(3): 351–378, 2001). The collection of such data has become increasingly feasible thanks to smartphones, which researchers can use to present surveys to travelers and to collect additional information through the phones’ location services and other sensors. This paper describes the design and implementation of the San Francisco Travel Quality Study, a multi-day research study conducted in autumn 2013 with 838 participants. The objective of the study was to investigate the link between transit service quality, the satisfaction and subjective well-being of transit riders, and travel choice behavior, with a particular interest in the influence of travelers’ choice history and personal experiences on future transit use. For that purpose, a rich panel data set was collected from multiple sources, including a number of mobile travel experience surveys capturing traveler satisfaction and emotions, two online surveys capturing demographics, attitudes and mode choice intentions, as well as high-resolution phone location data and transit vehicle location data. By fusing the phone location data with transit vehicle location data, individual-level transit travel diaries could be automatically created, and by fusing the location data with the survey responses, additional information about the context of the responses could be derived. While the behavioral and satisfaction-related findings of the study are detailed in other publications, this paper is intended to serve two purposes. First, it describes the study design, data collection effort and challenges faced in order to provide a learning opportunity for other researchers considering similar studies. Second, it discusses the key sociodemographic data and characteristics of the study population in order to provide a foundation and reference for further publications that make use of the data set described here. The authors would like to invite other researchers to collaborate with them on the evaluation of the data.