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).     

Preferences for alternative fuel vehicles by Dutch local governments

Using a choice model, we estimate the preferences for alternative fuel vehicles by Dutch local governments. The analysis shows that local governments are willing to pay between 25% and 50% extra for an alternative fuel vehicle without a serious loss of utility. Further, local emissions are an important criterion on which to base a decision, especially for municipalities and provinces. We also calculate the utility for a number of prominent alternative fuel vehicles. We find that show that local governments value the battery electric vehicle and biogas internal combustion engine equally. It is important, however, that the time to refuel for electric vehicles is reduced to about 30 min.     

Consumer preferences for alternative fuel vehicles: A discrete choice analysis

This paper analyzes the potential demand for privately used alternative fuel vehicles using German stated preference discrete choice data. By applying a mixed logit model, we find that the most sensitive group for the adoption of alternative fuel vehicles embraces younger, well-educated, and environmentally aware car buyers, who have the possibility to plug-in their car at home, and undertake numerous urban trips. Moreover, many households are willing to pay considerable amounts for greater fuel economy and emission reduction, improved driving range and charging infrastructure, as well as for enjoying vehicle tax exemptions and free parking or bus lane access. The scenario results suggest that conventional vehicles will maintain their dominance in the market. Finally, an increase in the battery electric vehicles’ range to a level comparable with all other vehicles has the same impact as a multiple measures policy intervention package.     

A network wide simulation strategy of alternative fuel vehicles

This paper presents an integrated simulator “CUIntegration” to evaluate routing strategies based on energy and/or traffic measures of effectiveness for any Alternative Fuel Vehicles (AFVs). The CUIntegration can integrate vehicle models of conventional vehicles as well as AFVs developed with MATLAB-Simulink, and a roadway network model developed with traffic microscopic simulation software VISSIM. The architecture of this simulator is discussed in this paper along with a case study in which the simulator was utilized for evaluating a routing strategy for Plug-in Hybrid Electric Vehicles (PHEVs) and Electric Vehicles (EVs). The authors developed a route optimization algorithm to guide an AFV based on that AFV driver’s choice, which included; finding a route with minimum (1) travel time, (2) energy consumption or (3) a combination of both. The Application Programming Interface (API) was developed using Visual Basic to simulate the vehicle models/algorithms developed in MATLAB and direct vehicles in a roadway network model developed in VISSIM accordingly. The case study included a section of Interstate 83 in Baltimore, Maryland, which was modeled, calibrated and validated. The authors considered a worst-case scenario with an incident on the main route blocking all lanes for 30 min. The PHEVs and EVs were represented by integrating the MATLAB-Simulink vehicle models with the traffic simulator. The CUIntegration successfully combined vehicle models with a roadway traffic network model to support a routing strategy for PHEVs and EVs. Simulation experiments with CUIntegration revealed that routing of PHEVs resulted in cost savings of about 29% when optimized for the energy consumption, and for the same optimization objective, routing of EVs resulted in about 64% savings.     

Locating refuelling stations for alternative fuel vehicles: a review on models and applications

The recent concerns on environmental issues have expedited the technological development of alternative fuel vehicles (AFVs), but the deployment of AFVs still remains at the initial stage mainly because of the lack of refuelling facilities. Recognising this, researchers have conducted various studies, proposing a variety of approaches to strategically locating refuelling stations. This paper presents a comprehensive review of the approaches, focusing more on applications than computational issues. The review identifies two main elements of the approaches: location modelling and refuelling demand estimation. Examining how the elements were handled in refuelling location studies, this paper suggests that future refuelling location models should properly reflect the intricate and various perspectives of three major AFV stakeholders: drivers, government agencies and refuelling service providers. This study is expected to help researchers efficiently set up their refuelling location problems and identify critical factors for seeking the solutions.     

Fuel economy,cost, and greenhouse gas results for alternative fuel vehicles in 2011

This paper presents in-service data collected from over 300 alternative fuel vehicles and over 80 fueling stations to help fleets determine what types of applications and alternative fuels may help them reduce their environmental impacts and fuel costs. The data were compiled in 2011 by over 30 organizations in New York State using a wide variety of commercial vehicle types and technologies. Fuel economy, incremental vehicle purchase cost, fueling station purchase cost, greenhouse gas reductions, and fuel cost savings data clarifies the performance of alternative fuel vehicles and fuel stations. Data were collected from a range of vehicle types, including school buses, delivery trucks, utility vans, street sweepers, snow plows, street pavers, bucket trucks, paratransit vans, and sedans. CNG, hybrid, LPG, and electric vehicles were tracked.     

Adoption of alternative fuel vehicles: Influence from neighbors,family and coworkers

During the last years, many governments have set targets for increasing the share of biofuels in the transportation sector. Understanding consumer behavior is essential in designing policies that efficiently increase the uptake of cleaner technologies. In this paper we analyze adopters and non-adopters of alternative fuel vehicles (AFVs). We use diffusion of innovation theory and the established notion that the social system and interpersonal influence play important roles in adoption. Based on a nationwide database of car owners we analyze interpersonal influence on adoption from three social domains: neighbors, family and coworkers. The results point primarily at a neighbor effect in that AFV adoption is more likely if neighbors also have adopted. The results also point at significant effects of interpersonal influence from coworkers and family members but these effects weaken or disappear when income, education level, marriage, age, gender and green party votes are controlled for. The results extend the diffusion of innovation and AFV literature with empirical support for interpersonal influence based on objective data where response bias is not a factor. Implications for further research, environmental and transport policy, and practitioners are discussed.     

Incentivizing alternative fuel vehicles: the influence of transport policies,attitudes and perceptions

This paper aims to evaluate the influence of policies, attitudes and perceptions when incentivizing alternative fuel vehicles. The impact of possible policies such as gasoline taxes increases, purchase price subsidies, tax exemptions, and increases in fuel recharging station availability for alternative fuelled vehicles is evaluated using hybrid choice models. The models also allow assessing the sensitivity of latent variables (i.e., attitudes and perceptions) in the car purchase behaviour. The models are estimated using data from a stated choice survey collected in five Colombian cities. The latent variables are obtained from the rating of statements related to the transport system, environmental concern, vehicle preferences, and technology. The modelling approach includes regression between latent variables. Results show that environmental concern and the support for green transport policies have a positive influence on the intention to purchase alternative fuel vehicles. Meanwhile, people who reveal to be car-dependent prefer to buy standard fuelled vehicles. The analysis among cities shows similar trends in individual behaviour, although there are differences in attribute sensitivities. The policy scenario analysis revealed high sensitivity to capital cost and the need for extensive investments in refuelling stations for alternative fuel vehicles to become attractive. Nevertheless, all policies should not only be directed at infrastructure and vehicles but also be focused on user awareness and acceptance of the alternative fuel vehicles. The analysis suggests that in an environmentally conscious market, people prefer alternative fuels. However, if the transport policies support private transport, the market shares of alternative fuel vehicles will decrease.     

Recursive expected conditional value at risk in the fleet renewal problem with alternative fuel vehicles

We study the fleet portfolio management problem faced by a firm deciding which alternative fuel vehicles (AFVs) to choose for its fleet to minimise the weighted average of cost and risk, in a stochastic multi-period setting. We consider different types of technology and vehicles with heterogeneous capabilities. We propose a new time consistent recursive risk measure, the Recursive Expected Conditional Value at Risk (RECVaR), which we prove to be coherent. We then solve the problem for a large UK based company, reporting how the optimal policies are affected by risk aversion and by the clustering for each type of vehicle.     

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.     

Life cycle model of alternative fuel vehicles: emissions,energy, and cost trade-offs

This paper describes a life cycle model for performing level-playing field comparisons of the emissions, costs, and energy efficiency trade-offs of alternative fuel vehicles (AFV) through the fuel production chain and over a vehicle lifetime. The model is an improvement over previous models because it includes the full life cycle of the fuels and vehicles, free of the distorting effects of taxes or differential incentives. This spreadsheet model permits rapid analyses of scenarios in plots of trade-off curves or efficiency frontiers, for a wide range of alternatives with current and future prices and levels of technology. The model is available on request.The analyses indicate that reformulated gasoline (RFG) currently has the best overall performance for its low cost, and should be the priority alternative fuel for polluted regions. Liquid fuels based on natural gas, M100 or M85, may be the next option by providing good overall performance at low cost and easy compatibility with mainstream fuel distribution systems. Longer term, electric drive vehicles using liquid hydrocarbons in fuel cells may offer large emissions and energy savings at a competitive cost. Natural gas and battery electric vehicles may prove economically feasible at reducing emissions and petroleum consumption in niches determined by the unique characteristics of those systems.     

Willingness-to-pay for road safety improvement

Few studies have explored, to date, the issue of the monetary valuation of non-fatal injuries caused by road traffic accidents. The present paper seeks to raise interest in this question and to estimate, by contingent valuation, French households’ willingness-to-pay (WTP) to improve their road safety level and reduce their risk of non-fatal injuries following a road accident. More precisely, a Tobit and a type-II Tobit model were estimated to identify factors for WTP. The results highlighted the significant positive influence of injury severity on WTP. Experience of road traffic accidents seemed to play an important role, positively influencing valuation of non-fatal injury.     

Integrated planning of static and dynamic charging infrastructure for electric vehicles

This paper investigates the optimal deployment of static and dynamic charging infrastructure considering the interdependency between transportation and power networks. Static infrastructure means plug-in charging stations, while the dynamic counterpart refers to electrified roads or charging lanes enabled by charging-while-driving technology. A network equilibrium model is first developed to capture the interactions among battery electric vehicles’ (BEVs) route choices, charging plans, and the prices of electricity. A mixed-integer bi-level program is then formulated to determine the deployment plan of charging infrastructure to minimize the total social cost of the coupled networks. Numerical examples are provided to demonstrate travel and charging plans of BEV drivers and the competitiveness of static and dynamic charging infrastructure. The numerical results on three networks suggest that (1) for individual BEV drivers, the choice between using charging lanes and charging stations is more sensitive to parameters including value of travel time, service fee markup, and battery size, but less sensitive to the charging rates and travel demand; (2) deploying more charging lanes is favorable for transportation networks with sparser topology while more charging stations can be more preferable for those denser networks.     

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.     

Identifying the early adopters of alternative fuel vehicles: A case study of Birmingham,United Kingdom

The transport sector has been identified as a significant contributor to greenhouse gas emissions. As part of its emissions reduction strategy, the United Kingdom Government is demonstrating support for new vehicle technologies, paying attention, in particular, to electric vehicles.Cluster analysis was applied to Census data in order to identify potential alternative fuel vehicle drivers in the city of Birmingham, United Kingdom. The clustering was undertaken based on characteristics of age, income, car ownership, home ownership, socio-economic status and education. Almost 60% of areas that most closely fitted the profile of an alternative fuel vehicle driver were found to be located across four wards furthest from Birmingham city centre, while the areas with the poorest fit were located towards the centre of Birmingham. The paper demonstrates how Census data can be used in the initial stages of identifying potential early adopters of alternative vehicle drivers. It also shows how such research can provide scope for infrastructure planning and policy development for local and national authorities, while also providing useful marketing information to car manufacturers.     

Deployment of stationary and dynamic charging infrastructure for electric vehicles along traffic corridors

As charging-while-driving (CWD) technology advances, charging lanes can be deployed in the near future to charge electric vehicles (EVs) while in motion. Since charging lanes will be costly to deploy, this paper investigates the deployment of two types of charging facilities, namely charging lanes and charging stations, along a long traffic corridor to explore the competitiveness of charging lanes. Given the charging infrastructure supply, i.e., the number of charging stations, the number of chargers installed at each station, the length of charging lanes, and the charging prices at charging stations and lanes, we analyze the charging-facility-choice equilibrium of EVs. We then discuss the optimal deployment of charging infrastructure considering either the public or private provision. In the former, a government agency builds and operates both charging lanes and stations to minimize social cost, while in the latter, charging lanes and stations are assumed to be built and operated by two competing private companies to maximize their own profits. Numerical experiments based on currently available empirical data suggest that charging lanes are competitive in both cases for attracting drivers and generating revenue.     

The impact of fuel availability on demand for alternative-fuel vehicles

We study the impact of fuel availability on demand for alternative-fuel vehicles, using data from a survey of potential car buyers in Germany. The survey was conducted as a computer-assisted personal interview and included a choice experiment involving cars with various fuel types. Applying a standard logit model, we show that alternative fuel availability influences choices positively, but its marginal utility diminishes with supply. Furthermore, we derive consumers’ marginal willingness-to-pay for an expanded service station network. The results suggest that a failure to expand the availability of alternative fuel stations represents a significant barrier to the widespread adoption of alternative-fuel vehicles.     

The evaluation of large cycling infrastructure investments in Glasgow using crowdsourced cycle data

Transportation - The benefits of cycling have been well established for several decades. It can improve public health and make cities more active and environmentally friendly. Due to the...     

Heterogeneous consumer preferences for alternative fuel cars in China

This paper models consumer preferences for alternative fuel cars and conventional, petrol fueled cars in China. It compares discrete choice model specifications where each specification reflects how consumers choose between alternative fuel cars and conventional types of cars. We find that, in China, consumers differentiate between the types of alternative fuel cars and are more likely to consider switching from petrol fuel vehicles to hybrid than to electric cars. Segmentation analysis is used to explore groups of consumers and their likelihood of adopting alternative fuel cars.     

Charging infrastructure demands of shared-use autonomous electric vehicles in urban areas

Ride-hailing is a clear initial market for autonomous electric vehicles (AEVs) because it features high vehicle utilization levels and strong incentive to cut down labor costs. An extensive and reliable network of recharging infrastructure is the prerequisite to launch a lucrative AEV ride-hailing fleet. Hence, it is necessary to estimate the charging infrastructure demands for an AEV fleet in advance. This study proposes a charging system planning framework for a shared-use AEV fleet providing ride-hailing services in urban area. We first adopt an agent-based simulation model, called BEAM, to describe the complex behaviors of both passengers and transportation systems in urban cities. BEAM simulates the driving, parking and charging behaviors of the AEV fleet with range constraints and identifies times and locations of their charging demands. Then, based on BEAM simulation outputs, we adopt a hybrid algorithm to site and size charging stations to satisfy the charging demands subject to quality of service requirements. Based on the proposed framework, we estimate the charging infrastructure demands and calculate the corresponding economics and carbon emission impacts of electrifying a ride-hailing AEV fleet in the San Francisco Bay Area. We also investigate the impacts of various AEV and charging system parameters, e.g., fleet size, vehicle battery capacity and rated power of chargers, on the ride-hailing system’s overall costs.