Analysis of plug-in hybrid electric vehicles’ utility factors using GPS-based longitudinal travel data

The benefit of using a PHEV comes from its ability to substitute gasoline with electricity in operation. Defined as the proportion of distance traveled in the electric mode, the utility factor (UF) depends mostly on the battery capacity, but also on many other factors, such as travel pattern and recharging pattern. Conventionally, the UFs are calculated based on the daily vehicle miles traveled (DVMT) by assuming motorists leave home in the morning with a full battery, and no charge occurs before returning home in the evening. Such an assumption, however, ignores the impact of the heterogeneity in both travel and charging behavior, such as going back home more than once in a day, the impact of available charging time, and the price of gasoline and electricity. Moreover, the conventional UFs are based on the National Household Travel Survey (NHTS) data, which are one-day travel data of each sample vehicle. A motorist’s daily travel distance variation is ignored. This paper employs the GPS-based longitudinal travel data (covering 3–18 months) collected from 403 vehicles in the Seattle metropolitan area to investigate how such travel and charging behavior affects UFs. To do this, for each vehicle, we organized trips to a series of home and work related tours. The UFs based on the DVMT are found close to those based on home-to-home tours. On the other hand, it is seen that the workplace charge opportunities significantly increase UFs if the CD range is no more than 40 miles.     

The impact of advance purchase deadlines on airline consumers’ search and purchase behaviors

Airlines frequently use advance purchase ticket deadlines to segment consumers. Few empirical studies have investigated how individuals respond to advance purchase deadlines and price uncertainties induced by these deadlines. We model the number of searches (and purchases) for specific search and departure dates using an instrumental variable approach that corrects for price endogeneity. Results show that search and purchase behaviors vary by search day of week, days from departure, lowest offered fares, variation in lowest offered fares across competitors, and market distance. After controlling for the presence of web bots, we find that the number of consumer searches increases just prior to an advance purchase deadline. This increase can be explained by consumers switching their desired departure dates by one or two days to avoid higher fares that occur immediately after an advance purchase deadline has passed. This reallocation of demand has significant practical implications for the airline industry because the majority of revenue management and scheduling decision support systems currently do not incorporate these behaviors.     

Applying the theory of reasoned action to understanding consumers’ intention to adopt hybrid electric vehicles in Saudi Arabia

To satisfy the global energy demand while accommodating the rapidly increasing consumption rate in its domestic market, Saudi Arabia must develop and implement fuel efficiency programs in many sectors. In the transportation sector, which is a major contributor to fuel consumption and emissions, hybrid electric vehicles (HEVs) could provide a viable solution, but they are not yet available in the Saudi market. Applying the theory of reasoned action (TRA) and an online questionnaire instrument (N = 847), this paper aims to identify the factors that could drive Saudi citizens’ intention to adopt such technology. We find that the TRA is appropriate to describe intention to adopt HEVs in the Saudi context, and that both subjective norms and attitudes are significant in explaining Saudi consumers’ intention, with subjective norms having three times as strong an effect as attitudes. The findings should be useful to relevant Saudi government officials as they develop and implement transportation-related initiatives and policies, as well as to global auto manufacturers and dealers seeking to tap into Saudi Arabia’s prospective HEV market.

     

How experience of use influences mass-market drivers’ willingness to consider a battery electric vehicle: A randomised controlled trial

Uptake of electric vehicles (EVs) by consumers could reduce CO₂ emissions from light duty road transport, but little is known about how mass-market consumer drivers will respond to them. Self-Congruity theory proposes that products are preferred whose symbolic meanings are congruent with personal identity. Further, Construal Level theory suggests that only those who are psychologically close to a new product category through direct experience with it can make concrete construals related to their lifestyles; most drivers lack this for EVs. For instance, potential performance benefits of EVs might offset range limitations for consumers who have such direct experience. The effect of direct experience was tested in a randomised controlled trial with 393 mass-market consumer drivers. An experimental group were given direct experience of a modern battery electric vehicle (BEV), and a control group an equivalent conventional car. Despite rating the performance of the BEV more highly than that of the conventional car, willingness to consider a BEV declined after experience, particularly if the range of the BEV considered was short. The participants willing to consider a short-range BEV were those high in self-congruity, for whom the BEV could act as a strong symbol of personal identity.     

Evaluating a concept design of a crowd-sourced ‘mashup’ providing ease-of-access information for people with limited mobility

This study investigates the impact of using a concept map-based ‘mashup’ (www.accessadvisr.net) to provide volunteered (i.e. user contributed) ease of access information to travellers with limited mobility. A scenario-based user trial, centred around journey planning, was undertaken with 20 participants, divided equally between (1) those who have physical restrictions on their mobility, due to disability, illness or injury, and (2) those with practical mobility constraints due to being parents with young children who have to use a child’s pushchair when using public transport. Both user groups found the concept useful, but its potential impact was less for the pushchair user group. There were mixed views in relation to the ability of the mashup to convey the trustworthiness, credibility and reliability of information necessary for journey planning. The study identified a number of key information-related user requirements which help enable effective design of user contributed web-based resources for travellers with mobility-related issues.     

Improving the behavioral realism of global integrated assessment models: An application to consumers’ vehicle choices

A large body of transport sector-focused research recognizes the complexity of human behavior in relation to mobility. Yet, global integrated assessment models (IAMs), which are widely used to evaluate the costs, potentials, and consequences of different greenhouse gas emission trajectories over the medium-to-long term, typically represent behavior and the end use of energy as a simple rational choice between available alternatives, even though abundant empirical evidence shows that real-world decision making is more complex and less routinely rational. This paper demonstrates the value of incorporating certain features of consumer behavior in IAMs, focusing on light-duty vehicle (LDV) purchase decisions. An innovative model formulation is developed to represent heterogeneous consumer groups with varying preferences for vehicle novelty, range, refueling/recharging availability, and variety. The formulation is then implemented in the transport module of MESSAGE-Transport, a global IAM, although it also has the generic flexibility to be applied in energy-economy models with varying set-ups. Comparison of conventional and ‘behaviorally-realistic’ model runs with respect to vehicle purchase decisions shows that consumer preferences may slow down the transition to alternative fuel (low-carbon) vehicles. Consequently, stronger price-based incentives and/or non-price based measures may be needed to transform the global fleet of passenger vehicles, at least in the initial market phases of novel alternatives. Otherwise, the mitigation burden borne by other transport sub-sectors and other energy sectors could be higher than previously estimated. More generally, capturing behavioral features of energy consumers in global IAMs increases their usefulness to policy makers by allowing a more realistic assessment of a more diverse suite of policies.     

Effects of different factors on drivers’ guidance compliance behaviors under road condition information shown on VMS

It is generally accepted that compliance behavior is affected by many factors. The purpose of this study is to investigate the effects of diverse factors on drivers’ guidance compliance behaviors under road condition information shown on graphic variable message sign (VMS), and based on this to find out a better information release mode. The involved data were obtained from questionnaire survey, and ordinal regression was used to analyze the casual relation between guidance compliance behavior and its influencing factors. Based on an overall analysis of conditions in driver’s route choice, an accurate method was proposed to calculate the compliance rate. The model testing information indicated that ordinal regression model with complementary log–log being the link function was appropriate to quantify the relation between the compliance rate and the factors. The estimation results showed that age, driving years, average annual mileage, monthly income, driving style, occupation, the degree of trust in VMS, the familiarity with road network and the route choice style were significant determinants of guidance compliance behavior. This paper also compared two different guidance modes which were ordinary guidance mode (M1) and predicted guidance mode (M2) through simulation. The average speed fluctuations and average travel time supported that M2 had better effect in improving traffic flow and balancing traffic load and resource. Some detailed suggestions of releasing guidance information were proposed with the explanation by flow-density curve and variation of traffic flows. These findings are the foundation to design and improve guidance systems by assessing guidance effect and modifying guidance algorithm.     

I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.     

Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.     

Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

Transportation - As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates...     

How to Capture the Passengers’ Point of View on a Transit Service through Rating and Choice Options

Abstract

In this paper, a methodology for capturing the transit passenger’s point of view by using both rating and choice options is proposed. For this purpose, some discrete choice logit models are introduced; the models allow the probability of choice of some alternative transit services to be calculated, and the importance of each service aspect to be determined. The models are calibrated by using data collected by a survey in which a stated preferences experiment was proposed to a sample of passengers, and some judgements were expressed by them about their transit services, in terms of perceptions and expectations. The introduced methodology provides a relevant contribution from a practical viewpoint because it allows the identification of the most important aspects on overall service quality; it is useful to the transit operators for measuring service quality and for investing on the various service aspects in order to effectively improve transit services.