Impact of vehicle usage on consumer choice of hybrid electric vehicles

We analyze the vehicle usage and consumer profile attributes extracted from both National Household Travel Survey and Vehicle Quality Survey data to understand the impact of vehicle usage upon consumers’ choices of hybrid electric vehicles in the US. In addition, the key characteristics of hybrid vehicle drivers are identified to determine the market segmentations of hybrid electric vehicles and the critical attributes to include in the choice model. After a compatibility test of two datasets, a pooled choice model combining both data sources illustrates the significant influences of vehicle usage upon consumers’ choices of hybrid electric vehicles. Even though the data-bases have in the past been used independently to study travel behavior and vehicle quality ratings, here we use them together.     

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.     

Factors affecting temporal changes in mode choice model parameters

In travel demand forecasting models, parameters are often assumed to be stable over time. The stability of these parameters, however, has been questioned. This study investigates the factors affecting temporal changes in mode choice model parameters using a method proposed by the author that jointly utilises repeated cross-sectional data. In this method, the parameters are assumed to follow functional forms and the parameter changes are modelled endogenously. While the author’s previous studies assumed that all parameters are the same function of the same variable, this study assumes that different parameters are different functions of different variables, including time (year) and macro-economic variables. The paper describes a case study of a journey-to-work mode choice analysis for Nagoya, Japan, that examines 288 combinations of the functional forms and variables. The analysis found that the functions of time had serious over-fitting problems and that parameter changes are more closely related to economic factors.     

Individual transport emissions and the built environment: A structural equation modelling approach

Increasing CO₂ emissions from the transport sector have raised substantial concerns among researchers and policy makers. This research examines the impact of the built environment on individual transport emissions through two mediate variables, vehicle usage and vehicle type choice, within a structural equation modelling (SEM) framework. We find that new-urbanism-type built environment characteristics, including high density, mixed land use, good connectivity, and easy access to public transport systems help reduce transport CO₂ emissions. Such mitigating effect is achieved largely through the reduced vehicle miles travelled (VMT) and is enhanced slightly by the more efficient vehicles owned by individuals living in denser and more diverse neighborhoods, all else being equal. Our research findings provide some new evidence that supports land use policies as an effective strategy to reduce transport CO₂ emissions.     

Traveled distance,stock and fuel efficiency of private vehicles in Canada: price elasticities and rebound effect

This paper presents estimates of the rebound effect and other elasticities for the Canadian light-duty vehicle fleet using panel data at the provincial level from 1990 to 2004. We estimate a simultaneous three-equation model of aggregate demand for vehicle kilometers traveled, vehicle stock and fuel efficiency. Price and income elasticities obtained are broadly consistent with those reported in the literature. Among other results, an increase in the fuel price of 10% would reduce driving by ~2% in the long term and by 1% the average fuel consumption rate. Estimates of the short- and long-term rebound effects are ~8 and 20%, respectively. We also find that an increase in the gross domestic product per capita of 10% would cause an increase in driving distance of 2–3% and an increase of up to 4% in vehicle stock per adult. In terms of policy implications, our results suggest that: (1) the effectiveness of new fuel efficiency standards will be somewhat mitigated by the rebound effect and (2) fuel price increases have limited impacts on gasoline demand.

Analysis of a consumer survey on plug-in hybrid electric vehicles

Plug-in Hybrid Electric Vehicles (PHEVs) show potential to reduce greenhouse gas (GHG) emissions, increase fuel efficiency, and offer driving ranges that are not limited by battery capacity. However, these benefits will not be realized if consumers do not adopt this new technology. Several agent-based models have been developed to model potential market penetration of PHEVs, but gaps in the available data limit the usefulness of these models. To address this, we administered a survey to 1000 stated US residents, using Amazon Mechanical Turk, to better understand factors influencing the potential for PHEV market penetration. Our analysis of the survey results reveals quantitative patterns and correlations that extend the existing literature. For example, respondents who felt most strongly about reducing US transportation energy consumption and cutting greenhouse gas emissions had, respectively, 71 and 44 times greater odds of saying they would consider purchasing a compact PHEV than those who felt least strongly about these issues. However, even the most inclined to consider a compact PHEV were not generally willing to pay more than a few thousand US dollars extra for the sticker price. Consistent with prior research, we found that financial and battery-related concerns remain major obstacles to widespread PHEV market penetration. We discuss how our results help to inform agent-based models of PHEV market penetration, governmental policies, and manufacturer pricing and marketing strategies to promote consumer adoption of PHEVs.     

汽车类环境标志产品的政府采购环境效益分析

实施政府绿色采购,引导和推动消费和生产向更可持续的方向发展是解决环境问题,实现经济社会全面可持续发展的必然要求。进行绿色采购成本效益分析是政府绿色采购政策可持续推进发展的有力保障。本文在介绍了中国环境标产品政府采购的现状和欧盟相关政府绿色采购环境效益研究的基础上,针对汽车产品展开了相应的环境效益分析,最后对广泛开展此项工作提出了发展建议。     

Constraints to green vehicle ownership: A focus group study

We consider constraints that prevent people with environmental concerns from buying “green” vehicles that are smaller, more fuel-efficient, and less polluting by using a series of focus group discussions. We find that the features of vehicles currently on the market, family and work responsibilities, residential choices, and routines and preferences all act as constraints. Serious misunderstandings about the environmental impacts of owning and using vehicles also were noted, making it difficult for many to accurately assess their alternatives. For some, environmental concerns are unlikely to influence future vehicle purchase decisions, even if constraints were removed altogether; other priorities have taken and will take precedence over the environmental impacts of their choices.     

Use of magnetic monitoring of vehicles for proactive strategy development

The proactive operational strategy in the transport system which is a parallel concept associated with the Intelligent Transportation System (ITS) seems to be modern and right direction of development. However, the basis for its operation is the “information” in the broad sense of the term, on the functioning of the transport system itself. As new technologies develop, there emerge new opportunities of putting to use a wide array of sensors which can deliver more complete traffic data but using complex matrix of sensors is often unjustified economically and confusing. The paper proposes use of magnetometers as an interesting alternative to pass the requirements of simplicity of application, minimum costs and maximum of acquired information. The process of obtaining the information requires analysis of the quantitative and qualitative changes of the magnetic field. The conducted analyses demonstrate the possibility of using the passive magnetic methods for the purpose of monitoring of vehicles. Placement of the magnetic sensor bear out an important issue and was also discussed. During the experimental research it has been proven that passive magnetic methods enable obtaining the information on the movement of a vehicle as well as on the vehicle itself. Estimations of such values as: vehicle’s moving direction, velocity, dimensions, clearance or mass having ferromagnetic properties and even the state of strain in a vehicle’s structure were confirmed in practice. In addition thanks to magneto-mechanical effects the theoretical possibility of assessing the stress occurring in the components of vehicles, which could be indicative of the volume of cargo carried. Moreover, the crash experiment showed the possibility of a collision detection using magnetic signal. The need for obtaining information increases from year to year, with information becoming the biggest asset which enables both development and effective use of the transport system. Included researches, proofs emerging opportunity of more extensive use of magnetic sensors and the passive magnetic methods which could be applied in the modern transportation system.     

Fuel economy testing of autonomous vehicles

Environmental pollution and energy use in the light-duty transportation sector are currently regulated through fuel economy and emissions standards, which typically assess quantity of pollutants emitted and volume of fuel used per distance driven. In the United States, fuel economy testing consists of a vehicle on a treadmill, while a trained driver follows a fixed drive cycle. By design, the current standardized fuel economy testing system neglects differences in how individuals drive their vehicles on the road. As autonomous vehicle (AV) technology is introduced, more aspects of driving are shifted into functions of decisions made by the vehicle, rather than the human driver. Yet the current fuel economy testing procedure does not have a mechanism to evaluate the impacts of AV technology on fuel economy ratings, and subsequent regulations such as Corporate Average Fuel Economy targets. This paper develops a method to incorporate the impacts of AV technology within the bounds of current fuel economy test, and simulates a range of automated following drive cycles to estimate changes in fuel economy. The results show that AV following algorithms designed without considering efficiency can degrade fuel economy by up to 3%, while efficiency-focused control strategies may equal or slightly exceed the existing EPA fuel economy test results, by up to 10%. This suggests the need for a new near-term approach in fuel economy testing to account for connected and autonomous vehicles. As AV technology improves and adoption increases in the future, a further reimagining of drive cycles and testing is required.     

Vehicle type choice under the influence of a tax reform and rising fuel prices

Differentiated vehicle taxes are considered by many a useful tool for promoting environmentally friendly vehicles. Various structures have been implemented in several countries, e.g. Ireland, France, The Czech Republic, and Denmark. In many countries the tax reforms have been followed by a steep change in new vehicle purchases toward more diesel vehicles and more fuel-efficient vehicles. The paper analyses to what extent a vehicle tax reform similar to the Danish 2007 reform may explain changes in purchasing behaviour. The paper investigates the effects of a tax reform, fuel price changes, and technological development on vehicle type choice using a mixed logit model. The model allows a simulation of the effect of car price changes that resemble those induced by the tax reform. This effect is compared to the effects of fuel price changes and technology improvements. The simulations show that the effect of the tax reform on fuel efficiency is similar to the effect of rising fuel prices while the effect of technological development is much larger. The conclusion is that while the tax reform appeared in the same year as a large increase in fuel efficiency, it seems likely that it only explains a small part of the shift in fuel efficiency that occurred and that the main driver was the technological development.     

The impact of private autonomous vehicles on vehicle ownership and unoccupied VMT generation

With 36 ventures testing autonomous vehicles (AVs) in the State of California, commercial deployment of this disruptive technology is almost around the corner (California Department of Transportation, 2016). Different business models of AVs, including Shared AVs (SAVs) and Private AVs (PAVs), will lead to significantly different changes in regional vehicle inventory and Vehicle Miles Travelled (VMT). Most prior studies have already explored the impact of SAVs on vehicle ownership and VMT generation. Limited understanding has been gained regarding vehicle ownership reduction and unoccupied VMT generation potentials in the era of PAVs. Motivated by such research gap, this study develops models to examine how much vehicle ownership reduction can be achieved once private conventional vehicles are replaced by AVs and the spatial distribution of unoccupied VMT accompanied with the vehicle reduction. The models are implemented using travel survey and synthesized trip profile from Atlanta Metropolitan Area. The results show that more than 18% of the households can reduce vehicles, while maintaining the current travel patterns. This can be translated into a 9.5% reduction in private vehicles in the study region. Meanwhile, 29.8 unoccupied VMT will be induced per day per reduced vehicles. A majority of the unoccupied VMT will be loaded on interstate highways and expressways and the largest percentage inflation in VMT will occur on minor local roads. The results can provide implications for evolving trends in household vehicles uses and the location of dedicated AV lanes in the PAV dominated future.     

Using census aggregates to proxy for household characteristics: an application to vehicle ownership

Traditionally, researchers studying transportation choice have used data either acquired from household surveys or broad, region-wide aggregates. At the disaggregate level, researchers usually do not have access to important variables or observations. This study investigates the potential usefulness of a proxy approach to modeling discrete choice vehicle ownership: substituting narrow area-based aggregate proxies for missing micro-level explanatory variables by accessing large, publicly maintained datasets. We use data from the 2000 Bay Area Travel Survey (BATS) and the contemporaneous U.S. Census file to compare three models of vehicle ownership, drawing area-wide proxies from increasing levels of aggregation. The models with proxies are compared with a parallel model that uses only survey data. The results indicate that the proxy models are preferred in terms of model selection criteria, and predict vehicle ownership as well or better than the survey model. Parameter values produced by the proxy method effectively approximate those returned by household survey models in terms of coefficient sign and significance, particularly when the aggregate variables are representative of their household-level counterparts. The proxy model with the narrowest level of aggregation achieved the best fit, coefficient precision, and percentage of correct prediction.

CO2 emissions correction of light-duty vehicles based on an energy deviation analysis method

In present study, a total of 3 types of light-duty vehicles were tested for 7 types of driving modes by using a vehicle dynamics-based simulation method. Deviations between the target and actual test vehicle speed and inaccurate chassis dynamometer road load settings are selected as the two main factors affecting the CO₂ emission measurement test. 126 cases of simulations were conducted to analyze the effects of the deviation between the target and actual test vehicle speed on CO₂ emissions and 63 cases of simulation were conducted to analyze the effectiveness of inaccurate road load test conditions on CO₂ emissions. Then, the CO₂ correlation method proposed by World Forum for Harmonization of Vehicle Regulations (WP.29, UN) was applied for simulated results in original simulation cases, and a correction to performance was validated by comparative analysis. From the simulation results, it is revealed that velocity and road load deviation are related to distinct changes in fuel efficiency and CO₂ emission rates of test vehicles. Also, it is revealed that CO₂ correction method show good performance to reduce the relative error lower than 2% in all test cases.     

External costs of electric vehicles

Electric vehicles (EV) are often considered a promising technology to decrease external costs of road transport. Therefore, main external cost components are estimated for EV and internal combustion engine vehicles (ICEV). These include costs of accidents, air pollution, climate change, noise, and congestion. All components are estimated over the product lifetime and, where appropriate, differentiated according to fuel type, vehicle size as well as emission location and time. The advantage of this differentiation is, however, compensated by high uncertainties of most cost estimates. Overall, the external costs of EV and ICEV do not differ significantly. Only for climate change, local air pollutants in congested inner-cities, and noise some advantageous effects can be observed for EV. The advantages depend strongly on the national electricity power plant portfolio and potentially also on the charging strategy. Controlled charging might allow for higher emission reductions than uncontrolled charging of EV.     

Real-world gaseous emission characteristics of Euro 6b light-duty gasoline- and diesel-fueled vehicles

To accurately investigate vehicle emissions that have become major contributors to global air pollutants and greenhouse gases, test conditions have been transferred from laboratory type approval test cycles to real-world driving conditions. In this study, the real-world driving emissions of carbon monoxide (CO), total hydrocarbons (THC), nitrogen oxides (NO_x), and carbon dioxide (CO₂) from one gasoline and two diesel Euro 6b light-duty passenger vehicles were investigated by a portable emission measurement system (PEMS) in Lyon, France. NO_x and CO₂ emission controls remain critical to addressing the real-world driving emissions of Euro 6b vehicles. Notably, the tested gasoline vehicle emitted higher CO₂ emissions than diesel vehicles on all types of roads, especially on the urban road with an excess of 29.3–48.3%. The highest emission factors of gaseous pollutants generally occurred on the motorway for the gasoline vehicle, while on the urban road for diesel vehicles. In particular, for high-speed driving conditions, the gasoline vehicle gaseous emissions, especially NO_x emissions, were more affected by acceleration than diesel vehicle emissions. In addition, the CO emissions, especially THC emissions, for the gasoline vehicle, were more influenced by warm-start, especially cold-start, than those for diesel vehicles.     

User preferences regarding autonomous vehicles

This study gains insight into individual motivations for choosing to own and use autonomous vehicles and develops a model for autonomous vehicle long-term choice decisions. A stated preference questionnaire is distributed to 721 individuals living across Israel and North America. Based on the characteristics of their current commutes, individuals are presented with various scenarios and asked to choose the car they would use for their commute. A vehicle choice model which includes three options is estimated:

• (1)Continue to commute using a regular car that you have in your possession.
• (2)Buy and shift to commuting using a privately-owned autonomous vehicle (PAV).
• (3)Shift to using a shared-autonomous vehicle (SAV), from a fleet of on-demand cars for your commute.

A factor analysis determined five relevant latent variables describing the individuals’ attitudes: technology interest, environmental concern, enjoy driving, public transit attitude, and pro-AV sentiments. The effects that the characteristics of the individual and the autonomous vehicle have on use and acceptance are quantified through random utility models including logit kernel model taking into account panel effects.Currently, large overall hesitations towards autonomous vehicle adoption exist, with 44% of choice decisions remaining regular vehicles. Early AV adopters will likely be young, students, more educated, and spend more time in vehicles. Even if the SAV service were to be completely free, only 75% of individuals would currently be willing to use SAVs. The study also found various differences regarding the preferences of individuals in Israel and North America, namely that Israelis are overall more likely to shift to autonomous vehicles.Methods to encourage SAV use include increasing the costs for regular cars as well as educating the public about the benefits of shared autonomous vehicles.     

Transitioning to energy efficient vehicles: An analysis of the potential rebound effects and subsequent impact upon emissions

Given the shift toward energy efficient vehicles (EEVs) in recent years, it is important that the effects of this transition are properly examined. This paper investigates some of these effects by analyzing annual kilometers traveled (AKT) of private vehicle owners in Stockholm in 2008. The difference in emissions associated with EEV adoption is estimated, along with the effect of a congestion-pricing exemption for EEVs on vehicle usage. Propensity score matching is used to compare AKT rates of different vehicle owner groups based on the treatments of: EEV ownership and commuting across the cordon, controlling for confounding factors such as demographics. Through this procedure, rebound effects are identified, with some EEV owners found to have driven up to 12.2% further than non-EEV owners. Although some of these differences could be attributed to the congestion-pricing exemption, the results were not statistically significant. Overall, taking into account lifecycle emissions of each fuel type, average EEV emissions were 50.5% less than average non-EEV emissions, with this reduction in emissions offset by 2.0% due to rebound effects. Although it is important for policy-makers to consider the potential for unexpected negative effects in similar transitions, the overall benefit of greatly reduced emissions appears to outweigh any rebound effects present in this case study.     

Preferences for shared autonomous vehicles

Shared autonomous vehicles (SAVs) could provide inexpensive mobility on-demand services. In addition, the autonomous vehicle technology could facilitate the implementation of dynamic ride-sharing (DRS). The widespread adoption of SAVs could provide benefits to society, but also entail risks. For the design of effective policies aiming to realize the advantages of SAVs, a better understanding of how SAVs may be adopted is necessary. This article intends to advance future research about the travel behavior impacts of SAVs, by identifying the characteristics of users who are likely to adopt SAV services and by eliciting willingness to pay measures for service attributes. For this purpose, a stated choice survey was conducted and analyzed, using a mixed logit model. The results show that service attributes including travel cost, travel time and waiting time may be critical determinants of the use of SAVs and the acceptance of DRS. Differences in willingness to pay for service attributes indicate that SAVs with DRS and SAVs without DRS are perceived as two distinct mobility options. The results imply that the adoption of SAVs may differ across cohorts, whereby young individuals and individuals with multimodal travel patterns may be more likely to adopt SAVs. The methodological limitations of the study are also acknowledged. Despite a potential hypothetical bias, the results capture the directionality and relative importance of the attributes of interest.     

A statistical analysis of consumer perceptions towards automated vehicles and their intended adoption

ABSTRACT

While automated vehicle (AV) development continues to progress rapidly, how the public will accept and adopt automated vehicles remains an open question. Using extensive survey data, we apply cluster analysis to better understand consumer perceptions toward potential benefits and concerns related to AVs with regard to factors influencing their AV adoption likelihood. Four market segments are identified – ‘benefits-dominated,’ ‘concerns-dominated,’ ‘uncertain,’ and ‘well-informed.’ A random parameters multinomial logit model is then estimated to identify factors influencing the probability of respondents belonging to one of these four market segments. Among other influences (such as socio-economic and current travel characteristics), it is found that ‘Millennials’ have a higher probability of belonging to the well-informed market segment, ‘Gen-Xers’ with a lower probability to the uncertain market segment, and ‘Baby Boomers’ with a higher probability to the concerns-dominated market (relative to the ‘Great Generation’). We also study the individuals’ expressed likelihood of AV adoption using separate random parameters ordered probit estimations for each of the four market segments. The substantial and statistically significant differences across each AV consumer market segment underscore the potentially large impact that different consumer demographics may have on AV adoption and the need for targeted marketing to achieve better market-penetration outcomes.