Vehicle mass as a determinant of fuel consumption and secondary safety performance

One interaction between environmental and safety goals in transport is found within the vehicle fleet where fuel economy and secondary safety performance of individual vehicles impose conflicting requirements on vehicle mass from an individual’s perspective. Fleet characteristics influence the relationship between the environmental and safety outcomes of the fleet; the topic of this paper. Cross-sectional analysis of mass within the British fleet is used to estimate the partial effects of mass on the fuel consumption and secondary safety performance of vehicles. The results confirmed that fuel consumption increases as mass increases and is different for different combinations of fuel and transmission types. Additionally, increasing vehicle mass generally decreases the risk of injury to the driver of a given vehicle in the event of a crash. However, this relationship depends on the characteristics of the vehicle fleet, and in particular, is affected by changes in mass distribution within the fleet. We confirm that there is generally a trade-off in vehicle design between fuel economy and secondary safety performance imposed by mass. Cross-comparison of makes and models by model-specific effects reveal cases where this trade-off exists in other aspects of design. Although it is shown that mass imposes a trade-off in vehicle design between safety and fuel use, this does not necessarily mean that it imposes a trade-off between safety and environmental goals in the vehicle fleet as a whole because the secondary safety performance of a vehicle depends on both its own mass and the mass of the other vehicles with which it collides.     

Feebates, footprints and highway safety

This paper presents an analysis of a market-based policy aimed at encouraging manufacturers to develop more fuel efficient vehicles without affecting the car buyer’s choice of vehicle size. A vehicle’s size is measured by its “footprint”, the product of track width and wheelbase. Traditional market-based policies to promote higher fuel economy, such as higher gasoline taxes or gas guzzler taxes, also induce motorists to purchase smaller vehicles. Whether or not such policies affect overall road safety remains controversial, however. Feebates, a continuous schedule of new vehicle taxes and rebates as a function of vehicle fuel consumption, can also be made a function of vehicle size, thus removing the incentive to buy a smaller vehicle. A feebate system based on a vehicle’s footprint creates the same incentive to adopt technology to improve fuel economy as simple feebate systems while removing any incentive for manufacturers or consumers to downsize vehicles.     

Willingness-to-pay for infrastructure investments for alternative fuel vehicles

This study investigates potential demand for infrastructure investment for alternative fuel vehicles by applying stated preference methods to a Japanese sample. The potential demand is estimated on the basis of how much people are willing to pay for alternative fuel vehicles under various refueling scenarios. Using the estimated parameters, the economic efficiency of establishing battery-exchange stations for electric vehicles is examined. The results indicate that infrastructural development of battery-exchange stations can be efficient when electric vehicle sales exceed 5.63% of all new vehicle sales. Further, we find a complementary relationship between the cruising ranges of alternative fuel vehicles and the infrastructure established.     

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.     

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.     

Abatement cost functions for incentive-based policies in the automobile market

This paper compares the outcomes of policies that target vehicle holdings with those that target vehicle usage using data from the US Consumer Expenditure Survey. Results show that a higher price of gasoline shifts vehicle holdings towards more fuel efficient vehicles and reduces the annual demand for miles, whereas imposing a fee on vehicles or a feebate program only shifts vehicle holdings towards more fuel efficient vehicles and has little to no impact on the demand for miles. While it is relatively expensive to reduce CO₂ emission through incentive-based policies, achieving any abatement level is more expensive through imposing fees on vehicles than gasoline taxes. In addition, the maximum amount of abatement attainable by a feebate program is relatively small and the same amount could be achieved by imposing a $0.73 gasoline tax per gallon.     

Making an Informed Vehicle Scrappage Decision

Abstract

According to the US Federal Highway Administration (FHWA), the number of publicly owned vehicles in the USA reached 3 913 999 in 2003. In order to maintain a stable vehicle fleet, government agencies must repeatedly make vehicle scrappage decisions because older vehicles must first retire to make room for newer vehicles. Typically, these decisions are made based on a deterministic ranking evaluation model to select candidate vehicles for replacement. The paper applied an objective and probabilistic method to a vehicle dataset collected by the DuPage County Forest Preserve District ((DCFPD), in the state of Illinois). A Weibull‐form survival model with time‐varying covariate and unobserved heterogeneity was estimated on the dataset. The results suggest that in addition to the fact that vehicle age is negatively related to the vehicle’s survival probability, there are other variables that also appear influential. The survival probabilities of alternative fuel vehicles are similar to those of reformulated unleaded gasoline vehicles. The results suggest that a probabilistic and objective model can benefit government agencies in their vehicle scrappage decisions.     

The effect of uncertainty on US transport-related GHG emissions and fuel consumption out to 2050

The future of US transport energy requirements and emissions is uncertain. Transport policy research has explored a number of scenarios to better understand the future characteristics of US light-duty vehicles. Deterministic scenario analysis is, however, unable to identify the impact of uncertainty on the future US vehicle fleet emissions and energy use. Variables determining the future fleet emissions and fuel use are inherently uncertain and thus the shortfall in understanding the impact of uncertainty on the future of US transport needs to be addressed. This paper uses a stochastic technology and fleet assessment model to quantify the uncertainties in US vehicle fleet emissions and fuel use for a realistic yet ambitious pathway which results in about a 50% reduction in fleet GHG emissions in 2050. The results show the probability distribution of fleet emissions, fuel use, and energy consumption over time out to 2050. The expected value for the fleet fuel consumption is about 450 and 350 billion litres of gasoline equivalent with standard deviations of 40 and 80 in 2030 and 2050, respectively. The expected value for the fleet GHG emissions is about 1360 and 850 Mt CO₂ equivalent with standard deviation of 130 and 230 in 2030 and 2050 respectively. The parameters that are major contributors to variations in emissions and fuel consumption are also identified and ranked through the uncertainty analysis. It is further shown that these major contributors change over time, and include parameters such as: vehicle scrappage rate, annual growth of vehicle kilometres travelled in the near term, total vehicle sales, fuel economy of the dominant naturally-aspirated spark ignition vehicles, and percentage of gasoline displaced by cellulosic ethanol. The findings in this paper demonstrate the importance of taking uncertainties into consideration when choosing amongst alternative fuel and emissions reduction pathways, in the light of their possible consequences.     

Assessing methods for comparing emissions from gasoline and diesel light-duty vehicles based on microscale measurements

This paper assess whether a real-world second-by-second methodology that integrates vehicle activity and emissions rates for light-duty gasoline vehicles can be extended to diesel vehicles. Secondly it compares fuel use and emission rates between gasoline and diesel light-duty vehicles. To evaluate the methodology, real-world field data from two light-duty diesel vehicles are used. Vehicle specific power, a function of vehicle speed, acceleration, and road grade, is evaluated with respect to ability to explain variation in emissions rates. Vehicle specific power has been used previously to define activity-based modes and to quantify variation in fuel use and emission rates of gasoline vehicles taking into account idle, acceleration, cruise, and deceleration. The fuel use and emission rates for light-duty diesel vehicles can also be explained using vehicle specific power -based modes. Thus, the methodology enables direct comparisons for different vehicle fuels and technologies. Furthermore, the method can be used to estimate average fuel use and emission rates for a wide variety of driving cycles.     

Characterising Green Light Optimal Speed Advisory trajectories for platoon-based optimisation

Conceptually, a Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles, allowing them to pass through an intersection during the green interval. In previous papers, a single speed is computed for each vehicle in a range between acceptable minimum and maximum values (for example between standstill and the speed limit). This speed is assumed to be constant until the beginning of the green interval, and sent as advice to the vehicle. The goal is to optimise for a particular objective, whether it be minimisation of emissions (for environmental reasons), fuel usage or delay. This paper generalises the advice given to a vehicle, by optimising for delay over the entire trajectory instead of suggesting an individual speed, regardless of initial conditions – time until green, distance to intersection and initial speed. This may require multiple acceleration manoeuvres, so the advice is sent as a suggested acceleration at each time step. Such advice also takes into account a suitable safety constraint, ensuring that vehicles are always able to stop before the intersection during a red interval, thus safeguarding against last-minute signal control schedule changes. While the algorithms developed primarily minimise delay, they also help to reduce fuel usage and emissions by conserving kinetic energy. Since vehicles travel in platoons, the effectiveness of a GLOSA system is heavily reliant on correctly identifying the leading vehicle that is the first to be given trajectory advice for each cycle. Vehicles naturally form a platoon behind this leading vehicle. A time loop technique is proposed which allows accurate identification of the leader even when there are complex interactions between preceding vehicles. The developed algorithms are ideal for connected autonomous vehicle environments, because computer control allows vehicles’ trajectories to be managed with greater accuracy and ease. However, the advice algorithms can also be used in conjunction with manual control provided Vehicle-to-Infrastructure (V2I) communication is available.     

Factors influencing the purchasing decisions of low emission cars: A study of Slovenia

This paper presents a study of customer preferences and opinions about alternative fuel vehicles. Nowadays the studies of green technologies, especially in the area of green transport, are interesting for policy makers, vehicle producers, customers and energy suppliers. Many stakeholders from public and private sector are investing a lot of effort to identify consumer behaviour for future improvements in development of their green products and strategies. This study is a modification of previously conducted research on customer behaviour on the same topic in Scotland. The survey includes almost 700 participants to identify the most important parameters of consumer behaviour related to purchase of alternative fuel vehicle. Therefore the questionnaire includes different factors, combining financial and technical aspects, environmental performance and vehicle design. Results have revealed that the most relevant factor for purchasing low emission vehicle is total vehicle price, and that the segment of potential alternative fuel vehicles buyers is much larger than we initially anticipated. Such vehicles are, surprisingly, also more attractive for the older population, according to our results.     

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.     

Rapid estimation of electric vehicle acceptance using a general description of driving patterns

A reliable estimate of the potential for electrification of personal automobiles in a given region is dependent on detailed understanding of vehicle usage in that region. While broad measures of driving behavior, such as annual miles traveled or the ensemble distribution of daily travel distances are widely available, they cannot be predictors of the range needs or fuel-saving potential that influence an individual purchase decision. Studies that record details of individual vehicle usage over a sufficient time period are available for only a few regions in the US. In this paper we compare statistical characterization of four such studies (three in the US, one in Germany) and find remarkable similarities between them, and that they can be described quite accurately by properly chosen set of distributions. This commonality gives high confidence that ensemble data can be used to predict the spectrum of usage and acceptance of alternative vehicles in general. This generalized representation of vehicle usage may also be a powerful tool in estimating real-world fuel consumption and emissions.     

A structural analysis of vehicle design responses to Corporate Average Fuel Economy policy

The US Corporate Average Fuel Economy (CAFE) regulations are intended to influence automaker vehicle design and pricing choices. CAFE policy has been in effect for the past three decades, and new legislation has raised standards significantly. We present a structural analysis of automaker responses to generic CAFE policies. We depart from prior CAFE analyses by focusing on vehicle design responses in long-run oligopolistic equilibrium, and we view vehicles as differentiated products, taking demand as a general function of price and product attributes. We find that under general cost, demand, and performance functions, single-product profit maximizing firm responses to CAFE standards follow a distinct pattern: firms ignore CAFE when the standard is low, treat CAFE as a vehicle design constraint for moderate standards, and violate CAFE when the standard is high. Further, the point and extent of first violation depends upon the penalty for violation, and the corresponding vehicle design is independent of further standard increases. Thus, increasing CAFE standards will eventually have no further impact on vehicle design if the penalty for violation is also not increased. We implement a case study by incorporating vehicle physics simulation, vehicle manufacturing and technology cost models, and a mixed logit demand model to examine equilibrium powertrain design and price decisions for a fixed vehicle body. Results indicate that equilibrium vehicle design is not bound by current CAFE standards, and vehicle design decisions are directly determined by market competition and consumer preferences. We find that with increased fuel economy standards, a higher violation penalty than the current stagnant penalty is needed to cause firms to increase their design fuel economy at equilibrium. However, the maximum attainable improvement can be modest even if the penalty is doubled. We also find that firms’ design responses are more sensitive to variation in fuel prices than to CAFE standards, within the examined ranges.     

A tool to optimize the initial distribution of hydrogen filling stations

An important barrier towards the introduction of fuel cell vehicles running on hydrogen is the lack of widespread refueling infrastructure. The niche of buses for public transport, taxis and deliverers with a local application area might not be large enough to generate the reductions of fuel cell vehicle costs that are necessary for a general technology switch. Thus, fuel availability at trunk roads probably plays a crucial role in generating demand for these also from private consumers. In this paper, we assume that consumers are more likely to consider buying a fuel cell vehicle the more frequently they are exposed to hydrogen refueling opportunities on long distant trips. We introduce a tool to test different small-scale initial distributions of hydrogen outlets within the German trunk road system for their potential success to generate a large-scale adoption of fuel cell vehicles. The tool makes use of agent-based trip modeling and geographic information system supported spatial modeling. We demonstrate its potentials by testing a ring shaped distribution of hydrogen outlets at highway filling stations. We find that the structure of an optimized initial distribution of filling stations depends on what drivers consider a sufficiently small distance between refueling opportunities.     

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.     

Platinum,fuel cells,and future US road transport

The rate at which fuel cell vehicles (FCVs) might displace the conventional fleet is examined under constraints imposed by the limited availability of platinum. It concludes that a transition period as short as 31 years is not feasible. Under the most favorable circumstances, a complete transition of the US fleet to this new technology would require about 66 years and 10,800 net tonnes of platinum. Platinum demand for the US auto industry alone would amount to 48% of world production during much of that transition period. The effect of that demand on the price of platinum would add to the problem of reducing vehicle cost to a competitive range. If US platinum consumption were to remain at its current level of 16% of annual world production, fleet conversion would require 146 years. These results imply that, without alternative catalysts, fuel cells alone cannot adequately address the issues facing the current system of road transport.     

Consumers’ willingness to pay for alternative fuel vehicles: A comparative discrete choice analysis between the US and Japan

This paper conducts a comparative discrete choice analysis to estimate consumers’ willingness to pay (WTP) for electric vehicles (EVs) and plug-in hybrid electric vehicles (PHEVs) on the basis of the same stated preference survey carried out in the US and Japan in 2012. We also carry out a comparative analysis across four US states. We find that on average US consumers are more sensitive to fuel cost reductions and alternative fuel station availability than are Japanese consumers. With regard to the comparative analysis across the four US states, consumers’ WTP for a fuel cost reduction in California is considerably greater than in the other three states. We use the estimates obtained in the discrete choice analysis to examine the EV/PHEV market shares under several scenarios. In a base case scenario with relatively realistic attribute levels, conventional gasoline vehicles still dominate both in the US and Japan. However, in an innovation scenario with a significant purchase price reduction, we observe a high penetration of alternative fuel vehicles both in the US and Japan. We illustrate the potential use of a discrete choice analysis for forward-looking policy analysis, with the future opportunity to compare its predictions against actual revealed choices. In this case, increased purchase price subsidies are likely to have a significant impact on the market shares of alternative fuel vehicles.     

A field evaluation case study of the environmental and energy impacts of traffic calming

This study quantifies the energy and environmental impact of a selection of traffic calming measures using a combination of second-by-second floating-car global positioning system data and microscopic energy and emission models. It finds that traffic calming may result in negative impacts on vehicle fuel consumption and emission rates if drivers exert aggressive acceleration levels to speed up to their journeys. Consequently by eliminating sharp acceleration maneuvers significant savings in vehicle fuel consumption and emission rates are achievable through driver education. The study also demonstrates that high emitting vehicles produce CO emissions that are up to 25 times higher than normal vehicle emission levels while low emitting vehicles produce emissions that are 15–35% of normal vehicles. The relative increases in vehicle fuel consumption and emission levels associated with the sample traffic calming measures are consistent and similar for normal, low, and high emitting vehicles.     

Fuel economy of hybrid-electric versus conventional gasoline vehicles in real-world conditions: A case study of cold cities in Quebec,Canada

This paper investigates the fuel efficiency of commercial hybrid electric vehicles (HEVs) and compares their performance with respect to standard gasoline vehicles in the context of cold Canadian urban environments. The effect of different factors on fuel efficiency is studied including road driving conditions (link type, city size), temperature, speed, cold-starts and eco-driving training. For this study, fuel consumption data at the link level in real-world conditions was used from a sample of 74 instrumented vehicles. From the study fleet, 21 vehicles were HEVs. Among other results, the beneficial fuel efficiency merits of hybrid vehicles were demonstrated with respect to gasoline cars, in particular at low speeds and in urban (city) environments. After controlling for other factors, sedan HEVs were 28% more efficient than sedan gasoline vehicles. However, the low temperatures (below 0 °C) observed regularly during winter season in the study cities were identified as a detrimental factor to fuel economy. In winter, the fuel efficiency of HEVs decrease about 20% with respect to summer. Other factors such as eco-driving training, city size, cold start and vehicle type were also found to be statistically significant.