Annual Mileage Research Articles

Quantifying the Economic Case for Electric Semi-Trucks

There has been considerable interest in the electrification of freight transport, particularly heavy-duty trucks to downscale the greenhouse-gas (GHG) emissions from the transportation sector. However, the economic competitiveness of electric semi-trucks is uncertain as there are substantial additional initial costs associated with the large battery packs required. In this work, we analyze the trade-off between the initial investment and the operating cost for realistic usage scenarios to compare a fleet of electric semi-trucks with a range of 500 miles with a fleet of diesel trucks. For the baseline case with 30% of fleet requiring battery pack replacements and a price differential of US\$50,000, we find a payback period of about 3 years. Based on sensitivity analysis, we find that the fraction of the fleet that requires battery pack replacements is a major factor. For the case with 100% replacement fraction, the payback period could be as high as 5-6 years. We identify the price of electricity as the second most important variable, where a price of US$0.14/kWh, the payback period could go up to 5 years. Electric semi-trucks are expected to lead to savings due to reduced repairs and magnitude of these savings could play a crucial role in the payback period as well. With increased penetration of autonomous vehicles, the annual mileage of semi-trucks could substantially increase and this heavily sways in favor of electric semi-trucks, bringing down the payback period to around 2 years at an annual mileage of 120,000 miles. There is an undeniable economic case for electric semi-trucks and developing battery packs with longer cycle life and higher specific energy would make this case even stronger.

Electric, plug-in hybrid, hybrid, or conventional? Polish consumers’ preferences for electric vehicles

Poland aims at stimulating the market to reach a target of 50,000 plug-in and battery electric vehicles by 2020. However, as in other Eastern European countries, the market penetration stays very low. In Poland, there were only 475 battery electric vehicles and 514 plug-in electric vehicles registered in 2017. To identify effective support measures, this paper examines the preferences of Polish consumers for three types of electric vehicles: battery, hybrid, and plug-in hybrid vehicles. We use a discrete choice experiment to estimate the willingness to pay of a representative sample of consumers intending to buy a car in Poland. We find that electric vehicles are significantly less preferred than conventional cars, even under a public programme that would enable slow-mode charging in places where respondents usually park. We quantify the marginal willingness to pay for increasing the driving range, reductions in charging time, the availability of fast-mode charging stations, and the provision of policy incentives. The novelty of the paper lies in presenting a scenario with the slow-mode and availability of several levels of fast-mode charging stations and examination of the extent to which the heterogeneity of consumer preferences is driven by place of residence (urban, suburban, rural), intention to buy a new versus a used car, and the annual mileage. This is also the first discrete choice experiment on electric vehicles conducted in Eastern Europe. To stimulate the electric vehicle market, we recommend a pricing policy that affects the operating costs and other incentives along with an effective up-front price incentive scheme.

An empirical study on the behavior of hybrid-electric vehicle purchasers

This paper empirically examines the factors influencing consumers’ choices concerning the purchase of hybrid electric vehicles (HEVs). In addition, the paper investigates whether purchasing HEVs causes a rebound effect through which purchasers of HEVs increase their annual vehicle mileage. The results suggest that consumers who are more attentive to fuel economy may be inclined to choose HEVs. Moreover, the existence of the direct rebound effect is confirmed by the finding that the purchase of an HEV leads to an increase in annual mileage per household. Greater mileage increases future fuel cost savings from using an HEV relative to a conventional vehicle, and thus potentially leads to increased fuel cost savings that offset the extra cost of choosing an HEV. However, if the purchase of HEVs leads to a considerable increase in annual mileage, overall annual automotive CO2 emissions per household may be significantly increased by using HEVs.

Fault analysis of the combustion engine used in public transport vehicles

The goal of this article is the fault analysis of combustion engines on the basis of the assessment of dependent variables: initial mileage, annual mileage and the annual number of orders made in the service and repair system compared with the independent factor, i.e. bus make. In the research four vehicle makes were tested: Jelcz M121, Mercedes-Benz Citaro, Solaris Urbino 12 and Autosan Sancity 12LF. The research encompassed the number of faults of a vehicle and its systems, the initial and annual mileage. It was conducted on the basis of 2015 data broken into particular months.

The impact of daily and annual driving on fuel economy and CO2 emissions of plug-in hybrid electric vehicles

The potential of plug-in hybrid electric vehicles (PHEV) to reduce greenhouse gas emissions highly depends on vehicle usage and electricity source. The electric driving share, i.e. the share of kilometres driven electrically, is specific to PHEV and a key factor for its fuel economy. Altogether, a detailed understanding of all factors influencing PHEV fuel economy is missing, especially with regard to driving distances. We analyse the influence of driving behaviour on fuel economy and more precisely the influences on the electric driving share based on mobility data. We applied a regression with mobility data of 780 vehicles to identify the main factors explaining this variation. Our results indicate that real-world fuel economy of PHEV differs widely among users. The resulting factors that explain up to 80% of the fuel economy are the all-electric range, the annual mileage, the regularity of daily driving, and the likelihood of long-distance trips. In our empirical analysis, the average electric driving share of N = 1,831 Chevrolet Volt in real-world driving is 78%. When the electricity for charging comes from renewable energy sources the resulting real-world well-to-wheel CO2 emissions of these PHEV are 37 gCO2/km. However, even with the current US electricity mix, the annual CO2 savings of all registered Chevrolet Volts in the U.S. amount to about 57 kt CO2 in comparison to conventional cars. Furthermore, a full charge per day is necessary for high fuel economy and any necessary recharging during the day increases the share of electric driving and lowers the consumption of conventional fuel. Current test-cycle fuel economy ratings neglect these factors. Although fuel economy ratings are good estimates for average usage patterns, they fail to account for the high variation in individual driving behaviour. This should be taken into account by future test-cycles and policies should incentivise high electric driving shares.

The hazard prediction test: A comparison of free-response and multiple-choice formats

Hazard perception skill is often related to lower crash risk, and the hazard perception test has been widely employed to measure this ability in drivers. An increasingly popular test-variant is the hazard prediction test: driving videos are occluded immediately prior to a hazard and participants are asked to predict how the situation will develop. Early versions of this test asked participants to provide a free-response answer which was subsequently coded. Later versions, however, have used a multiple-choice format where participants are provided with four options presented on screen. While the benefits of a multiple-choice format are obvious in terms of providing immediate feedback without relying on subjective coding, it is unclear whether this change in format affects the discriminative validity of the test. For the current study, a free-response test and a multiple-choice test were created using the same video clips. The free-response test (experiment 1) was found to successfully discriminate between novice and experienced drivers, with the latter predicting more hazards correctly. The answers provided by participants in Experiment 1 were then used to generate the options for a multiple-choice test (experiment 2). This second test was also found to discriminate between novice and experienced drivers, and a comparison between the two tests failed to reveal an advantage for one over the other. Despite this, correlations between prediction accuracy and both years of post-license driving, and annual mileage, were only significant for the multiple-choice test. The results suggest that the multiple-choice format is not only time- and cost-efficient, but is ostensibly as good as the free-response test in discriminating between driver groups.

Accident involvement among Iranian lorry drivers: Direct and indirect effects of background variables and aberrant driving behaviour

Road accident rates among Iranian lorry drivers are considerably high and, according to empirical evidence, aberrant driving behaviours, summed to certain demographic, psycho-social and work-related factors, may explain their accident involvement. Consequently, the main aim of the study was to examine the direct and indirect effects of background variables (i.e. annual mileage, lorry driving experience, demographic and socioeconomic factors) on accident involvement mediated through aberrant driving behaviour among Iranian lorry drivers. A cross-sectional questionnaire survey was conducted in 2012 among 914 lorry drivers in 10 selected provinces in Iran. The 27-item Driver Behaviour Questionnaire (DBQ) was used to measure aberrant driving behaviour. Results from valid observations (n = 785) confirmed a four-factor solution (including ordinary violations, aggressive violations, errors, and lapses) of the DBQ. Errors, ordinary violations and aggressive violations were positively associated with accident involvement. However, lapses were not significantly associated with accident involvement. The results of structural equation modeling (SEM) further showed that, in addition to direct effects of background variables on accident involvement, several variables had indirect effects mediated by three-DBQ factors; ordinary violations, aggressive violations, and errors. Higher age, having more lorry driving experience, having higher educational attainment, and married drivers were indirectly related to less accident involvement. Annual driving mileage and the resting rate of drivers was both directly and indirectly related to accident involvement. Higher income and car ownership were directly related to fewer accidents. Interventions could aim to decrease ordinary violations, aggressive violations and errors among younger, less educated and single lorry drivers. Initiatives targeted to increase the scheduled resting frequency of lorry drivers may also hold promise.

Investigating factors influencing drivers' speed selection behavior under reduced visibility conditions

ABSTRACTObjective: Driving speed is a major concern for driving safety under reduced visibility conditions. Many factors affect speed selection in low visibility, but few studies have been conducted examining drivers' characteristics, particularly in China. The present study aimed to investigate the correlation between drivers' demographic information, driving ability, and speed choice in low-visibility conditions using a sample of Chinese drivers.Methods: A self-designed driving ability scale was used to assess driving ability in reduced visibility conditions. The reliability and validity of 306 gathered questionnaires were examined in this article, and a structural equation model (SEM) was built to explore the predictors of drivers' speed selection behavior under reduced visibility conditions and to measure the relationships between various factors.Results: Age and driving experience have no direct relationship to speed selection behavior in reduced visibility, but the frequency of using expressways and annual mileage are significantly related to the speed on roads that have a speed restriction of 80 or 120 km/h. Under reduced visibility conditions, driving ability has a significant effect on speed selection behavior, and driving skill (DS) is the most influential on speed selection behavior on roads with a speed limit of 120 km/h; otherwise, the effect of risk perception (RP) does not differ by speed choice on 3 roads with different speed limits. Driving speed in good weather also has a positive influence on speed selection behavior in low visibility.Conclusion: Driving ability is directly associated with speed selection in reduced visibility conditions, and some demographic data indirectly influence speed selection. This study provides useful recommendations for drivers' training programs to reduce casualties from accidents in low-visibility conditions.

Comparing Inference Methods for Non‐probability Samples

SummarySocial and economic scientists are tempted to use emerging data sources like big data to compile information about finite populations as an alternative for traditional survey samples. These data sources generally cover an unknown part of the population of interest. Simply assuming that analyses made on these data are applicable to larger populations is wrong. The mere volume of data provides no guarantee for valid inference. Tackling this problem with methods originally developed for probability sampling is possible but shown here to be limited. A wider range of model‐based predictive inference methods proposed in the literature are reviewed and evaluated in a simulation study using real‐world data on annual mileages by vehicles. We propose to extend this predictive inference framework with machine learning methods for inference from samples that are generated through mechanisms other than random sampling from a target population. Describing economies and societies using sensor data, internet search data, social media and voluntary opt‐in panels is cost‐effective and timely compared with traditional surveys but requires an extended inference framework as proposed in this article.

Voluntary- and Involuntary-Distraction Engagement: An Exploratory Study of Individual Differences.

Objective The aim of this study was to explore individual differences in voluntary and involuntary driver-distraction engagement. Background Distractions may stem from intentional engagement in secondary tasks (voluntary) or failing to suppress non-driving-related stimuli or information (involuntary). A wealth of literature has examined voluntary distraction; involuntary distraction is not particularly well understood. Individual factors, such as age, are known to play a role in how drivers engage in distractions. However, it is unclear which individual factors are associated with voluntary- versus involuntary-distraction engagement and whether there is a relation between how drivers engage in these two distraction types. Method Thirty-six participants, ages 25 to 39, drove in a simulator under three conditions: voluntary distraction with a self-paced visual-manual task on a secondary display, involuntary distraction with abrupt onset of irrelevant visual-audio stimuli on the secondary display, and no distraction. Results The number of glances toward the secondary display under voluntary distraction was not correlated to that under involuntary distraction. The former was associated with gender, age, annual mileage, and self-reported distraction engagement; such associations were not observed for the latter. Accelerator release time in response to lead-vehicle braking was delayed similarly under both conditions. Conclusion Propensity to engage in voluntary distractions appears to be not related to the inability of suppressing involuntary distractions. Further, voluntary and involuntary distraction both affect braking response. These findings have implications for design of in-vehicle technologies, which may be sources of both distraction types.

Providing a foundation for road transport energy demand analysis: The development of a vehicle parc model for South Africa

AbstractIt is key for national economic planning to build the tools to forecast energy demand from major sectors like transport in a credible way. As a starting point, this requires building a sufficiently detailed ‘bottom-up’ picture of technologies and their activity levels in the recent past. A vehicle parc model was developed for South Africa to feed transport demand and data on the fleet into a national energy systems model, the South African TIMES model, which is a least-cost optimisation model of the TIMES/ MARKAL family. Detailed assumptions were developed for 24 vehicle typologies that included the vintage profile, annual mileage and its relationship with age, fuel economy and its improvement over time, and occupancy and load factor. Combining these assumptions, the model was successfully calibrated over 2000–2014 with the national registration database, national fuel sales statistics and, on the freight side, with estimates of the demand for ton.km published by the University of Stellen-bosch’s Department of Logistics (2014 only). A demand for passenger.km was also calculated, which agreed well with national transport surveys. A range of detailed indicators were produced for the vehicle typologies and some interesting trends observed, including the steady dieselisation of the light vehicle fleet over the study period and the stagnation of passenger car fuel economy, despite legislation in the European Union. The present study believes that this updated data-rich picture of the road transport vehicle parc will support other studies and national policy and planning initiatives.

Canada’s federal database is inadequate for the assessment of environmental consequences of oil and gas pipeline failures

In Canada, the National Energy Board (NEB) regulates inter-provincial oil and gas pipelines and maintains historical records that contain data on oil and gas pipeline accidents; these data include information about operators, the accidents’ cause, and the resulting consequences. New inter-provincial pipelines are being built in Canada to transport fuels, but no comprehensive statistical analysis of the risk to environmental receptors exists. This study assesses the quality and quantity of NEB pipeline failure data available in Canada with a focus on environmental consequences and investigates differences between Canada and a more thoroughly studied jurisdiction, the United States, in tracking accident data. The discrepancies in agencies’ jurisdiction and regulated mileage are analyzed, along with reporting criteria and initial recording year. The level of detail provided by the two agencies is compared, identifying deficiencies in data collection. The Pipeline and Hazardous Materials Safety Administration (PHMSA) regulates 76% of pipelines in the United States, whereas the NEB only monitors 9% of pipelines in Canada. PHMSA provides four databases that include accidents from the 1980s for most pipelines and from 2011 for liquefied natural gas facilities; the NEB database includes accident data starting from 2008, which derive primarily from transmission pipelines. Information about environmental consequences is quite detailed in the US database, which reports 21 descriptive fields, whereas in Canada only two NEB database fields describe environmental outcomes. Moreover, dissimilarities in accident reporting criteria prevent the combination of data from the two agencies. Consequently, the NEB database does not allow for statistically robust and system wide analysis of the environmental consequences of pipeline failures in Canada. Furthermore, to calculate failure rates (annual number of accidents per kilometre of pipeline) for regulated pipelines, annual total mileage estimates are required. Mileage per year is provided by PHMSA for gas gathering, transmission, and distribution pipelines starting from 1984, and for hazardous liquid pipelines from 2004; the NEB provides annual mileage from 2010, a shorter period of record. The Canadian federal agencies are encouraged to improve accuracy and consistency in recording past accidents and in collecting pipeline data, with the goal of preventing and minimizing future pipeline failures.

Environmental and economic comparison of diesel and battery electric delivery vans to inform city logistics fleet replacement strategies

Poor air quality in urban areas and environmental concerns attributable to road transportation are growing and significant problems for governments. Many different options have been proposed to lower emissions, and a critical one is the use of battery electric vehicles (BEVs).Since city-logistics accounts for about 25% of urban mobility emissions, we focus on battery electric and diesel delivery vans. The aim of this paper is to present a holistic view of the problem, comparing the environmental, social and economic impact of BEV and diesel delivery vans, providing useful insights to policy makers and fleet owners willing to replace or select delivery vans to include in their city-logistics fleets.In cities where new BEV vans replace old diesel vans and the electricity mix is relatively clean, CO2 emissions and air pollutants decrease by 93–98% and 85–99%, respectively. If BEVs use electricity coming from coal energy and are compared to new diesel vans, reductions of CO2 emissions and air pollutants are in the order of 12–13% and 0–92%, respectively. Longer battery life and greater annual mileage improve these results and decrease cost differences. Results also reveal that annual emissions benefits of replacing older diesel vans with BEVs are on the same order of magnitude of equivalent annual cost differences.From a strictly business perspective, BEV vans are already economically attractive in some cities with existing incentives; however, for other cities incentives equal to the value of their emissions reduction benefits are needed, but might not be sufficient to justify BEV acquisition.

An updated emission inventory of vehicular VOCs and IVOCs in China

Abstract. Currently, the emission inventory of vehicular volatile organic compounds (VOCs) is one of those with the largest errors and uncertainties due to suboptimal estimation methods and the lack of first-hand basic data. In this study, an updated speciated emission inventory of VOCs and an estimation of intermediate-volatility organic compounds (IVOCs) from vehicles in China at the provincial level for the year of 2015 are developed based on a set of state-of-the-art methods and an abundance of local measurement data. Activity data for light-duty vehicles are derived from trajectories of more than 70 000 cars for 1 year. The annual mileage of trucks are calculated from reported data by more than 2 million trucks in China. The emission profiles are updated using measurement data. Vehicular tailpipe emissions (VTEs) and four types of vehicular evaporation emissions (VEEs), including refueling, hot soak, diurnal and running loss, are taken into account. Results show that the total vehicular VOC emissions in China are 4.21 Tg (with a 95 % confidence interval range from 2.90 to 6.54 Tg) and the IVOC emissions are 200.37 Gg in 2015. VTEs are still the predominant contributor, while VEEs are responsible for 39.20 % of VOC emissions. The control of VEEs is yet to be optimized in China. Among VTEs, passenger vehicles emissions have the largest share (49.86 %), followed by trucks (28.15 %) and motorcycles (21.99 %). Among VEEs, running loss is the largest contributor (81.05 %). For both VTEs and VEEs, Guangdong, Shandong and Jiangsu province are three of the highest, with a respective contribution of 10.66, 8.85 and 6.54 % to the total amounts of VOCs from vehicles. 97 VOC species are analyzed in this VOC emission inventory. i-Pentane, toluene and formaldehyde are found to be the most abundant species in China's vehicular VOC emissions. The estimated IVOCs are another inconvenient truth, concluding that precursor emissions for secondary organic aerosol (SOA) from vehicles are much larger than previously estimated.

Motorcyclists' self-reported riding mileage versus actual riding mileage in the following year

IntroductionAccurate motorcyclist mileage estimates are important because self-evaluation of riding experience is related to riding behavior, the relationship of self-reported to actual or future mileage is necessary in targeting training and considering survey responses, and motorcycle crash statistics require accurate travel data. MethodThis study collected real‐world data from motorcyclists over the course of two months to two years per rider. This paper explores motorcyclists' self-reported annual riding mileage (obtained via pre-study surveys) and the actual amount of riding during the study (based upon odometer readings and GPS data). ResultsOf the 91 riders who had been riding for at least a year before the study, significantly more (73%) rode less the following year than reported for the previous year. The recorded annualized mileage averaged 89% of the reported mileage from the previous year. Analyses based on estimated average annual mileage were similar to those using the previous year estimation, and the pattern held regardless of age group, motorcycle type, or gender. The exception was novice or returning riders, who tended to either significantly underestimate or increase actual mileage as they began (or continued) to ride. ConclusionsMotorcyclists' estimation of riding experience expressed as mileage may not be indicative of current or future mileage. Practical applicationsReliance on self-reported mileage during training to categorize groups, for interpretation of studies, or to develop motorcycle travel data and safety statistics may be unrealistic. Certainly any use of self-reported mileage should incorporate the concept that mileage overestimation seems likely. Because questions about previous year and average annual mileage may elicit similar responses, motorcyclist surveys should be constructed to prompt the most thoughtful responses in terms of mileage estimations. In general, reported mileage should not be relied upon as an accurate predictor of future actual mileage.

A validation of the low mileage bias using naturalistic driving study data

IntroductionThis paper evaluated the low mileage bias (LMB) phenomenon for senior drivers using data mined from the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study. Supporters of the LMB construct postulate that it is only those seniors who drive the lowest annual mileage who are primarily responsible for the increased crash rates traditionally attributed to this population in general. MethodThe current analysis included 802 participants, all aged 65 or older who were involved in 163 property damage and injury crashes, and deemed to be at-fault in 123 (75%) of those instances. Poisson regression models were used to evaluate the association between annualized mileage driven and crash risk. ResultsResults show that the crash rate for drivers with lower annualized mileage (i.e., especially for those driving fewer than approximately 3000miles per year) was significantly higher than that of drivers with higher annualized mileage, and that 25% of the overall sample were low- mileage drivers according to this criterion. Data were also evaluated by gender and meta-age group (i.e., younger-old: 65–74 and older-old: 75–99), and the results were consistent across these sub-groups. ConclusionsThis study provides strong support for the existence of the LMB. Practical applicationsThese results can help to reshape how transportation safety stakeholders view senior drivers in general and help them to focus their efforts on those seniors most in need of either risk-reducing countermeasures or alternative means of transportation.

A consumer-oriented total cost of ownership model for different vehicle types in Germany

In Germany, market penetration by alternative powertrains has been generally processing at a slow pace. Therefore, reaching the 2020 target of one million registered electric vehicles (EVs) is a major challenge. We analyze the German market by advancing and refining existing consumer-oriented total cost of ownership (TCOC) models and demonstrate the validity of our model by comparing the cost-efficiency of EVs and internal combustion engine vehicles (ICEVs) including the battery resale value for second use and second life. The TCOC model was calculated for the ten most frequently registered battery electric vehicles (BEVs) and hybrid electric vehicles (HEVs) and compared with ICEVs in the same vehicle segments. The results are further validated through applying three typical annual mileage driver profiles and by Monte Carlo simulations under various scenarios. Results reveal that only a few BEVs and HEVs are economical without subsidies when compared with ICEVs in all considered scenarios. The subsidies only barely change the results. The mini and the medium vehicle segment remain uneconomical in all tested scenarios. Overall, we conclude that subsidies support the competitiveness of BEVs, but fail to lead to favorable TCOC within several vehicle segments and several tested annual mileages.

Something from nothing: Estimating consumption rates using propensity scores, with application to emissions reduction policies.

Consumption surveys often record zero purchases of a good because of a short observation window. Measures of distribution are then precluded and only mean consumption rates can be inferred. We show that Propensity Score Matching can be applied to recover the distribution of consumption rates. We demonstrate the method using the UK National Travel Survey, in which c.40% of motorist households purchase no fuel. Estimated consumption rates are plausible judging by households’ annual mileages, and highly skewed. We apply the same approach to estimate CO2 emissions and outcomes of a carbon cap or tax. Reliance on means apparently distorts analysis of such policies because of skewness of the underlying distributions. The regressiveness of a simple tax or cap is overstated, and redistributive features of a revenue-neutral policy are understated.

Electrification of the two-car household: PHEV or BEV?

In previous works, we have shown two-car households to be better suited than one-car households for leveraging the potential benefits of the battery electric vehicle (BEV), both when the BEV simply replaces the second car and when it is used optimally in combination with a conventional car to overcome the BEV’s range limitation and increase its utilization. Based on a set of GPS-measured car movement data from 64 two-car households in Sweden, we here assess the potential electric driving of a plug-in hybrid electric vehicle (PHEV) in a two-car household and compare the resulting economic viability and potential fuel substitution to that of a BEV.Using estimates of near-term mass production costs, our results suggest that, for Swedish two-car households, the PHEV in general should have a higher total cost of ownership than the BEV, provided the use of the BEV is optimized. However, the PHEV will increasingly be favored if, for example, drivers cannot or do not want to optimize usage. In addition, the PHEV and the BEV are not perfect substitutes. The PHEV may be favored if drivers require that the vehicle be able to satisfy all driving needs (i.e., if drivers don’t accept the range and charge-time restrictions of the BEV) or if drivers requires an even larger battery in the BEV to counter range anxiety.We find that, given a particular usage strategy, the electric drive fraction (EDF) of the vehicle fleet is less dependent on whether PHEVs or BEVs are used to replace one of the conventional cars in two-car households. Instead, the EDF depends more on the usage strategy, i.e., on whether the PHEV/BEV is used to replace the conventional car with the higher annual mileage (“the first car”), the less used car (“the second car”), or is used flexibly to substitute for either in order to optimize use. For example, from a fuel replacement perspective it is often better to replace the first car with a PHEV than to replace the second with a BEV.

The impact of local work and residential balance on vehicle miles traveled: A new direct approach

This paper introduces a new approach for explaining the effects of spatial competition for opportunities based on a cumulative opportunities measure of accessibility. We focus on the case of the labor market where some municipalities offer too few workplaces for their local population, forcing some of their residents to long drives. We apply this new accessibility measure to explain households' annual mileage in Switzerland using a Heckman model to account for carless households.We find that car travel demand is much greater in municipalities with a relative undersupply in the local labor market compared to a balanced or oversupplied local labor market. The results show that driving increases with greater distance to the labor market center. The model estimates allow policy makers and planners to quantify as a first assessment the expected average mileage in each municipality for new settlements and to identify municipalities with low expected annual mileage.