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This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.

We simulate the introduction of shared, automated, and electric vehicles (SAEVs) providing on-demand shuttles service in a large-scale transport digital twin of the San Francisco Bay Area region (California, USA) based on transit supply and demand data, and using the mesoscopic agent-based Behavior, Energy, Autonomy, and Mobility beta software (BEAM) developed at the Lawrence Berkeley National Laboratory (LBNL). The main goal of this study is to test the operations of this novel mobility service integrated with existing fixed-route public transportation service in a mesoscopic simulation of a real case scenario, while testing the BEAM beta software capabilities. In particular, we test the introduction of fleets of on-demand vehicles bound to operate within circular catchment areas centered on high-frequency transit stops, with the purpose of extending the reach of fixed-route transit by providing an alternative first- and last-mile connection at high-frequency public transport stations. Results show that on-demand automated shuttles represent the best solution for some users, increasing the overall transit ridership by 3%, and replacing mostly ride-hail trips, especially those connecting to transit stops, but also some walking trips. This type of service has the potential to reduce overall vehicle miles traveled (VMT), increase transit accessibility, and save energy, but future research is needed to optimize this type of service and make it more attractive to travelers.

We introduce a methodology to develop a geo-typology (geotype) that categorizes each location in the United States in terms of their main drivers of transportation demand and supply. We develop the first comprehensive set of geotypes for both urban and rural areas across the entire United States. This typology is designed to facilitate national level modeling of multi-modal transportation system's response to alternative investment strategies differentiated across different types of locations. We develop a two-stage clustering procedure to systematically and quantitatively characterize the ways in which locations across the nation are similar or different with respect to their potential response to investment strategies of interest. First, we cluster all 73,057 census tracts, using factor analysis and the CLARA clustering algorithm into “microtypes” based on their street network and economic characteristics. Then we cluster regions (core-basic statistical areas and counties) into “geotypes” using PAM clustering according to their commute configurations, polycentricity and density. The resulting set captures both local and regional variation. These microtypes and geotypes are comparable across all locations, enabling a national level perspective, while maintaining sufficient heterogeneity to support a variety of transportation analyses capturing critical geographic variation.

We estimate the effects of transportation network companies (TNCs) Uber and Lyft on vehicle ownership, fleet average fuel economy, and transit use in U.S. urban areas using a set of difference-in-difference propensity score-weighted regression models that exploit staggered market entry across the U.S. from 2011 to 2017. We find evidence that TNC entry into urban areas causes an average 0.7% increase in vehicle registrations with significant heterogeneity in these effects across urban areas: TNC entry produces larger vehicle ownership increases in urban areas with higher initial ownership (car-dependent cities) and in urban areas with lower population growth (where TNC-induced vehicle adoption outpaces population growth). We also find no statistically significant average effect of TNC entry on fuel economy or transit use but find evidence of heterogeneity in these effects across urban areas, including larger transit ridership reductions after TNC entry in areas with higher income and more childless households.

This report recalculates the estimated relationship between vehicle mass and societal fatality risk, using alternative groupings by vehicle weight, to test whether the trend of decreasing fatality risk from mass reduction as case vehicle mass increases, holds over smaller increments of the range in case vehicle masses. The NHTSA baseline regression model estimates the relationship using for two weight groups for cars and light trucks; we re-estimated the mass reduction coefficients using four, six, and eight bins of vehicle mass. The estimated effect of mass reduction on societal fatality risk was not consistent over the range in vehicle masses in these weight bins. These results suggest that the relationship indicated by the NHTSA baseline model is a result of other, unmeasured attributes of the mix of vehicles in the lighter vs. heavier weight bins, and not necessarily the result of a correlation between mass reduction and societal fatality risk. An analysis of the average vehicle, driver, and crash characteristics across the various weight groupings did not reveal any strong trends that might explain the lack of a consistent trend of decreasing fatality risk from mass reduction in heavier vehicles.

This report presents a new approach to analyze the relationship between vehicle mass and risk: tracking fatality risk by vehicle model year and mass, for individual vehicle models. This approach is appealing as it greatly minimizes the influence of driver characteristics and behavior, and crash circumstances, on fatality risk. However, only the most popular vehicle models, with the largest number of fatalities, can be analyzed in this manner. While the analysis of all vehicle models of a given type suggests that there is a relationship between increased mass and fatality risk, analysis of the ten most popular four-door car models separately suggests that this relationship is weak: in many cases when the mass of a specific vehicle model is increased societal fatality risk is unchanged or even increases. These results suggest that increasing the mass of an individual vehicle model does not necessarily lead to decreased societal fatality risk.

Previous analyses have indicated that mass reduction is associated with an increase in crash frequency (crashes per VMT), but a decrease in fatality or casualty risk once a crash has occurred, across all types of light-duty vehicles. These results are counter-intuitive: one would expect that lighter, and perhaps smaller, vehicles have better handling and shorter braking distances, and thus should be able to avoid crashes that heavier vehicles cannot. And one would expect that heavier vehicles would have lower risk once a crash has occurred than lighter vehicles. However, these trends occur under several alternative regression model specifications. This report tests whether these results continue to hold after accounting for crash severity, by excluding crashes that result in relatively minor damage to the vehicle(s) involved in the crash. Excluding non-severe crashes from the initial LBNL Phase 2 and simultaneous two-stage regression models for the most part has little effect on the unexpected relationships observed in the baseline regression models. This finding suggests that other subtle differences in vehicles and/or their drivers, or perhaps biases in the data reported in state crash databases, are causing the unexpected results from the regression models.

This report examines the sensitivity of the estimated effect of mass reduction on crash frequency in the National Highway Traffic Safety Administration (NHTSA) baseline regression model to including several additional vehicle and driver characteristics. The additional variables include handling and braking capability by vehicle model, from three Consumer Reports road tests; initial vehicle purchase price and vehicle manufacturer; average household income and “bad driver” rating by vehicle model; and whether the driver was using alcohol or drugs, or was properly restrained. The three Consumer Reports road tests are associated with an unexpected increase in crash frequency, in both all crashes and single vehicle crashes with a stationary object. For this reason they were not included in the sensitivity regression models. As expected, vehicle initial purchase price, median household income, and whether the driver was wearing a seat belt are associated with statistically significant decreases in crash frequency, while whether the driver was using alcohol or drugs is associated with a statistically significant increase in crash frequency. A poor average driving record by vehicle model is associated with an expected increase in crash frequency in cars, but unexpected decreases in crash frequency in light trucks and crossover utility vehicles (CUVs)/minivans. Including these variables, either individually or including all in the same regression model, does not change the general results of the baseline NHTSA regression model: that mass reduction is associated with an increase in crash frequency in all three types of vehicles, while footprint reduction is associated with an increase in crash frequency in cars and light trucks, but with a decrease in crash frequency in CUVs/minivans. The variable with the biggest effect is initial vehicle purchase price, which dramatically reduces the estimated increase in crash frequency in heavier-than-average cars and light trucks, and all CUVs/minivans. These results suggest that other, more subtle, differences in vehicles and their drivers account for the unexpected finding that lighter vehicles have higher crash frequencies than heavier vehicles, for all three types of vehicles.

This report summarizes an effort to replicate the results from a 2-stage regression model developed by Dynamic Research Inc. (DRI) to simultaneously estimate the effect of mass or footprint reduction on the two components of societal fatality risk per vehicle miles of travel, crashes per VMT (crash frequency) and fatality risk once a crash has occurred (crashworthiness/ compatibility). Lawrence Berkeley National Laboratory (LBNL) was not able to exactly replicate the results from DRI’s simultaneous 2-stage regression model. This may be because of discrepancies in how DRI and LBNL classified the state police-reported crash data into crash types. LBNL’s analysis of four alternate regression models suggests that the results from DRI’s method are sensitive to changes in what data are used in the analysis, or even the particular vehicles included in the decimation sample; in some cases the sign of the estimated relationship from DRI’s results changes under an alternate LBNL regression. However, for the most part LBNL’s alternate regressions confirm the general results from DRI’s simultaneous model, and LBNL’s analysis in its Phase 2 report: that mass reduction is associated with an increase in crash frequency (crashes per VMT), but a decrease in fatality risk once a crash has occurred, across all vehicle types. Similar results were obtained after using stopped rather than non-culpable vehicles as the induced exposure records, and replacing footprint with wheelbase and track width.

The National Highway Traffic Safety Administration (NHTSA) recently updated its 2003 and 2010 logistic regression analyses of the effect of a reduction in light-duty vehicle mass on US fatality risk per vehicle mile traveled (VMT). The current NHTSA analysis is the most thorough investigation of this issue to date. LBNL's assessment of the analysis indicates that the estimated effect of mass reduction on risk is smaller than in the previous studies, and statistically non-significant for all but the lightest cars. The effects three recent trends in vehicle designs and technologies have on societal fatality risk per VMT are estimated, and whether these changes might affect the relationship between vehicle mass and fatality risk in the future. Side airbags are found to reduce fatality risk in cars, but not necessarily light trucks or CUVs/minivans, struck in the side by another light-duty vehicle; reducing the number of fatalities in cars struck in the side is predicted to reduce the estimated detrimental effect of footprint reduction, but increase the detrimental effect of mass reduction, in cars on societal fatality risk. Better alignment of light truck bumpers with those of other vehicles appears to result in a statistically significant reduction in risk imposed on car occupants; however, reducing this type of fatality will likely have little impact on the estimated effect of mass or footprint reduction on risk. Finally, shifting light truck drivers into safer, car-based vehicles, such as sedans, CUVs, and minivans, would result in larger reductions in societal fatalities than expected from even substantial reductions in the masses of light trucks. A strategy of shifting drivers from truck-based to car-based vehicles would reduce fuel use and greenhouse gas emissions, while improving societal safety.

This report summarizes the layout and content of a website created for the CEC to make the information in the Energy Aware Planning Guide readily available to California cities and counties, to assist them in their efforts to reduce energy use. The website consists of an electronic version of the entire Planning Guide that allows easy maneuverability among the many sections of the document, and quick downloading of references cited in the document. The website also contains individual briefing pages for 46 land use- and transportation-related measures, including 21 briefs that provide information on additional transportation-related measures that are not specifically identified in the Planning Guide. The website also summarizes estimates of the effectiveness of individual measures described in the briefings, as well as the cost-effectiveness of each measure, derived from a comprehensive national study of the potential to reduce energy use from the transportation sector

One impediment to increasing the fuel economy standards for light-duty vehicles is the long-standing argument that reducing vehicle mass to improve fuel economy will inherently make vehicles less safe. This technical paper summarizes and examines the research that is cited in support of this argument, and presents more recent research that challenges it. We conclude that the research claiming that lighter vehicles are inherently less safe than heavier vehicles is flawed, and that other aspects of vehicle design are more important to the on-road safety record of vehicles. This paper was prepared for a workshop on experts in vehicle safety and fuel economy, organized by the William and Flora Hewlett Foundation, to discuss technologies and designs that can be taken to simultaneously improve vehicle safety and fuel economy; the workshop was held in Washington DC on October 3, 2006.