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Automated vehicle (AV) technology is expected to make roads safer. However, until recently only a handful of studies could test such hypotheses due to limited access to testing data. This study contributes to the literature by jointly analyzing the associated factors of three interrelated outcome variables--vehicle at fault, collision type, and injury outcome in AV-involved crashes. We use Bayesian networks to analyze the manually extracted data from reports of 333 AV-involved crashes that occurred in California between January 2017 and October 2021. The summary statistics indicate that rear-end collisions are the dominant (63.5%), while AVs are at fault for a small proportion of crashes (14.4%), and a majority of crashes (84.4%) are noninjury. The joint inferences of the Bayesian networks show that irrespective of the collision type, when the AV is at fault, the chance of the physical injury in a crash increases significantly. Further, the chance of an AV being at fault seems higher in parking locations, and during driving at wet pavements in unclear weather. The chances of AV rear-end collisions are lower in the parking lot, and when nonvehicular participants are involved but increase in high traffic flow roadways. We also find that the likelihood of physical injury is higher at high-speed locations, intersections, and wet pavements. These insights suggest specific areas (unsignalized intersections, less structured right-of-way rules, and wet pavements) where technological improvements could enhance the safety performance of AVs. [ABSTRACT FROM AUTHOR]

On-road remote sensing measurements of light and medium-duty gasoline vehicles collected within California's South Coast Air Basin since 1999 generally fall within the range of observed summer ambient molar NOx/CO measurements collected during morning rush hours. Compared with ambient and on-road emissions, the California Air Resources Board EMFAC model underpredicts 2018 gasoline vehicle NOx emission factors by more than a factor of 2.6. Contributing to these differences is that vehicles older than model year 2006 have NOx emission deterioration rates that are up to four times higher on-road than predicted by the EMFAC model. A fuel-based inventory using the 2018 on-road gasoline emission factors for CO and NOx results in total CO emissions similar to the basin inventory but NOx emissions that are 74% higher than the inventory. The higher NOx emission estimates from on-road gasoline vehicle measurements make their contribution to the inventory slightly larger than heavy-duty diesel vehicles. We have found LEV I (1994–2003) gasoline vehicles are a major source of these on-road emissions and that significant NOx reductions in the South Coast Air Basin are being overlooked by not targeting the high emitters for removal. Implications: A comparison of ambient and on-road vehicle molar NOx/CO ratios collected in California's South Coast Air Basin with those predicted by California's EMFAC2017 vehicle emissions model shows that the model significantly underpredicts NOx emission factors for gasoline vehicles. This results in a 74% underestimate of the contribution of gasoline vehicles to the basin's NOx inventory, with the contribution from gasoline vehicles comparable to that from heavy-duty diesel trucks. This likely means that current projections for future NOx emission reductions from mobile sources in the basin will not be realized unless additional NOx reductions are obtained from older gasoline vehicles. [ABSTRACT FROM AUTHOR]

• Framework for modeling well to wheels real-world plug-in vehicles CO 2 using big data. • Simulations conducted using open-source code and public data sets. • Considered sensitivity to fuel mix in electric grid as well as charging behavior. • Examined several electric range designs for three plug-in powertrain architectures. • Too long electric range can be detrimental to equivalent CO 2 emissions. This paper presents a modeling and simulation study of the greenhouse gas (GHG) emissions of plug-in vehicles subject to real-world driving conditions. The simulations utilize open-source software tools for estimation of second-by-second energy and/or fuel consumption in more than 65 thousand real-world recorded trips in the California household travel survey (CHTS) data set. Statistical distributions of the equivalent well-to-wheels (W2W) GHG emissions corresponding to CHTS trips are generated for three baseline public-domain plug-in vehicle models of: (i) parallel-drive plug-in hybrid, (ii) serial-drive plug-in hybrid, and (iii) pure electric vehicle. In addition to the baseline models, several derived vehicle models are created by upsizing of the battery and powertrain via an iterative procedure to attain the new all-electric range (AER). The emissions model also considers days when a pure electric vehicle cannot meet the travel demand by assuming that the driving on those days is done on a conventional vehicle. For each of the considered powertrain architectures, optimizing the AER for minimum GHG emissions becomes a trade-off between reducing the gasoline driven miles and maintaining reasonably sized (lightweight) powertrain components. As such, the equivalent GHG emissions of a vehicle are shown to have diminishing returns in terms of AER, and the optimum AER depends on both the carbon content in the electric grid, as well as the charging behavior. Within considered modeling assumptions and several vehicle usage conditions, the optimum AER is estimated to be between 30 and 60 miles for PHEVs, and between 120 and 200 miles for BEVs. [ABSTRACT FROM AUTHOR]

• PEV adoption can reduce greenhouse gas emission by up to 65% • Optimal PEV model assignment at household level is significantly different from vehicle level. • BEVs with 120+ miles range can benefit the most travel demand. • The achievable GHG emission reduction decreases by 2/3 if PHEV drivers never charge. There has been a continuous effort to quantify the expected greenhouse gas reductions from plug- in electric vehicles (PEVs). Most existing studies are based on standardized driving cycles to compare the performance of PEVs and conventional internal combustion engine (ICE) vehicles. This study is based on assigning the most efficient plug-in hybrid or full electric vehicle to a household based on the household travel behavior as collected by GPS tracking data of thousands of vehicles' real trips in California, and it found that PEV adoption can reduce greenhouse gas emission by up to 65%. We further explore the market share difference in assigning optimal vehicle models by considering vehicle-based and household-based travel demand, and we found that, given currently available technology, in many cases adoption of Plug-in Hybrid Electric vehicles (PHEVs) will not necessarily result in significantly higher greenhouse gas (GHG) emissions (2.8% higher) compared to battery electric vehicles (BEVs), while PHEVs have advantages to satisfy household's long-distance travel demand. However, if PHEV drivers never charge their vehicles, the achievable GHG emission reduction will be decreased by nearly 2/3. [ABSTRACT FROM AUTHOR]