Investigating Hydrogen Station Use and Station Access in California Using a Survey of Fuel Cell Vehicle Drivers

California has set a goal of reaching 100% zero emission vehicle (ZEV) sales by 2035. Most ZEV sales to date have been battery electric vehicles (BEVs) or plug-in hybrid electric vehicles (PHEVs), while fuel cell electric vehicles (FCEVs) make up only a small portion of ZEV sales. The market for FCEVs may be partially constrained because, unlike BEVs and PHEVs, they cannot use any existing infrastructure. This research investigates FCEV drivers use of hydrogen stations in California (of which there are 47 in operation) with the goal of informing the development of hydrogen infrastructure. Hydrogen station use was studied using results from a 2017 survey of 395 fuel cell electric vehicle (FCEV) owners and a 2018 survey of 328 FCEV owners. The results show FCEV drivers use on average 2.4 hydrogen stations. The average shortest distance FCEV owners would need to travel from home, work, or their commute to a hydrogen refueling station was 10 miles. Those whose most-used station was not the closest station available were more likely than those whose most-used station was the closest to use renewable hydrogen, suggesting that some drivers may prefer renewable hydrogen. Currently the percentage of California census block groups with one, two, and three hydrogen stations within 10 miles of households are 52.4%, 25.6%, and 22.5%; these census block groups are concentrated primarily in large metropolitan areas. Finally, 70% of FCEV owners said they would not have purchased the vehicle if their primary station had not been available, pointing the importance of station availability to FCEV adoption

Understanding the Impact of Charging Infrastructure on the Consideration to Purchase an Electric Vehicle in California

This research makes explicit and tests an implicit assumption in policies promoting public investment in plug-in electric vehicle (PEV) charging infrastructure: even people who are not already interested in PEVs see public PEV charging. Data from a survey representing all car-owning households in California are combined with per capita counts of public PEV charging locations and PEV registrations to estimate a structural equation model for two central variables: the extent to which participants have already considered acquiring a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV), and whether and how many places people see PEV charging. The model controls for socio-economic and demographic measures as well as participants’ awareness, knowledge, and assessments of PEVs. The model also controls for the known spatial correlation between PEV registrations and public PEV charging locations. The conclusion is there is no evidence of a relationship between public charging location density and participants reporting they see PEV charging locations. Nor is there a relationship between public charging location density and PEV purchase consideration. The evidence indicates there is little reason to assume building more public PEV charging means more people will see that charging or that more people will consider purchasing a PEV. Rather, awareness, knowledge, and positive assessments of PEVs allow people to see PEV charging in their local environment. In short, interest in PEVs is a prerequisite to people seeing PEV charging. Concomitant investments to increase awareness of PEVs and engagement in a transition to them as well as in PEV charging infrastructure may be a more effective way to grow the PEV market

More Public Charging Infrastructure Alone Will Not Increase Electric Vehicle Sales

Plug-in electric vehicles (PEVs), including battery electric vehicles and plug-in hybrid electric vehicles, are an important technology for decarbonizing transportation and reducing urban air pollution. A lack of public charging infrastructure is frequently cited as a primary barrier to continued, widespread PEV market growth. Public and private stakeholders are investing in public charging infrastructure, in part because they hope the presence of more infrastructure will encourage consumers to purchase PEVs. However, public charging infrastructure can only affect PEV sales if people—especially those who are not already PEV owners—see it, and by seeing it become more likely to consider purchasing a PEV. Researchers at UC Davis examined this relationship. They used data from a survey administered in the first quarter of 2021 of approximately 3,000 California car-owning residents, as well as data on PEV registrations and public charger locations. They modeled the relationships between multiple variables

The Relationship Between Vertical Loadrates and Tibial Acceleration Across Footstrike Patterns

Category: Other Introduction/Purpose: While the etiology of injuries is multifactorial, impact loading, as measured by the loadrate of the vertical ground reaction force has been implicated. These loadrates are typically measured with a force plate. However, this limits the measure of impacts to laboratory environments. Tibial acceleration, another measure of running impacts, is considered a surrogate for loadrate. It can be measured using new wearable technology that can be used in a runner’s natural environment. However, the correlation between tibial acceleration measured from mobile devices and vertical ground reaction force loadrates, measured from forceplates, is unknown. The purpose of this study was to determine the correlation between vertical and resultant loadrates to vertical and resultant tibial acceleration across different footstrike patterns (FSP) in runners. Methods: The study involved a sample of convenience made up of 169 runners (74 F, 95 M; age: 38.66±13.08 yrs) presenting at a running injury clinic. This included 25 habitual forefoot strike (FFS), 17 midfoot strike (MFS) and 127 rearfoot strike (RFS) runners. Participants ran on an instrumented treadmill (average speed 2.52±0.25 m/s), with a tri-axial accelerometer attached at the left distal medial tibia. Only subjects running with pain <3/10 on a VAS scale during the treadmill run were included to reduce the confounding effect of pain. Vertical average, vertical instantaneous and resultant instantaneous loadrates (VALR, VILR and RILR) and peak vertical and resultant tibial accelerations (VTA, RTA) were averaged for 8 consecutive left steps. Correlation coefficients (r) were calculated between tibial accelerations and loadrates. Results: All tibial accelerations were significantly correlated across all loadrates, with the exception of RTA with VILR for FFS (Table 1) which was nearly significant (p=0.068). Correlations ranged from 0.37-0.82. VTA was strongly correlated with all loadrates (r = 0.66). RTA was also strongly correlated with both loadrates for RFS and MFS, but only moderately correlated with loadrates for FFS (r = 0.47). Correlations were similar across the different loadrates (VALR, VILR, RILR). Conclusion: The stronger correlation between vertical tibial acceleration and all loadrates (VALR, VILR, RILR) suggests that it may be the best surrogate for loadrates when studying impact loading in runners

Long Distance Travel and Destination Attractiveness

This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings

Long Distance Travel and Destination Attractiveness

This report provides a summary of analyses using data of long distance tours by each household from an 8-week California Household Travel Survey travel log. The first analysis, uses Structural Equations Models (SEM) and a simpler variant called Path Analysis on three censored variables (tour miles by air, miles driving, and miles by public transportation) and two categorical variables (main trip tour purpose) and number of overnight stays. The second analysis, uses Latent Class Cluster Analysis (LCCA) to identify five distinct, informative patterns of long-distance travel. This analysis shows that long-distance tours for vacation, business travel, medical, and shopping are substantially distinct in terms of their travel characteristics and correspond to different combinations of other activities in the tour and they are done by different types of households. The methods used here to identify the typology of long distance travel can be easily expanded to include a variety of other explanatory variables of this type of behavior in more focused data collection settings