1 Powered by electric engines, electric vehicles (EVs) exhibit unique dynamic characteristics that may lead 2 to different crash characteristics and outcomes compared with traditional internal combustion engine 3 vehicles (ICEVs). This paper focuses on non-motorist crashes and estimates crash characteristics and 4 severity outcomes using statistical testing and regression analyses based on Chicago crash data from 2015 5 to 2022. Innovatively, this study supplements traditional police crash reports with Google Street View 6 (GSV) images and employs computer vision neural network models to uncover previously unreported 7 environmental variables at crash scenes. The results reveal both similarities and disparities in non-8 motorist crash characteristics between EV-involved and ICEV-involved incidents. The Likelihood Ratio 9 Test suggests parameter transferability in injury severity models for both vehicle types. However, notable 10 distinctions in factor distributions, such as non-motorist type, hit-and-run incidents, damage level, crash 11 hour, crash weekday, weather conditions, and road surface conditions, along with the influence of season 12 and road surface condition on injury severity, exist between EV and ICEV crashes. These distinctions 13 may be attributed to driver demographics, vehicle design, and usage characteristics. These insights can 14 guide the development of safety regulations for EVs and aid in devising specific safety measures and 15 policies for non-motorists, including pedestrians and cyclists. 16 17 Keywords: Non-motorist Crash, Electric Vehicles, Google Street View, Binary Probit Regression 18 19 J. Ling, X. Qian, and K. Gkritza 3 INTRODUCTION 1 Electric vehicles (EVs) refer to automobiles that utilize electricity as their primary or partial 2 source of power. They can be classified into four main categories: battery electric vehicles (BEVs), plug-3 in hybrid electric vehicles (PHEVs), hybrid electric vehicles (HEVs), and fuel cell electric vehicles 4 (FCEVs). Recent reductions in manufacturing costs and the progressive deployment of charging stations 5 have significantly bolstered the appeal of EVs. Particularly, BEV registrations increased by 110% and 6 PHEV registrations rose by 75% between 2016 and 2020 (1, 2). This surge in EV numbers highlights the 7 importance of addressing safety concerns, particularly pertaining to non-motorist safety. EVs display 8 unique dynamic features, such as operating almost silently without the traditional engine noise, which 9 calls for a reassessment of conventional safety measures. 10 The existing body of literature on non-motorist crashes involving EVs tends to focus 11 predominantly on pedestrian incidents involving HEVs. However, there is a significant research gap when 12 it comes to investigating non-motorist crashes involving BEVs and PHEVs. The distinct powertrain 13 technology, weight distribution, and overall vehicle design inherent to these various types of EVs 14 emphasize the importance of addressing this research gap. Given the extensive research available on 15 HEVs due to their early introduction (3), and the limited crash records pertaining to FCEVs, this study 16 primarily focuses on the analysis of BEVs and PHEVs (referred to as EVs later in this paper). In addition, 17 the objective of this study is to employ an integrated approach that combines Google Street View (GSV), 18 transformer models for image segmentation, and police reports to develop a comprehensive data 19 description for non-motorist crashes involving EVs. This integrated approach contributes to the analysis 20 of non-motorist crashes by providing comprehensive data integration, enhanced contextual understanding, 21 precise object identification, and evidence-based insights for addressing the unique safety challenges 22 associated with EVs. Subsequently, a binary probit model is estimated to identify the factors that 23 influence EV-related non-motorist crashes. 24 The paper is structured as follows. Firstly, a comprehensive literature review on EV-related crash 25 studies and use of GSV for crash analysis is presented. Subsequently, the data sources and statistical 26 models employed in this study are described. The ensuing section presents the outcomes and significant 27 findings derived from the statistical hypothesis testing and regression analyses. Finally, the paper 28 concludes by discussing the research implications, acknowledging the study’s limitations, and proposing 29 potential avenues for future research. 30 31 LITERATURE REVIEW 32 With the increasing number of EVs on the road, some studies have shed light on the statistical 33 analysis of EV crashes. Liu et al. (4) applied Pearson’s chi-squared test to confirm that the distribution of 34 severity levels for EV (mixing of BEV and PHEV) crashes is different from that of ICEV crashes. They 35 used the logistic regression model to identify essential factors influencing crash severity. Based on their 36 results, for EVs’ crash data, the presence of medians has a negative effect on crash severity, and collisions 37 with motorcycles have a positive effect on crash severity. However, this study only focused on 38 environmental variables without taking human-related and vehicle-related variables into consideration. 39 Moreover, several published government reports compared HEVs’ crash data with ICEVs’ crash data. 40 Chen et al. directly compared the crash statistics between HEVs and ICEVs and noted that occupants of 41 HEVs tended to be older than occupants of ICEVs, fire incidents were not common in both HEV and 42 ICEVs, and occupants of HEVs were more likely to experience arm, wrist, and hand injuries but less 43 likely to experience leg, ankle, and foot injuries when being compared with that of ICEVs (5). However, 44 the study did not account for roadway and environmental factors into consideration and only descriptive 45 analysis other than statistical models or testing is employed. 46 The former studies did not take heterogeneity into consideration, which may introduce biased 47 estimation and inferences. Taking heterogeneity and heteroskedasticity into consideration, Huang et al. 48 evaluated HEV crashes’ severity through a hierarchical mixed logit model and concluded that higher 49 occupant vehicles and older occupants were associated with higher injury counts, but crashes happen on 50 the wet road surface and regional artery roads (not expressway) result in fewer injury counts (6). Also, 51 J. Ling, X. Qian, and K. Gkritza 4 Huang et al. pointed out that the statistical analysis results could support strong heterogeneity effects in 1 crash data (6). Based on this conclusion, Seraneeprakarn et al. further validated the influence of 2 unobserved heterogeneity by comparing estimation from the mixed logit model, mixed logit model with 3 heterogeneity in means, and mixed logit models with heterogeneity in means and variance (7). These 4 studies identify the importance of taking heterogeneity effects into consideration when analyzing EVs’ 5 crash data. Still, these studies only analyzed crash involving HEVs instead of other EV types. 6 In addition to studies that use statistical modeling methods to analyze real crash data, Karaaslan et 7 al. used agent-based modeling to conduct traﬃc simulation and showed that EVs have a greater potential 8 of posing a threat to pedestrians than ICEVs by performing sensitivity analysis on the simulated crash 9 data (8). Furthermore, Karaaslan et al. confirmed the simulation results by analyzing the crash data from 10 the Fatal Analysis Reporting System (FARS) through a chi-squared test (8). This study further confirms 11 the idea proposed in earlier studies that EVs have a higher possibility to hit non-motorists than ICEVs. 12 Other than focusing on driver injuries in EV-involved crashes, some studies focused on 13 vulnerable road users (i.e., pedestrians or cyclists). Hanna studied pedestrian or cyclist crashes with HEVs 14 and ICEVs (3). Based on hypothesis testing results, Hanna concluded that motor vehicle crashes 15 involving pedestrians and cyclists usually happened on roads with low-speed limits under good lighting 16 and weather conditions (3). More importantly, HEVs were more likely to collide with pedestrians and 17 cyclists compared with ICEVs. Focusing on speed limits, vehicle actions, and crash locations, Wu et al. 18 further verified this conclusion through statistical methods including a case-control approach, relative 19 risk, and odds ratio (9). These studies suggest collision counterparts like pedestrians and bicycles are 20 worth taking into consideration when analyzing EV crash data. However, a limited number of variables 21 are explored in these studies. Such limitation makes it hard to identify potential differences and effects of 22 various factors in crash data between EVs and ICEVs. 23 Besides, various types of data are used in vehicle crash analysis including police records, Event 24 Data Recorder (EDR) data, and images of crash locations. Google Street View (GSV) has emerged as a 25 valuable tool for various research domains, including transportation crash analysis and public health 26 studies. Initially, it was primarily utilized for case study analysis, aiding in the interpretation of statistical 27 models. For instance, Hanson, Noland, & Brown employed GSV to present case studies of crashes, which 28 helped enhance the understanding of their statistical model results (10). 29 As the application of GSV expanded, researchers began utilizing trained auditors to extract 30 features from the images, thereby enhancing data collection. Mooney et al., for example, employed five 31 trained virtual street auditors, who collected data from the CANVAS system (11). They focused on 32 factors such as crosswalk presence, billboards, road or sidewalk condition, bus stops, and pedestrian 33 signals. Furthermore, with advancements in computer vision and neural networks, pre-trained Deep 34 Convolutional Neural Networks (DCNNs) have facilitated the recognition of different spatial categories 35 and scene types within Street View imagery. Kang et al. demonstrated the use of DCNNs for this purpose 36 (12). For instance, Stiles, Li, & Miller Pyramid employed a Scene Parsing Network (PSPNet) to segment 37 Street View imagery into up to 150 distinct object categories, enabling the identification of visual objects 38 (13). It should be noted, however, that while pre-trained neural networks can extract certain elements 39 from GSV images, some aspects, such as detailed road characteristics, may still require manual 40 examination by researchers. This includes assessing factors like the presence of sidewalks, bike lanes, and 41 other road conditions, as well as environmental conditions like blocked views or the presence of traffic 42 signs. Still, GSV has proven instrumental in research for extracting valuable information that may not be 43 included in traditional crash reports. 44 45 DATA DESCRIPTION AND PRELIMINARY ANALYSIS 46 This study utilizes crash data, which is based on police report, obtained from the electronic crash 47 reporting system (E-Crash) maintained by the Chicago Police Department (CPD) (14). The dataset covers 48 traffic crashes occurring on city streets within the jurisdiction of the City of Chicago, spanning the years 49 2015 to 2022. To ensure privacy, personally identifiable information like Vehicle Identification Number 50 (VIN) is not included in the given data. Moreover, the GSV API is utilized to obtain panoramic 360-51 J. Ling, X. Qian, and K. Gkritza 5 degree views, which consists of 4 pictures, each covering a 90-degree field of vision, and a 90-degree 1 pitch (horizontal visual angle) at each EV crash location, based on the given latitude and longitude 2 coordinates of crashes. 3 Street view images provide human-like perspectives of urban streetscapes. Google Street View 4 (GSV), launched in 2007, is the pioneering and leading provider in this domain (12). This study collected 5 360-degree GSV images from 2011 to 2023 with given latitude and longitude information of crash 6 location. The primary distribution of image years is as follows: 41.67% from 2021, 19.05% from 2022, 7 16.67% from 2019, and 10.71% from 2018, 3.57% from 2017, 2.38% from 2016, and 1.19% from 2011, 8 2013, 2015, 2020, and 2023. 9 This study also incorporates the use of Segformer, a Transformer model designed for semantic 10 segmentation of street view imagery, to aid the visual examination (17). Segformer consists of a 11 hierarchical Transformer encoder and a lightweight all-MLP decode head. Pre-trained on CityScapes data, 12 the model takes the input image and generates names of visual objects in the form of a list (20). 13 The distribution of crash locations of all victim types (including both motorist and non-motorists) 14 for different types of vehicles are demonstrated in Figure 1. 15 16 17 18 Figure 1 Crash Distribution for Difference Types of Vehicles 19 20 Police Report Data Processing 21 Due to the absence of VINs or other variables that explicitly identify EVs or ICEVs, the fuel type 22 classification for each vehicle is determined based on the vehicle’s make (brand), model, and year. This 23 determination is made by referencing fuel economy information provided by the U.S. Department of 24 Energy (15). 25 J. Ling, X. Qian, and K. Gkritza 6 Given the disparity in vehicle years and safety technologies between EVs and ICEVs in this 1 study, a rigorous filtering process is implemented to ensure a fair comparison. Only ICEVs from model 2 years 2010 to 2022 are considered to match the timeframe of the EVs under investigation. 3 Moreover, to address the critical aspects of accounting for geographical and temporal variations 4 in vehicle crash analyses, established recommendations are followed, including the adoption of buffer 5 analysis to control for spatial and temporal heterogeneity (16). For this study, three types of buffers are 6 employed to select relevant ICEV crashes that occurred in proximity to EV crashes. A geological buffer 7 of 50 meters, a seasonal buffer, and a time-period buffer are utilized. The time-period buffer involves 8 dividing each day into six distinct periods: 3:00-6:59, 7:00-10:59, 11:00-14:59, 15:00-18:59, 19:00-22:59, 9 and 23:00-2:59. Only ICEV crashes that transpired within the same specific time-period, season, and 10 geographical location as the corresponding EV crash are considered for further analysis. 11 In the end, there are 58 non-motorist crashes for BEVs, 17 non-motorist crashes for PHEVs, and 12 358 non-motorist crashes for ICEVs (327 from BEV crash buffer, 31 from PHEV crash buffer). The 13 statistical summary of data is presented in Table 1. 14 15 TABLE 1 Descriptive Statistics of Selected Variables 16 Electric Vehicles (EVs) Internal Combustion Engine Vehicles (ICEVs) Number Percentage % Number Percentage Injury Severity No Injury 21 28.0% 87 24.30% Lightly Injured 48 64.0% 234 65.36% Severely Injured/Fatal 6 8.0% 37 10.34% Non-motorist Type Bike 42 56.0% 122 34.08% Pedestrian 33 44.0% 236 65.92% Gender Male 40 53.33% 206 57.54% Female 35 46.67% 152 42.46% Safety Equipment True 14 18.67% 47 13.13% False 61 81.33% 311 86.87% Location Bike Lane 14 18.67% 33 9.22% Driveway Access 2 2.67% 10 2.79% Crosswalk On in 24 32.0% 113 31.56% Roadway 29 38.67% 129 36.03% Shoulder 0 0.0% 1 0.28% Other 6 8.0% 72 20.11% Driver Physical Condition Drug/Alcohol 1 1.33% 2 0.56% Emotional 2 2.67% 0 0.0% Normal 68 90.67% 338 94.41% Other 4 5.33% 18 5.03% Driver Vision Not Obscured 67 89.33% 321 89.66% Obscured 8 10.67% 37 10.33% J. Ling, X. Qian, and K. Gkritza 7 Speed Limit Under 35 mph 75 100.0% 341 95.25% 35–50 mph 0 0.0% 17 4.75% Above 50 mph 0 0.0% 0 0.0% Traffic Control Present & Function 37 49.33% 174 48.60% Not Present/Not Function 38 50.67% 184 51.40% Intersection True 28 37.33% 132 36.87% False 47 62.67% 226 63.12% Not Right of Way True 1 1.33% 27 7.54% False 74 98.67% 331 92.45% Hit and Run True 16 21.33% 128 35.75% False 59 78.67% 230 64.25% Person Ejected True 4 5.33% 13 3.63% False 71 94.67 345 96.37% Damage $500 or less 30 40.0% 202 56.42% $501 - $1,500 15 20.0% 51 14.25% over $1,500 30 40.0% 105 29.33% Hour 3:00-6:59 0 0.0% 1 0.28% 7:00-10:59 18 24.0% 38 10.61% 11:00-14:59 15 20.0% 86 24.02% 15:00-18:59 28 37.33% 173 48.32% 19:00-22:59 13 17.33% 50 13.97% 23:00-2:59 1 1.33% 10 2.79% Day of Week Monday 7 9.33% 29 8.10% Tuesday 7 9.33% 55 15.36% Wednesday 11 14.67% 57 15.92% Thursday 13 17.33% 42 11.73% Friday 15 20.0% 60 16.76% Saturday 11 14.67% 80 22.35% Sunday 11 14.67% 35 9.78% Season Fall (9, 10, 11) 31 41.33% 129 36.03% Spring (3, 4, 5) 10 13.33% 49 13.69% Summer (6, 7, 8) 23 30.67% 108 30.17% Winter (12, 1, 2) 11 14.67% 72 20.11% Weather Clear 67 89.33% 293 81.84% Cloudy 2 2.67% 15 4.19% Rainy 6 8.0% 38 10.61% Snowy 0 0.0% 12 3.35% J. Ling, X. Qian, and K. Gkritza 8 Surface Condition Dry 66 88.0% 292 81.56% Icy 0 0.0% 16 4.47% Wet 9 12.0% 50 13.97% 1 Table 1 contains the Safety Equipment variable, which denotes the presence of both regular 2 helmets and bicycle helmets, Traffic Control variable, which indicates the presence and proper 3 functionality of traffic signs, traffic signals, and police at the crash location, and Season variable, which is 4 categorized into Spring (March, April, May), Summer (June, July, August), Fall (September, October, 5 November), and Winter (December, January, February). 6 Table 1 shows that the majority of categorical variables for EV-related non-motorist crashes 7 closely resemble those of ICEV-related non-motorist crashes. Nonetheless, there are notable differences 8 in variables such as non-motorist type, hit-and-run incidents, damage level, crash hour, crash weekday, 9 weather conditions, and road surface conditions. The Chi-square tests confirm that all these differences 10 are statistically significant. 11 Regarding non-motorist type, it appears that EVs are more likely to be involved in crashes with 12 cyclists, while ICEVs are more frequently associated with pedestrian crashes. Additionally, non-motorist 13 crashes involving EVs exhibit a lower proportion of hit-and-run incidents. This observation aligns with 14 findings from a survey conducted by Jensen & Marbit, which indicated that households owning EVs tend 15 to have higher education and income levels, potentially leading to a reduced likelihood of hit-and-run 16 behavior (17). Furthermore, non-motorist crashes involving EVs result in higher financial damage levels, 17 as supported by a study conducted by Mersky et al., highlighting the generally higher cost of EVs 18 compared to traditional vehicles (19). 19 Notably, non-motorist crashes involving EVs are more prevalent during rush hours (7:00-10:59) 20 and on workdays compared to those involving ICEVs. Jensen & Marbit’s study provides an explanation 21 for this trend, suggesting that EVs are more commonly used for commuting purposes due to the ease of 22 planning home-work trips and a lower level of flexibility often required for this scenario, making EVs 23 well-suited for such usage patterns (17). 24 Finally, the proportion of non-motorist crashes involving EVs is lower during snowy weather and 25 icy road surface conditions. This can be attributed to the comparatively reduced usage of EVs during 26 winter, possibly due to concerns about reduced battery range and overall performance in cold weather 27 conditions. 28 29 Google Street View Image 30 Initially, the acquired images are processed through a pre-trained Segformer model, which is a 31 type of Transformer neural network, to identify visual objects within each image (17). The frequency of 32 occurrence and corresponding percentage for each identified object are then documented in Table 2. 33 During the visual examination of GSV images, 10 environment factors are considered: crosswalk 34 traffic light, traffic sign indicating pedestrians or cyclists, crosswalk type, number of lanes, number of 35 directions, near the intersection, lane divided, and area type. The visual examination data is presented in 36 Table 3. 37 To determine area types, as the geological boundary of the Chicago urban area is not obtained, 38 this study relies on GSV images. More specifically, the area types are deduced based on the building type, 39 which allows for a rough estimation of the population density of that area and, consequently, its area type. 40 Figure 2 provides an example of a crash location categorized as an urban area, while Figure 3 illustrates 41 an example of a crash location categorized as a suburban area. 42 43 J. Ling, X. Qian, and K. Gkritza 9 1 2 Figure 2 E Pearson St and N Michigan Ave, Chicago (Lat. 41.89763, Long. -87.62427) 3 4 5 6 Figure 3 W Armitage Ave, Chicago (Lat. 41.91794, Long. -87.6573) 7 8 Excluding mobile objects such as bicycles, buses, and cars, notable distinctions between non-9 motorist crashes involving EVs and ICEV in Table 2 are primarily attributed to the presence of terrain 10 and traffic lights. The analysis of Table 2 suggests that EV-related non-motorist crashes tend to occur 11 more frequently in suburban or rural areas, since terrains are more prevalent there compared to urban 12 areas. This observation finds support in the data presented in Table 3, which reveals that approximately 13 68% of non-motorist crashes involving EVs transpire in suburban locations. Moreover, Table 2 14 underscores that non-motorist crashes involving EVs often take place in areas lacking traffic lights. This 15 finding aligns with the reasonable expectation that urban areas, characterized by higher traffic volumes 16 and intricate road networks, typically exhibit a higher density of traffic signals compared to suburban 17 areas. 18 Table 3 indicates that non-motorist crashes involving EVs predominantly occur in proximity to 19 intersections, two-way two-divided-lane trafficways, and areas lacking traffic control devices such as 20 crosswalk lights and traffic signs. 21 22 J. Ling, X. Qian, and K. Gkritza 10 TABLE 2 Portion of Visual Objectives by Pre-trained Segformer Model 1 Electric Vehicles (EVs) Internal Combustion Engine Vehicles (ICEVs) Number Percentage Number Percentage Bicycle 46 61.33% 233 65.08% Building 75 100% 358 100% Bus 18 24% 159 44.41% Car 75 100% 358 100% Fence 75 100% 358 100% Motorcycle 34 45.33% 186 51.96% Person 71 94.67% 353 98.60% Pole 73 97.33% 356 99.44% Rider 23 30.67% 145 40.50% Road 75 100% 358 100% Sidewalk 75 100% 357 99.72% Sky 72 96.0% 358 100% Terrain 71 94.67% 314 87.71% Traffic light 57 76.0% 337 94.13% Traffic sign 75 100% 357 99.72% Train 34 45.33% 229 63.97% Truck 39 52.0% 240 67.04% Vegetation 72 96.0% 358 100% Wall 74 98.67% 354 98.88% 2 TABLE 3 Data by Visual Examination of GSV Image for EV-related Non-motorist Crash 3 Number Percentage Crosswalk Light False 44 58.67% True 31 41.33% Traffic Sign False 62 82.67% True 13 17.33% Crosswalk Type Block 48 64.0% Enhanced (Green) 2 2.67% Line 7 9.33% No Crosswalk 18 24.0% Sidewalk Exists False 5 6.67% True 70 93.33% Lane Count 0 1 1.33% 1 11 14.67% 2 41 54.67% 3 12 16.0% 4 9 12.0% 6 1 1.33% J. Ling, X. Qian, and K. Gkritza 11 Number of Direction 0 (Parking) 1 1.33 1 25 33.33 2 49 64.33 Near Intersection False 24 32.0% True 51 68.0% Lane Divided False 21 28.0% True 54 72.0% Area/Location Highway 2 2.67% Parking 4 5.33% Rural 1 1.33% Suburban 51 68.0% Urban 17 22.67% 1 METHODS 2 This study analyzes GSV images of non-motorist crash locations through visual examination and 3 pre-trained Transformer model, as described in the previous section. Additionally, the relationship 4 between non-motorist injury levels and vehicle type is explored using the Pearson’s chi-squared test and 5 Cramer’s V statistics. The crash severity level is further modeled using Binary Probit Regression model. 6 7 Statistical Analysis 8 Through statistical analysis, whether there is a significant relationship between non-motorist 9 injuries and vehicle type and evaluate the magnitude of that association could be determined. 10 Additionally, a Binary Probit Regression model is employed to understand how various factors impact the 11 severity of non-motorist crashes involving EVs. 12 13 Hypothesis Testing 14 The Pearson’s Chi-squared test is used to examine whether there is a significant association 15 between non-motorists’ injury counts and vehicle type (BEV, PHEV, and ICEV). This non-parametric 16 statistical test analyzes categorical data and compares the observed frequencies of non-motorist injuries 17 across different vehicle types with the expected frequencies under the assumption of independence (21). 18 Additionally, Cramer’s V statistic is employed to measure the strength of the association between the 19 variables. Suppose the 𝜒! is already obtained, 𝑘 is the number of vehicle types (𝑘=2), and 𝑟 is the 20 number of injury levels (𝑟=2), then Cramer V statistic is calculated through Equation 1: 21 22 ("!/$%&' {*+,,.+,} (1) 23 24 Statistical Models 25 Due to the limited sample size of 75 EV-related non-motorist crashes and 358 ICEV-related non-26 motorist crashes, probit regression models are preferred over other models, such as multinomial logit and 27 mixed logit, as they better mitigate the bias caused by the small sample (22). Additionally, due to the 28 scarcity of severe and fatal injury observations, the study combines injuries into two categories: injury 29 and no injury. Consequently, a binary probit regression is deemed a suitable and efficient approach for 30 this analysis. 31 The following specification is used that 𝑌0∗ is the latent continuous measure of injury severity 32 faced by non-motorist 𝑖 in a crash, 𝑥0 is a vector of explanatory variables describing the non-motorists, 33 J. Ling, X. Qian, and K. Gkritza 12 driver, traffic condition, and environmental condition, 𝛽 is a vector of parameters to be estimated, and 𝜖0 1 is a random error term which is assumed to follow standard normal distribution, Equation 2: 2 3 𝑌0∗=𝛽2𝑥0+𝜖0 (2) 4 5 Suppose 𝜇* represent unknown thresholds which need to be estimated along with 𝛽, the observed 6 discrete injury severity 𝑌0 is coded in Equation 3: 7 8 𝑌0=00 if 𝑌0∗≤0 (No Injury)1 if 𝑌0∗>0 (Injury or Fatal) (3) 9 10 Since 𝜖$ is assumed to follow the standard normal distribution, and 𝜙(∙) is the standard normal 11 cumulative distribution function, the probability that 𝑖th non-motorist experiences 𝑘 level of injury (𝑘=12 0,1) could be expressed in Equation 4-1 and Equation 4-2: 13 14 ℙ(𝑌0=0 | 𝑥0)=ℙ(𝑌0∗≤0)=ℙ(𝛽2𝑥0+𝜖0≤0)=ℙ(𝜖0≤−𝛽2𝑥0)=1− 𝜙(𝛽2𝑥0) (4-1) 15 ℙ(𝑌0=1 | 𝑥0)=1−ℙ(𝑌0∗≤0)=1−ℙ(𝛽2𝑥0+𝜖0≤0)=1− 𝜙(−𝛽2𝑥0)=𝜙(𝛽2𝑥0) (4-2) 16 17 In binary probit model, the binary classes display an increasing trend (0 for no injury, 1 for 18 injury). Positive signs in the associated variables indicate a higher likelihood of driver injury severity as 19 their values increase. Conversely, negative signs suggest a lower likelihood of driver injury severity. 20 21 Model Transferability 22 A Likelihood Ratio Test is proposed to test whether estimated parameters are transferable 23 spatially or temporally for a model (23). In this study, the same Likelihood Ratio Test is applied to test 24 examine whether estimated parameters can be transferred between EV-related and ICEV-related non-25 motorist injury severity models. 26 In this context, 𝐿𝐿(𝛽3), 𝐿𝐿(𝛽4), and 𝐿𝐿(𝛽5) represent the log-likelihoods at convergence of 27 models estimated using data from both EV-related and ICEV-related non-motorist crash, EV-related non-28 motorist crash only, and ICEV-related non-motorist crash only, respectively. 29 The test statistics 𝜒! would follow a chi-square distribution with the degree of freedom equal to 30 the sum of the number of estimated parameters in both EV-non-motorist and ICEV-non-motorist models 31 minus the number of estimated parameters in the overall model, if the null hypothesis (the parameters are 32 the same) holds. The calculation of the test statistic is performed using Equation 5: 33 34 𝜒!=−2[𝐿𝐿(𝛽3)−𝐿𝐿(𝛽4)−𝐿𝐿(𝛽5)] (5) 35 36 To ensure the accuracy of the transferability test, consistent variables are employed across all 37 models. However, due to the absence of visual examination of GSV images for ICEV-related non-38 motorist crashes, an EV-related non-motorist injury severity model is developed solely using police crash 39 report data. This model shares the same variables as the ICEV-related non-motorist injury severity model, 40 but its purpose is solely for testing rather than analysis and interpretation. 41 42 RESULTS 43 In this section, the results of our analysis are presented. The hypothesis testing section includes 44 the contingency table with Chi-square test statistics, P-values, and Cramer’s V statistics. Additionally, the 45 outcomes of the Likelihood Ratio Test for model transferability are provided. In the statistical model 46 section, the reference group for each categorical variable are discussed, and a table displaying estimated 47 coefficients and standard errors is presented. 48 49 J. Ling, X. Qian, and K. Gkritza 13 Hypothesis Testing 1 In Table 4, contingency tables display the frequency distribution of vehicle type and person type 2 and non-motorist type. This facilitates the examination of potential associations between vehicle type and 3 person type or non-motorist type. The Table 4 also provides the respective chi-square statistics and p-4 values for each contingency table. 5 6 TABLE 4 Contingency Table and Corresponding Chi-square Test Results 7 EV ICEV 𝝌𝟐 d.f. P-value Cramer V Non-Motorists 75 358 0.2826 1 0.595 0.003 Motorists 6,192 27,397 Cyclists 42 122 11.75 1 0.001 0.171 Pedestrians 33 236 8 The Likelihood Ratio Test for model transferability between EV-related and ICEV-related non-9 motorist injury severity models yields a test statistic 𝜒!=10.767 with 15 degrees of freedom. The 10 corresponding P-value for the null hypothesis (assuming the parameters are the same) is 0.769. 11 12 Statistical Model 13 The research model encompasses five variable groups: person, driver, traffic, environment, and 14 age. We have conducted Binary Probit Regression, using "No Injury" as the baseline severity level, and 15 the outcomes are presented in Table 5. 16 In this model, age is the only numerical variable. As for categorical variables, “female” represents 17 the reference group for Gender, “cyclist” for Non-motorist Type, “not obscured” for Driver Vision, 18 “false” for Hit and Run, “sidewalk” for location, “no control” for Traffic Control, “false” for Lane 19 Divided, “false” for Intersection, “one-way” for Number of Directions, “Fall” for season, “dry” for Road 20 Surface Condition, and “false” for Urban Area. 21 Additionally, Table 5 provides the corresponding standard errors and significance levels of 22 coefficient estimates. 23 24 TABLE 5 Binary Probit Model Estimation of Crash Severity Outcome 25 Electric Vehicles (EVs) Internal Combustion Engine Vehicles (ICEVs) Categories Variable Coef. Est. Std. Err. Coef. Est. Std. Err. Intercept 0.3222 1.2204 -0.4270 0.3472 Non-motorist Characteristics Gender Male -1.1500 0.5211 * -0.4471 0.1901 * Age 0.0035 0.0183 0.0127 0.0062 * Non-motorist Type Pedestrian 0.9435 0.6468 0.8964 0.2111 *** Driver Characteristics Driver Vision Obscured -1.2674 0.7370 0.0723 0.2891 Hit and Run True 0.1970 0.5079 0.3024 0.1874 Traffic Characteristics Location Crosswalk 1.1836 1.0795 0.3383 0.3394 Roadway 0.5247 0.7650 0.1582 0.2295 Bike lane 1.2166 0.8476 1.5440 0.3931 *** J. Ling, X. Qian, and K. Gkritza 14 Traffic Control Present & Function -0.2390 0.5636 -0.2832 0.2322 Lane Divided True 1.1823 0.6769 \ \ Intersection True 0.4533 0.6639 0.4229 0.3051 Num of Directions Two-way -0.6938 0.5158 \ \ Environmental Characteristics Season Spring 0.3295 0.7112 -0.4782 0.2559 Summer 0.2158 0.5500 0.1611 0.2164 Winter -0.5741 0.7363 0.2263 0.2623 Surface Condition Wet 1.6550 0.7363 -0.0497 0.2509 Icy \ \ -0.7975 0.4279 Urban Area True -0.3065 0.5569 \ \ Significance Level Codes: < 0.001 (***), < 0.01 (**), < 0.05 (*) Reference Severity Level: No Injury 1 DISCUSSION 2 Hypothesis Testing 3 Based on the Chi-square test with a p-value of 0.595 > 0.05, there is insufficient evidence to 4 reject the null hypothesis, indicating no significant association between vehicle type (EV, ICEV) and 5 person type (non-motorist, motorist). However, since exposure rates for non-motorist are not obtained, we 6 could only conclude that, based on police report based crash data, there is no sufficient evidence to 7 support the claim that there is an association between vehicle type (EV, ICEV) and victim type (non-8 motorist or not). 9 Nonetheless, EV-related and ICEV-related non-motorist crashes are not entirely identical, as 10 evidenced by the Chi-square test with a p-value of 0.0006 < 0.05, indicating a statistically significant 11 association between vehicle type (EV, ICEV) and non-motorist type (pedestrian, cyclist). However, the 12 Cramer V statistic suggests such association is not strong. The higher likelihood of crashes involving EVs 13 and bicycles in Chicago may be attributed to the city’s substantial EV adoption and efforts to enhance 14 bike-friendliness. With a wide array of public charging stations, bike lanes, and bike-sharing programs, 15 there is increased interaction between EVs and bicycles, which potentially leads to a higher crash risk. 16 The Likelihood Ratio Test for model transferability yields a p-value of 0.769 > 0.05, indicating 17 no sufficient evidence to reject the claim of parameter transferability between EV-related and ICEV-18 related non-motorist injury severity models. This suggests that the factors influencing non-motorist injury 19 severity in crashes involving EVs and ICEVs are similar and consistent. Thus, a unified set of variables 20 and coefficients can be used to model non-motorist injury severity in both EV and ICEV crashes. 21 22 Statistical Model 23 Given the limited observations, the study identified a limited set of significant variables for 24 predicting the severity of EV-related non-motorist crashes. Despite these limitations, the estimated 25 coefficients provide valuable insights into the impact of each factor on non-motorist injury severity in 26 J. Ling, X. Qian, and K. Gkritza 15 EV-related crashes. Additionally, comparing the estimates between EV-related and ICEV-related non-1 motorist injury severity models can offer valuable insights into potential differences between the two 2 types of crashes. Findings for different variable groups are discussed separately below. 3 4 Non-motorist Characteristics 5 The estimated coefficients for non-motorist characteristics, including gender, age, and type, do 6 not show a significant difference between EV-related and ICEV-related non-motorist injury severity 7 models. 8 For non-motorist victims, males have a lower likelihood of injury than female, aligning with prior 9 studies showing females’ increased vulnerability due to biomechanical differences (24). Moreover, injury 10 likelihood increases with age, consistent with previous research on non-motorist crashes (25). This can be 11 attributed to age-related factors such as decreased bone density, muscle strength, slower reaction time, 12 and medical conditions, making older non-motorists more susceptible during crashes. 13 Pedestrians are at a greater risk of injury compared to cyclists, which can be attributed to factors 14 such as visibility, speed, and protection. Cyclists’ greater visibility to drivers due to their height and 15 reflective clothing, their ability to travel faster and maneuver easily, and their use of protective gear like 16 helmets contribute to their lower vulnerability in crashes. 17 18 Driver Characteristics 19 The estimated coefficients for driver vision differ between EV-related and ICEV-related non-20 motorist injury severity models. Specifically, when driver vision is obscured, the likelihood of injury 21 decreases for EVs but increases for ICEVs. This could be attributed to reduced noise and vibration in 22 EVs, making it easier for drivers to detect hazards even with obscured vision. Additionally, EV drivers’ 23 heightened awareness of their quieter vehicles may contribute to exercising greater caution in the 24 presence of pedestrians or cyclists. 25 In both EV-related and ICEV-related non-motorist injury severity models, hit-and-run incidents 26 exhibit a higher likelihood of injury. This can be attributed to the delayed medical attention, potentially 27 worsening injuries, and association with severe injury or reckless driving behaviors such as driving under 28 the influence of alcohol or drugs (26). 29 30 Traffic Characteristics 31 For both EV-related and ICEV-related non-motorist crashes, injuries are more likely at 32 crosswalks, roadways, bike lanes, and intersections compared to sidewalks. This can be attributed to 33 higher traffic speeds and volumes at these locations, increasing the injury risk. Additionally, the presence 34 of traffic control devices, such as pedestrian signs and traffic lights, effectively reduces the likelihood of 35 injury, emphasizing their importance in reducing non-motorist injuries in both types of crashes. 36 Furthermore, utilizing GSV images, this study examines the impact of divided lanes and the 37 number of directions on the injury severity of EV-related non-motorist crashes. The estimated coefficients 38 suggest a higher likelihood of injury in EV-related non-motorist crashes on One-Way roads with divided 39 lanes (where lanes are used to separate parking spaces or bike lanes), potentially due to limited escape 40 routes and obstructed visibility caused by roadside vehicles for both drivers and non-motorists. 41 42 Environmental Characteristics 43 For EV-related non-motorist crashes, there is a higher likelihood of injuries in Spring and 44 Summer, but lower in Winter compared to Fall. Conversely, for ICEV-related non-motorist crashes, 45 injuries are more likely in Summer and Winter, but less likely in Spring compared to Fall. The precise 46 explanation for these seasonal differences remains challenging, but they could be attributed to the 47 interplay of seasonal patterns of non-motorist activities and the use of EVs. 48 The estimated coefficients for road surface condition show differences between EV-related and 49 ICEV-related non-motorist injury severity models, with EVs more likely to have injuries on wet road 50 surfaces, while ICEVs are less likely. The contrasting responses of EVs and ICEVs to wet road surfaces 51 J. Ling, X. Qian, and K. Gkritza 16 could be attributed to specific vehicle characteristics, although this study lacks relevant data for further 1 validation. EVs’ regenerative braking may be less effective on wet roads, impacting braking performance, 2 and their weight distribution might reduce traction. In contrast, ICEVs with ABS, traction control, and 3 front-wheel or all-wheel-drive configurations tend to handle wet and icy conditions better, particularly 4 with the use of winter tires. Additionally, EVs’ instant torque delivery can lead to sudden acceleration and 5 potential loss of control on wet surfaces. 6 This study investigates the influence of area type on the injury severity of EV-related non-7 motorist crashes using GSV images. The estimated coefficient reveals a lower likelihood of injuries in 8 urban areas compared to suburban and rural areas, possibly due to factors such as lower vehicle speeds, 9 shorter distances between intersections, better road infrastructure, and sufficient traffic control devices. 10 11 CONCLUSIONS 12 This study aims to bridge critical research gaps in the investigation of non-motorist crashes 13 involving EVs (BEVs and PHEVs) by conducting a comprehensive analysis of factors of crash and crash 14 injury severity levels in both EV and ICEV collisions with non-motorists. The research focuses on the 15 city of Chicago and employs various statistical methodologies to explore specific aspects of the crashes. 16 Through contingency table and Chi-square testing, the study investigates whether EVs are more prone to 17 colliding with non-motorists and examines which type of non-motorists is more likely to be involved in 18 EV crashes. Additionally, a likelihood ratio test is applied to assess the transferability of factors between 19 EV-related and ICEV-related non-motorist crash severity models. Furthermore, the research utilizes 20 Binary Probit Regression analyses to explore and compare three main groups of crash factors: human, 21 traffic, and environment, between EV-related and ICEV-related non-motorist crash injury severity 22 models. 23 The results reveal a significant scale of similarities between EV-related and ICEV-related non-24 motorist crashes. The Likelihood Ratio Test suggests parameter transferability between the injury severity 25 models for the two vehicle types, indicating that the factors influencing non-motorist injury severity apply 26 consistently across both EV and ICEV crashes. Also, the Chi-square test suggests there is not enough 27 evidence to claim that EVs are more likely to crash with non-motorists compared to ICEVs. Moreover, 28 the estimated coefficients for various factors, including non-motorist gender, age, type, hit and run 29 incidents, crash location, and presence of traffic control devices, do not exhibit significant differences 30 between the two vehicle types of injury severity models. 31 Nevertheless, significant variations in factor distributions, including non-motorist type, hit-and-32 run incidents, damage level, crash hour, crash weekday, weather conditions, and road surface conditions, 33 as well as the impact of season and road surface condition on injury severity, are observed between EV 34 and ICEV crashes. These disparities can be attributed to the demographic characteristics of drivers, 35 vehicle structure and design, and usage patterns unique to each vehicle type. 36 The study’s contributions extend beyond the statistical analyses. Through combining GSV images 37 with traditional police crash report data, this study reveals and uses previously unreported environmental 38 variables for analysis. This study also highlights the potential of machine learning models for computer 39 vision, such as Transformer Neural Network (Segformer), in enhancing crash analysis in certain 40 perspectives. Moreover, this study addresses a critical concern regarding the rise of EVs, contributing to 41 targeted safety measures and policies for non-motorists, including pedestrians and bicyclists. 42 Despite its strengths, this study does have several limitations. The data processing involves using 43 vehicle make, model, and year to filter EV-related crashes since VINs are not provided. Consequently, 44 some EV-related crashes might be overlooked, leading to potential underestimation of their numbers. 45 Non-motorist crashes involving ICEVs in locations similar to those where EV-related non-motorist 46 crashes occur, but not covered by EV-crash buffers, may also be disregarded. Additionally, due to a 47 limited number of observations, cyclists and pedestrian data are combined in one model, and only a 48 restricted set of variables is considered, with the usage of a simple regression model. Lastly, it is 49 important to note that the information extracted from GSV images may not accurately reflect the 50 circumstances at the time of the crashes. 51 J. Ling, X. Qian, and K. Gkritza 17 Future research should focus on investigating EV-related non-motorist crashes with improved 1 data quality and models that account for random effects and unobserved heterogeneity. Furthermore, 2 considering a wider range of variables, including driver characteristics (age, gender, physical condition), 3 vehicle characteristics (year, weight), and traffic characteristics (road class, trafficway type, pavement 4 type), could lead to a more comprehensive analysis of EV-related crash characteristics. Furthermore, 5 future research may explore how EV driver characteristics, the impact of Advanced Driver Assistance 6 Systems (ADAS), and some vehicle-related factors, such as weight and structure, influence EV-crash 7 characteristics. 8 9 ACKNOWLEDGMENTS 10 This study is partially supported by the Advancing Sustainability through Powered Infrastructure for 11 Roadway Electrification (ASPIRE) award, an Engineering Research Center program by the National 12 Science Foundation (NSF), grant no. EEC-1941524. The views and conclusions contained herein are 13 those of the authors and should not be interpreted as necessarily representing the official policies, either 14 expressed or implied, of NSF, or the U.S. Government. The U.S. Government is authorized to reproduce 15 and distribute reprints for governmental purposes notwithstanding any copyright annotation therein. 16 17 AUTHOR CONTRIBUTIONS 18 The authors confirm their contributions to the paper as follows: study conception and design: J. Ling, X. 19 Qian, K. Gkritza; data collection: J. Ling, X. Qian; analysis and interpretation of results: J. Ling, X. Qian; 20 draft manuscript preparation: J. Ling, X. Qian, K. Gkritza. All authors reviewed the results and approved 21 the final version of the manuscript. 22