Clustering and pedestrian crashes prediction modelling: Amman case

Pedestrian casualties are a severe domestic as well as international problem. This study analyses the spatial distribution of pedestrian casualties to define contributory factors and delineate the means for their prediction. Three years of crash data were collected along with other factors and analysed using kernel density estimation (KDE), spatial autocorrelation (Moran's I), cluster K-Means, spatial regression, and general linear regressions (GLM). Kernel density estimate defines a cluster of pedestrian deaths within 1250 meters. According to Moran's I, 17/22 attributes about casualties, road networks, demographics, and land use have positive values, indicating similar importance clustering. The spatial pattern of pedestrian casualties is random and insignificant and does not change with time. Casualties are negatively related to the surrounding attributes, indicating a tendency towards dispersion. A K-Means analysis of multiple variables revealed that when variables included in the clustering were higher, the variance explanation percentage was lower. In the multi-variable GLM assuming Poisson distribution, the road network length alone or with the house permits combined were the best predictors of casualties. Classic regressions were not significantly enhanced by spatial dimension, and none of the autoregressive coefficients were significant. The predictions from the Poisson-based GLM model are similar to the classic regressions.

Analysis of the injury severity of motor vehicle-pedestrian crashes at urban intersections using spatiotemporal logistic regression models

This paper conducted a comprehensive study on the injury severity of motor vehicle-pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.

The urban structure and pedestrian injuries: A typological analysis of pedestrian crashes in the city of Hermosillo, Mexico

OBJECTIVE

In this study, we aim to identify social typologies of pedestrian crashes considering demographics, health impacts, involved vehicle, temporality of the collision, and place of impact in Hermosillo, Mexico.

METHODS

A socio-spatial analysis was performed by using local urban planning information and vehicles-pedestrian crashes records collected by the police department (N = 950) between 2014 and 2017. Multiple Correspondence Analysis and Hierarchical Cluster Analysis were used to determine typologies. Geographical distribution of typologies was obtained with spatial analysis techniques.

RESULTS

The results suggest there are four typologies, which portray the physical vulnerability of pedestrians, which reflect the vulnerability to collisions associated to the variables age, gender, and street speed limits. Findings show that children are more likely to be injured during weekends in residential zones (Typology 1), while older females are more likely to be injured during the first three days of the week (Monday - Wednesday) in the downtown area (Typology 2). Injured males during the afternoon in arterial streets represented the most frequent cluster (Typology 3). Also, males were likely to be severely injured by heavy trucks during nighttime in peri-urban areas (Typology 4). These findings indicate that vulnerability and risk exposure vary according to the type of pedestrian involved in the crash, which are linked to the types of places they visit.

CONCLUSIONS

The design of the built environment plays a major role in the number of pedestrian injuries particularly when it favors motor vehicles over pedestrians or non-motorized vehicles. Because traffic crashes are considered preventable events, cities must embrace a diversity of mobility modes and incorporate the appropriate infrastructures that safeguard the lives of all their travelers, especially pedestrians.

Pedestrian crash frequency: Unpacking the effects of contributing factors and racial disparities

In this paper, we unpack the magnitude effects of the determinants of pedestrian crashes using a multivariate analysis approach. We consider four sets of exogenous factors that characterize residential neighborhoods as well as potentially affect pedestrian crashes and the racial composition of such crashes: (1) crash risk exposure (CE) attributes, (2) cultural variables, (3) built environment (BE) features, and (4) sociodemographic (SD) factors. Our investigation uses pedestrian crash and related data from the City of Houston, Texas, which we analyze at the spatial Census Block Group (CBG) level. Our results indicate that social resistance considerations (that is, minorities resisting norms as they are perceived as being set by the majority group), density of transit stops, and road design considerations (in particular in and around areas with high land-use diversity) are the three strongest determinants of pedestrian crashes, particularly in CBGs with a majority of the resident population being Black. The findings of this study can enable policymakers and planners to develop more effective countermeasures and interventions to contain the growing number of pedestrian crashes in recent years, as well as racial disparities in pedestrian crashes. Importantly, transportation safety engineers need to work with social scientists and engage with community leaders to build trust before leaping into implementing planning countermeasures and interventions. Issues of social resistance, in particular, need to be kept in mind.

Factors affecting injury severity at pedestrian crossing locations with Rectangular RAPID Flashing Beacons (RRFB) using XGBoost and random parameters discrete outcome models

This paper evaluates the effectiveness of Rectangular Rapid Flashing Beacons (RRFB) on crash severity. The study used and compared XGBoost and Random Parameters Discrete Outcome Models (RPDOM) respectively. The dataset comprises of 312 pedestrian crossing locations, among which 154 treatment locations were provided with the Rectangular Rapid Flashing Beacons (RRFB) and 158 control locations without RRFB. These control locations have similar roadway, traffic, and land use characteristics of that of the treatment locations but are not treated with RRFB or other pedestrian crossing countermeasures. This study shows the impact of RRFB and other factors on severity of nighttime, pedestrian, total and rear-end crashes. Crash severity data was compiled from driver, vehicle, and event level data of each crash. Due to availability of larger number of observations for total (35,553), rear-end (15,675) and nighttime crashes (8,144) XGBoost was used, and due to less observations for pedestrian crashes (369), it was modeled using RPDOM. The results showed positive impact of RRFB for the reduction of nighttime crashes. It was noted that RRFB reduces the K and A nighttime crashes according to the SHAP values from the XGBoost model but does not have the desired significance for rear end and overall total crashes in the study area. From the RPDOM, it was seen that RRFB showed statistically significant reduction in injury severity of pedestrian crashes and nighttime crashes. To compare the two models, nighttime crashes were modeled using both the techniques, the prediction accuracy of XGBoost Model was 97% which was much greater than that of the RPDOM at 73.8% prediction accuracy. Thus, both XGBoost and the RPDOM model for showed positive impact of installing RRFB in reducing the severity of nighttime crashes.

Investigation on the driver-victim pairs in pedestrian and bicyclist crashes by latent class clustering and random forest algorithm

Pedestrians and bicyclists from marginalized and underserved populations experienced disproportionate fatalities and injury rates due to traffic crashes in the US. This disparity among road users of different races and the increasing trend of traffic risk for underserved racial groups called for an urgent agenda for transportation policy making and research to ensure equity in roadway safety. Pedestrian and bicyclist crashes involved drivers and pedestrians/bicyclists; the latter were usually victims. Traditional safety studies did not account for the interaction between the two parties and assumed that they were independent from each other. In this study we paired the driver and pedestrian/bicyclist involved in the same crash to understand the socioeconomic and demographic make-up of the two parties involved in crashes and assessed the geographic distribution of these crashes and crash-contributing factors. For this purpose, we applied thelatent class clustering analysis (LCA) to classify different crash types and analyze the patterns of the crashes based on the income and ethnicity of both drivers and victims involved in pedestrian and bicyclist crashes. We then used random forest algorithms and partial dependence plots (PDPs) to model and interpreted the contributing factors of the clusters in both pedestrian and bicyclist models. The clustering results showed a pattern of social segregation in pedestrian and bicyclist crashes that drivers and victims with similar socioeconomic characteristics tend to be involved in one crash. Pedestrian/bicyclist exposure, driver's age, victim's age, year of the car in use, annual average daily traffic (AADT), speed limit, roadbed width, and lane width were the most influential factors contributing to this pattern. Crashes that involved drivers and victims with lower income and non-white ethnicity tended to happen in the location with higher pedestrian/bicyclist exposure, higher speed limit, and wider road. The findings of this research can help to inform the decision-making process for improving safety to ensure equitable and sustainable safety for all road users and communities.

The incidence burden of unreported pedestrian crashes in Illinois

OBJECTIVE

Communities with high rates of pedestrians struck by motor vehicles may miss out on mitigation resources and suffer worse medical outcomes if crashes there go unreported to police. This study investigates the places, people, and communities in Illinois where struck pedestrians are most likely to go unreported. A better understanding of the true burden and distribution of struck pedestrians will help guide policy and direct investments and interventions where they are most needed.

METHODS

Hospital records of pedestrians treated for injuries sustained by a motor vehicle that were not able to be linked with a corresponding crash report across three consecutive years are investigated. Discordance rates of struck pedestrians are calculated and disaggregated by region. A presentation of summary statistics is accompanied by an ordinary least squares predictive model to estimate the relationship between discordant struck pedestrians and sociodemographic factors.

RESULTS

The incidence of unreported struck pedestrians was not randomly distributed. Blacks struck by a motor vehicle were disproportionately likely to go unreported to police. Zip codes with the most unreported crashes per capita on average had double the poverty rate and 2.6 times the carless household rate as the rest of Illinois. Struck pedestrians diagnosed at the hospital with an intoxicating substance went unreported to police nearly 70% of the time. Generally, more severe head and thorax injuries were more likely to be reported. Struck pedestrians outside of Cook County averaged a 60% discordance rate, those within Cook County averaged a discordance rate of about 50%. Struck pedestrian cases reported to police averaged emergency department charges of about $2,500 more than unreported cases.

CONCLUSIONS

Underlying and contributing factors influential of a struck pedestrian's decision of whether to report to police is complex and layered by social constructs mixed with difficult economic decisions, often further complicated by the fog of impairment. Recommendations are made for community outreach to stress the importance of reporting incidents to police, along with adjusting case count numbers in police records using hospital data and discordance rates.

Exploring the impacts of street layout on the frequency of pedestrian crashes: A micro-level study

INTRODUCTION

Pedestrian safety has become a critical issue since walking is increasingly promoted as a sustainable transport mode. However, pedestrians are vulnerable to severe injury and mortality in road crashes. Therefore, it is important to understand the factors that affect the safety of pedestrians. This paper investigates the impacts of street layout on the frequency of pedestrian crashes by examining the interactive pattern of built environment, crossing facilities, and road characteristics.

METHOD

A surrogate exposure variable of pedestrian crashes at the road-segment level is proposed by considering the locations of crossing facilities, distribution of points of interest (POIs), road characteristics, and pedestrian activities. A network-based kernel density technique is used to identify the pedestrian crash risk at the road segment level. Bayesian spatial models based on different exposure variables are employed and compared.

RESULTS

The results suggest that models using the surrogate exposure of pedestrian crashes provide better model fit than the ones simply using the density of pedestrians. It is also found that the presence of POIs is related to a higher risk of pedestrian-vehicle crash. In addition, a significantly higher number of pedestrian crashes are found to occur on segments with more bus stops and metro stations. Results also show that the longer the distance between the crossing facilities and road segments, the more pedestrian crashes are observed.

CONCLUSIONS

The proposed aggregated indicator can provide more efficient exposure and higher prediction accuracy than the density of pedestrians. Besides, the POIs, crossing facilities, and road types were all significantly related to pedestrian crashes.

PRACTICAL APPLICATIONS

Our results suggest that the locations of POIs and transport facilities should be planned in a way that can decrease the number of road crossed or guide pedestrians to take safe crossing path.

Exposure based geographic analysis mode for estimating the expected pedestrian crash frequency in urban traffic zones; case study of Tehran

Predicting pedestrian crashes on urban roads is one of the most important issues related to urban traffic safety. Due to the lack of spatial correlation and instability in the crash data, the statistical reliability of Empirical Bayesian method in the combination of the observed and predicted crash frequency is questionable. In this study, an EB model has been developed to estimate the expected frequency of pedestrian crashes in urban areas using the over-dispersion parameter taking into account the spatial correlation of crash data. The objective of this study is to estimate the expected geographical frequency of pedestrian crashes using the Empirical Bayesian (EB) approach using weighted geographical regression models for pedestrian crashes in Tehran. For doing so, four models of geographic weighted Poisson regression (GWPR), geographic weighted zero-inflated Poisson regression (GWZIPR), geographic weighted Negative Binomial regression (GWNBR) and the geographic weighted zero-inflated Negative Binomial regression (GWZINBR) have been used. In this study, the areas analyzed for the development of the EB model based on pedestrian exposure variables include traffic analysis zones (TAZs). Finally, the EB model was extended to the Geographic Empirical Bayesian (Ge-EB) model. The results showed that GWZIPR and GWZINBR models make more accurate predictions. These models had the lowest values of Akaike Information Criterion (AIC), the lowest values of Cross Validation and the lowest values of Root Mean Square Error (RMSE). The Moran and Variance Inflated Factor (VIF) indices were also within acceptable limits. The weighted negative binomial distribution could moderate the amount of heterogeneity of crash data to some extent. This study has shown the dispersion and density of pedestrian crashes without having the volume of pedestrians and thus can be done by taking safety measures in places prone to pedestrian crashes.

Factors affecting pedestrian behaviors at signalized crosswalks: An empirical study

INTRODUCTION

Safety of pedestrians depends, among other factors, on their behavior while crossing the road. This study aims to assess behaviors of pedestrians at signalized crosswalks.

METHOD

Following a literature review and a pilot study, 25 vital pedestrian crossing factors and behaviors were determined. Then data was randomly collected for 708 pedestrians at 10 lighted crossings in Sharjah (UAE), five at road intersections and five mid-block crossings.

RESULTS

Results indicated that 17.4% of pedestrians observed crossed partly or fully on red and that crossing speed was 1.22 m/s, on the average, which is slightly faster than most speeds recorded in the literature. Moreover, female pedestrians were more likely to cross while chatting with others, less likely to cross on red, and more likely to walk slower than male pedestrians. Results also showed that pedestrians who crossed at road intersections walked slower than those who crossed at mid-block crossings. It was also found that longer red pedestrian times and narrower roads tended to encourage pedestrians to cross on red and that the majority of pedestrians did not look around before crossing.

PRACTICAL IMPLICATIONS

Use of the Health Belief Model for pedestrian safety are discussed.

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

INTRODUCTION

Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes.

METHOD

In this study, the research team analyzed Louisiana's traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels.

RESULTS

The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.

In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention

INTRODUCTION

A pedestrian crash occurs due to a series of contributing factors taking effect in an antecedent-consequent order. One specific type of antecedent-consequent order is called a crash causation pattern. Understanding crash causation patterns is important for clarifying the complicated growth of a pedestrian crash, which ultimately helps recommend corresponding countermeasures. However, previous studies lack an in-depth investigation of pedestrian crash cases, and are insufficient to propose a representative picture of causation patterns.

METHOD

In this study, pedestrian crash causation patterns were discerned by using the Driving Reliability and Error Analysis Method (DREAM). One hundred and forty-two pedestrian crashes were investigated, and five pedestrian pre-crash scenarios were extracted. Then, the crash causation patterns in each pre-crash scenario were analyzed; and finally, six distinct patterns were identified. Accordingly, 17 typical situations corresponding to these causation patterns were specified as well.

RESULTS

Among these patterns, the pattern related to distracted driving and the pattern related to an unexpected change of pedestrian trajectory contributed to a large portion of the total crashes (i.e., 27% and 24%, respectively). Other patterns also played an important role in inducing a pedestrian crash; these patterns include the pattern related to an obstructed line of sight caused by outside objects (9%), the pattern that involves reduced visibility (13%), and the pattern related to an improper estimation of the gap distance between the vehicle and the pedestrian (10%). The results further demonstrated the inter-heterogeneity of a crash causation pattern, as well as the intra-heterogeneity of pattern features between different pedestrian pre-crash scenarios. Conclusions and practical applications: Essentially, a crash causation pattern might involve different contributing factors by nature or dependent on specific scenarios. Finally, this study proposed suggestions for roadway facility design, roadway safety education and pedestrian crash prevention system development.

Pedestrian risky behavior and safety at zebra crossings in a Ghanaian metropolitan area

OBJECTIVE

This article assesses pedestrian behavior and safety at zebra crossings in the Cape Coast Metropolis.

METHOD

A mix of a naturalistic exploratory and descriptive study was conducted using both primary and secondary data. The primary data included an observational study of over 6,000 pedestrians using zebra crossings in the metropolis. The secondary data were obtained from the national road traffic crashes (RTCs) database at the Building and Road Research Institute covering information on pedestrian crashes between 2007 and 2016 in the metropolis. Analyses were conducted using frequencies and percentages with Pearson's chi-square correlation used to establish the relationship between independent and dependent variables.

RESULTS

The findings showed that the majority of the 328 pedestrian crashes between 2007 and 2016 resulted in either fatalities or serious injuries and occurred at locations away from a junction. Most of the pedestrians observed used the zebra crossing were alone and engaged in talking or using mobile phone. Age group, pedestrian status, and the location of the zebra crossings influenced pedestrians' risky behaviors.

CONCLUSION

The majority of the pedestrian crashes in the metropolis resulted in injuries resulting in hospitalization or fatalities and occurred at a midblock. Pedestrians largely exhibited risky behaviors predisposing the occurrence of RTCs at zebra crossings despite the fact that they are a pedestrian right-of-way. There is therefore the need for the National Road Safety Commission to carry out campaigns to educate pedestrians on the safer use of zebra crossings.

Walking under the influence of the alcohol: A case study of pedestrian crashes in Tennessee

Alcohol adversely affects human behavior and judgment, and it is one of the contributing factors in traffic crashes. Although a large body of research has investigated driving behavior under the influence of the alcohol, to the best of our knowledge, no study has investigated the crash characteristics of the pedestrians under the influences of the alcohol. Tennessee Police Crash Data from 2011 to 2016 was used in this study to identify crashes between motor vehicles and pedestrians who were walking under the influence of alcohol (WUI). Results indicate that the number of fatally injured pedestrians for WUI cases has increased since 2011. Alcohol was present in 7% of the pedestrian crashes. Tested pedestrians averaged BAC levels of 0.17 g/dL. As pedestrian injury severity increased, the share of the WUI crashes increased. WUI contributed in 22% of the fatally injured pedestrian and only in 2% of the pedestrian crashes with no-injury. Comparisons indicate that the WUI crashes had their characteristics, which distinguished them from non-WUI crashes. Analysis indicates that 83% of the WUI crashes occurred in the nights; moreover, 54%, 69%, and 85% of WUI crashes respectively occurred on weekends, mid-block section of the road, and areas with no traffic control device. Results of a binary logit regression indicate that pedestrian's age, males, posted speed limit, and nighttime crashes had a positive association with the WUI crashes. On the other hand, urban context, intersection crashes, driver maneuvers (i.e., parking-related, turning, and straight), and daylight had a negative association with the WUI crashes. Findings are discussed in line with road safety countermeasures.

Considering built environment and spatial correlation in modeling pedestrian injury severity

OBJECTIVE

This study looks at mitigating and aggravating factors that are associated with the injury severity of pedestrians when they have crashes with another road user and overcomes existing limitations in the literature by focusing attention on the built environment and considering spatial correlation across crashes.

METHOD

Reports for 6,539 pedestrian crashes occurred in Denmark between 2006 and 2015 were merged with geographic information system resources containing detailed information about the built environment and exposure at the crash locations. A linearized spatial logit model estimated the probability of pedestrians sustaining a severe or fatal injury conditional on the occurrence of a crash with another road user.

RESULTS

This study confirms previous findings about older pedestrians and intoxicated pedestrians being the most vulnerable road users and crashes with heavy vehicles and in roads with higher speed limits being related to the most severe outcomes. This study provides novel perspectives by showing positive spatial correlations of crashes with the same severity outcomes and emphasizing the role of the built environment in the proximity of the crash.

CONCLUSIONS

This study emphasizes the need for thinking about traffic calming measures, illumination solutions, road maintenance programs, and speed limit reductions. Moreover, this study emphasizes the role of the built environment, because shopping areas, residential areas, and walking traffic density are positively related to a reduction in pedestrian injury severity. Often, these areas have in common a larger pedestrian mass that is more likely to make other road users more aware and attentive, whereas the same does not seem to apply to areas with lower pedestrian density.

Macro-level safety analysis of pedestrian crashes in Shanghai, China

Pedestrian safety has become one of the most important issues in the field of traffic safety. This study aims at investigating the association between pedestrian crash frequency and various predictor variables including roadway, socio-economic, and land-use features. The relationships were modeled using the data from 263 Traffic Analysis Zones (TAZs) within the urban area of Shanghai - the largest city in China. Since spatial correlation exists among the zonal-level data, Bayesian Conditional Autoregressive (CAR) models with seven different spatial weight features (i.e. (a) 0-1 first order, adjacency-based, (b) common boundary-length-based, (c) geometric centroid-distance-based, (d) crash-weighted centroid-distance-based, (e) land use type, adjacency-based, (f) land use intensity, adjacency-based, and (g) geometric centroid-distance-order) were developed to characterize the spatial correlations among TAZs. Model results indicated that the geometric centroid-distance-order spatial weight feature, which was introduced in macro-level safety analysis for the first time, outperformed all the other spatial weight features. Population was used as the surrogate for pedestrian exposure, and had a positive effect on pedestrian crashes. Other significant factors included length of major arterials, length of minor arterials, road density, average intersection spacing, percentage of 3-legged intersections, and area of TAZ. Pedestrian crashes were higher in TAZs with medium land use intensity than in TAZs with low and high land use intensity. Thus, higher priority should be given to TAZs with medium land use intensity to improve pedestrian safety. Overall, these findings can help transportation planners and managers understand the characteristics of pedestrian crashes and improve pedestrian safety.

Safety effects of exclusive and concurrent signal phasing for pedestrian crossing

This paper describes the estimation of pedestrian crash count and vehicle interaction severity prediction models for a sample of signalized intersections in Connecticut with either concurrent or exclusive pedestrian phasing. With concurrent phasing, pedestrians cross at the same time as motor vehicle traffic in the same direction receives a green phase, while with exclusive phasing, pedestrians cross during their own phase when all motor vehicle traffic on all approaches is stopped. Pedestrians crossing at each intersection were observed and classified according to the severity of interactions with motor vehicles. Observation intersections were selected to represent both types of signal phasing while controlling for other physical characteristics. In the nonlinear mixed models for interaction severity, pedestrians crossing on the walk signal at an exclusive signal experienced lower interaction severity compared to those crossing on the green light with concurrent phasing; however, pedestrians crossing on a green light where an exclusive phase was available experienced higher interaction severity. Intersections with concurrent phasing have fewer total pedestrian crashes than those with exclusive phasing but more crashes at higher severity levels. It is recommended that exclusive pedestrian phasing only be used at locations where pedestrians are more likely to comply.

A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods

This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates.