Severity of Pedestrian Crashes in Developing Countries: Analysis and Comparisons Using Decision Tree Techniques

Little crash equity research has controlled for active transportation rates, examined the Southeastern United States, or used composite sociodemographic metrics. This study analyzed disparities in pedestrian and cyclist crashes and crash severity according to level of social vulnerability (SV) across South Carolina. Data about SV and its 4 dimensions (socioeconomic status, household composition and disability, minority status and language, and housing type and transportation) were compiled for all census tracts (N = 1103) within South Carolina. Data for all crashes involving a pedestrian (n = 10,688) and/or cyclist (n = 4802) from 2011 to 2021 were obtained from the South Carolina Department of Transportation and geocoded to the respective census tract. Total average pedestrian and cyclist crash severity (Equivalent Property Damage Only) were also calculated for each tract. Crash frequency and severity scores were adjusted using the average number of walking and cycling trips in the census tract per year using StreetLight Data. Mixed-model linear regression analyzed the relationships between overall SV and the 4 SV dimensions and 4 crash measures-pedestrian and cyclist frequency and severity. Stratified analyses were conducted for urban and rural tracts. Overall SV was positively and significantly associated with all 4 crash outcomes in urban areas: pedestrian crashes per trip (B = 0.048, SE = 0.012, P < .001), pedestrian crash severity per trip (B = 9.018, SE = 2.516, P < .001), cyclist crashes per trip (B = 0.093, SE = 0.029, P < .01), and cyclist crash severity per trip (B = 16.370, SE = 5.482, P < .01). In urban areas, greater SV was associated with more severe pedestrian and cyclist crash outcomes. Targeted policy and programmatic and infrastructure interventions are needed to improve active transportation safety and public health.

Pedestrian red-light violations at signalized crossings are an important traffic safety concern, particularly in densely populated cities. In this study, the authors quantitatively investigated the factors associated with pedestrian red-light violations and injury severity resulting from pedestrian–motor vehicle crashes at signalized crossings. Random parameter probit models are used to account for individual-specific heterogeneity that arises from a set of unmeasured factors related to traffic conditions and the pedestrians’ physical and mental status. Data for the analysis are based on the historical crash record maintained by the Hong Kong Transport Department. Children younger than age 11 years are not only associated with a higher likelihood of pedestrian red-light violations but also tend to have a higher probability of severe injuries. Factors including summer, dual carriageways with a central traffic island, and pedestrian age of 12 to 25 years are solely related to a higher likelihood of pedestrian red-light violations; meanwhile, variables solely associated with a higher probability of severe injuries include crashes that occur between 22:00 and 06:59, crashes occurring in rainy weather, crashes involving pedestrians older than age 46 years, and bus crashes. Appropriate countermeasures are recommended to curb pedestrian red-light violations and to reduce the injury severity of pedestrian crashes.

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach

Reducing fatal and serious injuries sustained in traffic crashes on tribal lands is a priority of federal, state, and local agencies in the United States. In the state of Arizona, the proportion of fatal and severe injury crashes on several areas of tribal land are 4.0% higher compared with statewide statistics. There is a need to investigate why higher proportions of fatal and severe injuries are occurring on tribal lands to plan effective countermeasures aimed at improving traffic safety in these areas. This study presents an analysis of factors affecting injury severity in crashes occurring within five tribal reservations in the state of Arizona. Crash data were obtained from the Arizona Department of Transportation, and the analysis included data for 9,597 persons involved in traffic crashes on these tribal lands for the years 2010–2016. An ordered logit model with random parameters was estimated using this data to identify factors significantly associated with severe injury outcomes in the event of a crash on tribal lands. Several person-, vehicle-, roadway-, and environmental-related variables were found to impact injury severity. For instance, alcohol and safety device usage were significantly associated with severity outcomes. The results of this study have the potential to aid transportation agencies effectively plan strategies to reduce traffic crash injuries and fatalities on tribal lands, and potential countermeasures considering the 4Es of traffic safety (engineering, education, enforcement, and emergency medical services) are discussed.

A novel approach for analyzing severe crash patterns on multilane highways

Traffic crashes involving pedestrians have a high frequency in developing countries. Among road users, pedestrians are the most vulnerable, as their involvement in traffic crashes is usually followed by severe and fatal injuries. This study aims to identify pedestrian crash patterns and reveal the random parameters in the dataset. A three-year (2015–2017) pedestrian crash dataset in Mashhad, Iran, was employed to investigate the influence of a rich set of factors on pedestrian injury severity, some of which have been less accounted for in previous studies (e.g., the vicinity to overpasses, the existence of vegetated buffers, and park lanes). A two-step method integrating latent class cluster analysis (LCA) and the mixed logit model was utilized to consider unobserved heterogeneity. The results demonstrated that various factors related to the pedestrian, vehicle, temporal, environmental, roadway, and built-environment characteristics are associated with pedestrian injuries. Furthermore, it was found that integrated use of LCA and mixed logit models can considerably reduce the unobserved heterogeneity and uncover the hidden effects influencing severity outcomes, leading to a more profound perception of pedestrian crash causation. The findings of this research can act as a helpful resource for implementing effective strategies by policymakers to reduce pedestrian casualties.

This study investigates the relationship between crash severity and risk factors, addressing the limitations of conventional methods in capturing spatial and temporal heterogeneity. We developed and compared five binary logit models: a basic model, temporal heterogeneity model, spatial heterogeneity model, spatiotemporal heterogeneity, and a partially constrained spatiotemporal heterogeneity model. The latter leverages the posterior distribution properties to ensure similar adjacent time and space regions share distribution. These models were calibrated and validated using 3,222 pedestrian crashes occurring at mid-blocks in Hong Kong (2010-2019). The results showed that both the unconstrained and partially constrained spatiotemporal models outperformed others in fit and predictive accuracy. Temporal heterogeneity analysis revealed evolving risk factors, while spatial heterogeneity analysis identified hot zones for crash severity. Key risk factors included pedestrian age, head injury, pedestrian actions, driver operations, vehicle type, and the first collision point. These insights support targeted, region- and time-specific pedestrian strategies.

ABSTRACTThis research studied potential factors associated with pedestrian injury severity levels sustained in train-pedestrian crashes at highway-rail grade crossings (HRGCs) using the Federal Railroad Administration's ten-year data. The analysis focused on nonsuicide pedestrian crashes and took into consideration the unobserved heterogeneity. Latent class clustering (LCC) addressed unobserved heterogeneity by creating distinct subgroups with relatively homogeneous attributes within each subgroup. HRGC inventory variables were the basis for the LCC; the process split the dataset into five distinguished clusters. Binary logit models for each cluster and the complete data set were estimated. A generalized linear mixed model, based on the complete data set, allowed examination of the clustering and comparison of the modeling results. Findings provided justification for the use of LCC as the first step in accounting for unobserved heterogeneity. Different HRGC, pedestrian, and crash characteristics were associated with pedestrian injury severity across different clusters. Higher train speed was associated with more severe injury propensity, regardless of the conditions of the HRGCs. Other variables including freight train involvement, train hitting pedestrian, HRGCs with the absence of flashing lights, advance warnings, rural areas, lower visibility conditions, and older pedestrians increased pedestrian injury severity levels with varying effects in different clusters.

Pedestrian injury has become a national traffic-safety concern as the share of pedestrian fatality continues to increase in the last decade. Pedestrian injury severities are influenced by many factors that include driver, pedestrian, vehicle, roadway, temporal, and environmental characteristics. Results indicate that some of the factors affecting pedestrian injury severity at intersection and non-intersection locations are statistically different and using the same model to perform the estimate at both locations may result in biased results. However, few studies have been conducted to explore different contributing factors at such locations. Mixed logit models are developed to independently identify the contributing factors to pedestrian injury severity resulting from crashes at intersections and non-intersections. The estimation shows factors such as male driver, alcohol, pedestrian above 65, truck, and higher speed limit significantly increase the probability of pedestrian serious injury severities in both locations. However, the impacts tend to be more severe at intersections. Urban and wet road surfaces decrease the likelihood of suffering fatal injury at intersections. Furthermore, crash time only has impacts at intersections, while traffic control, severe weather, and day-of-week only have impacts at non-intersections. The results provide insights on developing more effective countermeasures to promote pedestrian safety.

Objective Few existing studies in the literature devoted efforts to examine the driver injury severity in left-turn crashes. To fill this research gap, this paper aims to provide a comprehensive study of the contributing factors of left-turn crashes and the corresponding injury severities. Methods The hierarchical ordered probit (HOPIT) model is first applied to study driver injury severity in left-turn crashes. The HOPIT model can overcome the limitations of traditional ordered probit models since its thresholds are always positive and ordered. It is a function of unique explanatory parameters that do not necessarily affect the ordered probability outcomes directly. Considering the driving condition during the wintertime could be significantly different from other seasons, this study divided the overall crash dataset into “winter” and “other-season” subsets based on the temperature, snowing condition, and other factors. Results With the “other-season” dataset, results demonstrated that contributing factors, such as young drivers, male drivers, clear, light, and ramp intersection with crossroad, are associated with a decrease in injury severity. On the contrary, factors like drug, alcohol, disregard traffic control device, high-speed limit, the protected left-turn signal, etc., are related to an increase in injury severity. In winter, results revealed that only nine contributing factors are significant to the left-turn crash. Alcohol, disregard traffic control device, nighttime, high-speed limit, head-on collision, and state road are associated with an increase in injury severity. Also, two-vehicle involved, snow, ramp intersection with crossroad are related to a decrease in injury severity. Conclusions The HOPIT model is applied to examine contributing factors of left-turn crashes and the corresponding injury severity, based on left-turn crash records from 2010 to 2017 in Utah. Eighteen significant factors of left-turn crash injury severity are identified in the overall dataset. In seasons rather than winter, the significant factors are almost the same as that of the entire year. In the winter, less significant factors and different patterns are found compared with the overall crashes.

This study investigated the main factors affecting the severity of injury to pedestrians in taxi–pedestrian crashes on urban arterial roads. Video data recorded by an in-car black box were used. Because the video data provided direct crash observation, they were more reliable than the crash data, and video images and speed profiles retrieved from the black box were advantageous for safety studies. For analysis of the black box data, this study defined new explanatory variables that affected injury severity; these variables could not have been identified by the conventional method, which was based on crash reports. A multiple-indicator and multiple-cause model was used to investigate the relationship between the explanatory variables and injury severity. A total of 484 taxi–pedestrian crash scenes over 2 years was used for the multivariate analysis in the city of Incheon, South Korea. The crash characteristics most strongly associated with increased crash severity were failure by the pedestrian to watch for approaching vehicles, jaywalking by the pedestrian, the pedestrian being elderly, excessive vehicle speed, failure by the driver to immediately stop, limited driver vision, and nighttime. This study emphasized the potential of individualized black box video recording data for crash severity analysis and investigation of the causal factors of crashes.

A data-driven approach to child pedestrian crash analysis using dimension reduction, clustering, and explainable AI.

BackgroundDespite substantial progress, motor vehicle crashes remain a leading killer of US children. Previously, we documented significant positive impacts of Safe Routes to School interventions on school-age pedestrian and pedalcyclist...

The effects of neighborhood characteristics and the built environment on pedestrian injury severity: A random parameters generalized ordered probability model with heterogeneity in means and variances

To evaluate the effect of vehicle type (passenger vehicle vs. light truck vehicle) on crash trajectory and on the consequent source and severity of pedestrian injury, we analyzed data from the Pedestrian Crash Data Study (PCDS), conducted by National Highway Traffic Safety Administration (NHTSA) from 1994 to 1998. While 62% of the adults in PV (passenger vehicle)-related crashes were carried by the vehicle, such pedestrian-vehicle interaction was observed only in 28% of LTV (light truck vehicle)-adult crashes. Being thrown forward or knocked down were the most common (65%) type of pedestrian-vehicle interactions for LTV-adult crashes. For children, 93% of those struck by LTVs and 46% of those struck by PVs were thrown forward or knocked down. For adults, LTVs were more likely than PVs to cause thorax (37% vs. 20%) and abdomen injuries (33% vs. 18%). For children, LTVs were more likely than PVs to cause injuries to the upper extremity (71% vs. 56%) and abdomen (14% vs. 8%). For adults struck by PVs the most common sources of injury were windshield for head injuries (63%), hood surface for thorax (67%), abdomen (58%), spine (30%), and upper extremity (36%) injuries, and bumper for the lower extremity injuries (60%). The leading causes of injury for adult-LTV crashes were ground for head (39%) and upper extremity (37%) injuries, hood edge for thorax (48%) and abdomen (56%) injuries, hood surface for spine injuries (36%), and bumper for lower extremity injuries (45%). For child-PV crashes, ground was the most common source of face (37%) abdomen (83%), spine (43%), and upper extremity injuries (54%). For children hit by LTVs, 52% of face, 67% of abdomen, 100% of spine, and 60% of upper extremity injuries were attributed to ground contacts. Altogether, the major sources of injury were hood surface and windshield for PV-pedestrian crashes and hood surface and hood edge for LTV-pedestrian crashes. Changes in design, such as altering the geometry and stiffness of front-end structures, might be associated with considerable decrease in the frequency and severity of pedestrian injury.