Mixed logit models for examining pedestrian injury severities at intersection and non-intersection locations

Pedestrian injury in crashes at intersections often results from complex interaction among various factors. The factor identification is a critical task for understanding the causes and improving the pedestrian safety. A total of 2,614 crash records at signalized and non-signalized intersections were applied. A Partial Proportional Odds (PPO) model was developed to examine the factors influencing Pedestrian Injury Severity (PIS) because it can accommodate the ordered response nature of injury severity. An elasticity analysis was conducted to quantify the marginal effects of contributing factors on the likelihood of PIS. For signalized intersections, seven explanatory variables significantly affect the likelihood of PIS, in which five explanatory variables violate the Proportional Odds Assumption (POA). Local driver, truck, holiday, clear weather, and hit-and-run lead to higher likelihood of severer PIS. For non-signalized intersections, six explanatory variables were found significant to the PIS, in which three explanatory variables violate the POA. Young and adult drivers, senior pedestrian, bus/van, divided road, holiday, and darkness tend to increase the likelihood of severer PIS. The vehicles of large size and heavy weight (e.g. truck, bus/van) are significant factors to the PIS at both signalized and non-signalized intersections. The proposed PPO model has demonstrated its effectiveness in identifying the effects of contributing factors on the PIS.

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.

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

Automobile Versus Pedestrian Injuries: Does Gender Matter?

Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations

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.

This article focuses on identifying the appropriate ordered response structure for modeling pedestrian injury severity. The alternative ordered response approaches considered for the empirical analysis include ordered logit model (OL), generalized ordered logit model (GOL), and latent segmentation based ordered logit model (LSOL). The GOL and LSOL models enhance the traditional OL model in different ways. The GOL model relaxes the restrictive thresholds in the OL model by allowing for individual-level exogenous variable impacts on the threshold parameters. On the other hand, the LSOL model allows for differential impact on the alternatives by segmenting the pedestrian crash population into various segments with segment-specific OL parameters. In this study, the authors focus on examining the performance of these two model structures relative to the traditional OL model in the context of pedestrian injury severity. The performance of the formulated injury severity models are tested based on the New York City (NYC) Pedestrian Research Data Base for the years 2002 through 2006. To the authors’ knowledge, the study provides a first of its kind comparison exercise among OL, GOL, and LSOL models for examining pedestrian injury severity. The model estimation results clearly highlight the presence of segmentation based on the crash location attributes of pedestrian accidents. The crash location attributes that affect the allocation of pedestrians into these segments include regional county, functional classification of roadway, pedestrian location on roadway, number of travel lanes, and number of parking lanes in the roadway system. The key factors influencing pedestrian injury severity are weather condition, lighting condition, vehicle types, pedestrian age, and season. Overall, the results of the empirical analysis provide credence to the hypothesis that LSOL model is a promising ordered framework to accommodate population heterogeneity in the context of pedestrian injury severity.

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

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.

It is well known that adaptive deformable fronts attached to trucks reduce pedestrian injury. However, the suitability of these frontal attachment devices from a holistic point of view, considering both primary and secondary impacts, is questioned in this paper. Using head-injury-criteria index, the effects of pedestrian gait and different closing speeds are considered for evaluation of numerous truck-pedestrian side and rear impact situations. Numerical simulations, considering coupled rigid and deformable-body dynamics, have been carried out in this paper to arrive at conclusions. For primary side impact scenario DEA frontal attachment provides a performance improvement specifically for 0% gait; however, the performance improvement is non-uniform for other gait cycles. For primary rear impact scenarios, it is observed that DEA is not a useful alternative at all. For secondary collision scenarios (involving both side and rear impacts) DEA is not at all an effective means for performance improvement.

More than 2,000 pedestrians are involved in reported traffic crashes with vehicles in North Carolina every year. Of these, 10% to 20% of these pedestrians were killed or severely injured. Studies are needed to explore the reasons under such situation for improvement. Traditional methods for modeling crash injury severities use multinomial logit (MNL) models, mixed logit (ML) models, or ordered logit/probit models. However, traditional MNL and ML models treat injury severity levels as nonordered, ignoring the inherent hierarchical nature of crash injury severities, whereas the data used in ordered logit models should be strictly subjected to the proportional odds (PO) assumption. A partial proportional odds (PPO) logit model approach is applied here to address these concerns. The predictors in the PPO model can have different effects on different levels of the dependent variable who violates the PO assumption. This study uses police-reported pedestrian crash data collected from 2007 to 2014 in North Carolina. A variety of motorist, pedestrian, environmental, roadway characteristics are examined. Comparisons of different models are also made and results show that PPO outperforms others. Parameter estimates and associated marginal effects are calculated and used to interpret the model and evaluate the significance of each dependent variables, followed by recommendations in the Conclusion.

San Francisco, California, completed a robust, data-driven process to define and prioritize pedestrian safety improvement projects. The goal of this process was to identify the lowest cost and most effective strategy to meet a citywide goal to eliminate traffic fatalities. A team of planners, engineers, and epidemiologists examined longitudinal pedestrian collision data to identify the major factors that corresponded with pedestrian– vehicle crashes in San Francisco and focused on locations with the highest frequency and severity of pedestrian injuries. Collision profiles were developed to classify the most frequent types of vehicle–pedestrian collisions at each intersection. Corresponding pedestrian safety countermeasures, selected for their relatively high effectiveness and low cost, were matched with applicable collision profiles. A scenario planning process was used to formulate a final investment strategy: a prioritized pedestrian safety project list that targeted the specific needs at priority locations. The data-driven capital planning process helped to secure funding commitments to implement pedestrian safety projects at 195 intersections over the course of 5 years, which moved San Francisco closer to its policy objective to eliminate pedestrian fatalities. All cities face a challenge in the employment of rigorous analysis to select and prioritize improvement projects with limited funding. This study created a new, streamlined approach to capital-constrained project development and prioritization that is applicable to all types of safety improvements across any jurisdiction.

Building Machine-Learning Models for Reducing the Severity of Pedestrian Road-Traffic Injuries

<div>As pedestrians are among the most critical road users, this research analyzes their vulnerability characteristics and predicts the injury severity of pedestrian crashes through decision tree techniques, rather than using statistical regression models that have particular predefined causal relationships between dependent and independent variables. Five years have been studied in Nablus Governorate/Province (2012–2016), one of 16 governorates in Palestine, as a case study based on reported crash frequencies for developing countries. Tree techniques (CART [Classification and Regression Tree] and CHAID [Chi-Square Automatic Interaction Detector]) were applied to extract the main impacting factors on injury severity for pedestrian crashes. The main contributions considered a small regional context in developing countries and found differences between the results of various methods in injury severity. Fourteen independent variables have been analyzed. A CART model with Gini splitting has produced the most accurate model. The most important variables were the victim’s gender, followed by area classification as rural, and the age categories of pedestrians older than 65 and younger than 15 years. The least important variables were found to be the driver’s gender, land use, and pavement conditions. Results also showed that the proximity of crashes to schools is relatively high; therefore, some policies were suggested regarding children’s awareness, school zones, and driver behavior. It was found that the majority of factors influencing pedestrian crashes are related to human characteristics such as age, gender, or attitude whereas, in developed countries, they were related to vehicles and infrastructure. Based on the results of the study, tree techniques were considered effective in the analysis of injury severity of pedestrians in the context of developing countries to identify the main factors of vulnerability.</div>

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.