Pedestrian Safety in Compact and Mixed-Use Urban Environments: Evaluation of 5D Measures on Pedestrian Crashes

This study examined the impact of density, diversity, design, distance to transit, and destination accessibility, five measures, known as the 5Ds, that characterize the built environment, on pedestrian–vehicle crashes in Seoul, Korea. Using spatial analysis based on 500-m grid cells, this study employed negative binomial regression models on the frequencies of three specific types of pedestrian–vehicle crashes: crashes causing death, major injury, and minor injury to pedestrians. Analysis shows that compact and mixed-use urban environments represented by 5D measures have mixed effects on pedestrian safety. Trade-off effects are found between a higher risk for all types of pedestrian crashes, and a lower risk for fatal pedestrian crashes in 5D urban environments. As a design variable, a higher number of intersections is more likely to increase some types of pedestrian crashes, including fatal crashes, a finding which warrants policy attention to promote pedestrian safety near intersection areas. This study also confirms an urgent need to secure the travel safety of pedestrians near public transit stations due to the higher risk of pedestrian crashes near such facilities. Various destinations, such as retail stores, traditional markets, and hospitals, are associated with pedestrian crashes. Pedestrian safety measures should be implemented to reduce the likelihood of pedestrian crashes near major destination facilities.

Semi and Nonparametric Conditional Probability Density, a Case Study of Pedestrian Crashes

Background: Kernel-based methods have gained popularity as employed model residual’s distribution might not be defined by any classical parametric distribution. Kernel-based method has been extended to estimate conditional densities instead of conditional distributions when data incorporate both discrete and continuous attributes. The method often has been based on smoothing parameters to use optimal values for various attributes. Thus, in case of an explanatory variable being independent of the dependent variable, that attribute would be dropped in the nonparametric method by assigning a large smoothing parameter, giving them uniform distributions so their variances to the model’s variance would be minimal. Objectives: The objective of this study was to identify factors to the severity of pedestrian crashes based on an unbiased method. Especially, this study was conducted to evaluate the applicability of kernel-based techniques of semi- and nonparametric methods on the crash dataset by means of confusion techniques. Methods: In this study, two non- and semi-parametric kernel-based methods were implemented to model the severity of pedestrian crashes. The estimation of the semi-parametric densities is based on the adoptive local smoothing and maximization of the quasi-likelihood function, which is similar somehow to the likelihood of the binary logit model. On the other hand, the nonparametric method is based on the selection of optimal smoothing parameters in estimation of the conditional probability density function to minimize mean integrated squared error (MISE). The performances of those models are evaluated by their prediction power. To have a benchmark for comparison, the standard logistic regression was also employed. Although those methods have been employed in other fields, this is one of the earliest studies that employed those techniques in the context of traffic safety. Results: The results highlighted that the nonparametric kernel-based method outperforms the semi-parametric (single-index model) and the standard logit model based on the confusion matrices. To have a vision about the bandwidth selection method for removal of the irrelevant attributes in nonparametric approach, we added some noisy predictors to the models and a comparison was made. Extensive discussion has been made in the content of this study regarding the methodological approach of the models. Conclusion: To summarize, alcohol and drug involvement, driving on non-level grade, and bad lighting conditions are some of the factors that increase the likelihood of pedestrian crash severity. This is one of the earliest studies that implemented the methods in the context of transportation problems. The nonparametric method is especially recommended to be used in the field of traffic safety when there are uncertainties regarding the importance of predictors as the technique would automatically drop unimportant predictors.

Safety Evaluation of Pedestrian Countdown Signals: Definitive Results from Two Cities in the United States

The pedestrian countdown signals (PCS) treatment involves the display of a numerical countdown that shows how many seconds are left in the flashing DON’T WALK interval. Although many studies have attempted to evaluate the safety of PCS, the results have been inconsistent for many reasons, including inadequate sample size and the inability to control for possible bias from regression to the mean and from exposure. This study performed a before-after empirical Bayes analysis using data from 115 treated intersections in Charlotte, North Carolina and 218 treated intersections in Philadelphia, Pennsylvania to evaluate the safety effects of PCS. The evaluation also included 136 reference intersections in Charlotte, and 597 reference intersections in Philadelphia. Following the implementation of PCS, total crashes decreased by approximately 8% and rear-end crashes decreased by approximately 12%, and these reductions were statistically significant at the 95% confidence level. Pedestrian crashes decreased by about 9% and this reduction was statistically significant at the 90% confidence level. Economic analysis revealed a benefit-cost ratio of 23 with a low of 13 and a high of 32.

Pedestrian Compliance at Signalized Intersections along Major Arterials

Background: Pedestrian non-compliance at signalized crossings is unsafe and considered one of the causes of pedestrian crashes. The speed limit on most major urban roads is 60 km/hr or less. However, the speed on some urban roads is higher in some countries. In this case, the situation is more unsafe and increases the possibility of fatal injuries or fatalities in the case of a crash. Therefore, it is expected that the pedestrians will be more cautious on these roads. Aim: This study aims to explore pedestrian compliance at signalized intersections on major arterials with 80 km/hr speeds in Qatar. Methods: Video data were collected for pedestrian movements at multiple intersections. Results: The study reported a 68.1 percent compliance rate at the study locations. The results also revealed that 14.6 percent of the pedestrians crossed during the Flashing Don’t Walk interval and 17.3 percent crossed during the Steady Don’t Walk interval. These rates are considered high compared to other countries. Several variables that may influence pedestrians’ behavior were investigated. Binary and ordinal logistic regression models were developed to describe the pedestrian crossing behavior as a function of these variables. Conclusion: Male and middle-age pedestrians were more likely to cross during these two intervals. The analysis showed that female pedestrians, elder pedestrians, pedestrians crossing in groups, pedestrians waiting before crossing, and pedestrians crossing against a flow of other pedestrians are more likely to comply and cross during the Walk interval compared to other groups. Several solutions were proposed in the study to increase compliance rates.

ESTIMATING PEDESTRIAN SUSCEPTIBILITY TO TRAFFIC ACCIDENTS IN CURITIBA, BRAZIL

Even though pedestrians represented 40% of all urban displacements in Brazil in 2017, they are still highly vulnerable to traffic accidents, with a mortality rate of 2.89 per 100 thousand inhabitants in 2018. The literature suggests a relationship between the occurrence of traffic accidents and demographic, socioeconomic, and urban structure variables. In this study, this relationship was investigated through a data-driven statistical model (logistic regression) combined with GIS spatial analysis, applied to estimate the pedestrian susceptibility to traffic accidents in the City of Curitiba, in Southern Brazil. By adopting broadly available spatial information, the proposed methods were robust in estimating the events, presenting an area under the ROC curve of 0.82 in the cross-validation. Additionally, the results highlighted a strong and statistically significant correlation between the pedestrian crashes and the analyzed variables of road system hierarchy, presence of BRT routes, land-use, population density and per capita income.

Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

Vehicle to vulnerable road user (VRU) crashes occupy a large proportion of traffic crashes in China, and crash injury severity analysis can support traffic managers to understand the implicit rules behind the crashes. Therefore, 554 VRUs-involved crashes are collected from January, 2017, to February, 2021, in a city in northern China, including 322 vehicle-pedestrian crashes and 232 vehicle-bicycle crashes. First, a descriptive statistical analysis is conducted to investigate the characteristics of VRUs-involved crashes. Second, the extreme gradient boosting (XGBoost) model is introduced to identify the importance of risk factors (i.e., time of day, day of week, rushing hour, crash position, weather, and crash involvements) of VRUs-involved crashes. The statistical analysis demonstrates that the risk factors are closely related to VRUs-involved crash injury severity. Moreover, the results of XGBoost reveal that time of day has the greatest impact on VRUs-involved crashes, and crash position shows the minimum importance among these risk factors.

Spatial Equity Analysis of Nighttime Pedestrian Safety: Role of Land Use and Alcohol Establishments in Albuquerque, NM

The United States experienced a 53% increase in pedestrian fatalities between 2009 and 2018, with 2018 having a 3.4% increase from 2017. Of the 2018 pedestrian fatalities with known lighting conditions, 76% occurred in dark/nighttime conditions, with 50% occurring between 6:00 and 11:59 p.m. Despite past research exploring several contributing characteristics for nighttime pedestrian crashes, there is limited research that investigates the spatial aspects of land use attributes and sociodemographic factors. Have these nighttime pedestrian collisions been concentrated in certain land uses? Could an establishment with the capacity to serve alcohol invoke a greater risk of pedestrian crashes? Does sociodemographic status correlate with clustering for fatal crashes, severe crashes, or both? To better understand the spatial characteristics of the recent increase in pedestrian collisions, we analyzed crash data from Albuquerque, New Mexico for pedestrian fatalities and severe injuries from 2013 to 2018 relative to lighting condition, land use (with a focus on alcohol establishments), and race/ethnicity on the block group level. We used confidence intervals and Getis-Ord Gi* statistics to verify the statistical integrity of the trends. Findings suggested that pedestrian fatality and severe injury rates were higher within a quarter mile of bars at night and in areas with elevated concentrations of minority populations. Pedestrian fatality and severe injury hot spots appeared to have higher percentages of non-white residents, coupled with lower sidewalk coverage and more arterials or collectors.

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

This paper presents the results of an analysis focusing on large truck-involved work zone fatal crashes using seven-year crash data in the State of Florida. Decision tree/random forest models were applied to specifically detect critical crash patterns that result in a fatality outcome. Because of the imbalanced nature of crash severity data (very low frequency of fatal crashes compared with property damage only or injury), data were treated using random and systematic over-sampling techniques. Marginal effects were addressed using Shapley values to increase model explainability. From a methodological perspective, results showed that the combination of over-sampling techniques with ensemble random forests could significantly improve model performance in predicting fatal crashes (compared with conventional logistic regression models). Primary contributors included pedestrian involvement, lighting conditions, safety equipment, driver condition, driver age, and work zone locations. For pedestrian crashes, factors such as dark-not lighted conditions, distracted truck driver, and driver’s age (young drivers outside city limits, senior drivers inside city limits) were highly likely to be fatal. For non-pedestrian crashes, the combination of front airbag deployment with any restraint system other than shoulder and belt was quite likely to be fatal. Also, abnormal driver conditions increased the risk of a fatal outcome. Additionally, the presence of female drivers (as the second driver in multiple vehicle crashes) highly decreased crash severity, probably because females typically drive more carefully than males. Interestingly, truck driver actions and maneuvers as well as roadway design and other physical environment features (i.e., number of lanes, median type, roadway grade, and alignment) did not show significant contribution to the model.