ABSTRACT
INTRODUCTION: Walking and cycling for transportation provide immense benefits (e.g., health, environmental, social). However, pedestrians and bicyclists are the most vulnerable segment of the traveling public due to the lack of protective structure and difference in body mass compared with motorized vehicles. Numerous studies are dedicated to enhancing active transportation modes, but very few studies are devoted to the safety analysis of the transit stops, which serve as the important modal interface for pedestrians and bicyclists. METHOD: This study bridges the gap by developing joint models based on the multivariate conditional autoregressive (MCAR) priors with distance-oriented neighboring weight matrix. For this purpose, transit-oriented design (TOD) related data in Los Angeles County were used for model development. Feature selection relying on both random forest (RF) and correlation analysis was employed, which leads to different covariates inputs to each of the two joint models, resulting in increased model flexibility. An integrated nested Laplace approximation (INLA) algorithm was adopted due to its fast, yet robust, analysis. For a comprehensive comparison of the predictive accuracy of models, different evaluation criteria were utilized. RESULTS: The results demonstrate that models with correlation effect perform much better than the models without a correlation of pedestrians and bicyclists. The joint models also aid in the identification of the significant covariates contributing to the safety of each of the two active transportation modes. The findings show that population density, employment density, and bus stop density positively influence bicyclist-involved crashes, suggesting that an increase in population, employment, or the number of bus stops leads to more active modes involved collisions. PRACTICAL APPLICATIONS: The findings of this study may prove helpful in the development and implementation of the safety management process to improve the roadway environment for the active modes in the long run.
Subject(s)
Bicycling , Walking , Humans , TravelABSTRACT
Temporal trait of crashes has huge impact on road crash occurrence and a large proportion of research have considered different time periods to determine the causes and features of crash occurrence or frequency. Compared with other safety studies based on a single time interval, considerably less research has relied on the use of multiple time units, especially for the time intervals of less than one year. The research aims to fill the gap by investigating the temporal distribution of crash counts using multiple time spans including hour, weekday and month. To illustrate the most accurate results possible, both the Chi-square test and Cochran-Mantel-Haenzel tests were employed to explore the independence of various time units based on two-way and three-way contingency tables. Eight contingency table models were developed which can be classified into four groups including Complete Independence, Joint Independence, Conditional Independence and Homogeneous Association. Finally, a set of evaluation criteria were utilized for evaluation of the model performance. The results revealed the significant association existence in all time variables (hour, weekday, month) and the model with both main and all interactive effects of time variables provides best prediction performance. Also, the findings showed that Hour 18, weekdays 1, 6, 7 (Friday and Weekends), and month 8 (August) have the largest number of crash occurrences. It is suggested that both main and interactive effects of time variables should be included for model development, which otherwise might yield misleading information. It is anticipated that research results will benefit the safety professionals with better understanding of the temporal patterns of crashes with different time periods and allow the safety administrators to allocate the safety resources.
