Search

Crashes involving vulnerable roadway users (VRUs), such as pedestrians and cyclists, have increased in recent years. Understanding relationships between these crashes and explanatory factors is critical to reversing this trend. However, most statistical models of VRU-related crashes rely primarily on vehicular exposure alone. VRU exposure is difficult to capture because of a lack of available data. To address this concern, this study incorporates several exposure metrics that capture nonmotorized and public transportation use at the census-tract level. A macroscopic-level crash-prediction model for the Manhattan area of New York City is developed that considers roadway and demographic variables, as well as bike-share trip information, subway flows, taxi movements, and person trips to various points of interest (POIs) as measures of travel exposure. The models are developed using geographically weighted negative binomial regression and various functional forms are considered for three different types of crash frequency. The results show that the number of shared-bike trips and POI visits are positively correlated with increases in pedestrian and cyclist crash frequencies; however, these features are less descriptive of motorist crash frequency. In addition, the explanatory power of POI information can be improved by considering only a subset of POI categories that represent “essential” trips. The spatial variation between motorist-related crash frequency and the exposure metric is observed to be more significant than the pedestrian- and cyclist-related crash frequency are to their exposure, and the shared-bike trips’ influence on the pedestrian- and cyclist-related crash frequency appears to be homogeneous across the Manhattan area.

Traditional pavement maintenance, rehabilitation, and reconstruction (MRR) planning accounts for user cost by maximizing drivers’ comfort, reducing users’ travel delay, and minimizing fuel consumption, while controlling the agency’s costs of undertaking the necessary preservation activities. Safety performance is seldom considered in the MRR planning of pavements. However, the pavement condition has a large impact on the crash frequency and needs to be addressed in the process of pavement MRR planning. Therefore, this study first incorporates safety costs into the MRR planning process as a separate objective. To do so, the MRR planning process seeks to find an optimal maintenance strategy with the lowest agency cost while minimizing crash frequency and user cost based on the resulting pavement condition. A multi-objective optimization approach is proposed to identify the optimal MRR plans that utilize a semi-Markov international roughness index deterioration model developed based on real pavement condition inspection data from Pennsylvania between 2006 and 2018. The results suggest that the agency costs will increase by $9,716 (5.3%) per mile per lane during a 50-year analysis window for pavement starting in good condition when the crash frequency is considered in the MRR planning. This increased agency cost contributes to a reduction in predicted crash frequency by 0.27%. The results can be used to determine the amount agencies need to spend on MRR activities to reduce accidents under different traffic flow and vehicle fleet combinations. The conclusion of this study provided evidence that lower crash frequency can be achieved by developing better MRR planning from the roadway operation perspective.