| Description |
The interest in multimodal transportation improvements is increasing in cities across the U.S. Investing in multimodal infrastructure benefits the portion of urban population that is unable to drive due to a variety of reasons such as personal preference, age, and affordability. It is also well known that active transportation such as walking, biking, and taking transit, can improve public health due to increased physical activity, and reduce traffic congestion by reducing the average person's delay. While improved multimodal infrastructure and accessibility attracts new users, it can possibly increase their exposure to risk from crashes. In urban areas where the "safety in numbers phenomenon" does not exist, nonmotorized user vulnerability becomes a predominant risk factor when they are involved in a crash, even at lower vehicle speeds. This dissertation aims to explore the factors that are associated with safety outcomes in urban multimodal transportation systems, and develop methods that can be used to estimate safety effects of multimodal infrastructure and accessibility improvements. Using Chicago as a case study, a comprehensive dataset is developed that significantly contributes to the existing literature by including socio-economic, land use, road network, travel demand, and crash data. Area-wide analysis on the census tract level provides a broader perspective about safety issues that multimodal users encounter in cities. The characteristics of a multimodal transportation system are expressed through the presence of multimodal infrastructure, street connectivity and network completeness, and accessibility to destinations for multimodal users. A set of statistical areal safety models (SASM) based on both frequentist and Bayesian statistical inference is applied to estimate the factors that are associated with total and severe vehicular, pedestrian, and bicyclist crashes in urban multimodal transportation systems. The results show that the current safety evaluation methods need to acknowledge the complexity of multimodal transportation systems through the inclusion of diverse factors that may influence safety outcomes, particularly for more vulnerable users. The methods developed in this research can further be used to expand the current practice of evaluating multimodal transportation safety, and planning for city-wide investments in multimodal infrastructure and improved accessibility, while being able to estimate the expected safety outcomes. |
