Investigating Pedestrian Safety Near Transit Bus Stops

This research examined how road and bus stop design and characteristics influence pedestrian safety, shaping both user perceptions and behaviors, and affecting crash outcomes near transit stops. A three-stage methodology integrated quantitative analysis with empirical field research. The first stage involved a macro-level quantitative analysis of factors contributing to pedestrian crashes, fusing together data on historical crash records, transit operations, demographic data, road inventories, and vehicle counts, and capturing over 300 crash-relevant features. Traditional and machine learning models were applied to classify bus stops and predict crash occurrence and severity. The second stage focused on human factors through a micro-level qualitative investigation, collecting public perceptions of safety through electronic surveys and comparing them with objective risk measures. The third stage consisted of systematic field observations documenting pedestrian behaviors and assessing how environmental and design characteristics influence these behaviors. By integrating objective crash modeling, subjective user perceptions, and observed behavior, the study developed a comprehensive framework for enhancing pedestrian safety. The findings inform actionable design guidelines and policy recommendations, supporting Vision Zero objectives and guiding transit agency standards, municipal complete streets policies, and national street design manuals.