摘要: |
During the last decade, non-motorized modes of transportation, including cycling and walking, have been spreading as they are considered economical, eco-friendly, and energy-efficient. With the expansion of active transportation, statistics show a significant increase in the number of fatalities. Between 2010 and 2019, there was a 36 percent increase in bicycle deaths in the United States. Moreover, despite the 41 percent drop in traffic volume in response to spring lockdowns caused by the Covid-19 pandemic, 697 bicyclists lost their lives in crashes in 2020, and California with 118 fatalities was the deadliest state for bicyclists. In this project, we propose to develop a crash-risk scoring method for prioritizing Bicycle safety improvement projects in the county of San Diego. Caltrans has a great interest in this topic as many bicycle bridges need to be improved to meet new standards. The prioritization methodology will have a widespread applicability and can be adopted by Caltrans for similar projects. The majority of the studies on pedestrian and bicycle safety suffered from the limitation of the exposure data as they relied on traditional data collection methods. In this project, we will use a combination of traditional data (e.g., historical crash data, environment characteristics) as well as crowdsourced (e.g., STRAVA, StreetLight data) and image-based data in order to develop a robust model to identify high-risk locations. Since crowdsourced data could be biased towards males and more athletic people, we will investigate and utilize calibration methods to address this issue. Moreover, previous studies show a disproportionate bicycle crash distribution among people with different socio-demographic characteristics. We will investigate transportation equity factors to include in the proposed risk scoring method to account for social justice across all communities. |