Developing Bicycle-Vehicle Crash-Specific Safety Performance Functions in Alabama Using Different Techniques
| Autor: | Michael Anderson, Niloufar Shirani-bidabadi, Kirolos Haleem, Naveen Mallipaddi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Rural Population
Computer science Poison control Human Factors and Ergonomics Crash Downgrade Transport engineering Artificial Intelligence 0502 economics and business Humans 0501 psychology and cognitive sciences Built Environment Safety Risk Reliability and Quality Highway Safety Manual Annual average daily traffic 050107 human factors 050210 logistics & transportation Median Multivariate adaptive regression splines Models Statistical 05 social sciences Public Health Environmental and Occupational Health Accidents Traffic Mars Exploration Program Bicycling Alabama |
| Zdroj: | Accident; analysis and prevention. 146 |
| ISSN: | 1879-2057 |
| Popis: | This study develops bicycle-vehicle safety performance functions (SPFs) for five facilities in the Highway Safety Manual (HSM). These are urban two-lane undivided segments (U2U), urban four-lane divided/undivided segments (U4DU), rural two-lane undivided segments (R2U), urban four-leg and three-leg signalized intersections (USG), and urban four-leg and three-leg stop-controlled intersections (UST). Two modeling techniques were explored, the Conway-Maxwell-Poisson (COM-Poisson) model (to accommodate bicycle-vehicle crash under-dispersion) and a machine learning technique, the multivariate adaptive regression splines (MARS). MARS is a non-black-box model and can effectively handle non-linear crash predictors and interactions. A total of 1,311 bicycle-vehicle crashes from 2011 through 2015 in Alabama were collected and their respective police reports were reviewed in details. Results from the SPFs for roadway segments using COM-Poisson showed that bicycle-vehicle crash frequencies were reduced along curved and downgrade/upgrade stretches and when having heavy traffic flow (along U2U segments). For urban signalized (USG) intersections, the absence of right-turn lanes on minor roads, the presence of bus stops, and the increase in the major road annual average daily traffic (AADT) were significant factors contributing to the increase in the number of bicycle-vehicle crashes. However, the presence of divided medians on major approaches was found to reduce bicycle-vehicle crashes at USG and UST intersections. MARS outperformed the corresponding COM-Poisson models for all five facilities based on mean absolute deviance (MAD), mean square prediction error (MSPE), and generalized R-square. MARS is recommended as a promising technique for effectively predicting bicycle-vehicle crashes on segments and intersections. |
| Databáze: | OpenAIRE |
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