Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency
Bibliographic Data
| ID | 15514767 |
|---|---|
| Authors | Sai Chand (0000-0001-8084-1413, UNSW Sydney, corresponding author), Zhuolin Li (0000-0002-0535-0945, UNSW Sydney), Abdulmajeed Alsultan (0000-0002-8696-4534, Prince Sattam Bin Abdulaziz University, corresponding author), Vinayak Dixit (0000-0001-7643-409X, UNSW Sydney), Vinayak V Dixit (UNSW Sydney) |
| Year | 2022 |
| Volume | 19 |
| Issue | 9 |
| Pages | 5726-5726 |
| Publication date | 2022-05-08 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph19095726 |
| PMID | 35565121 |
| OpenAlex | W4229336823 |
| Language | EN |
| Citations received | 2 |
| References cited | 60 |
Road traffic crashes cause social, economic, physical and emotional losses. They also reduce operating speed and road capacity and increase delays, unreliability, and productivity losses. Previous crash duration research has concentrated on individual crashes, with the contributing elements extracted directly from the incident description and records. As a result, the explanatory variables were more regional, and the effects of broader macro-level factors were not investigated. This is in contrast to crash frequency studies, which normally collect explanatory factors at a macro-level. This study explores the impact of various factors and the consistency of their effects on vehicle crash duration and frequency at a macro-level. Along with the demographic, vehicle utilisation, environmental, and responder variables, street network features such as connectedness, density, and hierarchy were added as covariates. The dataset contains over 95,000 vehicle crash records over 4.5 years in Greater Sydney, Australia. Following a dimension reduction of independent variables, a hazard-based model was estimated for crash duration, and a Negative Binomial model was estimated for frequency. Unobserved heterogeneity was accounted for by latent class models for both duration and frequency. Income, driver experience and exposure are considered to have both positive and negative impacts on duration. Crash duration is shorter in regions with a dense road network, but crash frequency is higher. Highly connected networks, on the other hand, are associated with longer length but lower frequency
Covariate · Crash · Duration (music · Econometrics · Environmental health · Negative binomial distribution · Poison control · Statistics · Computer Science · Environmental Science · Injury Epidemiology and Prevention · Mathematics · Medicine · Traffic and Road Safety · Urban Transport and Accessibility
Deciding on the Number of Classes in Latent Class Analysis and Growth Mixture Modeling
Regression Models and Life-Tables
Relationship Between Traffic Volume and Accident Frequency at Intersections
A Descriptive Analysis on the Impact of Covid-19 Lockdowns on Road Traffic Incidents in Sydney, Australia
The effects of area-wide road speed and curvature on traffic casualties in England
Urban spatial order
| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2024 (3) |
| Highly cited | No |