Analyzing urban crash incidents
An advanced endogenous approach using spatiotemporal weights matrix
- authored by
- Reza Mohammadi, Mohammad Taleai, Philipp Otto, Monika Sester
- Abstract
Contemporary spatial statistics studies often underestimate the complexity of road networks, thereby inhibiting the strategic development of effective interventions for car accidents. In response to this limitation, the primary objective of this study is to enhance the spatiotemporal analysis of urban crash data. We introduce an innovative spatial-temporal weight matrix (STWM) for this purpose. The STWM integrates external covariates, including road network topological measurements and economic variables, offering a more comprehensive view of the spatiotemporal dependence of road accidents. To evaluate the functionality of the presented STWM, random effect eigenvector spatial filtering analysis is employed on Boston's traffic accident data from January to March 2016. The STWM improves analysis, surpassing distance-based SWM with a lower residual standard error of 0.209 and a higher adjusted R2 of 0.417. Furthermore, the study emphasizes the influence of road length on crash incidents, spatially and temporally, with random standard errors of 0.002 for spatial effects and 0.026 for non-spatial effects. This is particularly evident in the north and center of the study area during specific periods. This information can help decision-makers develop more effective urban development models and reduce future crash risks.
- Organisation(s)
-
Institute of Cartography and Geoinformatics
- External Organisation(s)
-
K.N. Toosi University of Technology (KNTU)
University of New South Wales (UNSW)
- Type
- Article
- Journal
- Transactions in GIS
- Volume
- 28
- Pages
- 368-410
- No. of pages
- 43
- ISSN
- 1361-1682
- Publication date
- 10.04.2024
- Publication status
- Published
- Peer reviewed
- Yes
- ASJC Scopus subject areas
- General Earth and Planetary Sciences
- Sustainable Development Goals
- SDG 3 - Good Health and Well-being
- Electronic version(s)
-
https://doi.org/10.1111/tgis.13138 (Access:
Open)
