INVESTIGATION OF ROADWAY GEOMETRIC AND TRAFFIC FLOW FACTORS FOR VEHICLE CRASHES USING SPATIOTEMPORAL INTERACTION
Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of various severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management processes are commonly implemented...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-09-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W7/1163/2017/isprs-archives-XLII-2-W7-1163-2017.pdf |
| Summary: | Traffic safety is a major concern in the transportation industry due to immense monetary and emotional burden caused by crashes of
various severity levels, especially the injury and fatality ones. To reduce such crashes on all public roads, the safety management
processes are commonly implemented which include network screening, problem diagnosis, countermeasure identification, and
project prioritization. The selection of countermeasures for potential mitigation of crashes is governed by the influential factors
which impact roadway crashes. Crash prediction model is the tool widely adopted by safety practitioners or researchers to link
various influential factors to crash occurrences. Many different approaches have been used in the past studies to develop better
fitting models which also exhibit prediction accuracy. In this study, a crash prediction model is developed to investigate the
vehicular crashes occurring at roadway segments. The spatial and temporal nature of crash data is exploited to form a spatiotemporal
model which accounts for the different types of heterogeneities among crash data and geometric or traffic flow variables. This study
utilizes the Poisson lognormal model with random effects, which can accommodate the yearly variations in explanatory variables
and the spatial correlations among segments. The dependency of different factors linked with roadway geometric, traffic flow, and
road surface type on vehicular crashes occurring at segments was established as the width of lanes, posted speed limit, nature of
pavement, and AADT were found to be correlated with vehicle crashes. |
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| ISSN: | 1682-1750 2194-9034 |