Predicting Segment-Intersection Crashes with Land Development Data
(2009) In Transportation Research Record p.9-17- Abstract
- Experience with crash prediction modeling has confirmed the importance of traffic volume, not only as exposure but also as a predictive variable. For intersection-related collisions, for example, angle collisions or any collisions involving turning vehicles, traffic volumes on both intersecting roads are necessary for sufficient prediction of crash count These collisions occur not only at intersections but any place where vehicles turn on or off the roadway, such as driveways. Intersecting traffic volumes at such locations are either not available or labor intensive to acquire. The objective of this study was to investigate the use of geographic information system (GIS) land use inventories to supplement observed traffic volumes as... (More)
- Experience with crash prediction modeling has confirmed the importance of traffic volume, not only as exposure but also as a predictive variable. For intersection-related collisions, for example, angle collisions or any collisions involving turning vehicles, traffic volumes on both intersecting roads are necessary for sufficient prediction of crash count These collisions occur not only at intersections but any place where vehicles turn on or off the roadway, such as driveways. Intersecting traffic volumes at such locations are either not available or labor intensive to acquire. The objective of this study was to investigate the use of geographic information system (GIS) land use inventories to supplement observed traffic volumes as exposure measures for estimating models for predicting segment-intersection crashes, defined as collisions occurring on road segments involving one or more turning or crossing vehicles. Model results for rural two-lane and urban two- and four-lane undivided roads indicate that the number of trips generated and the extent of surrounding land development itself act as excellent predictors for segment-intersection crashes and in fact work better than models using the number of access points. The reason is that those variables better describe the intensity of the traffic accessing the major artery. This is a valuable finding, since access points along a road segment cannot be counted automatically, but many jurisdictions have GIS land use inventories available for all sorts of planning purposes. Such a development will permit better accounting of exposure to segment-intersection crashes in crash prediction modeling. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1517904
- author
- Bindra, Sumit ; Ivan, John N. and Jonsson, Thomas LU
- organization
- publishing date
- 2009
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Transportation Research Record
- issue
- 2102
- pages
- 9 - 17
- publisher
- Transportation Research Board, Washington DC, USA
- external identifiers
-
- wos:000272002300002
- scopus:76249124019
- ISSN
- 0361-1981
- DOI
- 10.3141/2102-02
- language
- English
- LU publication?
- yes
- id
- 9c6e3b53-ed74-4cbd-afe8-8d767014ebae (old id 1517904)
- date added to LUP
- 2016-04-01 13:45:49
- date last changed
- 2022-01-27 20:54:03
@article{9c6e3b53-ed74-4cbd-afe8-8d767014ebae,
abstract = {{Experience with crash prediction modeling has confirmed the importance of traffic volume, not only as exposure but also as a predictive variable. For intersection-related collisions, for example, angle collisions or any collisions involving turning vehicles, traffic volumes on both intersecting roads are necessary for sufficient prediction of crash count These collisions occur not only at intersections but any place where vehicles turn on or off the roadway, such as driveways. Intersecting traffic volumes at such locations are either not available or labor intensive to acquire. The objective of this study was to investigate the use of geographic information system (GIS) land use inventories to supplement observed traffic volumes as exposure measures for estimating models for predicting segment-intersection crashes, defined as collisions occurring on road segments involving one or more turning or crossing vehicles. Model results for rural two-lane and urban two- and four-lane undivided roads indicate that the number of trips generated and the extent of surrounding land development itself act as excellent predictors for segment-intersection crashes and in fact work better than models using the number of access points. The reason is that those variables better describe the intensity of the traffic accessing the major artery. This is a valuable finding, since access points along a road segment cannot be counted automatically, but many jurisdictions have GIS land use inventories available for all sorts of planning purposes. Such a development will permit better accounting of exposure to segment-intersection crashes in crash prediction modeling.}},
author = {{Bindra, Sumit and Ivan, John N. and Jonsson, Thomas}},
issn = {{0361-1981}},
language = {{eng}},
number = {{2102}},
pages = {{9--17}},
publisher = {{Transportation Research Board, Washington DC, USA}},
series = {{Transportation Research Record}},
title = {{Predicting Segment-Intersection Crashes with Land Development Data}},
url = {{http://dx.doi.org/10.3141/2102-02}},
doi = {{10.3141/2102-02}},
year = {{2009}},
}