Development of Hazard Map Using GIS to Reduce Traffic Accidents

Koike, Hideki; Morimoto, Akinori; Hanzawa, Yusaku; Shiraishi, Norifusa

doi:10.1061/40513(279)28

Cited by 3 publications

(3 citation statements)

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“…Although accurate crash locations can be particularly useful for crash analyses, the majority of existing studies proceed to the analysis of crash location data without reporting any sort of prior processing (6). The developed crash-mapping approaches are closely related to the type of their locational input data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…(6)). The developed crash mapping approaches are closely related to the type of their locational input data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…[1][2][3][4][5]. The spatial nature of crashes makes crash locations as one of the most important and informative attributes of crash databases (5)(6)(7) that at the same time are very likely to be inaccurately reported (1)(2)(3)(4)7). Therefore, the refinement of the crash locations gives to the crash analyses the potential to improve in quality.…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas

Imprialou

Quddus

Pitfield

2015

Transportation Research Record

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The spatial nature of traffic crashes makes crash locations one of the most important and informative attributes of crash databases. It is however very likely that recorded crash locations in terms of easting and northing coordinates, distances from junctions, addresses, road names and types are inaccurately reported. Improving the quality of crash locations therefore has the potential to enhance the accuracy of many spatial crash analyses. The determination of correct crash locations usually requires a combination of crash and network attributes with suitable crash mapping methods. Urban road networks are more sensitive to erroneous matches due to high road density and inherent complexity. This paper presents a novel crash mapping method suitable for urban and metropolitan areas that matched all the crashes that occurred in London from 2010-2012. The method is based on a hierarchical data structure of crashes (i.e. candidate road links are nested within vehicles and vehicles nested within crashes) and employs a multilevel logistic regression model to estimate the probability distribution of mapping a crash onto a set of candidate road links. The road link with the highest probability is considered to be the correct segment for mapping the crash. This is based on the two primary variables: (a) the distance between the crash location and a candidate segment and (b) the difference between the vehicle direction just before the collision and the link direction. Despite the fact that road names were not considered due to limited availability of this variable in the applied crash database, the developed method provides a 97.1% (±1%) accurate matches (N=1,000). The method was compared with two simpler, non-probabilistic crash mapping algorithms and the results were used to demonstrate the effect of crash location data quality on a crash risk analysis.Keywords: crash location, crash mapping, metropolitan/urban networks, multilevel logistic regression Imprialou M.-I.M., Quddus M., Pitfield D.A. 3 INTRODUCTIONSustainable road safety programmes require constant enhancements to crash prevention policies and countermeasures. The precautionary measures should focus on the network areas where many crashes occur and the driving attitudes that are considered mostly responsible for crashes. Analysis of crash data aims to identify and explain the factors that lead to traffic crashes. The quality and reliability of the crash data that are used as an input for spatial crash analyses (e.g. identification of black spots, spatial crash modelling etc.) is closely related to the validity of their outcomes (e.g. [1][2][3][4][5]. The spatial nature of crashes makes crash locations as one of the most important and informative attributes of crash databases (5-7) that at the same time are very likely to be inaccurately reported (1-4, 7). Therefore, the refinement of the crash locations gives to the crash analyses the potential to improve in quality.Traffic crashes create major problems to society as they are related to personal injuri...

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Section: Literature Reviewmentioning

confidence: 99%

“…(6)). The developed crash mapping approaches are closely related to the type of their locational input data.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas

Imprialou

Quddus

Pitfield

2015

Transportation Research Record

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Factor analysis on relationship between a hazard perception and traffic accidents

Asari¹,

Koike²,

Morimoto³

2003

INFRASTRUCTURE PLANNING REVIEW

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Recently, it seems that the countermeasures to provide safety equipment and infrastructure is not effective enough to reduce traffic accidents. Therefore, it is necessary to introduce a new approach to cope with the increasing traffic accident. The objective of this study is to examine the relationship between locations where accidents occurred and the factor that people feels dangerous. A hazardous factor was analyzed by using the Conjoint analysis. From these results, it became apparent that the hazardous perception differs by the user according to the character in the district.

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Location of Speed Control Cameras on Highways: A Geospatial Analysis

Coronell¹,

Arellana²,

Cantillo³

2021

Transport

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This research proposes a methodology to identify critical sections of highways where the location of speeding control may be beneficial. The method relies on a spatial and statistical analysis of infrastructure risks, along with traffic accident frequency and severity. A relevant feature of this methodology is related to its potential to be used in areas where there are no detailed historical records about traffic crashes, which is common in Global South countries. We applied the methodology to a rural road network in Colombia, where a recent law established that technical criteria should support the location of speed cameras. The case study uses accident information from six years, and risk data from a road safety audit carried out in the area under study. Even though historical records of accidents in the area were not fully available, the methodology allowed prioritising speed camera installations in the zone and identifying the relevant variables to define camera location. The relevant variables were the geometric characteristics of the road, traffic flows, risk factors, and proximity to populated centres. The use of speed controls should be part of a road safety management system, which allows defining camera location according to robust technical criteria.

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Development of Hazard Map Using GIS to Reduce Traffic Accidents

Cited by 3 publications

References 0 publications

Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas

Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas

Factor analysis on relationship between a hazard perception and traffic accidents

Location of Speed Control Cameras on Highways: A Geospatial Analysis

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