High accuracy crash mapping using fuzzy logic

Imprialou, Maria-Ioanna M.; Quddus, Mohammed; Pitfield, David

doi:10.1016/j.trc.2014.03.002

Cited by 28 publications

(15 citation statements)

References 16 publications

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“…More recent algorithms consider additional attributes from crash reports such as vehicle direction just before the crash and employ advanced statistical or Artificial Intelligence (AI) concepts. For example, Artificial Neural Networks (Deka and Quddus, 2014) and Fuzzy Logic (Imprialou et al, 2014) have been applied to correct freeway crash locations achieving matching accuracy of 98.4% and 98.9% respectively (for a detailed overview of crash mapping algorithms the reader is referred to Imprialou et al 2015).…”

Section: Crash Location and Timementioning

confidence: 99%

Crash data quality for road safety research: Current state and future directions

Imprialou

Quddus

2019

Accident Analysis & Prevention

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141

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Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five "W" questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users' demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems.

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Section: Crash Location and Timementioning

confidence: 99%

Crash data quality for road safety research: Current state and future directions

Imprialou

Quddus

2019

Accident Analysis & Prevention

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141

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“…A similar approach includes the use of restrictive, pre-defined buffer zones along with some descriptive variables such as road name, class, speed limit and junction details (1). Although these approaches have the benefit of a simple theoretic background and are easier to implement, are shown to produce significantly less accurate results than methods that use the vehicle directions (7).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Deka & Quddus (4) developed an artificial neural network for matching crashes within the entire primary road network of the UK that considered the distance, vehicle direction, and the reported road name and type (accuracy level: 98.4%). Imprialou et al (7) employed an empirically set fuzzy-logic inference system based on distance and direction combined with road name and type filters (accuracy level: 98.9%). One of the main shortcomings of these three methods is the expression of the direction of a crash by a single measure (i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thus, each crash is attempted to be matched with road links that fall into the area that is defined by an error circle with its centre at the reported crash location and a predefined radius (henceforth: candidate links). This method, that is extensively discussed in Imprialou et al (7), is an efficient way to narrow down the number of the examined road links without compromising the accuracy of the results.…”

Section: A) Candidate Road Links Identificationmentioning

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%

See 2 more Smart Citations

Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas

Imprialou

Quddus

Pitfield

2015

Transportation Research Record: Journal of the Transportation R

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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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Research on Vector Road Aided Inertial Navigation by Using ICCP Algorithm

Liu

2021

Spatial Data and Intelligence

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High accuracy crash mapping using fuzzy logic

Cited by 28 publications

References 16 publications

Crash data quality for road safety research: Current state and future directions

Crash data quality for road safety research: Current state and future directions

Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas

Research on Vector Road Aided Inertial Navigation by Using ICCP Algorithm

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