Pedestrian crash analysis with latent class clustering method

Sun, Ming; Sun, Xiaoduan; Shan, Donghui

doi:10.1016/j.aap.2018.12.016

Cited by 125 publications

(54 citation statements)

References 27 publications

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“…The ulterior supposition of local independence must be verified and hence, the regular expression of LCMs is re-written: Finally, regarding the exposure traffic data, the exploratory analysis supports that 55% of the accidents took place on crosstown roads that have an AADT of more than 10,000 vehicles per day, followed by another 22% of them which have a value of less than 2500 vehicles per day. These results point towards the fact that more accidents occur on crosstown roads with higher traffic, a result which is inconsistent with that obtained in other studies [10]. However, in this case, the exploratory analysis only indicates that there are a greater number of crosstown roads with high volume traffic, regardless of the severity of the accident or the number of accidents, which will be analyzed later.…”

Section: Cluster Analysiscontrasting

confidence: 77%

“…The town population is far more exposed to road crashes and there are competing speed requirements: urban mobility needs low speeds and measures to encourage pedestrians' flows, while car inter-urban traffic demands higher speeds and continuous flows (without the interruption of traffic lights or Statistically, the database on crosstown road accidents shows a greater number of severe injury accidents involving pedestrians. Pedestrians are the most unprotected road user group (because of their limited tolerance to a vehicle collision and absence of protection) and for this reason, in case of urban roads, the scientific research has always focused on the identification of causes concerning pedestrian accident severity applying a modeling methodology that implies an initial segmentation of the sample of accidents [9,10]. Previous work on the Spanish crosstown roads, using official databases, have demonstrated [11] that there are clear risk factors related with pedestrian injury severity like offences committed by the pedestrian (such as not using pedestrian crossings, crossing illegally, etc.)…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

2020

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Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.

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Section: Cluster Analysiscontrasting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

2020

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“…Osman et al proposed a bi-level hierarchical classification methodology to identify different types of secondary tasks that drivers are engaged in using their driving behavior parameters [22]. Sun et al utilized the Latent Class Cluster (LCC) model as a preliminary tool to identify the major factors that contribute to the crashes [23]. Ding et al adopted a machine learning approach of Multiple Additive Poisson Regression Trees (MAPRT) to sort the relative importance of attributes in explaining pedestrian crashes [24].…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Wang

2019

PLoS ONE

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Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles.

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“…In order to identify seven clusters and analyse the severity of different types of traffic accidents, Depaire et al (2008) [22] applied this technique in combination with multinomial logit (MNL) models and demonstrated the importance of segmenting the data in the road safety analysis. In addition, in order to analyse the main factors in pedestrian crash severity, Sun et al (2019) [27] applied a latent class cluster model as a preliminary tool for segmenting 14,236 pedestrian crashes in Louisiana. Their results demonstrated the importance of the application of these clustering techniques, which help in identifying hidden relationships in traffic safety analyses.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It has been observed that meaningful relations can be concealed while analyzing traffic accidents in a large set of heterogeneous data. Many studies have demonstrated that segmenting the data into homogeneous groups helps in reducing heterogeneity and provides further information on traffic safety analysis [27,28]. On the other hand, some of the variables that have not been identified as meaningful in the entire database analysis are considered as determinative for a cluster.…”

Section: Injury Severity Analysis Using Mnlmentioning

confidence: 99%

Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

et al. 2019

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According to the Spanish General Traffic Accident Directorate, in 2017 a total of 351 pedestrians were killed, and 14,322 pedestrians were injured in motor vehicle crashes in Spain. However, very few studies have been conducted in order to analyse the main factors that contribute to pedestrian injury severity. This study analyses the accidents that involve a single vehicle and a single pedestrian on Spanish crosstown roads from 2006 to 2016 (1535 crashes). The factors that explain these accidents include infractions committed by the pedestrian and the driver, crash profiles, and infrastructure characteristics. As a preliminary tool for the segmentation of 1535 pedestrian crashes, a k-means cluster analysis was applied. In addition, multinomial logit (MNL) models were used for analysing crash data, where possible outcomes were fatalities and severe and minor injured pedestrians. According to the results of these models, the risk factors associated with pedestrian injury severity are as follows: visibility restricted by weather conditions or glare, infractions committed by the pedestrian (such as not using crossings, crossing unlawfully, or walking on the road), infractions committed by the driver (such as distracted driving and not respecting a light or a crossing), and finally, speed infractions committed by drivers (such as inadequate speed). This study proposes the specific safety countermeasures that in turn will improve overall road safety in this particular type of road.

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Pedestrian crash analysis with latent class clustering method

Cited by 125 publications

References 27 publications

Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective

Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches

Investigating the Risk Factors Associated with the Severity of the Pedestrians Injured on Spanish Crosstown Roads

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