Traffic accident segmentation by means of latent class clustering

Depaire, Benoît; Wets, Geert; Vanhoof, Koen

doi:10.1016/j.aap.2008.01.007

Cited by 212 publications

(139 citation statements)

References 28 publications

Supporting

Mentioning

133

Contrasting

Unclassified

Order By: Relevance

“…LC has the advantage over traditional partitioning clustering methods such as k-means that it does not depend on a distance between the elements: there is no need to normalize or standardize the data before processing. Consequently, variables of different types (ordinal, count, nominal, continuous) can be included in the analysis without special processing (10).…”

Section: Clustering Analysismentioning

confidence: 99%

“…In the field of safety analysis, some researchers trained a decision tree to analyze the injury severity (7), (8) and reported satisfying results in prediction and classification. Other researchers analyzed the accidents by clustering using k-means (8), (9) and LC (10). Finally, some researchers have recommended combining data mining and statistical techniques.…”

Section: Introductionmentioning

confidence: 99%

“…They suggested that CART and MARS can be used as a precursor to a more detailed logistic regression analysis. Depaire et al (10) used LC as a preliminary analysis to expose the hidden relationships and then applied the multinomial logit model to injury analysis. They found that this methodology is more powerful compared to applying only a multinomial logit model on the whole dataset.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada

et al. 2013

View full text Add to dashboard Cite

Understanding the underlying relationship between pedestrian injury severity outcomes and factors leading to more severe injuries is very important in dealing with the problem of pedestrian safety. To investigate injury severity outcomes, many previous works relied on statistical regression models. There has also been some interest for data mining techniques, in particular for clustering techniques which segment the data into more homogeneous subsets. This research combines these two approaches (data mining and statistical regression methods) to identify the main contributing factors associated with the levels of pedestrian injury severity outcomes. This work relies on the analysis of two unique pedestrian injury severity datasets from the City of New York, US (2002US ( -2006 and the City of Montreal, Canada (2003-2006. General injury severity models were estimated for the whole datasets and for subpopulations obtained through clustering analysis. This paper shows how the segmentation of the accident datasets help to better understand the complex relationship between the injury severity outcomes and the contributing geometric, built environment and socio-demographic factors. While using the same methodology for the two datasets, different techniques were tested. For instance, for New York, latent class with ordered probit method provides the best results. However, for Montreal, the K-means with multinomial logit model is identified as the most appropriate technique. The results show the power of using clustering with regression to provide a complementary and more detailed analysis. Among other results, it was found that pedestrian age, location at intersection, actions prior to accident, driver age, vehicle type, vehicle movement, driver alcohol involvement and lighting conditions have an influence on the likelihood of a fatal crash. Moreover, several features within the built environment are shown to have an effect. Finally, the research provides recommendations for policy makers, traffic engineers, and law enforcement to reduce the severity of pedestrian-vehicle collisions. KEYWORDS:Pedestrian safety, regression, latent class, clustering, severity, built environmental, land use variables Mohamed, Saunier, Miranda-Moreno, Ukkusuri 3 INTRODUCTIONRoad user safety is a primary concern, not only for traffic safety specialists and traffic engineers, but for educators and law enforcement as well. Most importantly, pedestrian safety is a vital traffic issue as all road users are pedestrians at one point or another. Since pedestrians are vulnerable road users and suffer more in road crashes, it is important to understand the factors affecting pedestrian injury severity levels. In this way, traffic engineers, planners, decision makers and law enforcement will be able to precisely target these factors through various counter-measures, such as improvements to motorized vehicles, roadway and pedestrian facility design, control strategies at conflict locations, and driver and pedestrian education programs.This pape...

show abstract

Section: Clustering Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Based on the goals of the research, 2 major types of data mining can be employed: predictive and descriptive techniques [12]. For traffic accident detection, the descriptive data mining technique of cluster analysis is used to divide heterogeneous data into several homogeneous classes or clusters [13]. The aim of this work is to observe the effectiveness of cluster analysis as a technique for detecting traffic accidents and to prevent subsequent traffic accidents.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, several data mining techniques have found their way into traffic safety research, such as rule induction [15], frequent item sets [16], artificial neural networks [17], and classification and regression trees [13,18].…”

Section: Introductionmentioning

confidence: 99%

Comparison of clustering techniques for traffic accident detection

Dogru¹,

Subaşı²

2015

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Transportation infrastructure in intelligent transportation systems (ITSs) is complemented with information and communication technologies to achieve better passenger safety and reduced transportation time, fuel consumption, and vehicle wear and tear. This paper shows how data mining techniques are used in ITSs for accident detection and prevention on motorways. In traffic, vehicles show similar behavior to that of vehicles in closed neighborhoods. Vehicles that show different behaviors than neighbor vehicles in cases like accidents, inappropriate lane changes, and speeding can be considered as anomalies and detected. In this paper, a traffic accident is simulated and the effectiveness of different clustering techniques is examined for detecting traffic accident. We show that if velocity and position values of each vehicle are given, vehicles' behavior can be analyzed and accidents can be detected easily. The success of the proposed algorithms is demonstrated in a highway scenario by means of simulations. Simulations showed that data mining tools successfully detected accidents with an average accident detection rate of 100% and a false alarm rate of 0% using DBSCAN and hierarchical clustering.

show abstract

Finding causation in occupational fatalities: A latent class analysis

Farina

Bianco

Bena

et al. 2018

American J Industrial Med

View full text Add to dashboard Cite

BackgroundThe method “Learning by mistakes” was developed in Italy to conduct occupational injury investigations and to collect information on the genesis of injuries. The aim is to analyze data classified with this method in order to identify patterns among the factors contributing to injury dynamics.MethodsData regarding 673 factors, corresponding to 354 occupational fatalities that occurred in the Piedmont region (north‐west of Italy) during 2005‐2014 were considered. Latent Class Analysis (LCA) was applied to find patterns among these factors.ResultsThe eight‐class model was selected. Most of the factors fell in the class “Fall from height or vehicle rollover due to incorrect practice” (40.56%) while the remaining factors where heterogeneously distributed in the other classes.ConclusionsAll the classes found allow for a logical interpretation. Systematic use of LCA could aid in uncovering new, unexpected patterns of factors not otherwise detectable by analysis of the single fatal accident.

show abstract

Traffic accident segmentation by means of latent class clustering

Cited by 212 publications

References 28 publications

A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada

A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada

Comparison of clustering techniques for traffic accident detection

Finding causation in occupational fatalities: A latent class analysis

Contact Info

Product

Resources

About