Using hierarchical tree-based regression model to predict train–vehicle crashes at passive highway-rail grade crossings

This paper presents the investigation about a problem situation that Electric Distributor Companies are facing in Chile resulting from transit accidents. The number of vehicle crashes to power distribution poles and street lighting has grown. This situation causes discomfort to citizen and mainly to the neighbors due to power cuts and even on occasion , losses of human lives because of the accident that have occurred. Based on previous research, the accidents are not random nor chance dependent, but the majority of transit accident follow parameters or variables from the scenery where it occurs. In order to analyze the variables and the degree this variables affect the accidents, a model of Perceptron and Multipercetron Artificial Neural Networks and a Multiple Nonlinear Regression model are proposed. An empirical study was made; collecting data from a distributor company and from Chilean National Traffic Safety Commission, where the more frequent variables involved in accidents were determined to develop the mentioned models. These variables were investigated and also their influence on the occurrence of vehicle crashes to power distribution poles could be confirmed. With this data, the prediction of post crashes was developed, where through the application of the neural network and multiple nonlinear regression, revealed 95.7% of acceptable predictions. This study will bring benefits to power distribution companies considering a risk index in the streets, based on the number of crashes of poles per street; this will allow optimal decisions in future electrical distribution projects avoiding critical areas.

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Modeling the Probability of Hazardous Materials Release in Crashes at Highway–Rail Grade Crossings

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118780885 ◽

2018 ◽

Vol 2672 (10) ◽

pp. 28-37 ◽

Cited By ~ 4

Author(s):

Amirfarrokh Iranitalab ◽

Yashu Kang ◽

Aemal Khattak

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Weather Conditions ◽

Hazardous Materials ◽

Class I ◽

Release Probability ◽

Data Subset ◽

Grade Crossings ◽

Using Data

Crashes at Highway–Rail Grade Crossings (HRGCs) that involve a truck or a train carrying hazardous materials (hazmat) expose people and the environment to potentially severe consequences of hazmat release. This research involved statistical modeling of the probability of hazmat release from trucks and/or trains in crashes at HRGCs to identify factors associated with hazmat release. The Federal Railroad Administration (FRA) HRGC crash dataset (2007–2016) yielded two subsets of crashes: 1) those involving hazmat-carrying trucks, and 2) those involving hazmat-carrying trains. Results from a logistic regression model using data subset 1 (crashes involving hazmat-carrying trucks) with hazmat release/no release as the response variable showed that standard flashing signal lights, railroad crossbucks, and railroad classes II and III (relative to railroad class I) were associated with lower hazmat release probability from hazmat-carrying trucks. Hazmat release probability from trucks was higher with freight train involvement. Results from a logistic regression model using data subset 2 (crashes involving hazmat-carrying trains) revealed that hazmat release probability from trains was lower with warmer temperature. However, the probability of release from trains was greater with railroad class II (relative to railroad class I), type of highway user (different types of trucks and motorcycle relative to automobiles), and weather conditions (fog, sleet or snow, relative to clear). A comparison of the results from this study with HRGC crash severity studies highlighted the importance and usefulness of this study.

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Analyzing Severity of Vehicle Crashes at Highway-Rail Grade Crossings: Multinomial Logit Modeling

Journal of the Transportation Research Forum ◽

10.5399/osu/jtrf.54.2.4282 ◽

2015 ◽

Cited By ~ 4

Author(s):

Wei (David) Fan ◽

Martin R. Kane ◽

Elias Haile

Keyword(s):

Logit Model ◽

High Speed ◽

Injury Severity ◽

Weather Conditions ◽

Multinomial Logit Model ◽

The United States ◽

Multinomial Logit ◽

Vehicle Crashes ◽

Grade Crossings ◽

The Impact

The purpose of this paper is to develop a nominal response multinomial logit model (MNLM) to identify factors that are important in making an injury severity difference and to explore the impact of such explanatory variables on three different severity levels of vehicle-related crashes at highway-rail grade crossings (HRGCs) in the United States. Vehicle-rail and pedestrian-rail crash data on USDOT highway-rail crossing inventory and public crossing sites from 2005 to 2012 are used in this study. A multinomial logit model is developed using SAS PROC LOGISTICS procedure and marginal effects are also calculated. The MNLM results indicate that when rail equipment with high speed struck a vehicle, the chance of a fatality resulting increased. The study also reveals that vehicle pick-up trucks, concrete, and rubber surfaces were more likely to be involved in more severe crashes. On the other hand, truck-trailer vehicles in snow and foggy weather conditions, development area types (residential, commercial, industrial, and institutional), and higher daily traffic volumes were more likely to be involved in less severe crashes. Educating and equipping drivers with good driving habits and short-term law enforcement actions, can potentially minimize the chance of severe vehicle crashes at HRGCs.

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