Modeling the accident prediction for at-grade highway-rail crossings

Yang, Xue; Li, Joshua; Zhang, Aonan; Zhan, You

doi:10.1093/iti/liac017

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Increased traffic can result The estimated parameters from both base and comparison model gave intuitive results, as previously shown in the literature. For instance, variables such as average annual daily traffic, flashing light as a warning device, maximum timetable speed, and total daily trains were also indicated in previous studies to be factors associated with crash occurrence at HRGCs [3,7,11,16,[29][30][31][32]. In addition to the United States, several similar studies have been conducted in various other countries worldwide to understand the factors of HRGC crashes [32][33][34][35].…”

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confidence: 94%

“…For instance, variables such as average annual daily traffic, flashing light as a warning device, maximum timetable speed, and total daily trains were also indicated in previous studies to be factors associated with crash occurrence at HRGCs [3,7,11,16,[29][30][31][32]. In addition to the United States, several similar studies have been conducted in various other countries worldwide to understand the factors of HRGC crashes [32][33][34][35]. It is believed that with the increase in average annual daily traffic and train traffic, exposure is increased as more vehicles and trains come into contact with the rail crossings, increasing the likelihood of crashes [2,3,16].…”

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confidence: 99%

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Investigating Highway–Rail Grade Crossing Inventory Data Quality’s Role in Crash Model Estimation and Crash Prediction

Farooq,

Khattak

2023

Applied Sciences

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The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration’s (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA’s 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and secondly, utilizing a field-validated inventory dataset for the same 560 HRGCs as input to the FRA’s 2020 Accident Prediction (AP) model. The findings indicated a significant statistical disparity in the predictions made with the two input datasets. Furthermore, two new Zero-inflated Negative Binomial (ZINB) models were estimated by employing 5-year reported HRGCs crashes and the two inventory datasets for the 560 HRGCs. These models facilitated the comparison of model parameter estimates and estimated marginal values. The results indicated that errors and missing values in the original FRA HRGCs inventory dataset resulted in crash predictions that statistically differed from those made using the more accurate and complete (validated in the field) HRGCs inventory dataset. Furthermore, the crash prediction model estimated upon the corrected inventory data demonstrated enhanced prediction performance, as measured by the statistical fitness criteria. The findings emphasize the importance of collecting complete and accurate inventory data when developing HRGCs crash frequency models. This will enhance models’ precision, improve their predictive capabilities to aid in better resource allocation, and ultimately reduce HRGCs crashes.

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confidence: 94%

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confidence: 99%

Investigating Highway–Rail Grade Crossing Inventory Data Quality’s Role in Crash Model Estimation and Crash Prediction

Farooq,

Khattak

2023

Applied Sciences

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“…The Poisson model has been extensively used for safety analysis and prediction since the crash frequency (count) data are discrete, random, and non-negative integers (Hirst et al 2004). Negative binomial models derived from the Poisson model were developed to solve the overdispersion problem in count data (Yang et al 2022). Previous studies on HRGCs have predominantly focused on analyzing crash severity through the utilization of simulation, statistical and mathematical modeling techniques, as well as data mining and machine learning algorithms (Fan et Lu and Tolliver applied a Poisson regression model to evaluate the HRGC crash frequency.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Yang et al (Yang et al 2022) applied the following six count data models for crash prediction: Poisson model, negative binomial model, zero-in ated Poisson model, zero-in ated negative binomial model, hurdle Poisson model, and hurdle negative binomial model. The zero-in ated negative binomial model performed better than the other ve models considering the statistical signi cance of factors, goodness-of-t, and zero in ations.…”

Section: Introductionmentioning

confidence: 99%

Modeling Highway-Rail Grade Crossing (HRGC) Crash Frequency

Soltaninejad,

Salum,

Kodi

et al. 2024

Preprint

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Substantial research in highway safety has focused on roadway crash frequency analysis; however, only a few studies have focused on the safety at highway-rail grade crossings (HRGCs). This study aimed to determine factors affecting the frequency of crashes at HRGCs. The statistical modeling and analysis were based on five years of HRGC crashes (2017–2021) that occurred on state-maintained HRGCs in Florida. Police reports of 2,092 crashes flagged as "railroad crossing" were downloaded and reviewed. The manual review of the police reports indicated that only 1,690 reports were classified as HRGC crashes. The Bayesian negative binomial model was applied to identify various contributing factors associated with the frequency of HRGC crashes. The following variables significantly increase the number of HRGC crashes: high-speed facilities, number of tracks, facilities with 4 to 5 lanes, presence of railroad crossing gates, proximity to a traffic control signal, and high traffic volume. To mitigate these risks, engineering treatments such as improved signing and pavement markings and implementation of Intelligent Transportation Systems (ITS) technologies are recommended.

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Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

Chhotu,

Suman

2024

Urban Rail Transit

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With the tremendous increase in the number of vehicles, the dense traffic created can lead to accidents and fatalities. In a traffic system, the costs for accidents are immeasurable. Numerous studies have been carried out to predict the cost of fatal accidents but have provided the actual values. Therefore, in this study, a monkey-based modular neural system (MbMNS) is developed to identify accident cost. The accident cases and cost data were collected and preprocessed to remove the noise, and the required features were extracted using the spider monkey function. Based on the extracted features, the accidents and the costs were identified. For rail engineering, this will support evaluating the number of railroad crossing accidents with different time intervals. The impact of every accident was also measured with different cost analysis constraints, including insurance, medical, and legal and administrative costs. Therefore, the present study contributes to the field by collecting and organizing the present railroad level crossing accident data from crossing inventory dashboards. Then, the introduction of a novel MbMNS for the cost analysis is the primary contribution of this study to further enrich the railroad level crossing protection system. The third contribution is the tuning of the prediction layer of a modular neural network to the desired level to achieve the highest predictive exactness score. Hence, the designed MbMNS was tested in the Python environment, and the results were validated with regard to recall, accuracy, F-measure, precision, and error values; a comparative analysis was also conducted to confirm the improvement. The novel MbMNS recorded high accuracy of 96.29% for accident and cost analysis, which is better than that reported for other traditional methods.

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Modeling the accident prediction for at-grade highway-rail crossings

Cited by 4 publications

References 18 publications

Investigating Highway–Rail Grade Crossing Inventory Data Quality’s Role in Crash Model Estimation and Crash Prediction

Investigating Highway–Rail Grade Crossing Inventory Data Quality’s Role in Crash Model Estimation and Crash Prediction

Modeling Highway-Rail Grade Crossing (HRGC) Crash Frequency

Cost Analysis and Prediction of Railroad Level Crossing Accidents for Indian Railways

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