A New Accident Prediction Model for Highway-Rail Grade Crossings Using the USDOT Formula Variables

This paper presents the ZINDOT model,a methodology utilizing a zero-inflated negative binomial model with the variables used in the United States Department of Transportation(USDOT)accident prediction formula,to determine the expected accident count at a highway-rail grade crossing.The model developed contains separate formulas to estimate the crash prediction value depending on the warning device type installed at the crossing:crossings with gates,crossings with flashing lights and no gates,and crossings with crossbucks.The proposed methodology also accounts for the observed accident count at a crossing using the Empirical Bayes method.The ZINDOT model estimates were compared to the USDOT model estimates to rank the crossings based on the expected accident frequency.It is observed that the new model can identify crossings with a greater number of accidents with Gates and Flashing Lights and Crossbucks in both Illinois(data which were used to develop the model)and Texas(data which were used to validate the model).A practitioner already using the USDOT formulae to estimate expected accident count at a crossing could easily use the ZINDOT model as it employs the same variables used in the USDOT formula.This methodology could be used to rank highway-rail grade crossings for resource allocation and safety improvement.

Accident and hazard prediction models for highway–rail grade crossings:a state-of-the-practice review for the USA

Highway–rail grade crossings(HRGCs)are one of the most dangerous segments of the transportation network.Every year numerous accidents are recorded at HRGCs between highway users and trains,between highway users and traffic control devices,and solely between highway users.These accidents cause fatalities,severe injuries,property damage,and release of hazardous materials.Researchers and state Departments of Transportation(DOTs)have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability.The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements.This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs.Furthermore,this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae.The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well.Based on the review results,the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs.However,certain states still prefer customized models due to some practical considerations.Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.

A Prediction Model for Railway Transportation Sofety and Its Application

This paper discusses the characterization of railway transportation safety, andapplies the Grey-Markov forecasting model to predict the occurrences of thedriving accident on railway according to their speciality. It will offer a reliableargument for taking measures to prevent driving accidents.

Safety Performance Functions for Traffic Signals:Phasing and Geometry

A significant proportion of urban crashes,especially serious and fatal crashes,occur at traffic signals.Many of the black-spots in both Australia and New Zealand cities occur at high volume and/or high-speed traffic signals.Given this,crash reduction studies often focus on the major signalised intersections.However,there is limited information that links the phasing configuration,degree of saturation and overall cycle time to crashes.While a number of analysis tools are available for assessing the efficiency of intersections,there are very few tools that can assist engineers in assessing the safety effects of intersection upgrades and new intersections.Safety performance functions have been developed to help quantify the safety impact of various traffic signal phasing configurations and level of intersection congestion at low and high-speed traffic signals in New Zealand and Australia.Data from 238 signalised intersection sites in Auckland,Wellington,Christchurch,Hamilton,Dunedin and Melbourne was used to develop crash prediction models for key crash-causing movements at traffic signals.Different variables(road features)effect each crash type.The models indicate that the safety of intersections can be improved by longer cycle times and longer lost inter-green times,especially all-red time,using fully protected right turns and by extending the length of right turn bays.The exception is at intersections with lots of pedestrians where shorter cycle times are preferred as pedestrian crashes increase with longer wait times.A number of factors have a negative impact on safety including,free left turns,more approach lanes,intersection arms operating near or over capacity in peak periods and higher speed limits.

Civil gas energy accidents in China from 2012-2021

In this study,civil gas energy accidents reported by the China Gas Network and related organizations from 2012 to 2021 were collected,and a comprehensive multidimensional correlation analysis was conducted considering factors such as accident timing,geography,causes,and casualties.The results identified July and August,Mondays and Sundays,and the morning,mid-day,and evening cooking times as the high-incidence months,days,and times for gas accidents,respectively.Gas accidents were found to occur more frequently in eastern coastal areas,provincial capitals,and larger cities,while residential and construction sites were identified as high-risk areas for gas accidents.Explosions were the most prevalent type of gas accident,followed by leaks,fires,and poisoning.Third-party construction and valve issues were identified as the primary factors contributing to gas leakage,whereas cooking was identified as the most common ignition source.An analysis of the Pearson correlation coefficient indicated a significant correlation among the gas accident factors.Moreover,a time-series prediction model was developed to forecast gas accidents in China,with the results demonstrating fluctuating gas accidents.This study proposes targeted preventive measures in terms of publicity,education,equipment,and facilities to provide scientific support to government units to improve civil gas energy security measures.

Predicting the severity of traffic accidents on mountain freeways with dynamic traffic and weather data

Traffic accident severity prediction is essential for dynamic traffic safety management.To explore the factors influencing the severity of traffic accidents on mountain freeways and to predict the severity of traffic accidents,four models based on machine learning algorithms are constructed using support vector machine(SVM),decision tree classifier(DTC),Ada_SVM and Ada_DTC.In addition,random forest(RF)is used to calculate the importance degree of variables and the accident severity influences with high importance levels form the RF dataset.The results show that rainfall intensity,collision type,number of vehicles involved in the accident and toad section type are important variables influencing accident severity.The RF feature selection method improves the classification performance of four machine leaming algorithms,resulting in a 9.3%,5.5%,7.2% and 3.6% improvement in prediction accuracy for SVM,DTC,Ada_SVM and Ada_DTC,respectively.The combination of the Ada_SVM integrated algorithm and RF feature selection method has the best prediction performance,and it achieves 78.9% and 88.4% prediction precision and accuracy,respectively.

Modern data sources and techniques for analysis and forecast of road accidents:A review

Road accidents are one of the most relevant causes of injuries and death worldwide,and therefore,they constitute a significant field of research on the use of advanced algorithms and techniques to analyze and predict traffic accidents and determine the most relevant elements that contribute to road accidents.The research of road accident prediction aims to respond to the challenge of offer tools to generate a more secure mobility environment,and ultimately,save lives.This paper aims to provide an overview of the state of the art in the prediction of road accidents through machine learning algorithms and advanced techniques for analyzing information,such as convolutional neural networks and long short-term memory networks,among other deep learning architectures.Furthermore,in this article,a compendium and study of the most used data sources for the road accident forecast is made.And a classification is proposed according to its origin and characteristics,such as open data,measurement technologies,onboard equipment and social media data.For the analysis of the information,the different algorithms employed to make predictions about road accidents are listed and compared,as well as their applicability depending on the types of data being analyzed,along with the results obtained and their ease of interpretation and analysis.The best results reported by the authors are obtained when two or more analytic techniques are combined,in such a way that analysis of the obtained results is strengthened.Among the future challenges in road traffic forecasting lies the enhancement of the scope of the proposed models and predictions by the incorporation of heterogeneous data sources,that include geo spatial data,information from traffic volume,traffic statistics,video,sound,text and sentiment from social media,that many authors concur that can improve the precision and accuracy of the analysis and predictions.