A Comprehensive Railroad-Highway Grade Crossing Consolidation Model: A Machine Learning Approach

Soleimani, Samira; Mousa, Saleh; Codjoe, Julius; Leitner, Michael

doi:10.1016/j.aap.2019.04.002

Cited by 34 publications

(18 citation statements)

References 15 publications

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“…Karamati et al (2020) applied random survival forest to HRGC crash data and found that adding audible alarm devices to crossings that already have gates and flashing lights can decrease crash likelihood by approximately 50% [12]. Soleimani et al (2019) used extreme gradient boosting to identify HRGCs that should be closed to prevent accidents [13]. Wali et al (2021) applied text mining to crash narrative data of railroad trespassing incidents and found that confirmed suicide attempts and the use of headphones or cellphones were more likely to result in fatal injuries [14].…”

Section: Related Workmentioning

confidence: 99%

Railroad accident analysis using extreme gradient boosting

Bridgelall

Tolliver

2021

Accident Analysis & Prevention

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Railroads are critical to the economic health of a nation. Unfortunately, railroads lose hundreds of millions of dollars from accidents each year. Trends reveal that derailments consistently account for more than 70% of the U.S. railroad industry's average annual accident cost. Hence, knowledge of explanatory factors that distinguish derailments from other accident types can inform more cost-effective and impactful railroad risk management strategies. Five feature scoring methods, including ANOVA and Gini, agreed that the top four explanatory factors in accident type prediction were track class, type of movement authority, excess speed, and territory signalization. Among 11 different types of machine learning algorithms, the extreme gradient boosting method was most effective at predicting the accident type with an area under the receiver operating curve (AUC) metric of 89%. Principle component analysis revealed that relative to other accident types, derailments were more strongly associated with lower track classes, non-signalized territories, and movement authorizations within restricted limits. On average, derailments occurred at 16 kph below the speed limit for the track class whereas other accident types occurred at 32 kph below the speed limit. Railroads can use the integrated data preparation, machine learning, and feature ranking framework presented to gain additional insights for managing risk, based on their unique operating environments.

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Section: Related Workmentioning

confidence: 99%

Railroad accident analysis using extreme gradient boosting

Bridgelall

Tolliver

2021

Accident Analysis & Prevention

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“…The cross-validation technique is utilized here along with the random and grid search approach. Grid search is a widely-used approach in fine-tuning of machine learning models (99)(100)(101).…”

Section: Feature Selection and Hyper Parameter Tuningmentioning

confidence: 99%

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

Gupta

Asgari

Azimi

et al. 2021

Transportation Research Record: Journal of the Transportation R

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This paper presents the results of an analysis focusing on large truck-involved work zone fatal crashes using seven-year crash data in the State of Florida. Decision tree/random forest models were applied to specifically detect critical crash patterns that result in a fatality outcome. Because of the imbalanced nature of crash severity data (very low frequency of fatal crashes compared with property damage only or injury), data were treated using random and systematic over-sampling techniques. Marginal effects were addressed using Shapley values to increase model explainability. From a methodological perspective, results showed that the combination of over-sampling techniques with ensemble random forests could significantly improve model performance in predicting fatal crashes (compared with conventional logistic regression models). Primary contributors included pedestrian involvement, lighting conditions, safety equipment, driver condition, driver age, and work zone locations. For pedestrian crashes, factors such as dark-not lighted conditions, distracted truck driver, and driver’s age (young drivers outside city limits, senior drivers inside city limits) were highly likely to be fatal. For non-pedestrian crashes, the combination of front airbag deployment with any restraint system other than shoulder and belt was quite likely to be fatal. Also, abnormal driver conditions increased the risk of a fatal outcome. Additionally, the presence of female drivers (as the second driver in multiple vehicle crashes) highly decreased crash severity, probably because females typically drive more carefully than males. Interestingly, truck driver actions and maneuvers as well as roadway design and other physical environment features (i.e., number of lanes, median type, roadway grade, and alignment) did not show significant contribution to the model.

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“…CatBoost is a recently developed tree-based ensemble algorithm that is widely recognized among the computer science community for its efficiency, accuracy, and generalization ability when handling categorical data. Treebased ensemble algorithms proved their robustness in handling datasets with high multicollinearity and high dimensionality which makes the CatBoost a well-fit algorithm for extracting patterns within the SHRP2-NDS data (14,19,20). The CatBoost algorithm was implemented in several studies related to driving style recognition and on-street parking availability prediction and demonstrated its high performance (21,22).…”

mentioning

confidence: 99%

“…Higher scores reflect a greater number of errors and more impairment. A score of ø 3 represents a cognitive deficit, while a score of 1 Higher values indicate greater tendency: (0-9),(10)(11)(12)(13)(14)(15)(16)(17)(18),(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35) 29 LOS Level of safety (LOS) of the event, either a safety-critical one (SCE) or normal baseline event (BLE)…”

mentioning

confidence: 99%

Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

Mousa

Fadaly

et al. 2021

Transportation Research Record: Journal of the Transportation R

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According to the National Highway Traffic Safety Administration (NHTSA), about seven million traffic accidents claimed more than 36,560 human lives in the U.S. in 2018 . These statistics have prompted researchers to investigate the driver characteristics associated with safety-critical events (SCE). This paper presents a hybrid CatBoost algorithm for identifying the feature levels associated with SCE. The model accounts for numerous difficulties and drawbacks reported in the literature. The model was trained and validated using the entire set of the Second Strategic Highway Research Program Naturalistic Driving Study (SHRP2-NDS) events (crash/near-crash and normal/baseline). Results indicate that secondary tasks (interacting with object in-vehicle, reaching for objects in the vehicle, pet interaction, cellphone/tablet use, and writing/texting), intersection influence (parking lots/driveway/entrance/exit, uncontrolled intersections, traffic signals, interchanges, and stop signs), income (under $29,000 and $100,000–$149,000), age (16–19 and 20–24), traffic density (level of service C, D, and E/F), high sensation-seeking tendency (scoring 18–35 on a scale of 35), low driving knowledge (scoring 0–8.9 on a 19-point scoring system questionnaire), and gender = female are the feature levels having an association with SCE with a probability varying between 51% and 87%. Results also revealed that passenger interactions, eating/drinking, driving away from intersections or interchanges, being age 70—79, or driving in traffic density = A are more related to safe driving. Consideration of these results can contribute to reducing roadway crashes and improve traffic safety.

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A Comprehensive Railroad-Highway Grade Crossing Consolidation Model: A Machine Learning Approach

Cited by 34 publications

References 15 publications

Railroad accident analysis using extreme gradient boosting

Railroad accident analysis using extreme gradient boosting

Analysis of Fatal Truck-Involved Work Zone Crashes in Florida: Application of Tree-Based Models

Hybrid Machine Learning Algorithm for Identifying Feature Levels Associated with Safety Critical Events

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