A proactive decision support system for predicting traffic crash events: A critical analysis of imbalanced class distribution

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics

Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.

Understanding the impact of vehicle dynamics, geometric and non-geometric roadway attributes on surrogate safety measure using connected vehicle data

Traditional safety research mostly relies on accident data to analyze the precedents to a crash. Alternatively, surrogate safety measures have the potential to proactively evaluate safety events. The era of connected vehicles and smart sensing has brought about tremendous innovations in safety research. GPS data from such vehicles form a useful case of big data analytics where surrogate safety measures have largely been unexplored. In this paper, we propose time to collision estimation from connected vehicle GPS data. The vehicle dynamics such as speed, acceleration, yaw rate, etc. are then coupled with geometric and non-geometric roadway attributes to understand the contributing factors for a traffic conflict. The dataset contains 2,568,421 GPS points from 14,753 unique journeys. 1:4 ratio of conflict to non-conflict events was used to select 15,258 samples with 28 independent vehicle dynamics, geometric, and non-geometric variables. Binary logit model was used to investigate the relationship of these variables with conflicts. Model results showed that out of 28 independent variables, 6 independent variables and 7 interaction variables were found significant. The results showed some interesting and unique relations of these variables with conflicts. Based on these significant variables, k-means clustering was performed to understand the threshold for the significant values for which the number of conflicts is significantly increased. Results from k-means clustering and two sample binomial proportion t-tests revealed that when absolute acceleration crossed 0.8 m/s², conflict probability increased by 8 percentage points.​ Moreover, when the yaw rate crossed 8 degrees/s, the conflict probability doubled. Besides, vehicles traveling at more than 140% of the recommended speed limit increased conflict probability by 7 percentage points.

Performance evaluation of surrogate measures of safety with naturalistic driving data

Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature. This study proposes such a framework that supports evaluating SMoS in multiple dimensions. We apply the framework to gain insights into the characteristics of six widely-used SMoS for longitudinal maneuvers, i.e., Time to Collision (TTC), single-step Probabilistic Driving Risk Field (S-PDRF), Deceleration Rate to Avoid a Crash (DRAC), Potential Index for Collision with Urgent Deceleration (PICUD), Proactive Fuzzy Surrogate Safety Metric (PFS), and the Critical Fuzzy Surrogate Safety Metric (CFS). To ensure comparability, all measures are calibrated with the same risk detection criterion. Four performance indicators, i.e., Prediction Accuracy, Timeliness, Robustness, and Efficiency are computed for all six SMoS and validated using naturalistic driving data. The strengths and weaknesses of all six measures are compared and analyzed elaborately. A key result is that not a single SMoS performs well in all performance dimensions. S-PDRF performs best in terms of Robustness but consumes the most time for computation. TTC is the most efficient but performs poorly in terms of Timeliness and Robustness. The proposed evaluation approach and the derived insights can support SMoS selection in active vehicle safety system design and traffic safety assessment.

Analysis and prediction of intersection traffic violations using automated enforcement system data

The automated enforcement system (AES) is an effective way of supplementing traditional traffic enforcement, and the traffic violation data from AES can also be effectively used for safety research. In this study, traffic violation data were used to analyze the influencing factors associated with traffic violations and to predict the probability of violations at intersections. The potential factors influencing violations include 24 independent factors related to time, space, traffic and weather. Results from a logistic model showed that the midday period, weekends, residential districts, collector roads, congested traffic conditions, high traffic flow, lower wind speed and low temperature would increase the probability of traffic violations. The probability of violations was predicted by the random forest algorithm, which was proven to be the best traffic violation prediction model among logistic regression, Gaussian naive Bayes, and support vector machine. Moreover, the proximity weighted synthetic oversampling technique (ProWSyn) method was applied to reduce the impact of the imbalance ratio (IR) and improve the model's prediction performance. The receiver operating characteristics (ROC) curves and Precision-Recall (PR) curves illustrated that the random forest algorithm using oversampling data had the best classifier prediction performance than undersampling data. The area under curve (AUC) and out-of-bag (OOB) error with IR = 1 reached 0.914 and 0.0787, which showed the better performance of the random forest algorithm using ProWSyn in dealing with imbalanced traffic violation data.

Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma

Recent hurricane experiences have created concerns for transportation agencies and policymakers to find better evacuation strategies, especially after Hurricane Irma-which forced about 6.5 million Floridians to evacuate and caused a significant amount of delay due to heavy congestion. A major concern for issuing an evacuation order is that it may involve a high number of crashes in highways. In this study, we present a matched case-control based approach to understand the factors contributing to the increase in the number of crashes during evacuation. We use traffic data for a period of 5 to 10 min just before the crash occurred. For each crash observation, traffic data are collected from two upstream and two downstream detectors of the crash location. We estimate models for three different conditions: regular period, evacuation period, and combining both evacuation and regular period data. Model results show that, if there exist a high volume of traffic at an upstream station and a high variation of speed at a downstream station, the likelihood of crash occurrence increases. Using a panel mixed binary logit model, we also estimate the effect of evacuation itself on crash risk and find that, after controlling for traffic characteristics, during evacuation the chance of a crash is higher than in a regular period. Our findings have implications for evacuation declarations and highlight the need for better traffic management strategies during evacuation. Future studies may develop advanced real-time crash prediction models which would allow us to deploy proactive countermeasures to reduce crash occurrences during evacuation.

Assessing Fitness-To-Drive among Older Drivers: A Comparative Analysis of Potential Alternatives to on-Road Driving Test

To enable older drivers to maintain mobility without endangering public safety, it is necessary to develop more effective means of assessing their fitness-to-drive as alternatives to an on-road driving test. In this study, a functional ability test, simulated driving test, and on-road driving test were carried out for 136 older drivers. Influencing factors related to fitness-to-drive were selected based on the correlation between the outcome measure of each test and the pass/fail outcome of the on-road driving test. Four potential alternatives combining different tests were considered and three modeling techniques were compared when constructing the fitness-to-drive assessment model for the elderly. As a result, 92 participants completed all of the tests, of which 61 passed the on-road driving test and the remaining 31 failed. A total of seven influencing factors from all types of tests were selected. The best model was trained by the technique of gradient boosted machine using all of the seven factors, generating the highest accuracy of 92.8%, with sensitivity of 0.94 and specificity of 0.90. The proposed fitness-to-drive assessment method is considered an effective alternative to the on-road driving test, and the results offer a valuable reference for those unfit-to-drive older drivers to either adjust their driving behavior or cease driving.