Traffic accident analysis and prediction using the NPMRDS

Traffic accidents are incidents caused by collisions between road vehicles or a vehicle with road infrastructures or pedestrians. Traffic accidents are a common cause for non-recurring traffic bottlenecks that, in turn, cause trip delays, an increase in fuel consumption and vehicle usage, and at the worst, loss of life and property. As part of this thesis, we were granted access to the Federal Highway Association’s (FHWA) National Performance Research Management Data Set (NPRMDS), which provide probe speed, average segment speed, reference speed, and travel time per segment, among other information. Statistical analysis is applied to the accident occurrence on Oklahoma roads, especially the I-35 highway corridor for the duration between 2017 and 2020 to show the effect of temporal and spatial factors, such as road segment and its geometry, time of day, day of the week, and month of the year. Multiple methodologies involving machine learning and deep learning were utilized to model accident detection using traffic speed data. Our desired outcome is ensuring a fast reaction time from an emergency response team. We produced a deployable model capable of providing a reliable detection of accident occurrences as an implementable alert system for the concerning state bodies. Using this approach, we were able to train an optimized Random Forest model, which detected 89.68 % of accidents with only a 13.92 % false detection rate. These are promising results for a real-time data environment. Speed turbulence classification was also implemented as a post processing application for classifying samples into free flow, congestion, and incident event based on historical data. The LSTM model outperformed others, especially when modelling is specified to a specific road segment. Accuracy was measured at above 87% in classification with greater than 75% accuracy in correctly classifying congestion and accident events

Automatic determination of traffic accidents based on KMC-based attribute weighting

In this study, the traffic accidents recognizing risk factors related to the environmental (climatological) conditions that are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of Geographical Information Systems (GIS) have been determined using the combination of K-means clustering (KMC)-based attribute weighting (KMCAW) and classifier algorithms including artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). The dynamic segmentation process in ArcGIS9.0 from the traffic accident reports recorded by District Traffic Agency has identified the locations of the motor vehicle accidents. The attributes obtained from this system are day, temperature, humidity, weather conditions, and month of occurred traffic accidents. The traffic accident dataset comprises five attributes (day, temperature, humidity, weather conditions, and month of occurred traffic accidents) and 358 observations including 179 without accident and 179 with accident. The proposed comprises two stages. In the first stage, the all attributes of dataset have been weighted using KMCAW method. The aims of this weighting method are both to increase the classification performance of used classifier algorithm and to transform from linearly non-separable traffic accidents dataset to a linearly separable dataset. In the second stage, after weighting process, ANN and ANFIS classifier algorithms have been separately used to determine the case of traffic accidents as with accident or without accident. In order to evaluate the performance of proposed method, the classification accuracy, sensitivity, specificity and area under the ROC (Receiver Operating Characteristic) curves (AUC) values have been used. While ANN and ANFIS classifiers obtained the overall prediction accuracies of 53.93 and 38.76\%, respectively, the combination of KMCAW and ANN and the combination of KMCAW and ANFIS achieved the overall prediction accuracies of 74.15 and 55.06\% on the prediction of traffic accidents. The experimental results have demonstrated that the proposed attribute weighting method called KMCAW is a robust and effective data pre-processing method in the prediction of traffic accidents on Konya-Afyonkarahisar highway in Turkey