Alternative approach for vehicle trajectory reconstruction under spatiotemporal side friction using lopsided network

Abdel-Aty

Transportation Research Record: Journal of the Transportation R

et al. 2023

This study proposes a three-stage framework for real-time crash likelihood and severity prediction. Firstly, a real-time crash likelihood prediction model was developed. Secondly, a real-time crash severity clustering model was proposed to cluster the crashes into different severity levels. Thirdly, a severity clustering validation model was developed to assess the performance of the proposed severity clustering model. Extensive data processing techniques were employed to collect real-time features from State Road 408 in Orlando, Florida, and a total of 6,750,072 events (625 crash events and 6,749,447 non-crash events) along with 24 real-time features were used. To develop the crash likelihood prediction model, nine machine-learning techniques were attempted, and the convolutional neural network model was found to provide the best result with respect to the sensitivity (0.916), false alarm rate (0.111), and area under the receiver operating characteristic curve (0.967). Davies–Bouldin index criteria were used to find the detector location that generated the most accurate traffic information to cluster the crashes into severity levels, and based on this traffic information, k-means clustering was applied to develop the severity clustering model. Finally, a severity clustering validation model was developed after investigating nine machine-learning techniques to validate the developed severity clustering model, and the decision tree model provided the best results based on three levels of sensitivity and specificity values. The developed framework has the potential to help traffic management centers to warn road users or develop transportation systems management and operations strategies in real time to avoid crashes or minimize the severity and, thus, can significantly contribute to improving road safety.

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Real-Time Framework to Predict Crash Likelihood and Cluster Crash Severity

Abdel-Aty

Transportation Research Record: Journal of the Transportation R

et al. 2023

“…On the other hand, the shopkeepers themselves are too poor to afford to close down shops for several days, and they are not provided with alternative places to peddle their trade. Hence, under the protection of local influential people, these shopkeepers are able to set up their shops again soon after eviction drives end [12,13,76,77]. Hence, the presence of illegal establishments is a significant and perpetual phenomenon in railway stations in Bangladesh.…”

Section: Unique Factorsmentioning

confidence: 99%

“…In addition, some shops have been set up illegally without government permission inside station premises. The majority of the shops sell low-quality food to consumers at exorbitant prices [12,13]. Moreover, the traffic congestion is extremely high at level crossings near railway stations, causing an additional delay for rail passengers both en route to stations and traveling by train [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

The Aspiration for Happy Train Journey: Commuters’ Perception of the Quality of Intercity Rail Services

et al. 2022

This paper assesses the perception of intercity rail passengers on station facilities at Joydebpur Railway Station in Bangladesh. The ordinal logistic regression (OLR) tool was applied to analyze 1000 responses of rail passengers on 24 selected service and 5 demographic parameters. Critical consideration from the perspective of a developing country revealed six unique factors, namely Level crossing facility, Illegal establishments, Illegal shops, Floating people, Arrival performance, and Departure performance, which have never been explored in any previous studies. The regression analysis identified that 13 service quality factors significantly affected commuters’ satisfaction level, particularly the Food and drinks, Road connectivity, Sanitation, and Waiting room facility at the station. Among the five demographic factors, age, occupation, and travel frequency significantly influenced overall passenger satisfaction (OPS). The model results have also been validated through a second survey at Kamalapur Railway Station, Bangladesh. The results suggest that policymakers should focus on the elderly, financially solvent people, and frequent travelers. Additionally, refreshment facilities, road connectivity, sanitation, and waiting room facilities should be given priority, as these will heavily impact passenger satisfaction according to this study. Subsequent attributes can then be prioritized as per the attributes ranked and according to budget considerations of the authority.

Journal of Advanced Transportation

“…So far, trajectory reconstruction has received a lot of attention from all over the world, and many scholars have done in-depth research on vehicle trajectory reconstruction [17][18][19]. Coifman [20] proposed an estimation method based on traffic flow theory (TFT) to reconstruct the vehicle trajectory on the expressway section, using the traffic data from a single dual loop detector which is extrapolated to the extended link.…”

Section: Introductionmentioning

confidence: 99%

An Unscented Kalman Filter-Based Method for Reconstructing Vehicle Trajectories at Signalized Intersections

Han

Zhang

et al. 2021

On-board data of detected vehicles play a critical role in the management of urban road traffic operation and the estimation of traffic status. Unfortunately, due to limitations of technology and privacy issues, the sampling frequency of the detected vehicle data is low and the coverage is also limited. Continuous vehicle trajectories cannot be obtained. To overcome the above problems, this paper proposes an unscented Kalman filter (UKF)-based method to reconstruct the trajectories at signalized intersections using sparse probe data of vehicles. We first divide the intersection into multiple road sections and use a quadratic programming problem to estimate the travel time of each section. The weight of each initial possible trajectory is calculated separately, and the trajectory is updated using the unscented Kalman filter (UKF); then, the trajectory between two updates is also obtained accordingly. Finally, the method is applied to the actual scenario provided by the NGSIM data and compared with the real trajectory. The mean absolute error (MAE) is adopted to evaluate the accuracy of the proposed trajectory reconstruction. Sensitivity analysis is provided in order to provide the requirement of sampling frequency to obtain highly accurate reconstructed vehicle trajectories under this method. The results demonstrate the applicability of the technique to the signalized intersection. Therefore, the method enables us to obtain richer and more accurate trajectory data information, providing a strong prior basis for future urban road traffic management and scholars using trajectory data for various studies.