scholarly journals TrajNet: An Efficient and Effective Neural Network for Vehicle Trajectory Classification

2021 ◽  
Author(s):  
Jiyong Oh ◽  
Kil-Taek Lim ◽  
Yun-Su Chung
Keyword(s):  
Neural Network ◽  
Trajectory Classification ◽  
Vehicle Trajectory

scholarly journals S2N2: An Interpretive Semantic Structure Attention Neural Network for Trajectory Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 58763-58773
Author(s):  
Canghong Jin ◽  
Ting Tao ◽  
Xianzhe Luo ◽  
Zemin Liu ◽  
Minghui Wu
Keyword(s):  
Neural Network ◽  
Semantic Structure ◽  
Trajectory Classification

Replicating Advanced Detection using Low Ping Frequency Probe Vehicle Trajectory Data to Optimize Signal Progression

2020 ◽  
Vol 2674 (7) ◽  
pp. 528-539
Author(s):  
Jonathan M. Waddell ◽  
Stephen M. Remias ◽  
Jenna N. Kirsch ◽  
Mohsen Kamyab
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Mean Squared Error ◽  
Linear Interpolation ◽  
Traffic Signal ◽  
Trajectory Data ◽  
Arrival Times ◽  
Probe Vehicle ◽  
The Neural Network ◽  
Vehicle Trajectory

Probe vehicle trajectory data has the potential to transform the current practice of traffic signal optimization. Current scalable trajectory data is limited in both the penetration rate and the ping frequency, or the length of time between vehicle waypoints. This paper introduces a methodology to create binary vehicle trajectories which can be used in a neural network to predict when vehicles will arrive at a virtual detector. The methodology allows for vehicles with ping frequencies of up to 60 s to be utilized for the optimization of offsets at signalized intersections. A nine-signal corridor in west Michigan was used to test the proposed methodology. The neural network was compared to traditional linear interpolation strategies and found to improve the root mean squared error of the arrival times by up to 6.18 s. Using the virtual detector data stacked over time to optimize the offsets of the corridor resulted in 77% of the benefit of an offset optimization performed with continuously collected high resolution signal controller data. In the era of big data, this alternative approach can assist with the large-scale implementation of traffic signal performance measures for improved operations.

scholarly journals Vehicle Trajectory Prediction Based on a Deep Neural Network

2018 ◽  
Vol 26 (2) ◽  
pp. 202-210
Author(s):  
Donggi Jeong ◽  
Minjin Baek ◽  
Woojoong Kim ◽  
Sang-Sun Lee
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Trajectory Prediction ◽  
Vehicle Trajectory ◽  
Vehicle Trajectory Prediction

scholarly journals Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 84
Author(s):  
Yuelei Xiao ◽  
Qing Nian
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Transformation Method ◽  
Information Loss ◽  
Location Prediction ◽  
The Neural Network ◽  
Vehicle Location ◽  
Vehicle Trajectory ◽  
Taxi Trajectory

Location prediction has attracted much attention due to its important role in many location-based services. The existing location prediction methods have large trajectory information loss and low prediction accuracy. Hence, they are unsuitable for vehicle location prediction of the intelligent transportation system, which needs small trajectory information loss and high prediction accuracy. To solve the problem, a vehicle location prediction algorithm was proposed in this paper, which is based on a spatiotemporal feature transformation method and a hybrid long short-term memory (LSTM) neural network model. In the algorithm, the transformation method is used to convert a vehicle trajectory into an appropriate input of the neural network model, and then the vehicle location at the next time is predicted by the neural network model. The experimental results show that the trajectory information of an original taxi trajectory is basically reserved by its shadowed taxi trajectory, and the trajectory points of the predicted taxi trajectory are close to those of the shadowed taxi trajectory. It proves that our proposed algorithm effectively reduces the information loss of vehicle trajectory and improves the accuracy of vehicle location prediction. Furthermore, the experimental results also show that the algorithm has a higher distance percentage and a shorter average distance than the other predication models. Therefore, our proposed algorithm is better than the other prediction models in the accuracy of vehicle location predication.

Identifying Wrong-Way Driving Incidents from Regular Traffic Videos Using Unsupervised Trajectory-based Method

2021 ◽  
pp. 036119812110363
Author(s):  
Qing Chang ◽  
Jiaxiang Ren ◽  
Huaguo Zhou ◽  
Yang Zhou ◽  
Yukun Song
Keyword(s):  
Urban Areas ◽  
Toll Roads ◽  
Trajectory Clustering ◽  
Vehicle Recognition ◽  
Detection Systems ◽  
Loop Detectors ◽  
Trajectory Classification ◽  
Thermal Cameras ◽  
Vehicle Trajectory ◽  
Transportation Agencies

Currently, transportation agencies have implemented different wrong-way driving (WWD) detection systems based on loop detectors, radar detectors, or thermal cameras. Such systems are often deployed at fixed locations in urban areas or on toll roads. The majority of rural interchange terminals does not have real-time detection systems for WWD incidents. Portable traffic cameras are used to temporarily monitor WWD activities at rural interchange terminals. However, it has always been a time-consuming task to manually review those videos to identify WWD incidents. The objective of this study was to develop an unsupervised trajectory-based method to automatically detect WWD incidents from regular traffic videos (not limited by mounting height and angle). The principle of the method includes three primary steps: vehicle recognition and trajectory generation, trajectory clustering, and outlier detection. This study also developed a new subtrajectory-based metric that makes the algorithm more adaptable for vehicle trajectory classification in different road scenarios. Finally, the algorithm was tested by analyzing 357 h of traffic videos from 14 partial cloverleaf interchange terminals in seven U.S. states. The results suggested that the method could identify all the WWD incidents in the testing videos with an average precision of 80%. The method significantly reduced person-hours for reviewing the traffic videos. Furthermore, the new method could also be applied in detecting and extracting other kinds of abnormal traffic activities, such as illegal U-turns.

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