scholarly journals Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Qianxia Cao ◽  
Zhongxing Zhao ◽  
Qiaoqiong Zeng ◽  
Zhengwu Wang ◽  
Kejun Long
Keyword(s):  
Real Time ◽  
Deep Neural Networks ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Target Vehicle ◽  
Time Prediction ◽  
Unsignalized Intersections ◽  
Vehicle Trajectories ◽  
Traffic Conflict ◽  
Vehicle Trajectory

Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction system is developed to collect vehicle trajectories and their left-turn, go straight, and right-turn labels to train turning recognition models and multilayer LSTM deep neural networks for the prediction task. Then, when performing vehicle trajectory prediction, we propose the vehicle heading angle change trend method to recognize the future move of the target vehicle to turn left, go straight, and turn right based on the trajectory data characteristics of the target vehicle before passing the stop line. Finally, we use the trained multilayer LSTM models of turning left, going straight, and turning right to predict the trajectory of the target vehicle through the intersection. Based on the TensorFlow-GPU platform, we use Yolov5-DeepSort to automatically extract vehicle trajectory data at unsignalized intersections. The experimental results show that the proposed method performs well and has a good performance in both speed and accuracy evaluation.

scholarly journals Incremental Road Network Generation Based on Vehicle Trajectories

2018 ◽  
Vol 7 (10) ◽  
pp. 382 ◽  
Author(s):  
Zhongyi Ni ◽  
Lijun Xie ◽  
Tian Xie ◽  
Binhua Shi ◽  
Yao Zheng
Keyword(s):  
Real Time ◽  
Road Network ◽  
Trajectory Data ◽  
Network Generation ◽  
The Real ◽  
The Road ◽  
Vehicle Trajectories ◽  
Representation Point ◽  
Road Planning ◽  
Vehicle Trajectory

Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.

scholarly journals Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data

2021 ◽  
Vol 13 (16) ◽  
pp. 9278
Author(s):  
Ruoxi Jiang ◽  
Shunying Zhu ◽  
Hongguang Chang ◽  
Jingan Wu ◽  
Naikan Ding ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Traffic Safety ◽  
Pearson Correlation ◽  
Highway Safety ◽  
Lane Change ◽  
Trajectory Data ◽  
Average Value ◽  
Traffic Conflict ◽  
Vehicle Trajectory ◽  
Safety Estimation

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator Ti was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/Ti indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for Ti, which Ti indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved Ti can overcome these shortcomings; thus, Ti has the highest correlation. More data are needed to verify and support the study.

scholarly journals Vehicle trajectory prediction and generation using LSTM models and GANs

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253868
Author(s):  
Luca Rossi ◽  
Andrea Ajmar ◽  
Marina Paolanti ◽  
Roberto Pierdicca
Keyword(s):  
Traffic Congestion ◽  
Short Term Memory ◽  
Autonomous Driving ◽  
Generative Models ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Displacement Error ◽  
Adversarial Network ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

scholarly journals Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Juyuan Yin ◽  
Peng Chen ◽  
Keshuang Tang ◽  
Jian Sun
Keyword(s):  
Mobile Internet ◽  
Urban Traffic ◽  
Internet Technology ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Trajectory Data ◽  
Adaptive Signal Control ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory ◽  
Adaptive Signal

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

scholarly journals A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7061
Author(s):  
Zhao Yang ◽  
Rong Tang ◽  
Jie Bao ◽  
Jiahuan Lu ◽  
Zhijie Zhang
Keyword(s):  
Real Time ◽  
Stable State ◽  
Prediction Method ◽  
Small Scale ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Unit Model ◽  
Proposed Model ◽  
Historical Trajectory ◽  
Gated Recurrent Unit

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

Resurrecting the Lost Vehicle Trajectories of Treiterer and Myers with New Insights into a Controversial Hysteresis

2018 ◽  
Vol 2672 (20) ◽  
pp. 25-38 ◽  
Author(s):  
Benjamin Coifman ◽  
Lizhe Li ◽  
Wen Xiao
Keyword(s):  
High Speed ◽  
Original Data ◽  
Research Community ◽  
Trajectory Data ◽  
Data Set ◽  
Car Following ◽  
Traffic Flow Theory ◽  
Vehicle Trajectories ◽  
Time Space ◽  
Vehicle Trajectory

The 1974 paper by Treiterer and Myers is a seminal work in traffic flow theory. This longevity is in part because of the impressive collection of manually extracted vehicle trajectories. To date, only a few studies have rivaled the scale of the empirical vehicle trajectory data used in Treiterer and Myers. Their data collection used high-speed aerial photography and manual data reduction to follow hundreds of vehicles. In spite of the Herculean collection effort, the trajectory data set was never released and has since been lost. Fortunately, the plots survive and the present work re-extracts the vehicle trajectory data from the time–space diagrams. The discussion places the value of the data in context and then uses the data to put an end to decades of misinterpretation that started with Treiterer himself. The central thesis of Treiterer and Myers generated considerable interest: a hysteresis whereby drivers exhibit different fundamental behavior depending on whether they are entering or exiting a disturbance. There has been extensive debate about the authors’ findings in the literature, but without the original data set any interpretation has required considerable speculation. With the resurrected trajectories, this work reexamines the vehicles underlying the hysteresis and finally quells the speculation. Rather than arising from car following behavior, it turns out that the enigmatic progression arose from a combination of lane change maneuvers and unremarkable transitions into or out of the congested regime. On publication, the re-extracted data from this paper will be released to the research community.

scholarly journals A Road Network Enhanced Gate Recurrent Unit Model for Gather Prediction in Smart Cities

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Mingchao Yuan ◽  
Ling Tian ◽  
Ke Yan ◽  
Xu Zheng
Keyword(s):  
Real Time ◽  
Network Structure ◽  
Road Network ◽  
Smart Cities ◽  
Great Influence ◽  
City Government ◽  
Model Parameters ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Unit Model

Gather prediction is an indispensable part of smart city projects. The city government can respond in advance based on gather predictions and greatly reduce the loss and risks caused by vicious gatherings. Compared with other trajectory prediction tasks (i.e., the recommendation of point of interest), gather prediction pay more attention to real-time trajectory data and requests stronger spatial-temporal dependence. At the same time, gather prediction is more focused on scenes with multiple types of trajectories. And the existing methods majorly rely on the trajectory data and ignore the great influence of geographical environment (i.e., road network structure). Therefore, this paper transforms the gather prediction into the trajectory prediction task with strong real-time condition in a certain city and conducts the gathering situations by predicting users’ aggregated movements in next minutes or hours. A novel Spatiotemporal Gate Recurrent Unit (STGRU) model is proposed, where spatiotemporal gates and road network gate are introduced to capture the spatiotemporal relationships between trajectories. Compared with existing methods, we improve the performance of the model by adding road network structure and external knowledges, as well as time and distance gates to reduce model parameters. The proposed STGRU is evaluated on three real-world trajectory datasets, and the experimental results demonstrate the effectiveness of the proposed model.

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