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 An Intersection-First Approach for Road Network Generation from Crowd-Sourced Vehicle Trajectories

2019 ◽  
Vol 8 (11) ◽  
pp. 473 ◽  
Author(s):  
Caili Zhang ◽  
Longgang Xiang ◽  
Siyu Li ◽  
Dehao Wang
Keyword(s):  
Road Network ◽  
Rapid Development ◽  
Road Networks ◽  
Uneven Distribution ◽  
Trajectory Data ◽  
Network Generation ◽  
The Road ◽  
Lower Accuracy ◽  
Road Intersections ◽  
Vehicle Trajectory

Extracting highly detailed and accurate road network information from crowd-sourced vehicle trajectory data, which has the advantages of being low cost and able to update fast, is a hot topic. With the rapid development of wireless transmission technology, spatial positioning technology, and the improvement of software and hardware computing ability, more and more researchers are focusing on the analysis of Global Positioning System (GPS) trajectories and the extraction of road information. Road intersections are an important component of roads, as they play a significant role in navigation and urban planning. Even though there have been many studies on this subject, it remains challenging to determine road intersections, especially for crowd-sourced vehicle trajectory data with lower accuracy, lower sampling frequency, and uneven distribution. Therefore, we provided a new intersection-first approach for road network generation based on low-frequency taxi trajectories. Firstly, road intersections from vector space and raster space were extracted respectively via using different methods; then, we presented an integrated identification strategy to fuse the intersection extraction results from different schemes to overcome the sparseness of vehicle trajectory sampling and its uneven distribution; finally, we adjusted road information, repaired fractured segments, and extracted the single/double direction information and the turning relationships of the road network based on the intersection results, to guarantee precise geometry and correct topology for the road networks. Compared with other methods, this method shows better results, both in terms of their visual inspections and quantitative comparisons. This approach can solve the problems mentioned above and ensure the integrity and accuracy of road intersections and road networks. Therefore, the proposed method provides a promising solution for enriching and updating navigable road networks and can be applied in intelligent transportation systems.

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 An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

2019 ◽  
Vol 8 (9) ◽  
pp. 411 ◽  
Author(s):  
Tang ◽  
Deng ◽  
Huang ◽  
Liu ◽  
Chen
Keyword(s):  
Road Network ◽  
Sampling Rate ◽  
Quality Data ◽  
Automatic Method ◽  
Trajectory Data ◽  
The Road ◽  
High Sampling Rate ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

scholarly journals RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

2022 ◽  
Author(s):  
Qibin Zhou ◽  
Qingang Su ◽  
Dingyu Yang
Keyword(s):  
Travel Time ◽  
Real Time ◽  
High Speed ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Trajectory Data ◽  
Real Time Traffic ◽  
Histogram Estimation ◽  
Traffic Estimation

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

Methodology of Inventory the Real Estate Components for the Purpose of Their Valuation Due to Road Construction – Case Study in Krakow

2017 ◽  
Author(s):  
Monika Siejka ◽  
Monika Mika
Keyword(s):  
Real Estate ◽  
Communication Systems ◽  
Road Network ◽  
Measurement Techniques ◽  
Road Construction ◽  
The Real ◽  
The Real Estate ◽  
The Road ◽  
Investment Process

The development of the communication systems determines the economic level of the country. In Poland, despite the successive investments in this area, it is still not enough beneficial solutions to the road network and international calls. The problem of the acquisition of property for public roads on both the valuation principles and the way of obtaining land for these purposes is constantly modified. These changes are intended to simplify the procedures, which have a significant impact on shortening of the investment process. The current provisions of law give the possibility of the start of road investment before a property owner receives compensation for land taken for this purpose. This situation requires an inventory of component parts of the property for the purposes of their valuation. The paper presents the methodology of inventory the real estate components for the needs of their valuation using modern measurement techniques GNSS and GIS.

scholarly journals Learning to Generate Maps from Trajectories

2020 ◽  
Vol 34 (01) ◽  
pp. 890-897 ◽  
Author(s):  
Sijie Ruan ◽  
Cheng Long ◽  
Jie Bao ◽  
Chunyang Li ◽  
Zisheng Yu ◽  
...  
Keyword(s):  
Road Network ◽  
Traditional Approach ◽  
Trajectory Data ◽  
Embedded Devices ◽  
The Road ◽  
Mobile Objects ◽  
Map Generation ◽  
Spatial View ◽  
Topological Connectivity ◽  
Wide Usage

Accurate and updated road network data is vital in many urban applications, such as car-sharing, and logistics. The traditional approach to identifying the road network, i.e., field survey, requires a significant amount of time and effort. With the wide usage of GPS embedded devices, a huge amount of trajectory data has been generated by different types of mobile objects, which provides a new opportunity to extract the underlying road network. However, the existing trajectory-based map recovery approaches require many empirical parameters and do not utilize the prior knowledge in existing maps, which over-simplifies or over-complicates the reconstructed road network. To this end, we propose a deep learning-based map generation framework, i.e., DeepMG, which learns the structure of the existing road network to overcome the noisy GPS positions. More specifically, DeepMG extracts features from trajectories in both spatial view and transition view and uses a convolutional deep neural network T2RNet to infer road centerlines. After that, a trajectory-based post-processing algorithm is proposed to refine the topological connectivity of the recovered map. Extensive experiments on two real-world trajectory datasets confirm that DeepMG significantly outperforms the state-of-the-art methods.

Visualisation and Detection of Road Traffic Events Using Complex Event Processing

2018 ◽  
Author(s):  
Paulo Figueiras ◽  
Hugo Antunes ◽  
Guilherme Guerreiro ◽  
Ruben Costa ◽  
Ricardo Jardim-Gonçalves
Keyword(s):  
Real Time ◽  
Road Network ◽  
Road Traffic ◽  
Complex Event Processing ◽  
Advanced Technology ◽  
Transport Systems ◽  
Automatic Identification ◽  
Event Processing ◽  
Time Data ◽  
The Road

In the recent decades, we have witnessed an increase in the number of vehicles using the road infrastructure, resulting in an increased overload of the road network. To mitigate such problems, caused by the increasing number of vehicles and increasing the efficiency and safety of transport systems has been integrated applications of advanced technology, denominated Intelligent Transport Systems (ITS). However, one problem still unsolved in current road networks is the automatic identification of road events such as accidents or traffic jams, being inhibitor to efficient road management. In order to mitigate this problematic, this paper proposes the development of a technological platform able to detect anomalies (abnormal traffic events) to typical road network status and categorize such anomalies. The proposed work, adopts a complex event processing (CEP) engine able to monitor streams of events and detect specified patterns of events in real time. Data is collectively collected and analysed in real-time from loop sensors deployed in Slovenian highways and national roads, providing traffic flows. This prototype will work with a large number of data, being used to process all data, complex event processing tools. All the data used to validate the present study is based on the Slovenian road network. This work has been carried out in the context of the OPTIMUM Project, funded by the H2020 European Research Framework Program.

scholarly journals Automatic Detection and Classification of Road, Car, and Pedestrian Using Binocular Cameras in Traffic Scenes with a Common Framework

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Yongchao Song ◽  
Jieru Yao ◽  
Yongfeng Ju ◽  
Yahong Jiang ◽  
Kai Du
Keyword(s):  
Real Time ◽  
Pedestrian Detection ◽  
Minimum Energy ◽  
Automatic Detection ◽  
Initial Contour ◽  
Target Area ◽  
Threshold Method ◽  
The Real ◽  
The Road ◽  
On The Road

In order to solve the problems of traffic object detection, fuzzification, and simplification in real traffic environment, an automatic detection and classification algorithm for roads, vehicles, and pedestrians with multiple traffic objects under the same framework is proposed. We construct the final V view through a considerate U-V view method, which determines the location of the horizon and the initial contour of the road. Road detection results are obtained through error label reclassification, omitting point reassignment, and so an. We propose a peripheral envelope algorithm to determine sources of vehicles and pedestrians on the road. The initial segmentation results are determined by the regional growth of the source point through the minimum neighbor similarity algorithm. Vehicle detection results on the road are confirmed by combining disparity and color energy minimum algorithms with the object window aspect ratio threshold method. A method of multifeature fusion is presented to obtain the pedestrian target area, and the pedestrian detection results on the road are accurately segmented by combining the disparity neighbor similarity and the minimum energy algorithm. The algorithm is tested in three datasets of Enpeda, KITTI, and Daimler; then, the corresponding results prove the efficiency and accuracy of the proposed approach. Meanwhile, the real-time analysis of the algorithm is performed, and the average time efficiency is 13 pfs, which can realize the real-time performance of the detection process.

Characteristics of Real-Time Imitation Control for Biped Robot: Falling Time Analysis

2013 ◽  
Vol 427-429 ◽  
pp. 983-986
Author(s):  
Yi Feng Cui ◽  
Su Goog Shon ◽  
Hee Jung Byun
Keyword(s):  
Real Time ◽  
Inverted Pendulum ◽  
Angular Position ◽  
Biped Robot ◽  
Trajectory Data ◽  
Time Analysis ◽  
The Real ◽  
Real Robot

The purpose of this paper is to show that a biped robot can walk by an imitation control. It proposes architecture and system for real-time imitation control of a biped robot. Using this method, the operator can interact with the robot to walk. The operator produces trajectory data necessary to start, stop, walk and redirect the robot. We have to send control commands or new angular position values for to the robot as fast as possible. To get intuition how fast the robot should be controlled, its falling time which as the primary time question is discussed. An inverted pendulum calculation example and the real robot fall down experiment were compared in this paper.

scholarly journals An advanced planner for urban freight delivering

2018 ◽  
Vol 4 (48) ◽  
pp. 27-40 ◽  
Author(s):  
Antonio COMI ◽  
Berta BUTTARAZZI ◽  
Massimiliano SCHIRALDI ◽  
Rosy INNARELLA ◽  
Martina VARISCO ◽  
...  
Keyword(s):  
Real Time ◽  
Web Application ◽  
Road Network ◽  
Time Windows ◽  
Mobile App ◽  
Delivery Time ◽  
Time Data ◽  
Urban Freight ◽  
The Road ◽  
On The Road

The paper aims at introducing an advanced delivery tour planner to support operators in urban delivery operations through a combined approach which chooses delivery bays and delivery time windows while optimizing the delivery routes. After a literature review on tools for the management and the control of the delivery system implemented for optimizing the usage of on-street delivery bays, a prototypical tour delivery planner is described. The tool allows transport and logistics operators to book the delivery bays and to have real-time suggestions on the delivery tour to follow, through the minimization of the total delivery time. Currently, at development phase, the tool has been tested in a target zone, considering the road network and time/city delivering constraints and real-time data about vehicles location, traffic and delivery bay availability. The tool identifies the possible tours based on the delivery preferences, ranks the possible solutions according to the total route time based on information on the road network (i.e. travel time forecasts), performs a further optimization to reduce the total travel times and presents the user the best alternative along with the indications of which delivery bay to use in each delivery stop. The developed prototype is composed by two main parts: a web application that manages communication between the database and the road network simulation, and, an Android mobile App that supports transport and logistic operators in managing their delivering, pre trip and en route, showing and updating routing based on real-time information.

Sign in / Sign up

Export Citation Format

Share Document