scholarly journals Map Matching for Urban High-Sampling-Frequency GPS Trajectories

2020 ◽  
Vol 9 (1) ◽  
pp. 31
Author(s):  
Minshi Liu ◽  
Ling Zhang ◽  
Junlian Ge ◽  
Yi Long ◽  
Weitao Che
Keyword(s):  
Traffic Management ◽  
Route Planning ◽  
Sampling Frequency ◽  
Urban Traffic ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Method ◽  
Matching Methods ◽  
High Sampling ◽  
Matching Rules

As a fundamental component of trajectory processing and analysis, trajectory map-matching can be used for urban traffic management and tourism route planning, among other applications. While there are many trajectory map-matching methods, urban high-sampling-frequency GPS trajectory data still depend on simple geometric matching methods, which can lead to mismatches when there are multiple trajectory points near one intersection. Therefore, this study proposed a novel segmented trajectory matching method in which trajectory points were separated into intersection and non-intersection trajectory points. Matching rules and processing methods dedicated to intersection trajectory points were developed, while a classic “Look-Ahead” matching method was applied to non-intersection trajectory points, thereby implementing map matching of the whole trajectory. Then, a comparative analysis between the proposed method and two other new related methods was conducted on trajectories with multiple sampling frequencies. The results indicate that the proposed method is not only competent for intersection matching with high-frequency trajectory data but also superior to two other methods in both matching efficiency and accuracy.

scholarly journals Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

2021 ◽  
Vol 10 (11) ◽  
pp. 787
Author(s):  
Chunchun Hu ◽  
Si Chen
Keyword(s):  
Traffic Management ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Moving Objects ◽  
Urban Traffic ◽  
Trajectory Data ◽  
Significant Group ◽  
Study Results ◽  
Movement Tendency ◽  
Graphics Processing

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

scholarly journals Method for Fast Map Construction Based on GPS Data and Compressed Grid Algorithm

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1322
Author(s):  
Jian Zhang ◽  
Shuai Ling ◽  
Ping Wang ◽  
Xiaoyang Hu ◽  
Lu Liu
Keyword(s):  
Traffic Management ◽  
Route Planning ◽  
Grid Cell ◽  
Urban Traffic ◽  
Gps Data ◽  
Storage Space ◽  
Resource Requirements ◽  
Map Generation ◽  
Time Update ◽  
Optimization Efficiency

Electronic maps play an important role in the field of urban traffic management, but the interface functions provided by map service agencies are limited, and commercial maps are generally expensive. Furthermore, the map generation algorithms based on the Global Positioning System (GPS) data can be very complex and take up a lot of storage space, which limits their application to specific practical problems, such as the real-time update of area maps, temporary road control, emergency route planning, and other scenarios. In order to solve this problem, an intuitive, extensible, and flexible method of constructing urban road maps is proposed. Using the Othello-coordinated method, the representation of the unit grid cell was redesigned. Through this method, the disadvantages of the raster map’s large storage space and computing resource requirements are compensated for during processing, improving the topological expression ability of the raster map and the speed with which the construction of the map is realized. The application potential of the proposed method is demonstrated by the evaluation of public transport service and road network resilience. In our experiments, the optimization efficiency of storage space was up to 99.914%, and the calculation accuracy of bus coverage was about 99.86%.

scholarly journals Research on map matching algorithm based on priority rule for low sampling frequency vehicle navigation data

2019 ◽  
Vol 3 (1) ◽  
pp. 2-13
Author(s):  
Zhishuo Liu ◽  
Yao Dongxin ◽  
Zhao Kuan ◽  
Wang Chun Fang
Keyword(s):  
Sampling Frequency ◽  
Experimental Results ◽  
Map Matching ◽  
Data Sampling ◽  
Priority Rule ◽  
Priority Rules ◽  
Trajectory Data ◽  
Content Type ◽  
Vehicle Trajectory ◽  
Better Than

Purpose There is a certain error in the satellite positioning of the vehicle. The error will cause the drift point of the positioning point, which makes the vehicle trajectory shift to the real road. This paper aims to solve this problem. Design/methodology/approach The key technology to solve the problem is map matching (MM). The low sampling frequency of the vehicle is far from the distance between adjacent points, which weakens the correlation between the points, making MM more difficult. In this paper, an MM algorithm based on priority rules is designed for vehicle trajectory characteristics at low sampling frequencies. Findings The experimental results show that the MM based on priority rule algorithm can effectively match the trajectory data of low sampling frequency with the actual road, and the matching accuracy is better than other similar algorithms, the processing speed reaches 73 per second. Research limitations/implications In the algorithm verification of this paper, although the algorithm design and experimental verification are considered considering the diversity of GPS data sampling frequency, the experimental data used are still a single source. Originality/value Based on the GPS trajectory data of the Ministry of Transport, the experimental results show that the accuracy of the priority-based weight-based algorithm is higher. The accuracy of this algorithm is over 98.1 per cent, which is better than other similar algorithms.

scholarly journals Effect Evaluation of Spatial Characteristics on Map Matching-Based Indoor Positioning

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6698
Author(s):  
Shuaiwei Luo ◽  
Fuqiang Gu ◽  
Fan Xu ◽  
Jianga Shang
Keyword(s):  
Particle Filtering ◽  
Spatial Information ◽  
Map Matching ◽  
Spatial Structures ◽  
Effect Evaluation ◽  
Matching Method ◽  
Open Spaces ◽  
Spatial Characteristics ◽  
Matching Methods ◽  
Proper Map

Map-matching is a popular method that uses spatial information to improve the accuracy of positioning methods. The performance of map matching methods is closely related to spatial characteristics. Although several studies have demonstrated that certain map matching algorithms are affected by some spatial structures (e.g., parallel paths), they focus on the analysis of single map matching method or few spatial structures. In this study, we explored how the most commonly-used four spatial characteristics (namely forks, open spaces, corners, and narrow corridors) affect three popular map matching methods, namely particle filtering (PF), hidden Markov model (HMM), and geometric methods. We first provide a theoretical analysis on how spatial characteristics affect the performance of map matching methods, and then evaluate these effects through experiments. We found that corners and narrow corridors are helpful in improving the positioning accuracy, while forks and open spaces often lead to a larger positioning error. We hope that our findings are helpful for future researchers in choosing proper map matching algorithms with considering the spatial characteristics.

scholarly journals Spatio-Temporal Visual Analysis for Urban Traffic Characters Based on Video Surveillance Camera Data

2021 ◽  
Vol 10 (3) ◽  
pp. 177
Author(s):  
Haochen Zou ◽  
Keyan Cao ◽  
Chong Jiang
Keyword(s):  
Video Surveillance ◽  
Traffic Management ◽  
Visual Analysis ◽  
Urban Traffic ◽  
License Plate ◽  
Road Vehicle ◽  
Urban Road ◽  
Surveillance Camera ◽  
Vehicle Density ◽  
Spatio Temporal

Urban road traffic spatio-temporal characters reflect how citizens move and how goods are transported, which is crucial for trip planning, traffic management, and urban design. Video surveillance camera plays an important role in intelligent transport systems (ITS) for recognizing license plate numbers. This paper proposes a spatio-temporal visualization method to discover urban road vehicle density, city-wide regional vehicle density, and hot routes using license plate number data recorded by video surveillance cameras. To improve the accuracy of the visualization effect, during data analysis and processing, this paper utilized Internet crawler technology and adopted an outlier detection algorithm based on the Dixon detection method. In the design of the visualization map, this paper established an urban road vehicle traffic index to intuitively and quantitatively reveal the traffic operation situation of the area. To verify the feasibility of the method, an experiment in Guiyang on data from road video surveillance camera system was conducted. Multiple urban traffic spatial and temporal characters are recognized concisely and efficiently from three visualization maps. The results show the satisfactory performance of the proposed framework in terms of visual analysis, which will facilitate traffic management and operation.

A prediction model of vessel trajectory based on generative adversarial network

2021 ◽  
pp. 1-11
Author(s):  
Senjie Wang ◽  
Zhengwei He
Keyword(s):  
Safety Management ◽  
Route Planning ◽  
Local Area ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Ship Traffic ◽  
Adversarial Network ◽  
Historical Trajectory ◽  
Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

Method of Urban Traffic Management

2021 ◽  
Author(s):  
A. G. Shevtsova ◽  
A. N. Novikov ◽  
V. V. Silyanov
Keyword(s):  
Traffic Management ◽  
Urban Traffic

scholarly journals Exploring the Influence of E-Hailing Applications on the Taxi Industry—From the Perspective of the Drivers

2021 ◽  
Vol 10 (2) ◽  
pp. 77
Author(s):  
Yitong Gan ◽  
Hongchao Fan ◽  
Wei Jiao ◽  
Mengqi Sun
Keyword(s):  
Driving Behavior ◽  
Urban Traffic ◽  
Behavior Changes ◽  
Transportation Industry ◽  
Trajectory Data ◽  
Long Distance ◽  
Taxi Drivers ◽  
Driving Behaviors ◽  
Taxi Industry ◽  
The Times

In China, the traditional taxi industry is conforming to the trend of the times, with taxi drivers working with e-hailing applications. This reform is of great significance, not only for the taxi industry, but also for the transportation industry, cities, and society as a whole. Our goal was to analyze the changes in driving behavior since taxi drivers joined e-hailing platforms. Therefore, this paper mined taxi trajectory data from Shanghai and compared the data of May 2015 with those of May 2017 to represent the before-app stage and the full-use stage, respectively. By extracting two-trip events (i.e., vacant trip and occupied trip) and two-spot events (i.e., pick-up spot and drop-off spot), taxi driving behavior changes were analyzed temporally, spatially, and efficiently. The results reveal that e-hailing applications mine more long-distance rides and new pick-up locations for drivers. Moreover, driver initiative have increased at night since using e-hailing applications. Furthermore, mobile payment facilities save time that would otherwise be taken sorting out change. Although e-hailing apps can help citizens get taxis faster, from the driver’s perspective, the apps do not reduce their cruising time. In general, e-hailing software reduces the unoccupied ratio of taxis and improves the operating ratio. Ultimately, new driving behaviors can increase the driver’s revenue. This work is meaningful for the formulation of reasonable traffic laws and for urban traffic decision-making.

scholarly journals Interdisciplinary Evaluation of Intersection Performances—A Microsimulation-Based MCDA

2021 ◽  
Vol 13 (4) ◽  
pp. 1859
Author(s):  
Kadir Diler Alemdar ◽  
Ahmet Tortum ◽  
Ömer Kaya ◽  
Ahmet Atalay
Keyword(s):  
Traffic Engineering ◽  
Traffic Management ◽  
Evaluation Criteria ◽  
Urban Traffic ◽  
Suitable Alternative ◽  
Order Preference ◽  
Energy Environment ◽  
Vehicle Delay ◽  
Hierarchy Process ◽  
Co Emission

Intersections are the most important regions in terms of urban traffic management. The intersection areas on the corridor should be analyzed together for consistency in traffic engineering. To do so, three intersections on the Vatan Street corridor in İstanbul, the most crowded city of Turkey, were examined. Various geometric and signal designs were performed for intersections and the most suitable corridor design was analyzed. The corridor designs were modeled with the PTV VISSIM microsimulation software. The most suitable corridor design was evaluated by using the results obtained from the microsimulation via analytical hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) from multi criteria decision analysis (MCDA) methods. The evaluation criteria in the study are vehicle delay, queue length, stopped delay, stops, travel time, vehicle safety, CO emission, fuel consumption, and construction cost. As a result, the current and the most suitable alternative corridors were compared according to the comparison parameters and up to 80% improvements were observed. Thus, some advantages were obtained in terms of energy, environment, time, and cost.

A Dynamic K-nearest Neighbor Map Matching Method Combined with Neural Network

2019 ◽  
Author(s):  
Ting Liu ◽  
Zhe Chen ◽  
Chuan Chen ◽  
Zongtao Duan ◽  
Bin Zhao
Keyword(s):  
Neural Network ◽  
Nearest Neighbor ◽  
Map Matching ◽  
K Nearest Neighbor ◽  
Matching Method
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