Identifying the Key Nodes and Sections of Urban Roadway Network Based on GPS Trajectory Data

This paper proposes a novel approach to identify the key nodes and sections of the roadway network. The taxi-GPS trajectory data are regarded as mobile sensor to probe a large scale of urban traffic flows in real time. First, the urban primary roadway network model and dual roadway network model are developed, respectively, based on the weighted complex network. Second, an evaluation system of the key nodes and sections is developed from the aspects of dynamic traffic attributes and static topology. At the end, the taxi-GPS data collected in Xicheng District of Beijing, China, are analyzed. A comprehensive analysis of the spatial-temporal changes of the key nodes and sections is performed. Moreover, the repetition rate is used to evaluate the performance of the identification algorithm of key nodes and sections. The results show that the proposed method realizes the expression of topological structure and dynamic traffic attributes of the roadway network simultaneously, which is more practicable and effective in a large scale.

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Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.238.503 ◽

2012 ◽

Vol 238 ◽

pp. 503-506 ◽

Cited By ~ 1

Author(s):

Zhi Cheng Li

Keyword(s):

Traffic Flow ◽

Network Model ◽

Large Scale ◽

Choice Model ◽

Transportation Systems ◽

Urban Traffic ◽

Guidance System ◽

Traffic Network ◽

Dynamic Traffic ◽

Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

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A deep convolutional neural network based approach for vehicle classification using large-scale GPS trajectory data

Transportation Research Part C Emerging Technologies ◽

10.1016/j.trc.2020.102644 ◽

2020 ◽

Vol 116 ◽

pp. 102644 ◽

Cited By ~ 1

Author(s):

Sina Dabiri ◽

Nikola Marković ◽

Kevin Heaslip ◽

Chandan K. Reddy

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Large Scale ◽

Deep Convolutional Neural Network ◽

Vehicle Classification ◽

Trajectory Data ◽

Gps Trajectory Data ◽

Gps Trajectory

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Dynamic Traffic Assignment Model Based on GPS Data and Point of Interest (POI) in Shanghai

Sensors ◽

10.3390/s21217341 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7341

Author(s):

Xueying Song ◽

Zheng Yang ◽

Tao Wang ◽

Chaoyang Li ◽

Yi Zhang ◽

...

Keyword(s):

Traffic Flow ◽

Road Network ◽

Traffic Assignment ◽

Trajectory Data ◽

Dynamic Traffic ◽

Urban Road ◽

Assignment Model ◽

Flow Assignment ◽

Gps Trajectory Data ◽

Gps Trajectory

Dynamic traffic flow, which can facilitate the efficient operation of traffic road networks, is an important prerequisite for the application of reasonable assignment of traffic demands in an urban road network. In order to improve the accuracy of dynamic traffic flow assignment, this paper proposes a dynamic traffic flow assignment model based on GPS trajectory data and the influence of POI. First, this paper explores the impact patterns of POI on regional road network congestion during peak hours through qualitative and quantitative analysis. Then, based on the user equilibrium theory, a dynamic traffic flow assignment model, in which the effect of POI on links is reflected using the link-node impedance function, is proposed. Finally, the accuracy of the model is validated by the GPS trajectory data and origin–destination (OD) traffic data of motor vehicles in Xuhui District, Shanghai, China. The results show that the model can be used to coordinate and optimize the traffic assignment of the regional road network under the influence of POI during peak hours and alleviate the congestion of the road network. The findings can provide a powerful reference for developing scientific and rational traffic assignment decisions and management strategies for urban road network traffic.

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Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China

Sustainability ◽

10.3390/su13042114 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2114

Author(s):

Bing Han ◽

Ziheng Wu ◽

Chaoyi Gu ◽

Kui Ji ◽

Jiangang Xu

Keyword(s):

Large Scale ◽

Operating Mode ◽

Time Of Day ◽

Trajectory Data ◽

Passenger Cars ◽

Drive Cycle ◽

Gps Trajectory Data ◽

Gps Trajectory ◽

Emission Modeling ◽

Vehicle Activity

A drive cycle describes the microscopic and macroscopic vehicle activity information that is crucial for emission quantification research, e.g., emission modeling or emission testing. Well-developed drive cycles capture the driving patterns representing the traffic conditions of the study area, which usually are employed as the input of the emission models. By considering the potential of large-scale GPS trajectory data collected by ubiquitous on-vehicle tracking equipment, the objective of this study is to demonstrate the capability of GPS-based trajectory data from rideshare passenger cars for urban drive cycle development. Large-scale GPS trajectory data and order data collected by an app-based transportation vehicle was used in this study. GPS data were filtered by thresholds of instantaneous accelerations and vehicle specific powers. The micro-trip selection-to-rebuild method with operating mode distribution was used to develop a series of speed-bin categorized representative drive cycles. Sensitivity of the time-of-day and day-of-week were analyzed on the developed drive cycles. The representativeness of the developed drive cycles was verified and significant differences exist when they are compared to the default light-duty drive cycles coded in MOVES. The findings of this study can be used for helping drive cycle development and emission modeling, further improving the understanding of localized emission levels.

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A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽

10.15388/informatica.2018.196 ◽

2019 ◽

Vol 30 (1) ◽

pp. 33-52 ◽

Cited By ~ 1

Author(s):

Pengfei HAO ◽

Chunlong YAO ◽

Qingbin MENG ◽

Xiaoqiang YU ◽

Xu LI

Keyword(s):

Compression Algorithm ◽

Trajectory Data ◽

Gps Trajectory Data ◽

Gps Trajectory

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Enabling Smart Urban Services with GPS Trajectory Data

10.1007/978-981-16-0178-1 ◽

2021 ◽

Author(s):

Chao Chen ◽

Daqing Zhang ◽

Yasha Wang ◽

Hongyu Huang

Keyword(s):

Trajectory Data ◽

Urban Services ◽

Gps Trajectory Data ◽

Gps Trajectory

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8090411 ◽

2019 ◽

Vol 8 (9) ◽

pp. 411 ◽

Cited By ~ 3

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.

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