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

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.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jing Wang ◽  
Chunjiao Dong ◽  
Chunfu Shao ◽  
Shichen Huang ◽  
Shuang Wang
Keyword(s):  
Network Model ◽  
Evaluation System ◽  
Large Scale ◽  
Urban Traffic ◽  
Identification Algorithm ◽  
Trajectory Data ◽  
Dynamic Traffic ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Key Nodes

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.

A Heading Maintaining Oriented Compression Algorithm for GPS Trajectory Data

Informatica ◽  
2019 ◽  
Vol 30 (1) ◽  
pp. 33-52 ◽  
Author(s):  
Pengfei HAO ◽  
Chunlong YAO ◽  
Qingbin MENG ◽  
Xiaoqiang YU ◽  
Xu LI
Keyword(s):  
Compression Algorithm ◽  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory

Enabling Smart Urban Services with GPS Trajectory Data

2021 ◽  
Author(s):  
Chao Chen ◽  
Daqing Zhang ◽  
Yasha Wang ◽  
Hongyu Huang
Keyword(s):  
Trajectory Data ◽  
Urban Services ◽  
Gps Trajectory Data ◽  
Gps Trajectory

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 Extended Classification Course Improves Road Intersection Detection from Low-Frequency GPS Trajectory Data

2020 ◽  
Vol 9 (3) ◽  
pp. 181
Author(s):  
Banqiao Chen ◽  
Chibiao Ding ◽  
Wenjuan Ren ◽  
Guangluan Xu
Keyword(s):  
Clustering Algorithm ◽  
Recall Rate ◽  
Location Based Services ◽  
Trajectory Data ◽  
Intersection Detection ◽  
Map Generation ◽  
Gps Trajectory Data ◽  
Road Intersections ◽  
Road Maps ◽  
Gps Trajectory

The requirements of location-based services have generated an increasing need for up-to-date digital road maps. However, traditional methods are expensive and time-consuming, requiring many skilled operators. The feasibility of using massive GPS trajectory data provides a cheap and quick means for generating and updating road maps. The detection of road intersections, being the critical component of a road map, is a key problem in map generation. Unfortunately, low sampling rates and high disparities are ubiquitous among floating car data (FCD), making road intersection detection from such GPS trajectories very challenging. In this paper, we extend a point clustering-based road intersection detection framework to include a post-classification course, which utilizes the geometric features of road intersections. First, we propose a novel turn-point position compensation algorithm, in order to improve the concentration of selected turn-points under low sampling rates. The initial detection results given by the clustering algorithm are recall-focused. Then, we rule out false detections in an extended classification course based on an image thinning algorithm. The detection results of the proposed method are quantitatively evaluated by matching with intersections from OpenStreetMap using a variety of distance thresholds. Compared with other methods, our approach can achieve a much higher recall rate and better overall performance, thereby better supporting map generation and other similar applications.

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