scholarly journals Understanding the Tourists’ Spatio-Temporal Behavior Using Open GPS Trajectory Data: A Case Study of Yuanmingyuan Park (Beijing, China)

2020 ◽  
Vol 13 (1) ◽  
pp. 94
Author(s):  
Qian Yao ◽  
Yong Shi ◽  
Hai Li ◽  
Jiahong Wen ◽  
Jianchao Xi ◽  
...  
Keyword(s):  
Hot Spots ◽  
Dwell Time ◽  
Average Dwell Time ◽  
Trajectory Data ◽  
Tourist Behavior ◽  
Gps Trajectory Data ◽  
Different Seasons ◽  
Gps Trajectory ◽  
Spatio Temporal ◽  
The Relationship

The visit paths, dwell time, and taking pictures are all variables of great significance to our understanding of tourists’ spatio-temporal behavior. Does having a large number of visitors mean that tourists are interested in a tourist location? What is the relationship between the dwell time and taking pictures? Are there differences in tourist behavior in different seasons? These issues are of great significance to tourism research but they have not been rigorously analyzed yet. This paper aims to understand the relationship between tourists’ visit path, dwell time, and taking pictures, and test whether there are differences in tourist behavior in different seasons. We used open global positioning systems (GPS) trajectory data at Yuanmingyuan Park from January 2014 to August 2020. Using Python and ArcGIS tools, we found hot spots of tourist passing, hot spots of tourist gathering, high average dwell time areas, and tourist interest areas. It is further found that: (1) passenger flow strongly explains dwell time, while the correlation between passenger flow and average dwell time is weak. (2) There was a close relationship between tourists’ stay and photo-taking behavior, which provided a theoretical basis for defining tourist photo behavior as tourists’ stay behavior. (3) Seasons did not significantly affect tourist behavior in Yuanmingyuan Park. This study presents a grid-based open GPS trajectory data processing framework that clarified the potential of an open GPS trajectory in tourist behavior research. Furthermore, our study explored the relationship between essential indicators and found that there is a strong consistency in tourist behavior across seasons.

scholarly journals Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

2016 ◽  
Vol 8 (3) ◽  
pp. 419-434 ◽  
Author(s):  
Seyed Morteza Mousavi ◽  
Aaron Harwood ◽  
Shanika Karunasekera ◽  
Mojtaba Maghrebi
Keyword(s):  
Trajectory Data ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Spatio Temporal

Statistical analysis of the stability of bus vehicles based on GPS trajectory data

2019 ◽  
Vol 33 (03) ◽  
pp. 1950015 ◽  
Author(s):  
Hui Zhang ◽  
Baiying Shi ◽  
Shuguang Song ◽  
Quanman Zhao ◽  
Xiangming Yao ◽  
...  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
Dwell Time ◽  
Trajectory Data ◽  
Running Time ◽  
Bus Bunching ◽  
Gps Trajectory Data ◽  
Gps Trajectory ◽  
Bus Service ◽  
The Stability

High quality bus service is considered as an efficient way to mitigate traffic congestion in big cities. Global positioning system (GPS) data provide sufficient sources to evaluate the performance of bus vehicles that both passengers and operator concern about. This paper aims to propose a framework to assess the operational performance of bus routes based on the GPS trajectory data collected from Jinan, China. Several important indicators of bus operation including travel time of routes, section running time, dwell time and bus bunching have been studied. The results show that the travel time of routes follow right skewed distributions. Moreover, section running time between two consecutive stations varies in different time period and it is larger in evening peak hours. Additionally, the dwell time has been discussed and the results show that there is no big variation in most stations except some stations, which provides a help to identify the key stations. Furthermore, we propose an approach to detect the bunching points. The results indicate the bunching points are easy to occur in the peak hours and the congested road section.

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.

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