scholarly journals SPATIO-TEMPORAL CHARACTERISTICS OF RESIDENT TRIP BASED ON POI AND OD DATA OF FLOAT CAR IN BEIJING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 99-105
Author(s):  
N. Mou ◽  
J. Li ◽  
L. Zhang ◽  
W. Liu ◽  
Y. Xu
Keyword(s):  
Research Unit ◽  
Concentric Circle ◽  
Relative Dispersion ◽  
Residential Areas ◽  
Trajectory Data ◽  
Periodic Patterns ◽  
Temporal Characteristics ◽  
The Road ◽  
Spatio Temporal ◽  
Taxi Trajectory

Due to the influence of the urban inherent regional functional distribution, the daily activities of the residents presented some spatio-temporal patterns (periodic patterns, gathering patterns, etc.). In order to further understand the spatial and temporal characteristics of urban residents, this paper research takes the taxi trajectory data of Beijing as a sample data and studies the spatio-temporal characteristics of the residents' activities on the weekdays. At first, according to the characteristics of the taxi trajectory data distributed along the road network, it takes the Voronoi generated by the road nodes as the research unit. This paper proposes a hybrid clustering method – based on grid density, which is used to cluster the OD (origin and destination) data of taxi at different times. Then，combining with the POI data of Beijing, this research calculated the density of the POI data in the clustering results, and analyzed the relationship between the activities of residents in different periods and the functional types of the region. The final results showed that the residents were mainly commuting on weekdays. And it found that the distribution of travel density showed a concentric circle of the characteristics, focusing on residential areas and work areas. The results of cluster analysis and POI analysis showed that the residents' travel had experienced the process of "spatial relative dispersion – spatial aggregation – spatial relative dispersion" in one day.

scholarly journals UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 263-268 ◽  
Author(s):  
P. X. Zhao ◽  
S. M. Zhao
Keyword(s):  
Traffic Flow ◽  
Flow Characteristics ◽  
Research Unit ◽  
Urban Traffic ◽  
Network Centrality ◽  
Trajectory Data ◽  
The Road ◽  
Analysis Process ◽  
Intersection Line ◽  
Taxi Trajectory

In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Experimental results indicate that urbaxperimental results indicate that urbaxperimental results indicate that urban traffic flow is relatively influenced by the road network structure. However, urban traffic flow also depends on the research unit size. Traditional centralities and traffic flow exhibit a low correlation at point granularity but exhibit a high correlation at line and area granularities. Furthermore, the conclusions of this study reflect the universality of the modifiable areal unit problem.

scholarly journals UNDERSTANDING URBAN TRAFFIC FLOW CHARACTERISTICS FROM THE NETWORK CENTRALITY PERSPECTIVE AT DIFFERENT GRANULARITIES

2016 ◽  
Vol XLI-B2 ◽  
pp. 263-268
Author(s):  
P. X. Zhao ◽  
S. M. Zhao
Keyword(s):  
Traffic Flow ◽  
Flow Characteristics ◽  
Research Unit ◽  
Urban Traffic ◽  
Network Centrality ◽  
Trajectory Data ◽  
The Road ◽  
Analysis Process ◽  
Intersection Line ◽  
Taxi Trajectory

In this study, we analyze urban traffic flow using taxi trajectory data to understand the characteristics of traffic flow from the network centrality perspective at point (intersection), line (road), and area (community) granularities. The entire analysis process comprises three steps. The first step utilizes the taxi trajectory data to evaluate traffic flow at different granularities. Second, the centrality indices are calculated based on research units at different granularities. Third, correlation analysis between the centrality indices and corresponding urban traffic flow is performed. Experimental results indicate that urbaxperimental results indicate that urbaxperimental results indicate that urban traffic flow is relatively influenced by the road network structure. However, urban traffic flow also depends on the research unit size. Traditional centralities and traffic flow exhibit a low correlation at point granularity but exhibit a high correlation at line and area granularities. Furthermore, the conclusions of this study reflect the universality of the modifiable areal unit problem.

scholarly journals Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data

2020 ◽  
Vol 13 (1) ◽  
pp. 112
Author(s):  
Helai Huang ◽  
Jialing Wu ◽  
Fang Liu ◽  
Yiwei Wang
Keyword(s):  
Waiting Time ◽  
Urban Areas ◽  
Transportation Systems ◽  
Trajectory Data ◽  
Peak Period ◽  
Important Indicator ◽  
Destination Attractiveness ◽  
Urban Accessibility ◽  
Spatio Temporal ◽  
Taxi Trajectory

Accessibility has attracted wide interest from urban planners and transportation engineers. It is an important indicator to support the development of sustainable policies for transportation systems in major events, such as the COVID-19 pandemic. Taxis are a vital travel mode in urban areas that provide door-to-door services for individuals to perform urban activities. This study, with taxi trajectory data, proposes an improved method to evaluate dynamic accessibility depending on traditional location-based measures. A new impedance function is introduced by taking characteristics of the taxi system into account, such as passenger waiting time and the taxi fare rule. An improved attraction function is formulated by considering dynamic availability intensity. Besides, we generate five accessibility scenarios containing different indicators to compare the variation of accessibility. A case study is conducted with the data from Shenzhen, China. The results show that the proposed method found reduced urban accessibility, but with a higher value in southern center areas during the evening peak period due to short passenger waiting time and high destination attractiveness. Each spatio-temporal indicator has an influence on the variation in accessibility.

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

scholarly journals VISUALIZATION OF SPATIO-TEMPORAL RELATIONS IN MOVEMENT EVENT USING MULTI-VIEW

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1469-1476
Author(s):  
K. Zheng ◽  
D. Gu ◽  
F. Fang ◽  
Y. Wang ◽  
H. Liu ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Multiple Scales ◽  
Temporal Relations ◽  
Discovery Process ◽  
Trajectory Data ◽  
Statistical Tools ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Visual Interface ◽  
Taxi Trajectory

Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multi-scale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatio-temporal relations of movement events from taxi trajectory data.

scholarly journals Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

2019 ◽  
Vol 8 (6) ◽  
pp. 257 ◽  
Author(s):  
Huihui Wang ◽  
Hong Huang ◽  
Xiaoyong Ni ◽  
Weihua Zeng
Keyword(s):  
Large Scale ◽  
Travel Demand ◽  
Chord Diagram ◽  
Future Research ◽  
Trajectory Data ◽  
Temporal Scales ◽  
Data Set ◽  
Ring Road ◽  
Temporal Characteristics ◽  
Taxi Trajectory

Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

scholarly journals TRAJECTORY COMPRESSION WITH CONSTRAINTS OF ROAD NETWORKS

2018 ◽  
Vol XLII-4 ◽  
pp. 751-755
Author(s):  
L. Zhang ◽  
M. Liu ◽  
Y. Long
Keyword(s):  
Data Storage ◽  
Moving Objects ◽  
Structural Characteristics ◽  
Road Networks ◽  
Map Matching ◽  
Trajectory Data ◽  
Temporal Characteristics ◽  
The Road ◽  
Social Big Data ◽  
The Social

<p><strong>Abstract.</strong> In recent, the trajectory data of moving objects is getting bigger and bigger, and it has become a very important part of the social big data. Its compression is an indispensable operation of data processing, and also it is the basis of the data storage, analysis and mining of moving objects. In the related research, there are two kinds of methods for the trajectory compression. One is to compress trajectory data based on its own spatial-temporal characteristics, another kind of methods is the map-matched trajectory compression. However, for offline trajectory compression, methods based on spatial-temporal characteristics do not take the road network constraints into account. If road networks are considered, the map matching is needed first, and it will greatly affect the efficiency of trajectory compression. Therefore, this paper proposes a new trajectory compression algorithm that combines the spatial-temporal characteristics from trajectories themselves and structural characteristics from road networks to improve the compression precision and efficiency.</p>

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