Mining Trip Attractive Areas Using Large-Scale Taxi Trajectory Data

Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.

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A WEB-BASED PLATFORM FOR VISUALIZING SPATIOTEMPORAL DYNAMICS OF BIG TAXI DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-1407-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 1407-1412 ◽

Cited By ~ 2

Author(s):

H. Xiong ◽

L. Chen ◽

Z. Gui

Keyword(s):

Large Scale ◽

Clustering Algorithm ◽

Trajectory Data ◽

Visualization System ◽

City Life ◽

Interactive Animation ◽

Dbscan Clustering ◽

Main Work ◽

Visualization Systems ◽

Taxi Trajectory

With more and more vehicles equipped with Global Positioning System (GPS), access to large-scale taxi trajectory data has become increasingly easy. Taxis are valuable sensors and information associated with taxi trajectory can provide unprecedented insight into many aspects of city life. But analysing these data presents many challenges. Visualization of taxi data is an efficient way to represent its distributions and structures and reveal hidden patterns in the data. However, Most of the existing visualization systems have some shortcomings. On the one hand, the passenger loading status and speed information cannot be expressed. On the other hand, mono-visualization form limits the information presentation. In view of these problems, this paper designs and implements a visualization system in which we use colour and shape to indicate passenger loading status and speed information and integrate various forms of taxi visualization. The main work as follows: 1. Pre-processing and storing the taxi data into MongoDB database. 2. Visualization of hotspots for taxi pickup points. Through DBSCAN clustering algorithm, we cluster the extracted taxi passenger’s pickup locations to produce passenger hotspots. 3. Visualizing the dynamic of taxi moving trajectory using interactive animation. We use a thinning algorithm to reduce the amount of data and design a preloading strategyto load the data smoothly. Colour and shape are used to visualize the taxi trajectory data.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060257 ◽

2019 ◽

Vol 8 (6) ◽

pp. 257 ◽

Cited By ~ 8

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.

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Urban Function Identification Based on POI and Taxi Trajectory Data

Proceedings of the 2019 3rd International Conference on Big Data Research ◽

10.1145/3372454.3372468 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xiaohui Mou ◽

Fei Cai ◽

Xin Zhang ◽

Jie Chen ◽

Rongrong Zhu

Keyword(s):

Trajectory Data ◽

Function Identification ◽

Urban Function ◽

Taxi Trajectory

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Taxi trajectory data based fast-charging facility planning for urban electric taxi systems

Applied Energy ◽

10.1016/j.apenergy.2021.116515 ◽

2021 ◽

Vol 286 ◽

pp. 116515

Author(s):

Hua Wang ◽

De Zhao ◽

Yutong Cai ◽

Qiang Meng ◽

Ghim Ping Ong

Keyword(s):

Facility Planning ◽

Trajectory Data ◽

Fast Charging ◽

Taxi Trajectory

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

Sustainability ◽

10.3390/su13010112 ◽

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.

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Mining Vehicle Trajectories to Discover Individual Significant Places: Case Study using Floating Car Data in the Paris Region

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121995500 ◽

2021 ◽

pp. 036119812199550

Author(s):

Danyang Sun ◽

Fabien Leurent ◽

Xiaoyan Xie

Keyword(s):

Large Scale ◽

City Planning ◽

Human Mobility ◽

Work Place ◽

Trajectory Data ◽

Floating Car Data ◽

Vehicle Trajectories ◽

Activity Types ◽

Paris Region

In this study we discovered significant places in individual mobility by exploring vehicle trajectories from floating car data. The objective was to detect the geo-locations of significant places and further identify their functional types. Vehicle trajectories were first segmented into meaningful trips to recover corresponding stay points. A customized density-based clustering approach was implemented to cluster stay points into places and determine the significant ones for each individual vehicle. Next, a two-level hierarchy method was developed to identify the place types, which firstly identified the activity types by mixture model clustering on stay characteristics, and secondly discovered the place types by assessing their profiles of activity composition and frequentation. An applicational case study was conducted in the Paris region. As a result, five types of significant places were identified, including home place, work place, and three other types of secondary places. The results of the proposed method were compared with those from a commonly used rule-based identification, and showed a highly consistent matching on place recognition for the same vehicles. Overall, this study provides a large-scale instance of the study of human mobility anchors by mining passive trajectory data without prior knowledge. Such mined information can further help to understand human mobility regularities and facilitate city planning.

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