scholarly journals XStar: a software system for handling taxi trajectory big data

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Xiang Li ◽  
Joseph Mango ◽  
Jiajia Song ◽  
Di Zhang
Keyword(s):  
Big Data ◽  
Complete Picture ◽  
Software System ◽  
Trajectory Data ◽  
Raw Data ◽  
Storage Structure ◽  
Analytical Functions ◽  
Traffic Systems ◽  
And Storage ◽  
Taxi Trajectory

AbstractAdvances in positioning and communicating technologies make it possible to collect large volumes of taxi trajectory data, quickly providing a complete picture of the ground traffic systems and thus being applied to different fields. However, there are still challenges for data users to handle such big data. In view of this, we have developed a software system named XStar to deal with trajectory big data. Its core is a scalable index and storage structure. Based on it, raw data can be saved in a more compact scheme and accessed more efficiently. A real taxi trajectory dataset is employed to demonstrate its performance. In general, XStar facilitates processing and analyzing trajectory data affordably and straightforwardly. Since its release in Jan. 2019, it has received downloads of over 4000 by May 2021. More analytical functions are being developed.

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

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

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 Analyzing Spatiotemporal Daily Travel Source Carbon Emissions Based on Taxi Trajectory Data

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Maopeng Sun ◽  
Chenlei Xue ◽  
Yanqiu Cheng ◽  
Ling Zhao ◽  
Zhiyou Long
Keyword(s):  
Carbon Emissions ◽  
Trajectory Data ◽  
Taxi Trajectory ◽  
Daily Travel

scholarly journals Challenges of Big Data analysis

2014 ◽  
Vol 1 (2) ◽  
pp. 293-314 ◽  
Author(s):  
Jianqing Fan ◽  
Fang Han ◽  
Han Liu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Measurement Errors ◽  
Modern Society ◽  
Big Data Analysis ◽  
Small Scale ◽  
Paradigm Change ◽  
Statistical Inferences ◽  
The One ◽  
And Storage

Abstract Big Data bring new opportunities to modern society and challenges to data scientists. On the one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This paper gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogenous assumptions in most statistical methods for Big Data cannot be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

scholarly journals Discovering Urban Traffic Congestion Propagation Patterns With Taxi Trajectory Data

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 69481-69491 ◽  
Author(s):  
Zhenhua Chen ◽  
Yongjian Yang ◽  
Liping Huang ◽  
En Wang ◽  
Dawei Li
Keyword(s):  
Traffic Congestion ◽  
Urban Traffic ◽  
Trajectory Data ◽  
Taxi Trajectory

scholarly journals Data Intensive Scalable Computing (DISC)

2020 ◽  
Author(s):  
Mario A. R. Dantas
Keyword(s):  
Big Data ◽  
Scalable Computing ◽  
Data Intensive ◽  
And Storage

This work presents an introduction to the Data Intensive Scalable Computing (DISC) approach. This paradigm represents a valuable effort to tackle the large amount of data produced by several ordinary applications. Therefore, subjects such as characterization of big data and storage approaches, in addition to brief comparison between HPC and DISC are differentiated highlight.

scholarly journals Infrastructure and Energy Conservation in Big Data Computing: A Survey

2019 ◽  
Vol 2 ◽  
pp. 73-82
Author(s):  
Ewa Niewiadomska-Szynkiewicz ◽  
Michał P. Karpowicz
Keyword(s):  
Big Data ◽  
High Performance ◽  
Physical Sciences ◽  
Communication Management ◽  
Data Intensive ◽  
Efficient Data ◽  
Software Platforms ◽  
Allocation Algorithms ◽  
And Storage ◽  
Big Data Computing

Progress in life, physical sciences and technology depends on efficient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of efficient HPC infrastructures. This paper addresses the vital problem of energy-efficient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

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