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Mobility Data-Driven Urban Traffic Monitoring

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-981-16-2241-0

ISBN: 978-981-16-2240-3

Publisher: Springer Singapore - Springer Journals

May 19, 2021

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Introduction

Liu, Zhidan; Wu, Kaishun

Pages: 1 –9

[In this chapter, we will introduce the basic concepts of widely available mobility dataMobility data in the urban city and their boosted applications. Then we will review and discuss the related works on urban traffic monitoringUrban traffic monitoring, and especially highlight these works built...
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Urban Traffic Monitoring from Mobility Data

Liu, Zhidan; Wu, Kaishun

Pages: 11 –16

[In thisMobility data chapter, weUrban traffic monitoring will present the problem description of mobility dataMobility data based urban traffic monitoring, and then elaborate our proposed typical workflow to address this problem. In particular, we describe the common types of mobility...
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A Compressive Sensing Based Traffic Monitoring Approach

Liu, Zhidan; Wu, Kaishun

Pages: 17 –29

[ThroughCompressive analysisSensing on real-world mobility data, we observe non-trivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct...
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A Dynamic Correlation Modeling Based Traffic Monitoring Approach

Liu, Zhidan; Wu, Kaishun

Pages: 31 –47

[This chapter presents a graph processing based traffic monitoring system, GPTE, which is able to achieve high accuracy and high scalability to support city scale traffic monitoring. GPTE benefits from its non-linear traffic correlation modelingTraffic correlation modeling and the graph-parallel...
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A Crowdsensing Based Traffic Monitoring Approach

Liu, Zhidan; Wu, Kaishun

Pages: 49 –63

[ThisCrowdsensing chapter presents a crowdsensing based urban traffic monitoringUrban traffic monitoring system. Different from existing works that heavily rely on intrusive sensingSensing or full cooperation from probe vehicles, our system exploits the power of participatory sensingSensing and...
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Conclusion and Future Work

Liu, Zhidan; Wu, Kaishun

Pages: 65 –67

[In this chapter, we firstly summarize the main content in the book, and then discuss several interesting future research directions.]

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