Detecting crowdedness spot in city transportation

Siyuan Liu, Yunhuai Liu, Lionel Ni, Minglu Li, Jianping Fan

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Crowdedness spot is a crowded area with an abnormal number of objects. Detecting the crowdedness spots of moving vehicles in an urban area is essential to many applications. An intuitive method is to cluster the objects in areas to get the density information. Unfortunately, the data capturing vehicle mobility possesses some new features, such as highly mobile environments, supremely limited size of sample objects, and nonuniform biased samples, and all these features have raised new challenges that make traditional density-based clustering algorithms fail to retrieve the real clustering property of objects, making the results less meaningful. In this paper, we propose a novel nondensity-based approach called mobility-based clustering. The key idea is that sample objects are employed as 'sensors' to perceive the vehicle crowdedness in nearby areas using their instant mobility rather than the 'object representatives.' As such, the mobility of samples is naturally incorporated. Several key factors beyond the vehicle crowdedness have been identified, and techniques to compensate these effects are accordingly proposed. Furthermore, taking the detected crowdedness spots as a label of the taxi, we can identify one particular taxi to be a crowdedness taxi that crosses a number of different crowdedness spots. We evaluate the performance of our methods and baseline approaches based on real traffic situations (to retrieve the real traffic crowdedness) and real-life data sets. Finally, the interesting findings are provided for further discussions.

Original languageEnglish (US)
Article number6374701
Pages (from-to)1527-1539
Number of pages13
JournalIEEE Transactions on Vehicular Technology
Volume62
Issue number4
DOIs
StatePublished - Jan 1 2013

Fingerprint

Clustering algorithms
Traffic
Clustering
Labels
Urban Areas
Instant
Clustering Algorithm
Biased
Object
Intuitive
Baseline
Sensors
Sensor
Evaluate
Life

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Aerospace Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Liu, Siyuan ; Liu, Yunhuai ; Ni, Lionel ; Li, Minglu ; Fan, Jianping. / Detecting crowdedness spot in city transportation. In: IEEE Transactions on Vehicular Technology. 2013 ; Vol. 62, No. 4. pp. 1527-1539.
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Detecting crowdedness spot in city transportation. / Liu, Siyuan; Liu, Yunhuai; Ni, Lionel; Li, Minglu; Fan, Jianping.

In: IEEE Transactions on Vehicular Technology, Vol. 62, No. 4, 6374701, 01.01.2013, p. 1527-1539.

Research output: Contribution to journalArticle

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