Clustering Smart Card Data for Urban Mobility Analysis

Mahrsi, Mohamed Khalil El; Côme, Étienne; Oukhellou, Latifa; Verleysen, Michel

doi:10.1109/tits.2016.2600515

Cited by 157 publications

(89 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Thus, the availability of MaaS data provides more useful information for understanding the mobility changes that have occurred in the urban areas. To understand integrated urban mobility, cluster analysis [13] [14] [15] and classification [16] [17] can be performed to identify the latent spatial-temporal patterns. With multi-source datasets, computational models are constructed for uncovering and optimizing urban mobility patterns.…”

Section: Machine Learning Methodologiesmentioning

confidence: 99%

Data Fusion for MaaS: Opportunities and Challenges

Zhou

Cai³

et al. 2018

2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))

View full text Add to dashboard Cite

Abstract-Computer Supported Cooperative Work (CSCW)in design is an essential facilitator for the development and implementation of smart cities, where modern cooperative transportation and integrated mobility are highly demanded. Owing to greater availability of different data sources, data fusion problem in intelligent transportation systems (ITS) has been very challenging, where machine learning modelling and approaches are promising to offer an important yet comprehensive solution. In this paper, we provide an overview of the recent advances in data fusion for Mobility as a Service (MaaS), including the basics of data fusion theory and the related machine learning methods. We also highlight the opportunities and challenges on MaaS, and discuss potential future directions of research on the integrated mobility modelling.

show abstract

Section: Machine Learning Methodologiesmentioning

confidence: 99%

Data Fusion for MaaS: Opportunities and Challenges

Zhou

Cai³

et al. 2018

2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design ((CSCWD))

View full text Add to dashboard Cite

show abstract

“…They investigated spatial and temporal patterns at stop levels for commuters and noncommuters [17]. El Mahrsi et al (2016) presented two different approaches for clustering smart card dataset. First one is stop-oriented, which clusters stops based on the frequency of the boarding and alighting transactions; and the second one is passenger-oriented, which clusters passengers based on the boarding times of their trips [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…El Mahrsi et al (2016) presented two different approaches for clustering smart card dataset. First one is stop-oriented, which clusters stops based on the frequency of the boarding and alighting transactions; and the second one is passenger-oriented, which clusters passengers based on the boarding times of their trips [18]. Yu and He (2017) proposed a visualisation model to present travel demand at stop level using heat maps.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

Faroqi

Mesbah

Kim

2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

The increasing availability of public transit smart card data has enabled several studies to focus on identifying passengers with similar spatial and/or temporal trip characteristics. However, this paper goes one step further by investigating the relationship between passengers' spatial and temporal characteristics. For the first time, this paper investigates the correlation of the spatial similarity with the temporal similarity between public transit passengers by developing spatial similarity and temporal similarity measures for the public transit network with a novel passenger-based perspective. The perspective considers the passengers as agents who can make multiple trips in the network. The spatial similarity measure takes into account direction as well as the distance between the trips of the passengers. The temporal similarity measure considers both the boarding and alighting time in a continuous linear space. The spatial-temporal similarity correlation between passengers is analysed using histograms, Pearson correlation coefficients, and hexagonal binning. Also, relations between the spatial and temporal similarity values with the trip time and length are examined. The proposed methodology is implemented for four-day smart card data including 80,000 passengers in Brisbane, Australia. The results show a nonlinear spatial-temporal similarity correlation among the passengers.

show abstract

“…Transportation applications, for example by avoiding traffic congestion and crowded areas, for collective monitoring and prediction of user traffic [3], by adding a social layer of driving [30], urban transport fluidization [5], or optimized taxi sharing [6]; 4)…”

Section: )mentioning

confidence: 99%

A survey of people movement analytics studies in the context of smart cities

Lohan

Kauppinen

Debnath

2016

2016 19th Conference of Open Innovations Association (FRUCT)

View full text Add to dashboard Cite

With the advent of the newest emergency call mandates in US and Europe, with the advances in cellular-based and WiFi-based localization solutions, and with the developments of cloud computing and web-based social networks, the location information and movement-related data is becoming easier and easier to collect from the user mobile devices and from the user cloud data and it is more and more used in a variety of Location Based Services and for various network planning and management tasks. The last decade has seen significant research efforts dedicated to analyze the user location and movement data, to extract mobility patterns and features and to use the predicted patterns for a more efficient resource allocation and for better location-based services. In the context of what is called today 'the smart city', user mobility and location data are becoming key components of the smart city architecture and applications. The goal of this paper is to give a compact and comprehensive overview of the challenges and solutions related to collecting, storing, analyzing, visualizing, using or distributing people's movement data and to summarize the purposes of such data in the context of the smart cities and the Internet of Things.

show abstract

Clustering Smart Card Data for Urban Mobility Analysis

Cited by 157 publications

References 39 publications

Data Fusion for MaaS: Opportunities and Challenges

Data Fusion for MaaS: Opportunities and Challenges

Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data

A survey of people movement analytics studies in the context of smart cities

Contact Info

Product

Resources

About