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

Faroqi, Hamed; Mesbah, Mahmoud; Kim, Jiwon

doi:10.1155/2017/1318945

Cited by 26 publications

(23 citation statements)

References 32 publications

(42 reference statements)

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“…The trip similarity is examined in two parallel steps of the spatial and temporal similarity measures. Both spatial and temporal similarity measures are adopted from Faroqi et al [15] where more details can be found. The measures are developed specifically for smart card dataset that include boarding and alighting transactions (not like GPS trajectories that include measurements every few meters).…”

Section: Methodsmentioning

confidence: 99%

“…In addition, a passenger can have one or more trips during a day, all of which should be considered in measuring the spatial or temporal similarity with other passengers. Spatial and/or temporal similarity measures between passengers based on their trips can be used as a measure to study the closeness or relationship between the passengers [15]. Therefore, to have a comprehensive insight of travel patterns, both spatial and temporal dimensions of the trips should be considered in the clustering of the passengers.…”

Section: Introductionmentioning

confidence: 99%

“…Spatial space is a two-dimensional space with units such as meters or inches; however, temporal space is a onedimensional space with units such as minutes. Hence, defining a unique spatiotemporal similarity measure might be an ambiguous technique because it needs to merge these two different spaces [15]. Moreover, to have passenger clusters with both spatial and temporal similarities, it is necessary to cluster them in two steps (sequentially) or combine values of the spatial and temporal similarities (that are calculated separately) and then cluster them in one step.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Faroqi

Mesbah

Kim

2019

Mathematical Problems in Engineering

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Smart card datasets in the public transit network provide opportunities to analyse the behaviour of passengers as individuals or as groups. Studying passenger behaviour in both spatial and temporal space is important because it helps to find the pattern of mobility in the network. Also, clustering passengers based on their trips regarding both spatial and temporal similarity measures can improve group-based transit services such as Demand-Responsive Transit (DRT). Clustering passengers based on their trips can be carried out by different methods, which are investigated in this paper. This paper sheds light on differences between sequential and combined spatial and temporal clustering alternatives in the public transit network. Firstly, the spatial and temporal similarity measures between passengers are defined. Secondly, the passengers are clustered using a hierarchical agglomerative algorithm by three different methods including sequential two-step spatial-temporal (S-T), sequential two-step temporal-spatial (T-S), and combined one-step spatiotemporal (ST) clustering. Thirdly, the characteristics of the resultant clusters are described and compared using maps, numerical and statistical values, cross correlation techniques, and temporal density plots. Furthermore, some passengers are selected to show how differently the three methods put the passengers in groups. Four days of smart card data comprising 80,000 passengers in Brisbane, Australia, are selected to compare these methods. The analyses show that while the sequential methods (S-T and T-S) discover more diverse spatial and temporal patterns in the network, the ST method entails more robust groups (higher spatial and temporal similarity values inside the groups).

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Faroqi

Mesbah

Kim

2019

Mathematical Problems in Engineering

Self Cite

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“…Briand et al [17] analyzed the behavioral habits of public transport passengers using a real dataset of smart card data covering a period of five years. Faroqi et al [18] investigated the relationship between passengers' spatial and temporal characteristics with a novel passenger-based perspective using smart card data. It is implemented for fourday smart card data including 80,000 passengers in Brisbane, Australia.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

Luo

Cai

et al. 2018

Journal of Advanced Transportation

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The metro passenger route choice, influenced by both train schedule and time constraints, is important to metro operation and management. Smart card data (Automatic Fare Collection (AFC) data in metro system) including inbound and outbound swiping time are useful for analysis of the characteristics of passengers’ route choices in metro while they could not reflect the property of train schedule directly. Train schedule is used in this paper to trim smart card data through removing inbound and outbound walking time to/from platforms and waiting time. Thus, passengers’ pure travel time in accord with trains’ arrival and departure can be obtained. Synchronous clustering (SynC) algorithm is then applied to analyze these processed data to calculate passenger route choice probability. Finally, a case study was conducted to illustrate the effectiveness of the proposed algorithm. Results showed the proposed algorithm works well to analyze metro passenger route choice. It was shown that passenger route choice during both peak period and flat period could be clustered automatically, and noise data are isolated. The probability of route choice calculated through SynC algorithm can be used to revise traditional model results.

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“…Hamed et al [12] investigated the relationship between the spatial and temporal characteristics of public transit passengers, by using four days' worth of smart-card data from 80,000 passengers in Brisbane, Australia. Hamed et al developed measures of spatial and temporal similarity with a perspective on new passengers, in which passengers were able to travel multiple times within the network.…”

Section: Smart-card Datamentioning

confidence: 99%

Automatic detection and analysis of long-term changes in travel patterns of public transport passengers using Smart-Card data

Moon¹

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All around the world, a large number of people rely on public transport. These days, most transport users automatically pay with a smart-card when using public transport systems. Due to people's frequent usage of public transport, researchers are invested in better understanding passengers' behaviour patterns. The smart-card system is an ideal source of data for researchers as it is capable of storing large amounts of data on transit users, and when used for a long time, can be the source needed for detailed investigation into passengers' trip patterns, both habitual and sporadic.There are two types of changes in the variation of passengers' movements. First, there exists a short-term pattern, which is expressed as a momentary variation in movement pattern. Second is more long-term and of specific interest to transport officials who are interested in understanding the underlying factors that may disturb the more regular and longer lasting habits. As such, the nature of this study is designed so as to improve detection and the analysis of temporal long-term changes in passengers' movement patterns by relying on the smart-card system in order to obtain detailed information about passengers, such as their time-of-day and day-of-week behaviour patterns.One of the main problems with using big data, however, is that the processing of it is time-inefficient and expensive when performed manually. The study therefore aims to process the smart-card data using an automated approach where the proposed framework relies on machine learning and pattern recognition techniques.This result strives to aid public transit operators to improve the provision of public transport to commuters by taking better account specific changes in passengers' movement behaviour. The research aims to provide public transit operators information that will serve to improve understanding of commuters' behaviours. As a result, public transit operators may better design customer retention strategies that will improve transit services and provide greater efficiency in inter-regional migration.

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

Cited by 26 publications

References 32 publications

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Comparing Sequential with Combined Spatiotemporal Clustering of Passenger Trips in the Public Transit Network Using Smart Card Data

Using Smart Card Data Trimmed by Train Schedule to Analyze Metro Passenger Route Choice with Synchronous Clustering

Automatic detection and analysis of long-term changes in travel patterns of public transport passengers using Smart-Card data

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