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Spatial-Temporal Similarity Correlation between Public Transit Passengers Using Smart Card Data 时间:2017-09-26 22:31 来源:未知作者:admin 点击: 次 Abstract: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.
1. Introduction
Analysing passengers’ movements in a public transit network is important in understanding passengers’ travel behaviours and designing more customised public transit services. By identifying passengers with similar spatial and/or temporal trip patterns and understanding their characteristics, transit operators could design transit services that better meet different needs of different passenger groups and develop strategies to influence travellers to use the existing transit network more efficiently. For instance, if a group of passengers every day boards on a specific bus route at a specific stop and time, and they change the bus at another stop to arrive at their final destination, then a specific bus (or a minibus) can be allocated to that group of passengers between their first boarding and last alighting stop for particular periods.
With the availability of transit smart card data that provide information on boarding and alighting locations and times for each passenger trip, it is now possible to analyse spatial and temporal movement patterns for each passenger and compare them across passengers, thereby allowing a deeper understanding of individual passengers and their relationships. And each spatial and temporal dimension of the movement has its measures and units, which makes it difficult to study these dimensions simultaneously [1]. Transit authorities have developed automated fare collection (AFC) systems around the world since two decades ago. These systems not only aim to gather fares but also they turn valuable datasets out of trips as a by-product. The datasets include time and place of transactions for boarding on and/or alighting from the public transit system [2, 3]. The datasets help researchers to expand the studies and investigate nested interactions among public transit passengers [4, 5]. Previously, datasets for transport studies were gathered mostly from surveys, which were expensive to run and limited in size. Hence, smart card datasets provide opportunities to explore travel behaviours of public transit passengers in large and detailed scales.
Exploring similarities among passengers’ trips, where trip similarity can be defined regarding spatial and/or temporal dimensions, can discover relationships among the passengers. By identifying how similar two passengers’ trips are spatial and temporal, the “passenger similarity” can be defined as a composite measure of trip similarity between two passengers. Such a passenger-level similarity measure can help the analysis of passenger characteristics. These measures can help the design and development of various customer-centric transit services and mobility applications. Examples of such applications include demand responsive transport (DRT) systems [6], friend recommendation systems [7, 8], and traffic flow prediction models [9].