Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Sui, Tao; Rohde, David; Corcoran, Jonathan

doi:10.1016/j.jtrangeo.2014.08.006

Cited by 128 publications

(69 citation statements)

References 37 publications

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“…A new geo-visual technique, the flow-comap Tao, Rohde, & Corcoran, 2014), was employed to map spatial patterns of bus passenger flows (captured by the difference in flow volumes) and their relationship to variations in apparent temperature. The flow-comap is an augmentation of two well established cartographic techniques, the flow map and the comap or Conditional Map (Brunsdon, 2001).…”

Section: Methodsmentioning

confidence: 99%

Mapping spatial patterns of bus usage under varying local temperature conditions

Corcoran

Sui

2017

Journal of Maps

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Weather influences our daily travel decisions in a number of important ways. At the individual scale, weather has been shown to influence trip-making behaviours where inclement conditions can induce re-scheduling, re-routing and cancellation of a planned journey. Furthermore at the transit system-level, poor weather can increase traffic congestion and reduce operational efficiencies. While some research has examined the weather-transit relationship, focus on the spatial dimension remain in their infancy. In this paper we adopt a visual analytic approach to spatially explore the complex weather-transit relationship at a micro geographical scale. We demonstrate that through spatially integrating a large disaggregate smart card database of bus ridership with hourly local weather measurements we can reveal how 'heat stress' changes the way in which passengers use the public transit system in subtropical Brisbane, Australia. Our approach has the potential for broader application across other public and private transport and climatic contexts to unveil the way in which weather influences our daily travel behaviour. ARTICLE HISTORY

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Section: Methodsmentioning

confidence: 99%

Mapping spatial patterns of bus usage under varying local temperature conditions

Corcoran

Sui

2017

Journal of Maps

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“…A partir de los datos de la tarjeta inteligente de transporte se pueden generar matrices origendestino (Munizaga et al, 2010) y explorar las dinámicas espacio-temporales y los flujos en transporte público (Tao et al, 2014). Otra fuente muy útil son los registros GPS de los movimientos de los usuarios (tracks), a partir de los cuales se puede identificar el origen y destino de cada viaje, la ruta seguida, las velocidades, tiempos de viaje, pendientes, etc.…”

Section: Análisis De Movilidadunclassified

Big Data y nuevas geografías: la huella digital de las actividades humanas

Puebla

2018

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ResumenEl término Big Data se ha popularizado en los últimos años y hace referencia a la producción de cantidades ingentes de datos. La actividad humana es captada a través de múltiples redes de sensores y dispositivos, dejando por tanto una huella digital. El análisis de esta huella digital tiene un gran potencial para la investigación geográfica del comportamiento humano. En este artículo se describen las principales características del Big Data y se destaca la importancia de los datos masivos para la ciencia y particularmente para la Geografía, centrando la atención en el estudio de los patrones espacio-temporales de la actividad humana. Palabras clave: Big Data; datos geolocalizados; comportamiento humano Resum. Big Data i noves geografies: l'empremta digital de les activitats humanesEl terme Big Data s'ha popularitzat en els últims anys i fa referència a la producció de quantitats ingents de dades. L'activitat humana és captada a través de múltiples xarxes de sensors i dispositius i, per tant, deixa una empremta digital. L'anàlisi d'aquesta empremta digital té un gran potencial per a la investigació geogràfica del comportament humà. En aquest article es descriuen les principals característiques del Big Data i es destaca la importància de les dades massives per a la ciència i, particularment, per a la Geografia, centrant l'atenció en l'estudi dels patrons espaciotemporals de l'activitat humana. Paraules clau: Big Data; dades geolocalitzades; comportament humà * Este artículo se basa parcialmente en la conferencia «Big Data y nuevas geografías», pronunciada en la Universidad Autónoma de Barcelona en 2017, y en la ponencia «El uso del Big Data en la investigación de la ciudad, la movilidad y el turismo», presentada en el XXV Congreso de la Asociación de Geógrafos Españoles. Résumé. Big Data et nouvelles géographies : l'empreinte digitale des activités humainesLe terme Big Data est devenu populaire ces dernières années et il se réfère à la production d'énormes quantités de données. L'activité humaine est captée par une multitude de réseaux de capteurs et de dispositifs, laissant ainsi une empreinte digitale. L'analyse de cette empreinte présente un grand potentiel pour l'étude géographique du comportement humain. Cet article décrit les principales caractéristiques du Big Data et il souligne l'importance des données massives pour la science et en particulier pour la géographie, en se concentrant sur l'étude des modèles spatio-temporels de l'activité humaine. Mots-clés: Big Data; données géolocalisées; comportement humain Abstract. Big data and new geographies: The digital footprint of human activityThe term 'big data' has become popular in recent years and refers to the production of huge amounts of data. Human activity is captured through multiple networks of sensors and devices, thus leaving a digital footprint. The analysis of this digital footprint has a great potential for geographical research on human behavior. This article describes the main characteristics of big data and highlights the importance of m...

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“…focused on the passengers instead of stops for discovering the patterns among the passengers. They used a smart card dataset from Brisbane and divided it into 5-time windows for further analysis of spatial patterns of the passengers' trips [13]. Kieu et al (2015) used a modified DBSCAN algorithm to discover spatial travel patterns at stop levels from a smart card dataset [14].…”

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

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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.

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Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap

Cited by 128 publications

References 37 publications

Mapping spatial patterns of bus usage under varying local temperature conditions

Mapping spatial patterns of bus usage under varying local temperature conditions

Big Data y nuevas geografías: la huella digital de las actividades humanas

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

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