Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore
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Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore Yi Zhu1
© Springer Science+Business Media, LLC, part of Springer Nature 2018
Abstract Understanding individual daily activity patterns is essential for travel demand management and urban planning. This research introduces a new method to infer transit riders’ activities from their smart card transaction records. Using Singapore as an example, activity type classification models were built using household travel survey and a rich set of urban built environment measures to reveal the spatial and temporal correspondences that indicate the activity participation of transit riders. The calibrated model is then applied to the transit smart card dataset to extract the embedded activity information. The proposed approach enables to spatially and temporally quantify, visualize, and examine urban activity landscapes in a metropolitan area and provides real-time decision support for the city. This study also demonstrates the potential value of combining new ‘‘big data’’ such as transit smart card data and “small data” such as traditional travel surveys to create better insights of urban travel demand and activity dynamics. Keywords Activity inference · Transit smart card data · Spatio-temporal correspondence · Urban sensing · Activity landscape
Introduction To urban policy makers, the complexity in the decision making process is escalating as a result of various trends including decentralization, gentrification, deindustrialization, globalization and energy conservation occurring simultaneously in the context of urban management. Meanwhile, behaviors and preferences of people and firms have been changing rapidly given the explosion of information, improved mobility and more differentiated lifestyles, which also require urban management strategies to be more adaptive and responsive to the issues exposed. Success in urban management calls for * Yi Zhu [email protected] 1
School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, China
13
Transportation
innovative decision making paradigms that can provide timely and informative analyses and forecasts grounded on a sound and profound understanding of the dynamics of urban systems. From the point of view of transportation and land use planning and management, knowledge of peoples’ activity-travel patterns is important, because activities and activity-derived travels are indicative of demands for transportation services as well as other urban services and opportunities for various activities. Moreover, the spatiotemporal distributions of people and activities are also crucial for a series of urban management tasks like emergency planning, disaster management, infrastructure services and resources allocation (Krygsman et al. 2007). In the last couple of decades, trips and activities are mainly studied within the framework of travel demand forecasting, which has evolved a long way from the trip-based, aggreg
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