PFlow: Reconstructing People Flow Recycling Large-Scale Social Survey Data

Sekimoto, Yoshihide; Shibasaki, Ryosuke; Kanasugi, Hiroshi; Usui, Tomotaka; Shimazaki, Yoshiaki

doi:10.1109/mprv.2011.43

Cited by 86 publications

(49 citation statements)

References 5 publications

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“…If there was a pick-up event in the grid, then pick-up location was estimated based on the distribution of real passenger trips that had originated in that grid. With pick-up location successfully estimated, the route to the pick-up location was implemented using the Dijkstra algorithm [45] which was based on OSM network. For each road network segment, travel time was estimated and described in Section 4.6.…”

Section: Passenger Pick-up Modulementioning

confidence: 99%

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Ranjit

Witayangkurn

Nagai

et al. 2018

IJGI

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Abstract:Taxi behavior is a spatial-temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent's parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query/search/retrieval of taxi agent's parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique.

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Section: Passenger Pick-up Modulementioning

confidence: 99%

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Ranjit

Witayangkurn

Nagai

et al. 2018

IJGI

Self Cite

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“…There are several methods for estimating the density of people in urban areas [17], [18], [19]. Mobile space statistics [19] presented by NTT DOCOMO tracks populations of each area by counting mobile phone users observed at each base station.…”

Section: Related Workmentioning

confidence: 99%

“…However, this approach aims at large-scale statistics such as the population in a city, which is different from our target. Reference [17] proposes a method to reconstruct the people flow from existing person-trip survey data. Reference [18] proposes a method for density estimation using coarse location information obtained from mobile phone call data.…”

Section: Related Workmentioning

confidence: 99%

Self-Estimation of Neighborhood Distribution for Mobile Wireless Nodes

Sakai

Uchiyama

Yamaguchi

et al. 2013

Journal of Information Processing

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Abstract:In this paper, we propose a method to estimate the node distribution for pedestrians with information terminals. The method enables us to provide situation-aware services such as intellectual navigation that tells the user the best route to go around congested regions. In the proposed method, each node is supposed to know its location roughly (i.e., within some error range) and to maintain a density map covering its surroundings. This map is updated when a node receives a density map from a neighboring node. Each node also updates the density map in a timely fashion by estimating the change of the density due to node mobility. Node distribution is obtained from the density map by choosing cells with the highest density in a greedy fashion. The simulation experiments have been conducted and the results have shown that the proposed method could keep average position errors less than 10 m.

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“…This is true in terms of comprehensive qualities including spatial/temporal accuracy, acquisition/process costs, and value to the user as a service. For example, our group had pointed out the necessity of three aspects of people flow data sets including "sufficiently large scale," "temporal completeness," and "realistic spatial accuracy" [6]. Moreover, mobile phone data for everyone is not usually available, compared with social survey data acquired for public benefits such as transportation or disaster prevention planning.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, mobile phone data for everyone is not usually available, compared with social survey data acquired for public benefits such as transportation or disaster prevention planning. Hence, our group had proposed a data process for the reconstruction of the spatio-temporal positions of large numbers of people using existing person trip survey (hereinafter referred to as PTS) data, conducted by various transportation planning agencies or commissions consisting of several Japanese cities [6][7]. Some results are summarized on the "People Flow Project" web site.…”

Section: Introductionmentioning

confidence: 99%

Digital Archiving of People Flow by Recycling Large-Scale Social Survey Data of Developing Cities

Sekimoto

Watanabe

Nakamura

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Data on people flow has become increasingly important in the field of business, including the areas of marketing and public services. Although mobile phones enable a person's position to be located to a certain degree, it is a challenge to acquire sufficient data from people with mobile phones. In order to grasp people flow in its entirety, it is important to establish a practical method of reconstructing people flow from various kinds of existing fragmentary spatio-temporal data such as social survey data. For example, despite typical Person Trip Survey Data collected by the public sector showing the fragmentary spatio-temporal positions accessed, the data are attractive given the sufficiently large sample size to estimate the entire flow of people. In this study, we apply our proposed basic method to Japan International Cooperation Agency (JICA) PT data pertaining to developing cities around the world, and we propose some correction methods to resolve the difficulties in applying it to many cities and stably to infrastructure data.

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PFlow: Reconstructing People Flow Recycling Large-Scale Social Survey Data

Cited by 86 publications

References 5 publications

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data

Self-Estimation of Neighborhood Distribution for Mobile Wireless Nodes

Digital Archiving of People Flow by Recycling Large-Scale Social Survey Data of Developing Cities

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