Understanding intra-urban trip patterns from taxi trajectory data

Liu, Yu; Kang, Chaogui; Gao, Song; Xiao, Yu; Yuan, Tian

doi:10.1007/s10109-012-0166-z

Cited by 290 publications

(193 citation statements)

References 31 publications

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“…In order to perform this analysis, which is fundamental for city planners, trajectory data are collected from inexpensive GPS-equipped devices, such as taxicabs. Taxi trip data is widely-used for mining traffic flow patterns, as described in [34,35]. For example, the NYC Open Data Plan has built a portal for publishing its digital public data for city-wide aggregation, as required by local law.…”

Section: Spatial Datasets For a Spatial Query Processing Casementioning

confidence: 99%

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Huang

Zhang

et al. 2017

IJGI

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Abstract:Geospatial big data analysis (GBDA) is extremely significant for time-constraint applications such as disaster response. However, the time-constraint analysis is not yet a trivial task in the cloud computing environment. Spatial query processing (SQP) is typical computation-intensive and indispensable for GBDA, and the spatial range query, join query, and the nearest neighbor query algorithms are not scalable without using MapReduce-liked frameworks. Parallel SQP algorithms (PSQPAs) are trapped in screw-processing, which is a known issue in Geoscience. To satisfy time-constrained GBDA, we propose an elastic SQP approach in this paper. First, Spark is used to implement PSQPAs. Second, Kubernetes-managed Core Operation System (CoreOS) clusters provide self-healing Docker containers for running Spark clusters in the cloud. Spark-based PSQPAs are submitted to Docker containers, where Spark master instances reside. Finally, the horizontal pod auto-scaler (HPA) would scale-out and scale-in Docker containers for supporting on-demand computing resources. Combined with an auto-scaling group of virtual instances, HPA helps to find each of the five nearest neighbors for 46,139,532 query objects from 834,158 spatial data objects in less than 300 s. The experiments conducted on an OpenStack cloud demonstrate that auto-scaling containers can satisfy time-constraint GBDA in clouds.

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Section: Spatial Datasets For a Spatial Query Processing Casementioning

confidence: 99%

Elastic Spatial Query Processing in OpenStack Cloud Computing Environment for Time-Constraint Data Analysis

Huang

Zhang

et al. 2017

IJGI

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“…Moreover, aiming at several applications for location-based services including a personalized point of interest (POI) recommendation for users [4], regional development [5], urban planning [6], and policymaking [7], several studies have addressed a question of how to model people flow in a specific area and understand the characteristics of the are with such large amount of mobility data.…”

Section: Modeling Characteristics Of Geographical Areas Using Mobilitmentioning

confidence: 99%

“…More recently, several studies have used the mobility data for regional development [5], urban planning [6], and policymaking [7]. One of key questions in those studies is how to model and predict people flow in a specific area where the mobility data have been collected.…”

Section: Introductionmentioning

confidence: 99%

Geospatial Area Embedding Based on the Movement Purpose Hypothesis Using Large-Scale Mobility Data from Smart Card

Ochi¹,

Nakashio²,

Ruttley³

et al. 2016

IJCNS

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With the deployment of modern infrastructure for public transportation, several studies have analyzed movement patterns of people using smart card data and have characterized different areas. In this paper, we propose the "movement purpose hypothesis" that each movement occurs from two causes: where the person is and what the person wants to do at a given moment. We formulate this hypothesis to a synthesis model in which two network graphs generate a movement network graph. Then we develop two novel-embedding models to assess the hypothesis, and demonstrate that the models obtain a vector representation of a geospatial area using movement patterns of people from large-scale smart card data. We conducted an experiment using smart card data for a large network of railroads in the Kansai region of Japan. We obtained a vector representation of each railroad station and each purpose using the developed embedding models. Results show that network embedding methods are suitable for a large-scale movement of data, and the developed models perform better than existing embedding methods in the task of multi-label classification for train stations on the purpose of use data set. Our proposed models can contribute to the prediction of people flows by discovering underlying representations of geospatial areas from mobility data.

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“…Holleczek et al (2014) or GPS coordinates to analyse and describe the patterns that characterise people behaviour (i.e. Jiang et al (2009) and Liu et al (2012).…”

Section: Mobility Patterns With Intelligent Transport Systemsmentioning

confidence: 99%