Identifying types of staying facilities from traffic behavior log data

Yu Sugawara; Takeshi Morita; Hidenao Abe; Shuichi Matsumoto; Takahira Yamaguchi

doi:10.1016/j.procs.2014.08.232

Identifying types of staying facilities from traffic behavior log data

Yu Sugawara, Takeshi Morita, Hidenao Abe, Shuichi Matsumoto, Takahira Yamaguchi

Department of Industrial and Systems Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

Traffic behavior surveys by hand require both a lot of money and human resources. Recently, traffic behavior surveys using information technology have been carried out. In this study, we propose a method to extract staying points from GPS-based positional data and identify the types of staying facilities by using Google Places API, a facility ontology, the regularity which is analyzed from trip chains about traffic behavior. This method could identify 68.5% types of staying facilities correctly in the evaluation using GPS location data from the Traffic Behavior Survey in Nagasaki.

Original language	English
Pages (from-to)	1495-1504
Number of pages	10
Journal	Procedia Computer Science
Volume	35
Issue number	C
DOIs	https://doi.org/10.1016/j.procs.2014.08.232
Publication status	Published - 2014 Jan 1
Event	International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014 - Gdynia, Poland Duration: 2014 Sept 15 → 2014 Sept 17

Keywords

Automation
Log Data
Person Trip Survey
Positional Data
Traffic Behavior Survey

ASJC Scopus subject areas

Computer Science(all)

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10.1016/j.procs.2014.08.232

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title = "Identifying types of staying facilities from traffic behavior log data",

abstract = "Traffic behavior surveys by hand require both a lot of money and human resources. Recently, traffic behavior surveys using information technology have been carried out. In this study, we propose a method to extract staying points from GPS-based positional data and identify the types of staying facilities by using Google Places API, a facility ontology, the regularity which is analyzed from trip chains about traffic behavior. This method could identify 68.5% types of staying facilities correctly in the evaluation using GPS location data from the Traffic Behavior Survey in Nagasaki.",

keywords = "Automation, Log Data, Person Trip Survey, Positional Data, Traffic Behavior Survey",

author = "Yu Sugawara and Takeshi Morita and Hidenao Abe and Shuichi Matsumoto and Takahira Yamaguchi",

year = "2014",

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doi = "10.1016/j.procs.2014.08.232",

language = "English",

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journal = "Procedia Computer Science",

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TY - JOUR

T1 - Identifying types of staying facilities from traffic behavior log data

AU - Sugawara, Yu

AU - Morita, Takeshi

AU - Abe, Hidenao

AU - Matsumoto, Shuichi

AU - Yamaguchi, Takahira

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Traffic behavior surveys by hand require both a lot of money and human resources. Recently, traffic behavior surveys using information technology have been carried out. In this study, we propose a method to extract staying points from GPS-based positional data and identify the types of staying facilities by using Google Places API, a facility ontology, the regularity which is analyzed from trip chains about traffic behavior. This method could identify 68.5% types of staying facilities correctly in the evaluation using GPS location data from the Traffic Behavior Survey in Nagasaki.

AB - Traffic behavior surveys by hand require both a lot of money and human resources. Recently, traffic behavior surveys using information technology have been carried out. In this study, we propose a method to extract staying points from GPS-based positional data and identify the types of staying facilities by using Google Places API, a facility ontology, the regularity which is analyzed from trip chains about traffic behavior. This method could identify 68.5% types of staying facilities correctly in the evaluation using GPS location data from the Traffic Behavior Survey in Nagasaki.

KW - Automation

KW - Log Data

KW - Person Trip Survey

KW - Positional Data

KW - Traffic Behavior Survey

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DO - 10.1016/j.procs.2014.08.232

M3 - Conference article

AN - SCOPUS:84924126286

SN - 1877-0509

VL - 35

SP - 1495

EP - 1504

JO - Procedia Computer Science

JF - Procedia Computer Science

IS - C

T2 - International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2014

Y2 - 15 September 2014 through 17 September 2014

ER -

Identifying types of staying facilities from traffic behavior log data

Abstract

Keywords

ASJC Scopus subject areas

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