Urban link travel time estimation using large-scale taxi data with partial information

•Uses large scale GPS traces of taxi cabs in New York City.•A model for estimating link travel times is developed using large scale data.•Developed insights into the congestion issues in New York City.•Rigorous numerical results are performed to test the approach. Taxicabs equipped with Global Posit...

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Veröffentlicht in:	Transportation research. Part C, Emerging technologies Jg. 33; S. 37 - 49
Hauptverfasser:	Zhan, Xianyuan, Hasan, Samiul, Ukkusuri, Satish V., Kamga, Camille
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier India Pvt Ltd 01.08.2013 Elsevier
Schlagworte:	Applied sciences Exact sciences and technology GPS-enabled taxicab Ground, air and sea transportation, marine construction Large scale data analysis Path inference Probe vehicle Road transportation and traffic Traffic state estimation Transportation planning, management and economics Urban networks Traffic state estimation Probe vehicle GPS-enabled taxicab Large scale data analysis Path inference Urban networks Data analysis Travel time GPS system Forecast model Modeling Urban district network Urban road traffic Taxi Large scale
ISSN:	0968-090X, 1879-2359
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Abstract	•Uses large scale GPS traces of taxi cabs in New York City.•A model for estimating link travel times is developed using large scale data.•Developed insights into the congestion issues in New York City.•Rigorous numerical results are performed to test the approach. Taxicabs equipped with Global Positioning System (GPS) devices can serve as useful probes for monitoring the traffic state in an urban area. This paper presents a new descriptive model for estimating hourly average of urban link travel times using taxicab origin–destination (OD) trip data. The focus of this study is to develop a methodology to estimate link travel times from OD trip data and demonstrate the feasibility of estimating network condition using large-scale geo-location data with partial information. The data, collected from the taxicabs in New York City, provides the locations of origins and destinations, travel times, fares and other information of taxi trips. The new model infers the possible paths for each trip and then estimates the link travel times by minimizing the error between the expected path travel times and the observed path travel times. The model is evaluated using a test network from Midtown Manhattan. Results indicate that the proposed method can efficiently estimate hourly average link travel times. This research provides new possibilities for fully utilizing the partial information obtained from urban taxicab data for estimating network condition, which is not only very useful but also is inexpensive and has much better coverage than traditional sensor data.
AbstractList	•Uses large scale GPS traces of taxi cabs in New York City.•A model for estimating link travel times is developed using large scale data.•Developed insights into the congestion issues in New York City.•Rigorous numerical results are performed to test the approach. Taxicabs equipped with Global Positioning System (GPS) devices can serve as useful probes for monitoring the traffic state in an urban area. This paper presents a new descriptive model for estimating hourly average of urban link travel times using taxicab origin–destination (OD) trip data. The focus of this study is to develop a methodology to estimate link travel times from OD trip data and demonstrate the feasibility of estimating network condition using large-scale geo-location data with partial information. The data, collected from the taxicabs in New York City, provides the locations of origins and destinations, travel times, fares and other information of taxi trips. The new model infers the possible paths for each trip and then estimates the link travel times by minimizing the error between the expected path travel times and the observed path travel times. The model is evaluated using a test network from Midtown Manhattan. Results indicate that the proposed method can efficiently estimate hourly average link travel times. This research provides new possibilities for fully utilizing the partial information obtained from urban taxicab data for estimating network condition, which is not only very useful but also is inexpensive and has much better coverage than traditional sensor data.
Author	Zhan, Xianyuan Kamga, Camille Hasan, Samiul Ukkusuri, Satish V.
Author_xml	– sequence: 1 givenname: Xianyuan surname: Zhan fullname: Zhan, Xianyuan email: zhanxianyuan@gmail.com organization: School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA – sequence: 2 givenname: Samiul surname: Hasan fullname: Hasan, Samiul email: samiul.hasan@gmail.com organization: School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907-2051, USA – sequence: 3 givenname: Satish V. surname: Ukkusuri fullname: Ukkusuri, Satish V. email: sukkusur@purdue.edu organization: School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA – sequence: 4 givenname: Camille surname: Kamga fullname: Kamga, Camille email: ckamga@utrc2.org organization: Civil Engineering, Marshak Hall, Suite 910, 160 Convent Avenue, The City College of New York, New York, NY 10031, USA
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Keywords	Traffic state estimation Probe vehicle GPS-enabled taxicab Large scale data analysis Path inference Urban networks Data analysis Travel time GPS system Forecast model Modeling Urban district network Urban road traffic Taxi Large scale
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Snippet	•Uses large scale GPS traces of taxi cabs in New York City.•A model for estimating link travel times is developed using large scale data.•Developed insights...
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SubjectTerms	Applied sciences Exact sciences and technology GPS-enabled taxicab Ground, air and sea transportation, marine construction Large scale data analysis Path inference Probe vehicle Road transportation and traffic Traffic state estimation Transportation planning, management and economics Urban networks
Title	Urban link travel time estimation using large-scale taxi data with partial information
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