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Requirements and Potential of GPS-based Floating Car Data for Traffic Management: Stockholm Case Study
KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301).ORCID iD: 0000-0001-8750-8242
KTH, School of Architecture and the Built Environment (ABE), Transport and Economics (closed 20110301).
IBM.
2010 (English)In: 2010 13th International IEEE Conference on Intelligent Transportation Systems, 2010, 730-735 p.Conference paper, Published paper (Refereed)
Abstract [en]

The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. The paper presents the development of a laboratory designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control. It also presents results to illustrate the scope of traffic information that can be provided by GPS-based data, using the city of Stockholm as a case study. The preliminary analysis shows that network coverage, especially during peak weekday hours, is adequate. Further investigation is needed to validate the data, and increase its value through fusion with complementary data from other sources.

Place, publisher, year, edition, pages
2010. 730-735 p.
Series
Intelligent Transportation Systems (ITSC), ISSN 2153-0009
Keyword [en]
GPS, stockholm, intelligent transportation systems
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-50296DOI: 10.1109/ITSC.2010.5625177Scopus ID: 2-s2.0-78650456864ISBN: 978-1-4244-7657-2 (print)OAI: oai:DiVA.org:kth-50296DiVA: diva2:461536
Conference
13th International IEEE Conference on Intelligent Transportation Systems, ITSC 2010. Funchal. 19 September 2010 - 22 September 2010
Note
QC 20111207. XML_importAvailable from: 2011-12-05 Created: 2011-12-05 Last updated: 2015-05-25Bibliographically approved
In thesis
1. Path Inference of Sparse GPS Probes for Urban Networks: Methods and Applications
Open this publication in new window or tab >>Path Inference of Sparse GPS Probes for Urban Networks: Methods and Applications
2012 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The application of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place in urban areas with significant number of mobile sensors moving around covering expansive areas of the road network. Most travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Currently, systems that collect floating car data are designed to transmit the data in a limited form and relatively infrequently due to the cost of data transmission. That means the reported locations of vehicles are far apart in time and space. In order to extract traffic information from the data, it first needs to be matched to the underlying digital road network. Matching such sparse data to the network, especially in dense urban, area is challenging.

This thesis introduces a map-matching and path inference algorithm for sparse GPS probes in urban networks. The method is utilized in a case study in Stockholm and showed robustness and high accuracy compared to a number of other methods in the literature. The method is used to process floating car data from 1500 taxis in Stockholm City. The taxi data had been ignored because of its low frequency and minimal information. The proposed method showed that the data can be processed and transformed into information that is suitable for traffic studies.

The thesis implemented the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging traffic and traffic-related data for traffic monitoring and control.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xi, 53 p.
Series
Trita-TSC-LIC, ISSN 1653-445X ; 12:010
Keyword
map-matching, path inference, sparse GPS probes, FCD, urban area, digital road network, Stockholm, taxi, iMobility Lab, MapViz, travel time
National Category
Transport Systems and Logistics
Research subject
SRA - Transport
Identifiers
urn:nbn:se:kth:diva-104524 (URN)978-91-85539-98-7 (ISBN)
Presentation
2012-11-26, V2, Teknikringen 76, KTH, Stockholm, 14:30 (English)
Opponent
Supervisors
Funder
TrenOp, Transport Research Environment with Novel Perspectives
Note

QC 20121107

Available from: 2012-11-07 Created: 2012-11-05 Last updated: 2013-09-06Bibliographically approved
2. Urban Travel Time Estimation from Sparse GPS Data: An Efficient and Scalable Approach
Open this publication in new window or tab >>Urban Travel Time Estimation from Sparse GPS Data: An Efficient and Scalable Approach
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The use of GPS probes in traffic management is growing rapidly as the required data collection infrastructure is increasingly in place, with significant number of mobile sensors moving around covering expansive areas of the road network. Many travelers carry with them at least one device with a built-in GPS receiver. Furthermore, vehicles are becoming more and more location aware. Vehicles in commercial fleets are now routinely equipped with GPS.

Travel time is important information for various actors of a transport system, ranging from city planning, to day to day traffic management, to individual travelers. They all make decisions based on average travel time or variability of travel time among other factors.

AVI (Automatic Vehicle Identification) systems have been commonly used for collecting point-to-point travel time data. Floating car data (FCD) -timestamped locations of moving vehicles- have shown potential for travel time estimation. Some advantages of FCD compared to stationary AVI systems are that they have no single point of failure and they have better network coverage. Furthermore, the availability of opportunistic sensors, such as GPS, makes the data collection infrastructure relatively convenient to deploy.

Currently, systems that collect FCD are designed to transmit data in a limited form and relatively infrequently due to the cost of data transmission. Thus, reported locations are far apart in time and space, for example with 2 minutes gaps. For sparse FCD to be useful for transport applications, it is required that the corresponding probes be matched to the underlying digital road network. Matching such data to the network is challenging.

This thesis makes the following contributions: (i) a map-matching and path inference algorithm, (ii) a method for route travel time estimation, (iii) a fixed point approach for joint path inference and travel time estimation, and (iv) a method for fusion of FCD with data from automatic number plate recognition. In all methods, scalability and overall computational efficiency are considered among design requirements.

Throughout the thesis, the methods are used to process FCD from 1500 taxis in Stockholm City. Prior to this work, the data had been ignored because of its low frequency and minimal information. The proposed methods proved that the data can be processed and transformed into useful traffic information. Finally, the thesis implements the main components of an experimental ITS laboratory, called iMobility Lab. It is designed to explore GPS and other emerging data sources for traffic monitoring and control. Processes are developed to be computationally efficient, scalable, and to support real time applications with large data sets through a proposed distributed implementation.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2015. xiv, 50 p.
Series
TRITA-TSC-PHD, 15:005
Keyword
map-matching, path inference, sparse GPS probes, floating car data, arterial, urban area, digital road network, iterative travel time estimation, fixed point problem, Stockholm, taxi
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-167798 (URN)978-91-87353-72-7 (ISBN)
Public defence
2015-06-05, Kollegiesalen, Brinellvägen 8, KTH, Stockholm, 13:30 (English)
Opponent
Supervisors
Note

QC 20150525

Available from: 2015-05-25 Created: 2015-05-22 Last updated: 2015-05-25Bibliographically approved

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