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Urban Travel Time Estimation from Sparse GPS Data: An Efficient and Scalable Approach
KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.ORCID iD: 0000-0001-8750-8242
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 [en]
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: urn:nbn:se:kth:diva-167798ISBN: 978-91-87353-72-7 (print)OAI: oai:DiVA.org:kth-167798DiVA: diva2:813447
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
List of papers
1. Requirements and Potential of GPS-based Floating Car Data for Traffic Management: Stockholm Case Study
Open this publication in new window or tab >>Requirements and Potential of GPS-based Floating Car Data for Traffic Management: Stockholm Case Study
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.

Series
Intelligent Transportation Systems (ITSC), ISSN 2153-0009
Keyword
GPS, stockholm, intelligent transportation systems
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-50296 (URN)10.1109/ITSC.2010.5625177 (DOI)2-s2.0-78650456864 (Scopus ID)978-1-4244-7657-2 (ISBN)
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
2. Path inference from sparse floating car data for urban networks
Open this publication in new window or tab >>Path inference from sparse floating car data for urban networks
2013 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 30, 41-54 p.Article in journal (Refereed) Published
Abstract [en]

The use of probe vehicles in traffic management is growing rapidly. The reason is that the required data collection infrastructure is increasingly in place in urban areas with a significant number of mobile sensors constantly moving and covering expansive areas of the road network. In many cases, the data is sparse in time and location and includes only geo-location and timestamp. Extracting paths taken by the vehicles from such sparse data is an important step towards travel time estimation and is referred to as the map-matching and path inference problem. This paper introduces a path inference method for low-frequency floating car data, assesses its performance, and compares it to recent methods using a set of ground truth data.

Keyword
Map-matching, Path inference, Sparse floating car data, GPS
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-123426 (URN)10.1016/j.trc.2013.02.002 (DOI)000318387500003 ()2-s2.0-84875483253 (Scopus ID)
Note

QC 20130610

Available from: 2013-06-10 Created: 2013-06-10 Last updated: 2017-12-06Bibliographically approved
3. Non-parametric estimation of route travel time distributions from low-frequency floating car data
Open this publication in new window or tab >>Non-parametric estimation of route travel time distributions from low-frequency floating car data
2015 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 58B, 343-362 p.Article in journal (Refereed) Published
Abstract [en]

The paper develops a non-parametric method for route travel time distribution estimation using low-frequency floating car data (FCD). While most previous work has focused on link travel time estimation, the method uses FCD observations for estimating the travel time distribution on a route. Potential biases associated with the use of sparse FCD are identified. The method involves a number of steps to reduce the impact of these biases. For evaluation purposes, a case study is used to estimate route travel times from taxi FCD in Stockholm, Sweden. Estimates are compared to observed travel times for routes equipped with Automatic Number Plate Recognition (ANPR) cameras with promising results. As vehicles collecting FCD (in this case, taxis) may not be a representative sample of the overall vehicle fleet and driver population, the ANPR data along several routes are also used to assess and correct for this bias. The method is computationally efficient, scalable, and supports real time applications with large data sets through a proposed distributed implementation.

Place, publisher, year, edition, pages
Elsevier, 2015
Keyword
Floating car data, Automatic number plate recognition, Kernel estimation, Route travel time distribution, Sampling bias, Non-parametric
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-167648 (URN)10.1016/j.trc.2015.01.015 (DOI)000361923400014 ()2-s2.0-84940462344 (Scopus ID)
Note

QC 20170201

Available from: 2015-05-22 Created: 2015-05-22 Last updated: 2017-12-04Bibliographically approved
4. Travel Time Estimation from Sparse Floating Car Data with Consistent Path Inference: A Fixed Point Approach
Open this publication in new window or tab >>Travel Time Estimation from Sparse Floating Car Data with Consistent Path Inference: A Fixed Point Approach
(English)Manuscript (preprint) (Other academic)
Abstract [en]

An important application of sparse floating car data (FCD) is the estimation of network link travel times, which requires pre-processing by map-matching and path inference filters. Path inference, in general, requires some a priori assumption about link travel times to infer paths that are reasonable and temporally consistent with observations. Path inference and travel time estimation is thus a joint problem. This paper proposes a fixed point approach to the travel time estimation problem with consistent path inference.The methodology is applied in a case study to estimate travel times from taxi FCD in Stockholm, Sweden. In this case study, existing methods for path inference and travel time estimation, without any particular assumptions about path choice models or travel time distributions, are used. The results show that standard fixed point iterations converge quickly to a solution where input and output travel times are consistent. The solution is robust under different initial travel times and data sizes. Using historical initial travel times reduces bias. The results highlight the value of the fixed point estimation process, in particular for accurate path finding and route optimization.

Keyword
floating car data, fixed point problem, travel time estimation, path inference, iterative process
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-167731 (URN)
Note

QS 2015

Available from: 2015-05-22 Created: 2015-05-22 Last updated: 2015-05-25Bibliographically approved
5. Floating Car and Camera Data Fusion for Non-Parametric Route Travel Time Estimation
Open this publication in new window or tab >>Floating Car and Camera Data Fusion for Non-Parametric Route Travel Time Estimation
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The paper proposes a non-parametric route travel time estimation method based on fusion of floating car data (FCD) and automated number plate recognition (ANPR) data. Today’s traffic management utilizes heterogeneous data collection systems which can be stationary or mobile. Each data collection system has its own advantages and disadvantages. Stationary sensors usually have less measurement noise than mobile sensors but their network coverage is limited. On the other hand, mobile sensors, commonly installed in fleet vehicles, cover relatively wider areas of the network but they suffer from low penetration rate and low sampling frequency. Traffic state estimations can benefit from fusion of data collected by various sources as they complement each other. The proposed estimation method is implemented using FCD from taxis and the ANPR data from Stockholm, Sweden. The results suggest that the fusion increases the robustness of the estimation, meaning that the fused estimates are always better than the worst of the two (FCD or ANPR), and it sometimes outperforms the two single sources.

Series
2014 17th IEEE International Conference on Intelligent Transportation Systems, ITSC 2014, 6957864
Keyword
Floating car data, ANPR, data fusion, route travel time estimation
National Category
Transport Systems and Logistics
Research subject
Transport Science
Identifiers
urn:nbn:se:kth:diva-167792 (URN)10.1109/ITSC.2014.6957864 (DOI)000357868701058 ()2-s2.0-84937113926 (Scopus ID)
Conference
2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014. Qingdao, China
Note

QC 20150525

Available from: 2015-05-22 Created: 2015-05-22 Last updated: 2015-08-14Bibliographically approved

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