Estimating Arterial Traffic Conditions using Sparse Probe Data

Estimating and predicting traffic conditions in arterial networks using probe data has proven to be a substantial challenge. In the United States, sparse probe data represents the vast majority of the data available on arterial roads in most major urban environments. This article proposes a probabilistic modeling framework for estimating and predicting arterial travel time distributions using sparsely observed probe vehicles. We evaluate our model using data from a fleet of 500 taxis in San Francisco, CA, which send GPS data to our server every minute. The sampling rate does not provide detailed information about where vehicles encountered delay or the reason for any delay (i.e. signal delay, congestion delay, etc.). Our model provides an increase in estimation accuracy of 35% when compared to a baseline approach for processing probe vehicle data.

Record URL:
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/9781424476572
Authors:
- Herring, Ryan
- Hofleitner, Aude
- Abbeel, Pieter
- Bayen, Alexandre M
- 0000-0002-6697-222X
Conference:
- 13th International IEEE Conference on Intelligent Transportation Systems (ITSC 2010)
- Location: Madeira Island , Portugal
- Date: 2015-9-19 to 2015-9-22
Publication Date: 2010

Language

English

Media Info

Media Type: Web
Features: Figures; References; Tables;
Pagination: pp 929-936

Subject/Index Terms

TRT Terms: Global Positioning System; Markov chains; Probe vehicles; Traffic congestion; Traffic data; Traffic delays; Traffic models
Geographic Terms: San Francisco (California)
Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory; I73: Traffic Control;

Filing Info

Accession Number: 01342686
Record Type: Publication
Source Agency: UC Berkeley Transportation Library
ISBN: 9781424476572
Files: TLIB
Created Date: Jun 23 2011 9:07AM