Quality of Private Sector Travel-Time Data on Arterials
Publication: Journal of Transportation Engineering
Volume 142, Issue 4
Abstract
Accurate traffic state information is essential for both travelers and transportation agencies. In the past, traffic condition data were usually collected by a government agency using its own sensors. Recently, a number of private sector companies have started selling travel-time and speed data collected using probe vehicles, which provides a viable opportunity to outsource traffic data collection. Because these data sources and their related algorithms are proprietary, the reliability and accuracy of this private sector data is often an important issue for transportation agencies. Previous studies have examined the accuracy of private sector data on freeways, but arterials have not been examined extensively. Arterials represent a fundamentally more challenging environment for probe vehicle data given the larger variance in travel times created by traffic signals and other intermediate access points. In the research, the quality of private sector data on arterials is evaluated by utilizing Bluetooth travel-time data as the ground truth. The evaluation is conducted from two perspectives: the ability to track real-time conditions, and the ability to identify long-term traffic state changes. The study sites are three signalized arterials in the state of Virginia. The results indicate that the private sector data evaluated were not suitable for real-time applications, but could be used to measure long-term traffic state changes for performance measurement programs.
Get full access to this article
View all available purchase options and get full access to this article.
References
Belzowski, B. M., and Ekstrom, A. (2013). “Stuck in traffic: Analyzing real time traffic capabilities of personal navigation devices and traffic phone applications.” 〈http://deepblue.lib.umich.edu/bitstream/handle/2027.42/100187/102967.pdf?sequence=1〉 (Jan. 20, 2015).
Cambridge Systematics. (2012). Travel time data collection white paper, Florida Dept. of Transportation, Tallahassee, FL.
Chase, R. T., Williams, B. M., Rouphail, N. M., and Kim, S. (2012). “Comparative evaluation of reported speeds from corresponding fixed-point and probe-based detection systems.” Transportation Research Record, 2308, 110–119.
Coifman, B., and Kim, S. (2013). “Assessing the performance of the SpeedInfo sensor.” 〈http://www.dot.state.oh.us/Divisions/Planning/SPR/Research/reportsandplans/Reports/2014/Planning/134723_FR.pdf〉 (Jan. 20, 2015).
Eisele, W. L., Schrank, D. L., Schuman, R., and Lomax, T. J. (2013). “Estimating urban freight congestion costs: Methodologies, measures, and applications.” 〈http://docs.trb.org/prp/13-1344.pdf〉 (Jan. 21, 2015).
Elefteriadou, L., Kondyli, A., and George, B. S. (2014). “Comparison of methods for measuring travel time at Florida freeways and arterials.” 〈http://www.dot.state.fl.us/research-center/Completed_Proj/Summary_PL/FDOT-BDV32-977-02-rpt.pdf〉 (Jan. 22, 2015).
Fontaine, M. D., Chun, P., and Cottrell, B. H. (2014). “Using private sector travel time data for project-level work zone mobility performance measurement.” 〈http://docs.trb.org/prp/14-2186.pdf〉 (Jan. 23, 2015).
Haghani, A., Hamedi, M., and Sadabadi, K. F. (2008). “Corridor coalition.” 〈http://www.i95coalition.org/i95/Portals/0/Public_Files/uploaded/Vehicle-Probe/I-95%20CC%20VP%20EX%20SUMM%20FINAL%20JAN%2022%2009%20W%20COVER.PDF〉 (Jan. 27, 2015).
Jia, C., Li, Q., Oppong, S., Ni, D., Collura, J., and Shuldiner, P. W. (2013). “Evaluation of alternative technologies to estimate travel time on rural interstates.” Transportation Research Board 92nd Annual Meeting,.
Kim, S., and Coifman, B. (2014). “Comparing INRIX speed data against concurrent loop detector stations over several months.” Transp. Res. Part C, 49, 59–72.
Lattimer, C., and Glotzbach, G. (2012). “Evaluation of third party travel time data.” 〈http://itswc.confex.com/itswc/AM2012/webprogram/Paper10870.html〉 (Jan. 23, 2015).
Quayle, S. M., Koonce, P., DePencier, D., and Bullock, D. M. (2010). “Arterial performance measures with media access control readers: Portland, Oregon, pilot study.” Transportation Research Record, 2192, 185–193.
Schneider, W. H., Turner, S. M., Roth, J., and Wikander, J. (2010). “Statistical validation of speeds and travel times provided by a data service vendor.” 〈http://www.dot.state.oh.us/Divisions/TransSysDev/Research/reportsandplans/Reports/2010/Traffic/134416_FR.pdf〉 (Jan. 23, 2015).
Turner, S. N. (2011). Technical memorandum, Minnesota Dept. of Transportation, St. Paul, MN.
Wang, Y., Araghi, B. N., Malinovskiy, Y., Corey, J., and Cheng, T. (2014). “Error assessment for emerging traffic data collection devices.” 〈http://www.wsdot.wa.gov/research/reports/fullreports/810.1.pdf〉 (Jan. 21, 2015).
Wikander, J., Eisele, W. L., and Schrank, D. L. (2014). “Automated congestion bottleneck identification for MAP-21 performance measure reporting using large statewide speed data sets.” 〈http://docs.trb.org/prp/14-4356.pdf〉 (Jan. 25, 2015).
Young, S. (2010). “Memorandum to vehicle probe project arterial data quality committee.” 〈〉 (Jan. 23, 2015).
Information & Authors
Information
Published In
Journal of Transportation Engineering
Volume 142 • Issue 4 • April 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Mar 29, 2015
Accepted: Sep 8, 2015
Published online: Jan 19, 2016
Published in print: Apr 1, 2016
Discussion open until: Jun 19, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.