Analyzing intra-metropolitan variation in highway traffic performance in Los Angeles using archived real-time data

References

• Brennan Jr, T. M., S. M. Remias, and L. Manili. 2015. “Performance Measures to Characterize Corridor Travel Time Delay Based on Probe Vehicle Data.” Transportation Research Record: Journal of the Transportation Research Board 2526: 39–50.
   Google Scholar
• Cambridge Systematics Inc. 2014. Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes. SHRP Report S2-L05-RW-1. Transportation Research Board of the National Academies, Washington, DC.
   Google Scholar
• Cambridge Systematics Inc., Dowling Associates, Inc., System Metrics Group, Inc. & Texas Transportation Institute. 2008. NCHRP Report 618: Cost-Effective Performance Measures for Travel Time Delay, Variation, and Reliability. Transportation Research Board of the National Academies, Washington, DC.
   Google Scholar
• Cambridge Systematics Inc., Transmode Consultants, National Cooperative Highway Research Program, American Association of State Highway and Transportation Officials. 1998. Multimodal corridor and capacity analysis manual. Publication no. 399, Transportation Research Board of the National Academies, Washington, DC.
   Google Scholar
• Chakrabarti, S. 2015. “The Demand for Reliable Transit Service: New Evidence Using Stop Level Data From the Los Angeles Metro bus System.” Journal of Transport Geography 48: 154–164.
   Web of Science ®Google Scholar
• Chakrabarti, S., and G. Giuliano. 2015. “Does Service Reliability Determine Transit Patronage? Insights From the Los Angeles Metro bus System.” Transport Policy 42: 12–20.
   Web of Science ®Google Scholar
• Federal Highway Administration (FHWA). 2013. Traffic Monitoring Guide. U.S. Department of Transportation, Office of Highway Policy Information. Accessed July 31, 2015. http://www.fhwa.dot.gov/policyinformation/tmguide/tmg_fhwa_pl_13_015.pdf.
   Google Scholar
• Giuliano, G., S. Chakrabarti, and M. Rhoads. 2016. “Using Regional Archived Multimodal Transportation System Data for Policy Analysis A Case Study of the LA Metro Expo Line.” Journal of Planning Education and Research 36 (2): 195–209.
   Web of Science ®Google Scholar
• Giuliano, G., M. Rhoads, and S. Chakrabarti. 2014. “New data, new applications: using transportation system data for regional monitoring.” Transport Research Arena (TRA) 5th Conference: Transport Solutions from Research to Deployment.
   Google Scholar
• Innamaa, S. 2006. “Effect of Monitoring System Structure on Short-Term Prediction of Highway Travel Time.” Transportation Planning and Technology 29 (2): 125–140.
   Web of Science ®Google Scholar
• Jackson, D. L., T. L. Shaw, G. Morgan, D. McLeod, and A. Vandervalk. 2000. Florida’s Reliability Method. Tallahassee: Florida Department of Transportation.
   Google Scholar
• Jeng, S. T., K. S. Nesamani, and S. G. Ritchie. 2013. “A new Approach to Estimate Vehicle Emissions Using Inductive Loop Detector Data.” Journal of Intelligent Transportation Systems 17 (3): 179–190.
   Web of Science ®Google Scholar
• Kim, S., and B. Coifman. 2014. “Comparing INRIX Speed Data Against Concurrent Loop Detector Stations Over Several Months.” Transportation Research Part C: Emerging Technologies 49: 59–72.
   Web of Science ®Google Scholar
• Lomax, T., S. Turner, G. Shunk, H. S. Levinson, R. H. Pratt, P. N. Bay, and G. B. Douglas. 1997. NCHRP report 398: Quantifying Congestion Volume 1 Final Report. Publication Project 7-13, Transportation Research Board, National Research Council, Washington, D.C.
   Google Scholar
• Margiotta, R. A., T. Lomax, M. Hallenbeck, S. Turner, A. Skabardonis, C. Ferrell, and B. Eisele. 2006. Guide to Effective Freeway Performance Measurement: Final Report and Giudebook. NCHRP Project 3-68, Transportation Research Board of the National Academies, Washington, DC.
   Google Scholar
• Meyer, M. D. 1995. Alternative performance measures for transportation planning: evolution toward multimodal planning. Publication FTA-GA-26-7000-95-1, Federal Transit Administration.
   Google Scholar
• National Transportation Statistics. 2019. Accessed July 23, 2019. https://www.bts.gov/topics/national-transportation-statistics.
   Google Scholar
• Pratt, R., and T. Lomax. 1996. “Performance Measures for Multimodal Transportation Systems.” Transportation Research Record: Journal of the Transportation Research Board 1518: 85–93.
   Google Scholar
• Pu, W., and A. J. Meese. 2013. Using New Data Sources to Meet MAP-21 Requirements for Performance-Based Planning: National Capital Region’s Experience in Monitoring Congestion and Reliability.” Presented at the 92nd Annual Meeting of the Transportation Research Board, Washington, D.C.
   Google Scholar
• SCAG. 2007. State of the Region Report. Southern California Association of Governments, 2007. Accessed July 23, 2019. http://www.scag.ca.gov/NewsAndMedia/Pages/State%20of%20The%20Region.aspx.
   Google Scholar
• Schrank, D., B. Eisele, and T. Lomax. 2019. 2019 Urban Mobility Report. Texas Transportation Institute, Texas A&M University. Accessed September 17, 2020. https://static.tti.tamu.edu/tti.tamu.edu/documents/mobility-report-2019.pdf.
   Google Scholar
• Schuman, R., S. Turner, and J. Corrales. 2017. “National Performance Management Research Data Set (NPMRDS) Descriptive Metadata Document 1.0.” No longer available online; available from the authors by request.
   Google Scholar
• Shaw, T. 2003. Performance measures of operational effectiveness for highway segments and systems. Synthesis 311, Transportation Research Board of the National Academies, Washington, D.C.
   Google Scholar
• Sweet, M. N., C. J. Harrison, and P. S. Kanaroglou. 2015. “Gridlock in the Greater Toronto Area: Its Geography and Intensity During key Periods.” Applied Geography 58: 167–178.
   Web of Science ®Google Scholar
• Teng, H., and N. Wang. 2011. “Estimating Vehicle Miles Traveled Combined with ITS Data.” Transportation Planning and Technology 34 (8): 777–794.
   Web of Science ®Google Scholar
• Turner, S. M., M. E. Best, and D. L. Schrank. 1996. Measures of effectiveness for major investment studies. Publication SWUTC/96/467106-1, Texas Transportation Institute and Southwest Region University Transportation Center.
   Google Scholar
• Yang, H., K. Ozbay, and K. Xie. 2015. “Improved Travel Time Estimation for Reliable Performance Measure Development for Closed Highways.” Transportation Research Record: Journal of the Transportation Research Board 2526: 29–38.
   Google Scholar
• Zhang, Y., M. Hamedi, A. Haghani, S. Mahapatra, and X. Zhang. 2015. “How Data Affect Travel Time Reliability Measures: An Empirical Study.” Transportation Research Board 94th Annual Meeting, no. 15-4330.
   Google Scholar