Evaluation of countermeasures for red light running by traffic simulator–based surrogate safety measures

References

• Agarwal NK. Estimation of Pedestrian Safety at Intersections Using Simulations and Surrogate Safety Measures [PhD dissertation]. Lexington, KY: University of Kentucky; 2011.
   Google Scholar
• Alhajyaseen WKM. The development of conflict index for the safety assessment of intersections considering crash probability and severity. Procedia Comput Sci. 2014;32:364–371.
   Google Scholar
• AASHTO. The Highway Safety Manual. Washington, DC: Association of State Highway Transportation Professionals; 2010.
   Google Scholar
• Archer J, Young W. Signal treatments to reduce the likelihood of heavy vehicle crashes at intersections: microsimulation modeling approach. J Transp Eng. 2010;136:632–639.
   Web of Science ®Google Scholar
• Argade PD. Quantifying the Safety Effects of Access Management Using VISSIM and SSAM: A Case Study [Master's thesis]. Lawrence, KS: University of Kansas; 2014.
   Google Scholar
• Ariza A. Validation of Road Safety Surrogate Measures as a Predictor of Crash Frequency Rates on a Large-Scale Microsimulation Network [Master's thesis]. Toronto, ON: University of Toronto; 2011.
   Google Scholar
• Beeber J. Safer Streets in Los Angeles: Why Engineering Countermeasures Are More Effective Than Photo Enforcement in Reducing Red-Light Related Crashes. Los Angeles, CA: Safer Streets in Los Angeles; 2011.
   Google Scholar
• Bonneson J, Zimmerman K, Brewer M. Engineering Countermeasures to Reduce Red-Light-Running. College Station, TX: Texas Transportation Institute; 2002. FHWA/TX 03/4027-2.
   Google Scholar
• Caliendo C, Guida M. Microsimulation approach for predicting crashes at unsignalized intersections using traffic conflicts. J Transp Eng. 2012;138:1453–1467.
   Web of Science ®Google Scholar
• Council FM, Eccles K, Lyon C, Griffth MS. Safety Evaluation of Red-Light Cameras. Washington, DC: Federal Highway Administration, U.S. Department of Transportation; 2005. FHWA HRT 05–048.
   Google Scholar
• Cunningham CM, Hummer JS. Evaluating the Used of Red Light Running Photographic Enforcement Using Collisions and Red Light Running Violations. Raleigh, NC: Institute for Transportation Research and Education, North Carolina State University; 2004.
   Google Scholar
• Decina LE, Thomas L, Srinivasan R, Staplin L. Automated Enforcement: A Compendium of Worldwide Evaluation of Results. Washington, DC: NHTSA, U.S. Department of Transportation; 2007. DOT HS 810-763.
   Google Scholar
• Dowling R, Skabardonis A, Alexiadis V. Traffic Analysis Toolbox Volume III: Guidelines for Applying Traffic Microsimulation Modeling Software. Washington, DC: Federal Highway Administration, U.S. Department of Transportation; 2004. FHWA HRT 04–040.
   Google Scholar
• Fairfax County Department of Planning and Zoning. Fairfax County Comprehensive Plan. Fairfax County, VA: Fairfax County Government; 2013.
   Google Scholar
• Fambro DB, Koppa RJ, Picha DL, Fitzpatrick K. Driver braking performance in stopping sight distance situations. Transp Res Rec. 2010;3251:9–16.
   Google Scholar
• Federal Highway Administration. Surrogate Safety Assessment Model (SSAM). Washington, DC: U.S. Department of Transportation; 2008. TechBrief; FHWA HRT 08–049.
   Google Scholar
• Federal Highway Administration. Manual on Uniform Traffic Control Devices. Washington, DC: U.S. Department of Transportation; 2009.
   Google Scholar
• Federal Highway Administration. Red-Light Running Fatalities (2000–2009). Washington, DC: U.S. Department of Transportation; 2014. Available at: http://safety.fhwa.dot.gov/intersection/redlight/data/rlr_fatal/. Accessed June 24, 2015.
   Google Scholar
• Fitzsimmons EJ, Hallmark S, McDonald T, Orellana M, Matulac D. The Effectiveness of Iowa's Automated Red Light Running Enforcement Programs. Ames, IA: Center for Transportation Research and Education, Iowa State University; 2007. CTRE Project 05–226.
   Google Scholar
• Garber NJ, Haas PR, Gosse C. Development of Safety Performance Functions for Two-Lane Roads Maintained by the Virginia Department of Transportation. Charlottesville, VA: Virginia Transportation Research Council; 2010. FHWA/VTRC 10-R25.
   Google Scholar
• Gates T, Noyce D, Laracuente L, Nordheim E. Analysis of dilemma zone driver behaviors at signalized intersections. Transp Res Rec. 2007;2030:29–39.
   Web of Science ®Google Scholar
• Gazis D, Herman R, Maradudin A. The problem of the amber signal light in traffic flow. Oper Res. 1960;8:112–132.
   Web of Science ®Google Scholar
• Gettman D, Head L. Surrogate Safety Measures from Traffic Simulation Models: Final Report. Washington, DC: Federal Highway Administration, U.S. Department of Transportation; 2003. FHWA RD 03–050.
   Google Scholar
• Gettman D, Pu L, Sayed T, Shelby S. Surrogate Safety Assessment Model and Validation: Final Report. Washington, DC: Federal Highway Administration, U.S. Department of Transportation; 2008. FHWA HRT 08–051.
   Google Scholar
• Habtemichael FG, Santos LP. Safety evaluations of aggressive driving on motorways through microscopic traffic simulation and surrogate measures. In: Safety Data, Analysis and Evaluation [CD-ROM]. Washington, DC: Transportation Research Board of the National Academies; 2011.
   Google Scholar
• Hallmark S, Oneyear N, McDonald T. Toolbox of Countermeasures to Reduce Red Light Running. Ames, IA: Center for Transportation Research and Education, Iowa State University; 2012. InTrans Project 10–386.
   Google Scholar
• Hu W, McCartt AT, Teoh ER. Effects of red light camera enforcement on fatal crashes in large U.S. cities. J Safety Res. 2011;42(4):277–282.
   PubMed Web of Science ®Google Scholar
• Hyden C. The Development of a Method for Traffic Safety Evaluation: The Swedish Conflicts Technique. Lund, Sweden: Department of Traffic Planning and Engineering, Lund Institute of Technology; 1987.
   Google Scholar
• Insurance Institute for Highway Safety. Communities Using Red Light and/or Speed Cameras. Arlington, VA: 2015. Available at: http://www.iihs.org/iihs/topics/laws/printablelist?print-view. Accessed June 25, 2015.
   Google Scholar
• Killi DV, Vedagiri P. Proactive evaluation of traffic safety at an unsignalized intersection using microsimulation. Journal of Traffic and Logistics Engineering. 2014;2(2):140–145.
   Google Scholar
• Li Z, Chitturi MV, Zheng D, Bill AR, Noyce DA. Modeling reservation-based autonomous intersection control in VISSIM. Transp Res Rec. 2013;2381:81–90.
   Web of Science ®Google Scholar
• Lyon C, Haq A, Persaud B, Kodama S. Safety performance functions for signalized intersections in large urban areas: development and application to evaluation of left-turn priority treatment. Transp Res Rec. 2005;1908:165–171.
   Google Scholar
• Malone B, Hadayeghi A, White C. Red light cameras: surprising new safety results. Paper presented at: ITE 2010 Annual Meeting and Exhibit; 2010.
   Google Scholar
• McGee HW. Making Intersections Safer: A Toolbox of Engineering Countermeasures to Reduce Red-Light Running—An Informational Report. Washington, DC: Institute of Transportation Engineers; 2003.
   Google Scholar
• McGee HW, Moriarty K, Eccles K, Liu M, Gates T, Retting R. Guidelines for Timing Yellow and All-Red Intervals at Signalized Intersections. Washington, DC: Transportation Research Board; 2012. National Cooperative Highway Research Program. Report 731.
   Google Scholar
• Milazzo JS, Hummer JE, Prothe LM. A Recommended Policy of Automated Electronic Traffic Enforcement of Red-Light Running Violations in North Carolina. Raleigh, NC: Institute for Transportation Research and Education, North Carolina State University; 2001.
   Google Scholar
• Milton J, Mannering F. The relationship among highway geometrics, traffic-related elements and motor-vehicle accident frequencies. Transportation. 1998;25:395–413.
   Web of Science ®Google Scholar
• Mountain L, Fawaz B, Jarrett D. Accident prediction models for roads with minor junctions. Accid Anal Prev. 1996;28:695–707.
   PubMed Web of Science ®Google Scholar
• Nadimi N, Behbahani H, Shahbazi, H. Calibration and validation of a new time-based surrogate safety measure using fuzzy inference system. Journal of Traffic and Transportation Engineering. 2016;3:51–58.
   Google Scholar
• Park B, Chen Y, Hourdos J. Opportunities to preventing rear-end vehicle crashes: findings from analyzing actual crash data. Journal of Transportation Safety & Security. 2011;3(2):95–107.
   Google Scholar
• Parker M, Zegeer C. Traffic Conflict Technique for Safety and Operations—Engineer's Guide. Washington, DC: Federal Highway Administration, U.S. Department of Transportation; 1988. FHWA/IP 88/026.
   Google Scholar
• Parsonson PS, Santiago A. Design standards for timing the traffic signal clearance interval must be improved to avoid liability. In: Compendium of Technical Papers. Washington, DC: Institute of Transportation Engineers; 1980:67–71.
   Google Scholar
• Polus A, Cohen M. A new, non-canonical Poisson regression model for the prediction of crashes on low-volume rural roads. IATSS Res. 2012;35(2):98–103.
   Google Scholar
• PTV. VISSIM 5.40 User Manual. Karlsruhe, Germany: Planung Transport Verkehr AG; 2012.
   Google Scholar
• Qin X, Ivan JN, Ravishanker N. Selecting exposure measures in crash rate prediction for two-lane highway segments. Accid Anal Prev. 2004;36(2):183–191.
   PubMed Web of Science ®Google Scholar
• Radwan E, Yan X, Harb R, Klee H, Abdel-Aty M. Red-Light Running and Limited Visibility Due to LTV's Using the UCF Driving Simulator. Orlando, FL: Center for Advanced Transportation Systems Simulation, University of Central Florida; 2005.
   Google Scholar
• Retting RA, Williams AF, Farmer CM, Feldman AF. Evaluation of red light camera enforcement in Fairfax, VA, USA. ITE J. 1999a;69(8):30–34.
   Web of Science ®Google Scholar
• Retting RA, Williams AF, Farmer CM, Feldman AF. Evaluation of red light camera enforcement in Oxnard, California. Accid Anal Prev. 1999b;31(3):169–174.
   PubMed Web of Science ®Google Scholar
• Robertson HD. ITE Manual of Transportation Engineering Studies. Englewood Cliffs, NJ: Prentice Hall; 1994.
   Google Scholar
• Rrecaj AA, Bombol KM. Calibration and validation of the VISSIM parameters—state of the art. TEM J. 2015;4:255–269.
   Google Scholar
• Ruby DE, Hobeika AG. Assessment of red light running cameras in Fairfax County, Virginia. Transportation Quarterly. 2003;57(3):33–48.
   Google Scholar
• Sayed T, Brown G, Navin F. Simulation of traffic conflicts at unsignalized intersections with TSC-Sim. Accid Anal Prev. 1994;26:593–607.
   PubMed Web of Science ®Google Scholar
• Shahdah U, Saccomanno F, Persaud B. Integrated traffic conflict model for estimating crash modification factors. Accid Anal Prev. 2014;71:228–235.
   PubMed Web of Science ®Google Scholar
• So J, Lim IK, Kweon YJ. Exploring traffic conflict–based surrogate approach for safety assessment of highway facilities. Transp Res Rec. 2015;2513:56–62.
   Web of Science ®Google Scholar
• So J, Park B, Wolfe SM, Dedes G. Development and validation of a vehicle dynamics integrated traffic simulation environment assessing surrogate safety. Journal of Computing in Civil Engineering. 2014;29.
   Web of Science ®Google Scholar
• Stevanovic A, Kergaye C, Haigwood J. Assessment of surrogate safety benefits of an adaptive traffic control system. Paper presented at: 3rd International Conference on Road Safety and Simulation; 2011; Indianapolis, IN.
   Google Scholar
• Tang K, Nakamura H. Safety evaluation for intergreen intervals at signalized intersections based on probabilistic methodology. Transp Res Rec. 2009;2128:226–235.
   Web of Science ®Google Scholar
• Tegge RA, Jo J-H, Ouyang Y. Development and Application of Safety Performance Functions for Illinois. Rantoul, IL: Illinois Center for Transportation, University of Illinois Urbana–Champaign; 2010. ICT 10–066.
   Google Scholar
• Trafficware. Synchro Studio 8: Getting Started and What's New in Version 8. Sugar Land, TX: Trafficware Group, Inc.; 2013.
   Google Scholar
• Van Der Horst ARA. A Time-Based Analysis of Road User Behaviour in Normal and Critical Encounters [PhD dissertation]. Delft: The Netherlands: Delft University of Technology; 1990.
   Google Scholar
• Van Der Horst R, Hogema J. Time-to-collision and collision avoidance systems. Paper presented at: 6th ICTCT Workshop; 1993; Salzburg, Austria.
   Google Scholar
• Vasconcelos L, Silva AB, Seco A. Safety analysis of turbo-roundabouts using the SSAM technique. Paper presented at: CITTA 6th Annual Conference on Planning Research; 2013; Lisbon, Portugal.
   Google Scholar
• Virginia Department of Transportation. Annual Traffic Crash Data, Traffic Monitoring System; 2012.
   Google Scholar
• Vogel K. A comparison of headway and time to collision as safety indicators. Accid Anal Prev. 2003;35:427–433.
   PubMed Web of Science ®Google Scholar
• Washington S, Shin K. The Impact of Red Light Cameras (Automated Enforcement) on Safety in Arizona. Chandler, AZ: Arizona Department of Transportation; 2005. FHWA AZ 05–550.
   Google Scholar
• Wortman RH, Matthias JS. Evaluation of driver behavior at signalized intersections. Transp Res Rec. 1983;904:10–20.
   Google Scholar
• Xie F, Gladhill K, Dixon KK, Monsere CM. Calibrating the highway safety manual predictive models for Oregon state highways. Transp Res Rec. 2011;2241:19–28.
   Web of Science ®Google Scholar
• Zhou H, Huang F. Development of traffic safety evaluation method based on simulated conflicts at signalized intersections. Procedia Soc Behav Sci. 2013;96:881–885.
   Google Scholar