University of Maryland Department of Civil & Environmental Engineering By G.L. Chang, M.L. Franz, Y. Liu, Y. Lu & R. Tao BACKGROUND SYSTEM DESIGN DATA.

Slides:



Advertisements
Similar presentations
Flashing Yellow Arrow Jerry Kotzenmacher Minnesota Department of Transportation.
Advertisements

Traffic Crash Characteristics for Jackson Area Transportation Study Wayne State University Transportation Research Group November 26, 2007.
Flashing Yellow Arrows Joel McCarroll, Region 4 Traffic Manager on behalf of Edward L. Fischer State Traffic Engineer Oregon Department of Transportation.
F IRST C APITAL E NGINEERING 48 S. Richland Ave. York PA | Phone: | Fax: | TRAFFIC REPORT Kingston Road &
Chapter #8 Study Guide Answers.
INTRODUCTION TO TRANSPORT Lecture 7 Introduction to Transport Lecture 7: Signal Coordination.
Vehicle Motion Human Factors
1 Austin Transportation Department Ali Mozdbar, P.E., PTOE Division Manager, Traffic Signals Traffic Signal Features for Pedestrians & Bicyclists.
CE 515x Train Acceleration, Deceleration, and Impact on Capacity Initial Instructions Work in teams of 2 - Get a new team mate (i.e., one who is not your.
HERO UNIT Training Module Work Zone Traffic Control And Incident Management Operations.
Chapter 221 Chapter 22: Fundamentals of Signal Timing: Actuated Signals Explain terms related to actuated signals Explain why and where actuated signals.
Spring Sampling Frame Sampling frame: the sampling frame is the list of the population (this is a general term) from which the sample is drawn.
INTRODUCTION TO TRANSPORT Lecture 4 Introduction to Transport Lecture 4: Signal Timing.
CE 4640: Transportation Design
Lec 24, Ch.19: Actuated signals and detectors (Objectives) Learn terminology related to actuated signals Understand why and where actuated signals are.
Lec 15, Ch.8, pp : Signal Timing (Objective)
June 16, 2004 Dr. Robert Bertini Michael Rose Evaluation of the “COMET” Incident Response Program Oregon Department of Transportation.
Advanced Public Transit Systems (APTS) Transit ITS CEE582.
Month XX, 2004 Dr. Robert Bertini Using Archived Data to Measure Operational Benefits of ITS Investments: Ramp Meters Oregon Department of Transportation.
Detours – Selection and Design Highways & Engineering Conference March 2, 2006.
Red Light Photo Enforcement & Crime Scene Investigation William Fisher Photo Safety Program.
Peter Koonce TRB Annual Meeting January 9, 2005 Best Practices for Signal Operations Best Practices for Signal Operations – Lessons Learned from the Portland.
2015 Traffic Signals 101 Topic 7 Field Operations.
Transportation Engineering
Regional Traffic Monitoring System for Maryland’s Eastern Shore Dr. Gang-Len Chang Traffic Safety and Operations Lab University of Maryland, College Park.
Analysis of the SHRP 2 Naturalistic Driving Study Data [S08(B)] Evaluation of Offset Left-Turn Lanes Jessica M. Hutton Presentation to MCTRS August 15,
Report Samples. 2 Stop Report Shows where, when and for how long a vehicle has stopped.
Intelligent Transportation System (ITS) ISYM 540 Current Topics in Information System Management Anas Hardan.
State Traffic Engineer
Incident Management in Central Arkansas: Current Settings and Proposed Extensions Weihua Xiao Yupo Chan University of Arkansas at Little Rock.
Demo. Overview Overall the project has two main goals: 1) Develop a method to use sensor data to determine behavior probability. 2) Use the behavior probability.
Transit Priority Strategies for Multiple Routes under Headway-based Operations Shandong University, China & University of Maryland at College Park, USA.
July 29, 2009 George Saylor, PE ODOT Senior ITS Engineer.
Travel Speed Study of Urban Streets Using GPS &GIS Tom E. Sellsted City of Yakima, Washington Information Systems and Traffic.
Chapter 9: Speed, travel time, and delay studies
Evaluation of Alternative Methods for Identifying High Collision Concentration Locations Raghavan Srinivasan 1 Craig Lyon 2 Bhagwant Persaud 2 Carol Martell.
Chapter 20: Actuated Signal Control and Detection
Signal Warrants. Slide 2 Signal Warrants  r2/part4/part4c.htm r2/part4/part4c.htm.
MD : ATIKUL ISLAM JUNIOR INSTRACTOR DEPT. OF. CIVIL SUB: SURVEYING3 SUB CODE : 6452 MOB:
September 25, 2013 Greg Davis FHWA Office of Safety Research, Development and Test Overview of V2I Safety Applications.
University of Minnesota Intersection Decision Support Research - Results of Crash Analysis University of Minnesota Intersection Decision Support Research.
Portland State University 11 By Maisha Mahmud Li Huan Evaluation Of SCATS Adaptive Traffic Signal Control System.
Engineering Study Griffin View Drive & Harbor Hills Blvd. Prepared for Lake County Public Works Engineering 437 Ardice Avenue Eustis, Florida Purchase.
I-270/MD 355 Simulator: An Intelligent Online Traffic Management System Dr. Gang-Len Chang Nan Zou Xiaorong Lai University of Maryland Saed Rahwanji Maryland.
Iihs.org Automated enforcement. Number of U.S. communities with speed cameras and red light cameras January 2016 Automated enforcement uses technology.
Design and Evaluation of An Advanced Dilemma Zone Protection System: Advanced Warning Sign and All-Red Extension by Sung Yoon Park, Liu Xu, Gang-Len Chang.
City of Portland - Isolated Timing Operations January 9, 2005 Isolated Timing Operations - Workshop on Best Practices for Signal Timing Bill Kloos Signals.
Conducted by: Tyler Womble. “A place where two or more roads meet, especially when at least one is a major highway” Can be regulated by: Stop Signs Traffic.
TRAFFIC SAFETY AND OPERATIONS LAB DESIGN OF A DILEMMA ZONE PROTECTION SYSTEM US MD 910C (WESTERN MARYLAND PARKWAY)
The Safety Impact of the Automated Photo Red Light Program Prepared by Lieutenant Jim Lemmon San Leandro Police Department Traffic Division.
Shlomo Bekhor Transportation Research Institute Technion – Israel Institute of Technology Monitoring and analysis of travel speeds on the national road.
ParkNet: Drive-by Sensing of Road-Side Parking Statistics Irfan Ullah Department of Information and Communication Engineering Myongji university, Yongin,
LOW COST SAFETY IMPROVEMENTS Practitioner Workshop The Tools – Identification of High Crash Locations – Session #2.
National Highway Institute 5-1 REV-2, JAN 2006 EQUIPMENT FACTORS AFFECTING INERTIAL PROFILER MEASUREMENTS BLOCK 5.
Module 3 Brianna James Percy Antoine. Entering the Roadway/Moving to the Curb/Backing  The seven steps to safely pull from a curb. Place foot firmly.
Signals, pavement markings, and proper turns QUIZ
SUMMARY AND CONCLUSIONS
MOVA Traffic Signal Control Trial
Adaptive Signals & ALDOT
* Topic 7 Field Operations
Exploratory Analysis of Crash Data
Establishing Safe and Realistic Speed Limits
Traffic Study Presented by Keith Wenners, pe, ptoe
Dilemma Zone Protection at An Isolated Signalized Intersection Using Dynamic Speed Guidance Wenqing Chen.
CE 3500 Transportation Engineering Elements of Traffic Signals
School of Civil Engineering
HERO UNIT Training Module
School of Civil Engineering
Example of cones and signs as traffic control at a roadway incident.
Transportation Engineering Calculating Signal Delay February 23, 2011
Presentation transcript:

University of Maryland Department of Civil & Environmental Engineering By G.L. Chang, M.L. Franz, Y. Liu, Y. Lu & R. Tao BACKGROUND SYSTEM DESIGN DATA ANALYSIS & RESULTS PERFORMANCE EVALUATION SURVEY DATA REDUCTION Traditional dilemma zone protection systems using spot vehicle detections from loop detectors provide protection for only those vehicles traveling at the posted speed limit. Recent technology has been developed to dynamically track individual vehicles as they approach an intersection of interest. Using real-time vehicle speed and distance from the intersection, the manufacturer of this technology claim that such a system can provide dilemma zone protection for each vehicle; thus improving the overall safety of the intersection. The goal of this research was to develop a system that utilizes the dynamic detection technology and to evaluate the performance of this system at an intersection suffering from crashes susceptible to correction by sufficient dilemma zone protection. Vehicle Detection that Called the All-Red Extension Vehicle Position After 3 Seconds of Red Map of System Design Acknowledgements: Thank you to the Maryland SHA for sponsoring this research Thank you to Mr. Bob DeBlase of SHA for his input on the signal logic Thank you to Mr. Larry Gredlein of SHA for providing temporary traffic control during tube installation Thank you to our colleagues at the Traffic Safety & Operation Lab for assisting in data collection and data reduction Synchronizing Equipment with GPS Video Reduction Software Summary of Data Analysis Vehicle Position at the End of the All-Red Extension Map of Equipment Location for East Bound Data Collection RESEARCH MOTIVATION The site selected for installation of the dynamic dilemma zone protection system was the high speed (55 mph) rural intersection of US40 at Red Toad Road in Cecil County, MD. This site was selected based on the frequency and severity of crashes that may be corrected by dilemma zone protection (i.e. right angle crashes). From the years , the intersection of US40 & Red Toad Road experienced 89 crashes, 40 (45%) of which were right angle crashes. PRE-DESIGN SURVEY To properly design the dynamic dilemma zone protection system, a pre- design survey was conducted at the target intersection. The survey collected data on vehicle speeds and reaction to the yellow interval. The following table and figures summarize driver behavior during the yellow interval on the major approach (US40): Summary of Vehicle Speeds During the Yellow Interval Speed Distribution During Yellow Interval (By Direction) Observing the wide distribution of speeds during the yellow interval suggests that traditional dilemma zone protection provided by static loop detectors may not be appropriate. This realization is illustrated in the following figure, showing the dilemma zone location for various approach speeds, calibrated to observed driver behavior at this intersection: Dilemma Zone Locations for Various Speeds To address large speed variance of approaching vehicles during the yellow interval, a system state-of-the-art microwave sensor that dynamically tracks approaching vehicles was selected for implementation. The data collected from these sensors was used to control the signal logic, including green extensions, gap-outs and all-red extensions. The all red extension provided dilemma zone protection by calculating an estimated time of arrival for each vehicle during the first 3 seconds of the US40 red interval. To address the over-representation of severe crashes in the eastbound direction, a two sensor system was designed for this direction. The location and detection range of each sensor is illustrated below: System Control Logic: Call a green extension after reaching the minimum green time if a vehicle was detected within 500ft of either intersection stop bar with a minimum speed of 27 mph; Call an all-red extension if a vehicle is detected within 500ft of either US 40 approach at a minimum speed of 56 mph at the onset of the US 40 red interval. The length of the extended all-red interval is determined by the vehicle’s speed and its distance from the stop bar with a maximum extension of 2.5 seconds. Additional dilemma zone protection for EB US 40 was provided by sensor 3. The section of EB US 40 covered uniquely by the second EB sensor (from 500ft to 875ft relative to the EB stop bar) was used only for all red extension. Within this range, a vehicle must be detected with a minimum speed of 67 mph for an all-red extension to be called. Approximately 18 months after the system was installed, a performance evaluation was conducted on the eastbound direction. Data was collected for 4 hours via tube detectors, video cameras and the signal log file. The location and function for each piece of data collection equipment is presented below: The evaluation was performed by identifying missed all-red extension calls. False positives were those events in which an all-red extension was called, but no vehicles met the criteria to do so (at the cost of efficiency). False negatives were the events in which an all-red extension was not called but at least one vehicle warranted an all red extension (at the cost of safety). For the analysis of the all-red extension function, only those vehicles detected within 3 seconds of onset of US 40 red interval where considered. During the 4 hours analysis period of the eastbound direction: 521 were detected within 3 seconds of the onset of US40 red interval. Only one all red extension was called during this period This call was verified (See figures to the left) The length of the all-red extension allowed the target vehicle to clear the intersection before the side street traffic was released. The remaining 520 vehicles detected within 3 seconds of the onset of US40 did not meet the criteria for an all-red extension Other than the vehicle that called the all-red extension, no other vehicles ran the red signal Based on the results of the survey and reduction in right angle crashes since the installation of the system, the Distance From Stop BarEquipment UsedData Collected 200ftVideo Camera 2Speed, Class, Lane 275ftVideo Camera1Signal 300ftTube Detector 1Speed, Class, Lane 400ftVideo Camera 3Speed, Class, Lane 500ftTube Detector 2Speed, Class, Lane 600ftVideo Camera 4Speed, Class, Lane 700ftTube Detector 3Speed, Class, Lane 800ftVideo Camera 5Speed, Class, Lane 875ftTube Detector 4Speed, Class, Lane Summary of Equipment Used Since each piece of data collection equipment had its own internal clock, all clocks were synchronized using hand-held GPS units (Figure below, left). While the tube detectors had “out-of-the-box” software to compute vehicle speeds, the video data had to be manually reduced. Using construction cones with known spacing, software was developed to calculate vehicle speeds (Figure below, right). This software was also used to record the timing of each signal interval change.