Research and Deployment of Technologies on Florida Highways and Streets

Transcription

1 Research and Deployment of Technologies on Florida Highways and Streets Essam Radwan, Ph.D., P.E. Professor of Engineering CATSS Executive Director Center for Advanced Transportation Systems Simulation University of Central Florida

2 Outline CATSS at UCF Completed Projects: CATSS Driving Simulators research Red Light Running New Projects: New Marking Design FTE DSS

3 UCF Facts Faculty: 1,400 Students: 44,000 Academic Programs: Baccalaureate: 77 Master's Programs: 56 Doctoral Programs: 16 Research Administration $105 Million

4 History of CATSS University Transportation Center (TEA 21) US Department of Transportation RITA- Tier 1 Florida Department of Transportation aeradwan@mail.ucf.edu

5 Organizational Chart Dr. Essam Radwan Executive Director Jack Selter Director Vijay Balram Office Manager Dr. Mohamed Abdel-Aty Program Director Transportation Safety and Operations Harold Klee Program Director Simulation Laboratory Patrick Kerr Program Director Data Management and Web Applications Amr Oloufa Program Director Advanced Intelligent Transportation Technologies & Communications Ron Tarr Program Director Simulation & Performance Technologies for Advanced Transportation Applications

6 Mission Advance and deploy value-added, cost effective technology into the U.S. intermodal surface transportation system to increase its overall safety and effectiveness.

7 Return on investment of $2.67 for every $1 spent by CATSS To date CATSS has generated $12.7 million in project funding from federal, state, and companies

8 Education Since its inception, 36 MS and 14 Ph.D. degrees have been awarded This year alone, there are a total of 35 graduate students affiliated with the center and close to 25 faculty and staff from three colleges and one institute. 6 Faculty from 3 additional Universities

9 The center has produced 67 projects and 30 technical reports posted on the CATSS Web site. Last year alone, CATSS faculty published 27 peer reviewed journal papers. The Web site received 27,100 hits last academic year. January,06 TRB CATSS faculty will present 15 papers

10 High Bay Area Three Stories High Hexagonal room Ten - Ton Crane Storage Space for Dismounted Cabs UCF Driving Simulator

11 UCF Driving Simulator Control Room Load Scenarios Control Environment Monitor Subjects Collect Data

12 Cabs UCF Driving Simulator Tractor Trailer Truck Municipal, Cement Trucks, Military, Bus, etc. On-The-Fly Reconfigurable Transmission Interior Controls, Gauges, AC

13 UCF Driving Simulator Cabs Saturn Sedan Automatic Transmission Intercom with Video available in both cabs

14 Capabilities: 3D World Creation Geometry Triangles Simulation is Balance Texture Edited Photos Created from Scratch Again, Balance is Key

15 Scenario Creation PC & Microsoft-Based Development Platform/Applications Worlds Urban Suburban Highway Industrial Park Rural European Desert Mountain Terrain Geo-Specific

16 Lower End Technology

17 NADS

18 Driving Simulator Applications Multi-disciplinary investigations and analyses on a wide range of issues associated with traffic safety, highway engineering, Intelligent Transportation System (ITS), human factors, and motor vehicle product development. Evaluating a pavement marking countermeasure for reducing red-light running.

19 Reducing Red-Light Running A pavement-marking countermeasure is proposed to help drivers make a clear decision at the onset of yellow change interval to reduce red-light running rate and improve intersection safety. Safely Stop at the Intersection Safely Cross the Intersection

20 Reducing Red-Light Running Three independent factors: Pavement type: with marking or without marking Speed limit: 30 mph and 45 mph Yellow onset distance: eight distances for each speed limit Yellow onset distance Approaching Vehicle Amber phase onset distance 30 mph: from 82 to 278 ft; 45 mph: from 180 to 360 ft.

21 Reducing Red-Light Running Key Findings ---- Red-light running rate RLR Rate (%) Without Marker With Marker mph 45mph Total Speed Limit Potentially, the pavement marking could results in a 65 percent reduction in red-light running. Chi-square test showed that the p-value is

22 Reducing Red-Light Running Key Findings ---- Stop/go decisions Stop Probability With Marking Without Marking Distance to the stop bar at onset of the yellow (ft) With marking for the 45 mph, the uncertainty distance between 20% and 80% probability of stopping is 50 ft (102 ft Vs 52 ft) shorter than without marking.

23

24 Deployment of the New Marking Design for RLR Concept The project will be carried out in the following stages: 1. Data collection sites selection 2. Data collection cameras installation 3. Data collection for the Before treatment 4. New Marking design installation 5. Data collection for the After treatment No information about marking Media information 6. Data analysis and conclusions

25 Site Selection

26 ITERIS Cameras The Iteris Vantage camera V2 Rack for Single or Dual Camera

27 ITERIS Cameras Installation

28 ITERIS Cameras Installation

29 Behavior Observations Preliminary Results Drivers Behavior Observations Using Video data to observe: drivers yellow-entry time red-entry time stop/go probability as a function of approaching distance and other potential factors such as speed, lane position, and vehicle type. Observed 4 hours during off-peak period

30 probability of stop/go decision Shifting the cameras to the locations at 400 ft upstream of the intersection to attain 100% stop probability.

31 Testing the Cameras Data collected for a time span from May 31 at14:00:00 to June 6 at 15:00:00, a total of 121 hours. There were 1278 RLR violations occurred at the test intersection, which represents a RLR rate of 10.5 RLR/hour There were 332 RLR violations occurred at the control intersection, which represents a RLR rate of 2.7 RLR/hour.

32 Testing the Cameras observations at each 15-min interval for each lane of the three-through lanes Additionally, the RLR data collected by cameras were also categorized by vehicle size: small vehicle (0-18 ft), median vehicle (18-26 ft), and large vehicle (larger than 26 ft).

33 Data Analyses for Left Lane Test Intersection Control Intersection

34 Preliminary Findings RLR peaks are not corresponding to traffic peaks Drivers are more likely to run red lights at the left lane Test intersection has higher RLR rates than controlled intersection

35 Decision Support System For Florida Turnpike Toll Facilities The objectives of the study are: Provide a calibrated and validated tool that calculates the capacity of the toll plazas operated by FTE Design or alter FTE toll plazas lane configuration to optimize their throughput Provide a visual display, in DSS, of a portion of the FTE system

36 What is a DSS? DSS are interactive computer-based systems and subsystems intended to help decision makers use communication technologies, data, documents, knowledge, models, algorithms etc. to complete decision process tasks.

37 Our DSS Maps of a highway network divided up into segments Computes the capacity of each segment Compares: Segment-Approach-Traffic-Volumes to Segment-Capacities Displays the bottleneck status by color at each segment on the maps

38 OOCEA Network of Toll Roads TOLL 417 TOLL 408 TOLL 408 TOLL TOLL 417 TOLL 528 TOLL 528 UMASS DARTMOUTH Physics Department Tel No: Fax No: 285 Old Westport Road North Dartmouth MA Title: Capacity of the OOCEA Network of Toll Roads with ETC Orange County, Orlando, FL TOLL 417 TOLL 417 Prepared For: Center for Advanced Transportation Systems Simulation ( CATSS ) Date: 08/26/01 Field/ Calc: MLZ Check: Drawn: AKM Scale: Not to scale Phase: 1 Sheet 1 of 1

39 Processing Rates for the different Customer-Groups Customer- Group X Processing Rates (vph) S X Description M 498 ± 48 Manual services : pay a toll collector cash A 618 ± 30 Automatic Coin-M achine Service T 138 ± 78 manual services for semi-trucks E 1560 ± 120 ETC or Electronic Toll Collection Services

40 DSS Application to OOCEA ZOOM IN Green:No Bottleneck

41 DSS Application to OOCEA Change the approach volumes in various places on the network the maps display the new bottleneck ORANGE: locations Near Bottleneck RED: Bottleneck

42 FTE

43 Dynamic Lane Merging Project Two main questions: When do we force late merge at lane closures? Can we trust the merge logic in computer simulation programs?

44 DLM Project The system architecture involves: 1. Queue detectors (like RTMS) installed close to static signs with flashing lights before entering the work zone at a uniform space to automatically determine whether traffic queuing is formed in open lanes before the work zone.

45 DLM Project 2. Appropriate threshold values for queue build up coupled with decision algorithm are used to activate the static sign with flashing lights close to the end of queue to display DO NOT PASS WHEN FLASHING..

46 DLM Project

47 DLM Project

48 DLM Project

49 DLM Project

50 Any Questions?