Research and Deployment of Technologies on Florida Highways and Streets

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

Outline

CATSS at UCF

Completed Projects:

CATSS Driving Simulators research

Red Light Running

New Projects:

New Marking Design FTE DSS

UCF Facts

UCF Facts

History of CATSS

•

University Transportation Center

(TEA 21)

•

US Department of Transportation –

RITA- Tier 1

•

Florida Department of

Transportation

•

http://catss.ucf.edu/

•

Email: [email protected]

Dr. Essam Radwan Executive Director Dr. Mohamed Abdel-Aty Program Director Transportation Safety and Operations Harold Klee Program Director Simulation Laboratory Jack Selter Director Amr Oloufa Program Director Advanced Intelligent Transportation Technologies & Communications Vijay Balram Office Manager Ron Tarr Program Director Simulation & Performance

Technologies for Advanced Transportation Applications Patrick Kerr Program Director Data Management and

Web Applications

Mission

Advance and deploy value-added, cost effective technology into the U.S.

intermodal surface transportation system to increase its overall safety and

• Return on investment of

$2.67 for every $1 spent by CATSS

• To date CATSS has

generated $12.7 million in

project funding from

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

• 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

UCF Driving Simulator

High Bay Area

Three Stories High

Hexagonal room

Ten - Ton Crane

Storage Space for Dismounted Cabs

Control Room

Load Scenarios

Control Environment

Monitor Subjects

Collect Data

Cabs

Tractor Trailer Truck

Municipal, Cement Trucks, Military, Bus, etc.

On-The-Fly Reconfigurable Transmission

Interior Controls, Gauges, AC

Cabs

Saturn Sedan

Automatic Transmission

Intercom with Video available in both cabs

Capabilities:

3D World Creation

Created from Scratch

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

Scenario Creation

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.

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.

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

30 mph: from 82 to 278 ft; 45 mph: from 180 to 360 ft.

Amber phase onset distance Approaching Vehicle

Reducing Red-Light Running

Key Findings ---- Red-light running rate

Potentially, the pavement marking could results in a 65 percent reduction in red-light running. Chi-square test showed that the p-value is 0.005.

4.46 3.27 3.87 1.19 1.49 1.34 0.00 1.00 2.00 3.00 4.00 5.00 30mph 45mph Total Speed Limit R L R R a te ( % ) Without Marker With Marker

Reducing Red-Light Running

Key Findings ---- Stop/go decisions

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.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 40 80 120 160 200 240 280 320 360 400 440

Distance to the stop bar at onset of the yellow (ft)

S to p P ro b a b il it y With Marking Without Marking

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

ITERIS Cameras

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.

probability of stop/go decision

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

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.

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).

Data Analyses for Left Lane

Test

Intersection

Control

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

Decision Support System For Florida Turnpike

Decision Support System For Florida Turnpike

Toll Facilities

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

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.

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

UMASS DARTMOUTH

Physics Department

285 Old Westport Road North Dartmouth MA 02747 Tel No: 508-999-9268

Fax No:

Title:

Capacity of the OOCEA Network of Toll Roads with

ETC

Orange County, Orlando, FL

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 4 408 408 417 TOLL 417 TOLL 417 TOLL 528 TOLL 528 TOLL 408 TOLL TOLL 417 4 TOLL TOLL

OOCEA Network of Toll Roads

Processing Rates for the

different Customer-Groups

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

DSS Application to OOCEA

Green:No Bottleneck ZOOM IN

• Change the approach volumes in various places on the network the maps display

the new bottleneck locations

DSS Application to OOCEA

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?

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.

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”..

Any Questions?