1 Bidoura Khondaker MASc. (Transportation Engg), University of British Columbia, Vancouver. PhD candidate (Transportation Engg.), University of Calgary.

Slides:



Advertisements
Similar presentations
1.Transform Roadway network into a mathematical model using Petri Net (PN) as illustrated in Figure 1. This work has been partially supported by the U.S.
Advertisements

Proactive Traffic Merging Strategies for Sensor-Enabled Cars
Complete Street Analysis of a Road Diet Orange Grove Boulevard Pasadena, CA Aaron Elias Engineering Associate Kittelson & Associates Bill Cisco Senior.
Driver Behavior Models NSF DriveSense Workshop Norfolk, VA Oct Mario Gerla UCLA, Computer Science Dept.
Learning Objectives To define and apply basic measures of traffic flow, including; Speed Volume Density Spacing and Headway Lane occupancy Clearance and.
How Do Traffic Control Measures Affect Vehicle Gas Emissions Presented by: Ryan O’Connell Co-Authors: Kevin Lu Dr. Wen Cheng Dr. Xudong Jia.
G4 Apps The Impact of Connected Vehicles on Traffic Operations ISMA Traffic Expo October 1, 2014.
The INTEGRATION Modeling Framework for Estimating Mobile Source Energy Consumption and Emission Levels Hesham Rakha and Kyoungho Ahn Virginia Tech Transportation.
Real-time Estimation of Accident Likelihood for Safety Enhancement Jun Oh, Ph.D., PE, PTOE Western Michigan University March 14, 2007.
Transportation Data Palooza Washington, DC May 9, 2013 Steve Mortensen Federal Transit Administration Data for Integrated Corridor Management (ICM) Analysis,
TRANSIMS Research and Deployment Project TRACC TSM Staff Dr. Vadim Sokolov Dr. Joshua Auld Dr. Kuilin Zhang Mr. Michael Hope.
Mining Motion Sensor Data from Smartphones for Estimating Vehicle Motion Tamer Nadeem, PhD Department of Computer Science NSF Workshop on Large-Scale Traffic.
The City of Gdynia City rights in 1926 With Sopot and Gdańsk forms the Tri-City agglomeration It has inhabitants Port city, employment structure:
® ® Contributor Session on Smart Mobility Performance Measures.
Evaluation Tools to Support ITS Planning Process FDOT Research #BD presented to Model Advancement Committee presented by Mohammed Hadi, Ph.D., PE.
TRB Lianyu Chu *, K S Nesamani +, Hamed Benouar* Priority Based High Occupancy Vehicle Lanes Operation * California Center for Innovative Transportation.
Transportation System Issues and Challenges
Advanced Public Transit Systems (APTS) Transit ITS CEE582.
ITS-SAFETEA-LU Title V-Subtitle C page 662 Goals (Partial) –Enhance surface transportation efficiency –Achieve transportation safety goals –Protect and.
Introduction Transportation System Objectives : Military; Knit together the inhabitants of a territory by providing mutual access and communication; Economic.
Measure 26 Strategic Traffic Management Katerina Oktabcova Usti nad Labem Municipality.
Dynamic Speed Limits to improve local air quality Henk Stoelhorst Rijkswaterstaat, Centre for Transport and Navigation.
Hypothesis 1: Narrow roadways and roadways with higher speed limits will increase risk of vehicle/bicycle crash Hypothesis 2: Bicycle lanes and signage.
Design of Cooperative Vehicle Safety Systems Based on Tight Coupling of Communication, Computing and Physical Vehicle Dynamics Yaser P. Fallah, ChingLing.
Office of Traffic, Safety and Technology Chapter 9 New Technologies Traffic Signs 101 November 20, 2014.
Odysa ® Experiences with an individual “green wave” Marcel Willekens / Arjan Bezemer / Kristiaan Langelaar.
Navigating SB 375: CEQA Streamlining and SB 743 Transportation Analysis 2014 San Joaquin Valley Fall Policy Conference.
An Intelligent Transportation System Evaluation Tool in the FSUTMS Regional Demand Modeling Environment By Mohammed Hadi, Florida International University.
ITS Sketch Planning Tool Webinar 2:00 – 4:00 PM January 8, 2009.
Intelligent Transportation System (ITS) ISYM 540 Current Topics in Information System Management Anas Hardan.
1 Development and Evaluation of Selected Mobility Applications for VII (a.k.a. IntelliDrive) Steven E. Shladover, Sc.D. California PATH Program Institute.
The Essentials of Long Combination Vehicles Presented to FHWA Talking Freight May 20, 2009 John Woodrooffe.
BPAC. “Congestion management is the application of strategies to improve transportation system performance and reliability by reducing the adverse impacts.
Incident Management in Central Arkansas: Current Settings and Proposed Extensions Weihua Xiao Yupo Chan University of Arkansas at Little Rock.
Innovative ITS services thanks to Future Internet technologies ITS World Congress Orlando, SS42, 18 October 2011.
1 IntelliDrive SM Research, Development and Emerging Technologies National ITS Perspective Panel Joseph I. Peters, Ph.D. Federal Highway Administration.
Opportunities for ATSSA in ITS Mike Schagrin ITS Joint Program Office US Department of Transportion.
TRANSPORTATION ENGINEERING Planes, Trains, Automobiles and More Ardrey Kell High School February 23, 2012.
November 15, 2005 Dr. Robert Bertini Dr. Sue Ahn Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System.
Efficient Traffic Flow:
Traffic Safety and Operations Lab Dept. of Civil and Environmental Engineering University of Maryland, College Park Maryland State Highway Administration.
Is Transportation Sustainable?. Objectives By the end of this unit, students will be able to: 1.Examine and prioritize transportation project impacts.
Calibrating Model Speeds, Capacities, and Volume Delay Functions Using Local Data SE Florida FSUTMS Users Group Meeting February 6, 2009 Dean Lawrence.
June 13-14, 2006 Scanning tour FHWA Hard Shoulder Running (HSR) in the Netherlands Bert Helleman Senior Consultant Smart roads and ATMS AVV Transport Research.
1 Using Intelligent Transportation Systems (ITS) Technologies and Strategies to Better Manage Congestion Jeffrey F. Paniati Associate Administrator of.
DEPARTMENT OF CIVIL & ENVIRONMENTAL ENGINEERING Proactive Optimal Variable Speed Limit Control for Recurrently Congested Freeway Bottlenecks by Xianfeng.
Robert Brydia Project Lead, I-35 Traveler Information During Construction Texas A&M Transportation Institute WORK ZONES & LARGE TRUCKS THE CENTRAL TEXAS.
June 14th, 2006 Henk Taale Regional Traffic Management Method and Tool.
Expressway Driving Entering, lane changing, and exiting.
Public Transportation Planning: Rapid transit solutions for adequate mass movement Mobility.
Hcm 2010: BASIC CONCEPTS praveen edara, ph.d., p.e., PTOE
Expressway Driving Legacy High School Drivers Education.
Section 3: New and Experimental Technologies
Transportation Systems Management and Operations: Why It Matters Presenter Name Date AGENCY LOGO Photo: © Shutterstock.com/iofoto ( )
School of Systems, Engineering, University of Reading rkala.99k.org April, 2013 Motion Planning for Multiple Autonomous Vehicles Rahul Kala Congestion.
Office of Highway Safety Introduction David S. Rayburn.
ABJ60 – Spatial Data and Information Science – Operations and Congestion Operations and Congestion.
Intelligent and Non-Intelligent Transportation Systems 32 Foundations of Technology Standard 18 Students will develop an understanding of and be able to.
1 Bidoura Khondaker MASc. (Transportation Engg), University of British Columbia, Vancouver. PhD candidate (Transportation Engg.), University of Calgary,
ATDM Analytical Methods for Urban Streets Urban Streets Subcommittee Meeting January 10, 2016 David Hale.
Chapter 12: Urban Transportation Policy “Everything in life is somewhere else, and you get there in a car.” E. B. White, One Man’s Meat, (NY: Harper &
METRO Dynamic Traffic Assignment in Action COST Presentation ODOT Region 4 April 1,
(Expressway Traffic Management System)
Metro Railway How it began
Ottawa AV Innovation ‘201’ Changing the way traffic moves
Shock Wave Analysis Definition: Flow-speed-density states change over space and time. When these changes of state occur, a boundary is established that.
ATM in California ITS Virginia June 5, 2014 Presented By
What is TSMO? TSMO encompasses a broad set of strategies that aim to optimize the safe, efficient, and reliable use of existing and planned transportation.
What is TSMO? TSMO encompasses a broad set of strategies that aim to optimize the safe, efficient, and reliable use of existing and planned transportation.
Module 6 A 21st Century Transportation Network
Presentation transcript:

1 Bidoura Khondaker MASc. (Transportation Engg), University of British Columbia, Vancouver. PhD candidate (Transportation Engg.), University of Calgary.

 NSERC (Natural Science and Engineering Research Council of Canada)  Industry Sponsor: Stantec 2

 ITS solution : Dynamic change of posted speed limit wrt to changing traffic condition.  Utilize traffic speed and volume detection, weather information technology to determine appropriate speeds. 3

4 Work ZonesCongestion Bad Weather Accident

The phenomena “Capacity Drop”: 5 Outflow before onset of congestion > Outflow when congestion occurs at bottleneck.

 Main Idea: Limiting Inflow Temporarily. 6 Default speed limit=100 kmph

 Limit inflow temporarily  Reduce speed variability  Increase safety  Reduce fuel consumption  Reduce Co2 emission  More efficient use of roadway  Less travel time 7

8

 Study Area 9 Figure : Deerfoot Trail

10 Surveillance System Traffic State: Speed and Occupancy Short Term Traffic Prediction Model: Predicted Traffic Volume, Speed and Densities. Non-recurrent congestion: Incident Work zone Optimizing speed limit values Control Action: Optimum Speed Limits are implemented Weather Information, Roadway conditions System should be proactive!!

11 1. Detector : Need extensive coverage Expensive!!! Provides speed information in the vicinity. 2. Probe Vehicle: GPS enabled device Low cost Excellent coverage

12

Multi-Criteria Objective Function  Minimizing Total Travel Time  Minimizing Fuel consumption and  Minimizing C02 Emission 13

14

 Microscopic, time-step traffic simulation model  Can simulate a multimodal world – Bicycles – Pedestrians – Cars/trucks – Light rail 15 Transit Signal Priority Interaction with motorized vehicle Multimodal

16

17 3 km 2 km 1 km Scenerio 1 Scenerio 2 Scenerio 3 Default speed limit = 100 km/hr 2 km 1 km m detectors

18

19

20 Scenario 1 Scenario 2 Scenario 3

21 Deerfoot and 64th Deerfoot and Mcknight

22 Driver’s compliance/Enforcement modeling Control Speed variable: (1+  )*V control  = compliance rate of driver’s V control = {60,70,80,90,100 kmph)

23 Another promising area:  Incorporation of Differential Speed Limit (DSL) with VSL.  The possible outcome will be explored by introducing DSL for different lanes where the limit on the outside lanes could be higher than the inside lanes, where traffic joins and exits.

24 Sensitivity Analysis: Finally, the performance of proposed framework will be rigorously evaluated using:  Varying congestion levels (normal, moderate and heavy congestion).  Different frequency of incident occurrence such as weather conditions, collisions (major, minor etc).

25

Questions/Comments/Suggestions?????? 26