Automated Bicycle Data Collection: A Case Study from Portland, OR Western District Annual ITE Meeting June 26th, 2012 Presenter: Sirisha Kothuri

Slides:



Advertisements
Similar presentations
Pedestrian Responsive Signal Timing Strategies Oregon ITE Winter Workshop February 26 th, 2015 Sirisha Kothuri, Chris Monsere, Andy Kading - PSU Peter.
Advertisements

Transit Signal Priority Applications New Technologies, New Opportunities Peter Koonce, PE APTA BRT Conference – Seattle, WA Wednesday, May 5, 2009 Technology.
An Integral Perspective on the S.E. 17 Corridor October 29, 2013 Calgary.
1 NATMEC 2008 Christopher Monsere Kristin Tufte, Robert L. Bertini, and Soyoung Ahn Intelligent Transportation Systems Laboratory Maseeh College of Engineering.
1 Austin Transportation Department Ali Mozdbar, P.E., PTOE Division Manager, Traffic Signals Traffic Signal Features for Pedestrians & Bicyclists.
HAWK Evaluation NE/SE 41 st Ave & E Burnside St Sirisha Kothuri William Farley Kimber Miller Aaron Rieck Civil & Environmental Engineering.
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.
February 9, 2006TransNow Student Conference Using Ground Truth Geospatial Data to Validate Advanced Traveler Information Systems Freeway Travel Time Messages.
June 16, 2004 Dr. Robert Bertini Michael Rose Evaluation of the “COMET” Incident Response Program Oregon Department of Transportation.
1 Vehicular Sensor Networks for Traffic Monitoring In proceedings of 17th International Conference on Computer Communications and Networks (ICCCN 2008)
Month XX, 2004 Dr. Robert Bertini Using Archived Data to Measure Operational Benefits of ITS Investments: Ramp Meters Oregon Department of Transportation.
Optimal Adaptive Signal Control for Diamond Interchanges Using Dynamic Programming Optimal Adaptive Signal Control for Diamond Interchanges Using Dynamic.
Customized Simulation Modeling Using PARAMICS Application Programming Interface Henry Liu, Lianyu Chu & Will Recker.
Abstract Bicycle use as a primary means of commuting to work increased 145% (American Community Survey, US Census Bureau) from 1996 to 2006 in Portland,
A Method to Verify Railroad Interconnect with Highway Traffic Signal Systems Adam Moore Portland Bureau of Transportation Oregon ITE Winter Workshop February.
Peter Koonce TRB Annual Meeting January 9, 2005 Best Practices for Signal Operations Best Practices for Signal Operations – Lessons Learned from the Portland.
Florida Department of Transportation District 4 TSM&O Program Advanced Transportation Management System (ATMS) Installation in South Broward County ATMS.
Transportation Research Group University of Nevada, Reno 2012 ITE District 6 Annual Meeting SIGNAL TIMING WITHOUT TRAFFIC COUNTS Zong Z. Tian, Ph.D., P.E.
Murray Boulevard and Farmington Road Intersection Sirisha Kothuri Wei Feng Ping Guo Meead Saberi CEE 550 Transportation Safety Analysis.
Applied Transportation Analysis ITS Application SCATS.
Congestion Management Innovations in Oregon Christopher Monsere Assistant Professor Portland State University Civil and Environmental Engineering Director,
Intelligent Transportation System (ITS) ISYM 540 Current Topics in Information System Management Anas Hardan.
Incident Management in Central Arkansas: Current Settings and Proposed Extensions Weihua Xiao Yupo Chan University of Arkansas at Little Rock.
1 A Google-Map-Based Arterial Traffic Information (GATI) System Authors: Yao-Jan Wu Dr. Yinhai Wang Dr. Dalin Qian Date: 10/03/2007.
Jeff’s slides. Transportation Kitchener Transportation Master Plan Define and prioritize a transportation network that is supportive of all modes of.
June 2006 ITE District 6 Annual Meeting June Evaluation of Single-Loop Detector Vehicle-Classification Algorithms using an Archived Data User.
A Model for Improving Operations through Archived Data 2005 ITS America Annual Meeting Mark Carter – SAIC Robert Haas - SAIC May 2 nd, 2005 i Florida’s.
NATMEC June 5, 2006 Comparative Analysis Of Various Travel Time Estimation Algorithms To Ground Truth Data Using Archived Data Christopher M. Monsere Research.
Abstract Transportation sustainability is of increasing concern to professionals and the public. This project describes the modeling and calculation of.
Southwest Washington ITS Traffic Data Collection & Analysis: A Tale of 3 Projects Jill MacKay ITE Traffic Simulation Roundtable October 4, 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.
TRAFFIC SIGNAL OPTIMIZATION: A Coordinated Effort Tom Dancey, P.E. Signal System Engineer City of Springfield CITY OF SPRINGFIELD & MISSOURI DEPARTMENT.
What are Intelligent Transportation Systems? Intelligent Transportation Systems (ITS) are existing and new technologies, including information processing,
2010 Fall Transportation Conference A Guideline for Choosing Cycle Length to Maximize Two-Way Progression in Downtown Area Saeedeh Farivar Zong Tian University.
Using Real Time Transportation System Performance Measures to Fuel a Regional Congestion Management System Robert L. Bertini Portland State University.
Robert L. Bertini Sirisha M. Kothuri Kristin A. Tufte Portland State University Soyoung Ahn Arizona State University 9th International IEEE Conference.
January 23, 2006Transportation Research Board 85 th Annual Meeting Using Ground Truth Geospatial Data to Validate Advanced Traveler Information Systems.
Comparative Analysis Of Various Travel Time Estimation Algorithms To Ground Truth Data Using Archived Data Christopher M. Monsere Research Assistant Professor.
1 Using Automatic Vehicle Location Data to Determine Detector Placement Robert L. Bertini, Christopher Monsere, Michael Wolfe and Mathew Berkow Portland.
Using Signal Systems Data and Buses as Probes to Create Arterial Performance Measures Mathew Berkow, Michael Wolfe, John Chee, Robert Bertini,
Portland State University 11 By Maisha Mahmud Li Huan Evaluation Of SCATS Adaptive Traffic Signal Control System.
SIGNAL OPTIMIZATION STUDY COMMUNITY DEVELOPMENT DEPARTMENT.
26 June 2006Institute of Transportation Engineers District 6 Annual Meeting Intelligent Transportation Systems Lab Christopher M. Monsere Research Assistant.
National Institute for Transportation and Communities (NITC) UTC SAFETY SUMMIT MARCH 19-20, 2015.
July 13, 2005ITE District 6 Annual Meeting Using Ground Truth Geospatial Data to Validate Advanced Traveler Information Systems Freeway Travel Time Messages.
Morning Sessions 1 & 2 -- Detector Design and Timing Peter Koonce KITTELSON & ASSOCIATES, INC. Best Practices for Signal Operations.
Using Archived Data to Measure Operational Benefits of a System-wide Adaptive Ramp Metering (SWARM) System Data Collection Plan / Experimental Design May.
Centre for Transport Studies Imperial College 1 Congestion Mitigation Strategies: Which Produces the Most Environmental Benefit and/or the Least Environmental.
ITE District 6 June 27, 2006 Incorporating Incident Data into a Freeway Data Archive for Improved Performance Measurement ITE District 6 June 27, 2006.
Traffic Signals & ITS to Encourage Walking & Cycling
1 TRB 88 th Annual Meeting January 12, 2009 – TRB 88 th Annual Meeting Mathew Berkow, Robert L. Bertini, Christopher Monsere, Michael Wolfe, Portland State.
ITE District 6 June 27, 2006 Incorporating Incident Data into a Freeway Data Archive for Improved Performance Measurement ITE District 6 June 27, 2006.
Do Mobility-Based Performance Measures Reflect Emissions Trends? Congestion and Emissions Co-performance Alex Bigazzi & Dr. Miguel Figliozzi ITE Western.
City of Portland - Isolated Timing Operations January 9, 2005 Isolated Timing Operations - Workshop on Best Practices for Signal Timing Bill Kloos Signals.
East-West Corridor Connectivity Study – Study Recommendations November 17, 2009.
City of Joliet - Sustainability City of Joliet Sustainability Initiatives American Planning Association National Conference April 16, 2013.
Intelligent and Non-Intelligent Transportation Systems 32 Foundations of Technology Standard 18 Students will develop an understanding of and be able to.
Key Trends Shaping Transportation System Management Operations Timothy Papandreou CIO, Director Office of Innovation San Francisco Municipal Transportation.
Grant Eligible Capital Improvement Plan Memorial City TIRZ THE GOODMAN CORPORATION DECEMBER 2015.
A Waterloo Region Case Study.  Waterloo Region  Connecting residents to your work  Ideas start off small  Plan for change  Moving forward  Your.
Virginia House Bill 2 – Funding the Right Projects Intelligent Transportation System Activities May 19, 2016.
Thinking Inside the Box

Outline Sensys SensMetrics Solution SensMetrics Performance Measures
REGIONAL BICYCLE AND PEDESTRIAN PLAN
2018/5/14 QUANTIFYING PHYSICAL ACTIVITY USING AN ACTIVITY-BASED TRAVEL DEMAND MODEL My topic today is---READ Question try to address is- READ I want to.
БЕЗБЕДНОСТ БИЦИКЛИСТА И МОГУЋНОСТИ РАЗВОЈА БИЦИКЛИЗМА НА ТЕРИТОРИЈИ ОПШТИНЕ СУРЧИН SAFETY OF CYCLISTS AND OPPORTUNITIES FOR THE DEVELOPMENT OF CYCLING.
School of Civil Engineering
IoT Traffic Signal Panel
Presentation transcript:

Automated Bicycle Data Collection: A Case Study from Portland, OR Western District Annual ITE Meeting June 26th, 2012 Presenter: Sirisha Kothuri Authors: Sirisha Kothuri Titus Reynolds Christopher Monsere Peter Koonce Automated Bicycle Data Collection 1

Outline  Introduction  Study Area  Bicycle Counts  Bicycle Delay  Summary  Next Steps Automated Bicycle Data Collection 2

3 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Our intentions are to be as sustainable a city as possible. That means socially, that means environmentally and that means economically. The bike is great on all three of those factors. You just can’t get a better transportation return on your investment than you get with promoting bicycling. – Mayor Sam Adams Source: P Koonce

Portland’s Bicycle Network Automated Bicycle Data Collection 4 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps  2010 Bicycle Network  Bicycle boulevards  Bicycle lanes  Off street paths  Total system (314 mi) Source: R Geller

Portland’s Bicycle Network Automated Bicycle Data Collection 5 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Source: R Geller

Where are we going? Automated Bicycle Data Collection 6 Introduction | Study Area | Bicycle Data | Pedestrian Data | Summary | Next Steps

Why is this important?  Bicycle Data  Gaps/Deficiencies in data  Evaluation of system performance  Current demand estimation  Future infrastructure and operational needs  Prioritize investments  Improve safety Automated Bicycle Data Collection 7 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

Objective  Utilize existing infrastructure to develop a long term monitoring and collection system to monitor bicycle activity. Automated Bicycle Data Collection 8 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

Study Area Automated Bicycle Data Collection 9 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps I-205 I-5 I-84 US-26 Downtown OR -217  Bicycle Data  Bicycle Counts  Bicycle Delay  Pedestrian Data  Push button actuations  Pedestrian Delay

Bicycle Data Automated Bicycle Data Collection 10 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps  Single inductive loops  Advance loop counts  Criteria for counts  Bicycle lane  Advance loop in bike lane  Individual loop wire  Communication

Automated Bicycle Data Collection 11 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Count Verification Outbound Loop Inbound Loop  Video and loop counts  Underestimation of loop counts

Automated Bicycle Data Collection 12 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Daily Trend

Automated Bicycle Data Collection 13 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Weekday Trends

Automated Bicycle Data Collection 14 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Bicycle Delay  Active at one intersection  Internal logic commands  Latch is set -  Bike is detected  Light status ≠ green  Latch is released  Light status = green  Delay = Duration of latch  Maximum delay per cycle

Automated Bicycle Data Collection 15 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps Bicycle Delay  Delay Reduction Strategies  Coordinated  Free  Increase in permissive length

PORTAL  Regional data archive  Data currently archived:  Freeway loop detector  Weather  Incidents  Bluetooth  Bike and Ped  Arterial Automated Bicycle Data Collection 16 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

Conclusions  Growing need for bicycle data  Operations  Planning  Bicycle counts from inductive loops  Cost effective  Potential for undercounting  Affected by placement, sensitivity and calibration Automated Bicycle Data Collection 17 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

Next Steps  Expansion and verification of counts  Expansion of bicycle delay to other intersections  Optimizing signal timing based on delay Automated Bicycle Data Collection 18 Introduction | Study Area | Bicycle Counts | Bicycle Delay | Summary | Next Steps

Thank you! Sirisha Kothuri Titus Reynolds oregon.gov Christopher Monsere Peter Koonce Automated Bicycle Data Collection 19