Integrated Travel Demand Model Challenges and Successes Tim Padgett, P.E., Kimley-Horn Scott Thomson, P.E., KYTC Saleem Salameh, Ph.D., P.E., KYOVA IPC.

Slides:



Advertisements
Similar presentations
THURSTON REGION MULTIMODAL TRAVEL DEMAND FORECASTING MODEL IMPLEMENTATION IN EMME/2 - Presentation at the 15th International EMME/2 Users Group Conference.
Advertisements

Feedback Loops Guy Rousseau Atlanta Regional Commission.
OVERVIEW OF CMAPS ADVANCED TRAVEL MODEL CADRE Kermit Wies, Deputy Executive Director for Research and Analysis AMPO Modeling Group, November 2010.
GIS and Transportation Planning
Dynamic Traffic Assignment: Integrating Dynameq into Long Range Planning Studies Model City 2011 – Portland, Oregon Richard Walker - Portland Metro Scott.
GREATER NEW YORK A GREENER Travel Demand Modeling for analysis of Congestion Mitigation policies October 24, 2007.
Simpson County Travel Demand Model July 22, 2003.
What is the Model??? A Primer on Transportation Demand Forecasting Models Shawn Turner Theo Petritsch Keith Lovan Lisa Aultman-Hall.
SCAG Region Heavy Duty Truck Model Southern California Region Heavy Duty Truck Model.
Chapter 4 1 Chapter 4. Modeling Transportation Demand and Supply 1.List the four steps of transportation demand analysis 2.List the four steps of travel.
Session 11: Model Calibration, Validation, and Reasonableness Checks
Norman W. Garrick Travel Flow Data Some Basic Concepts Good travel flow data for all modes of travel is important for transportation planning and design.
Interfacing Regional Model with Statewide Model to Improve Regional Commercial Vehicle Travel Forecasting Bing Mei, P.E. Joe Huegy, AICP Institute for.
Framework for Model Development General Model Design Highway Network/Traffic Analysis Zones (TAZs) Development of Synthetic Trip Tables Development of.
Milton-Madison Bi-State Travel Demand Model Rob Bostrom Planning Application Conference Houston, Texas May 19, 2009.
Presented to presented by Cambridge Systematics, Inc. Transportation leadership you can trust. An Integrated Travel Demand, Mesoscopic and Microscopic.
Source: NHI course on Travel Demand Forecasting (152054A) Session 10 Traffic (Trip) Assignment Trip Generation Trip Distribution Transit Estimation & Mode.
SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY San Francisco DTA Project: Model Integration Options Greg Erhardt DTA Peer Review Panel Meeting July 25 th,
BALTIMORE METROPOLITAN COUNCIL MODEL ENHANCEMENTS FOR THE RED LINE PROJECT AMPO TRAVEL MODEL WORK GROUP March 20, 2006.
Evaluating Robustness of Signal Timings for Conditions of Varying Traffic Flows 2013 Mid-Continent Transportation Research Symposium – August 16, 2013.
Network Benefit Cost Analysis: An Overview of the Application of NET_BC Software for Caltrans District 5’s System Analysis Study TRB Planning Applications.
Integrated Corridor Analysis Tool (ICAT) William W. Stoeckert I-95 Corridor Coalition.
June 15, 2010 For the Missoula Metropolitan Planning Organization Travel Modeling
Modelling of Trips using Strategic Park-and-Ride Site at Longbridge Railway Station Seattle, USA, Oct th International EMME/2 Users Conference.
© 2014 HDR, Inc., all rights reserved. COUNCIL BLUFFS INTERSTATE SYSTEM MODEL Jon Markt Source: FHWA.
Transportation leadership you can trust. presented to TRB Planning Applications Conference presented by Vamsee Modugula Cambridge Systematics, Inc. May.
Simpson County Travel Demand Model Mobility Analysis November 7, 2003.
Computers in Urban Planning Computational aids – implementation of mathematical models, statistical analyses Data handling & intelligent maps – GIS (Geographic.
Methodology for the Use of Regional Planning Models to Assess Impact of Various Congestion Pricing Strategies Sub-network Extraction A sub-network focusing.
How to Put “Best Practice” into Traffic Assignment Practice Ken Cervenka Federal Transit Administration TRB National Transportation.
Connectivity & Mobility
PERFORMANCE MEASURES FOR HIGHWAY CAPACITY DECISION MAKING WEST VIRGINIA PLANNING CONFERENCE – SEPTEMBER 16, 2015 SALEEM SALAMEH, P.E., PH.D. KYOVA IPC.
Transportation Planning, Transportation Demand Analysis Land Use-Transportation Interaction Transportation Planning Framework Transportation Demand Analysis.
TRB Planning Applications Identifying the Long-Range Transportation Improvement and Funding Needs for Urban Areas in Texas By Kevin M. Hall, Texas Transportation.
Travel Demand Modeling Experience Bellevue-Kirkland-Redmond Travel Demand Modeling Experience Jin Ren, P.E. City of Bellevue, Washington, USA October 19,
Traffic Parameters for use with MOBILE6 in Ky. - Update by Jesse Mayes, P.E. Division of Multimodal Programs July 22, 2003.
David B. Roden, Senior Consulting Manager Analysis of Transportation Projects in Northern Virginia TRB Transportation Planning Applications Conference.
+ Creating an Operations-Based Travel Forecast Tool for Small Oregon Communities TRB National Transportation Planning Applications Conference May 20, 2009.
Getting to Know Cube.
Interpreting Demand and Capacity for Street and Highway Design Lecture 6 CE 5720 Norman Garrick Norman W. Garrick.
Major Transportation Corridor Studies Using an EMME/2 Travel Demand Forecasting Model: The Trans-Lake Washington Study Carlos Espindola, Youssef Dehghani.
MATRIX ADJUSTMENT MACRO (DEMADJ.MAC AND DEMADJT.MAC) APPLICATIONS: SEATTLE EXPERIENCE Murli K. Adury Youssef Dehghani Sujay Davuluri Parsons Brinckerhoff.
Exploring Cube Base and Cube Voyager. Exploring Cube Base and Cube Voyager Use Cube Base and Cube Voyager to develop data, run scenarios, and examine.
FDOT Transit Office Modeling Initiatives The Transit Office has undertaken a number of initiatives in collaboration with the Systems Planning Office and.
Forecasting and Evaluating Network Growth David Levinson Norah Montes de Oca Feng Xie.
Dynamic Tolling Assignment Model for Managed Lanes presented to Advanced Traffic Assignment Sub-Committee presented by Jim Hicks, Parsons Brinckerhoff.
Bharath Paladugu TRPC Clyde Scott Independent Consultant
an Iowa State University center SIMPCO Traffic Modeling Workshop Presented by: Iowa Department of Transportation and Center for Transportation Research.
Comparative Analysis of Traffic and Revenue Risks Associated with Priced Facilities 14 th TRB National Transportation Planning Applications Conference.
Analyzing the Mobility Impacts of TOD Level of Service in Transit Oriented Districts Service for Who?
BUSINESS SENSITIVE 1 Network Assignment of Highway Truck Traffic in FAF3 Maks Alam, PE Research Leader Battelle.
Jack is currently performing travel demand model forecasting for Florida’s Turnpike. Specifically he works on toll road project forecasting to produce.
INCORPORATING INCOME INTO TRAVEL DEMAND MODELING Brent Spence Bridge Case Study October 13, 2015.
Interstate 95 Managed Lanes PD&E Study (95 Express) Project Development and Environment Study SE FSUTMS Users Group The Corradino Group November 2, 2007.
TRAVEL TIME ANALYSIS Use of Data IN-KY-OH Traffic Incident Management Conference October 9, 2015 Dayton, OH.
1 Toll Modeling Analysis for the SR 520 Bridge Replacement and HOV Project 19 th Annual International EMME/2 Users’ Conference October 19-21, 2005 Presented.
Minnesota State Planning Conference September 28, 2011.
Travel Demand Forecasting: Traffic Assignment CE331 Transportation Engineering.
Transportation Modeling – Opening the Black Box. Agenda 6:00 - 6:05Welcome by Brant Liebmann 6:05 - 6:10 Introductory Context by Mayor Will Toor and Tracy.
2040 LONG RANGE PLAN UPDATE Congestion Management Process Plan (CMPP) Major Update February 24, 2016.
METRO Dynamic Traffic Assignment in Action COST Presentation ODOT Region 4 April 1,
Mesoscopic Modeling Approach for Performance Based Planning
Performance Measure Exploration Preparing for the 2018 RTP
Validating Trip Distribution using GPS Data
APPLICATIONS OF STATEWIDE TRAVEL FORECASTING MODEL
2017 SCORT Conference Washington, DC
Technical Advisory Committee
Presented to 2017 TRB Planning Applications Conference
Ventura County Traffic Model (VCTM) VCTC Update
Minnesota State Planning Conference September 28, 2011
Presentation transcript:

Integrated Travel Demand Model Challenges and Successes Tim Padgett, P.E., Kimley-Horn Scott Thomson, P.E., KYTC Saleem Salameh, Ph.D., P.E., KYOVA IPC 2015 WV Planning Conference September 16 Davis, West Virginia

“There is really no hope that a mathematical model can ever accurately predict the future, given the uncertainty in demographics, technological shifts, and social changes.” – JD Hunt “But we still try.” – Tim

Forecasting Background Why do we use forecasting models?

Model Background History Model versions over time Previous model and transition to current

2015 KYOVA Travel Demand Model

Useful Planning Tool CMP, MTP/TIP, corridor studies, and other applications Three Current Models KYOVA, Ashland, and RIC Limitations Not all are time-of-day Speeds and capacities calculated differently “Apples vs. oranges”

What’s Different Boundary Capacity methodology

Model Update Expand KYOVA model to include Ashland model Integrate HCM 2010 methods for speed, capacity Incorporate HERE travel time data Update external travel data Incorporate truck network, trip matrix V/C calculations Documentation and training

Highway Capacity Manual 2010 Free-flow speeds and capacities  traffic forecasts, V/C Major update underway Changes in methods (e.g., Urban Streets FFS prediction)

Additional Features Free-flow speed override Simplified user interface

Automatic Reporting RMSE VMT by county and functional class Congested speeds by county and functional class VHT by county and functional class

Travel Demand Model Integration Issues Network attributes -Integrate state linear referencing systems (Road analyzer, HIS, and LRS) Household model -Household composition and trip rates for additional counties Employment data -Coordinate employment classes and data sources Traffic counts daily, hourly, and truck counts for calibration and validation Trip distribution -Re-estimate gravity model parameters to reflect expanded area

Challenges and Successes Challenges Data Multiple jurisdictions Successes Coordination and cooperation

Thinking Beyond the Model

When the Model isn’t Enough Travel demand models don’t do a great job with: -Transit and Transit Suitability -Freight Planning -Alternatives Analysis Balance between complexity of model, ease of use, and cost

Mode Share What is it? District of Columbia Multimodal Long Range Transportation Plan What was the question? How much can we increase non-auto mode share by expanding transportation choices and improving the reliability of all transportation modes?

Mode Share How did we answer the question? Used elasticities to account for introduction of new service and expansion of existing service Used GIS to determine influence areas and applied this to our model trip tables to shift trips between modes

Mode Share

Results and Conclusions Answers would not have come from the travel demand model alone Interesting—showed that investment alone wasn’t enough to achieve their goal of 75% non-auto mode share

KYOVA’s Spatial Decision Support System (SDSS) Integration

What is a Spatial Decision Support System (SDSS)? “…an interactive, flexible, and adaptable computer- based information system, especially developed for supporting the solution of a non-structured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker’s own insights…” (Turban, 1995)

Spatial Decision Support System Characteristics

Current SDSS Maps CMP Map Figures KYOVA Transportation Management Area boundary CMP network Major river crossings Fixed-route transit coverage Computed crash rates compared to statewide average Congested locations from stakeholder workshops Downtown railroad underpass/viaduct locations

Current DSS Maps CMP Map FigureTime of DayYears Volumes from Traffic Model Assignments AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Capacities from Traffic Model Assignments AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 V/C Ratios from Traffic Model Assignments AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Levels of Service AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Travel Time Indices AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040 Planning Time Indices AM Peak, PM Peak, Average Daily Base Year, 2020, 2030, 2040

Third Party Data Sources

Traffic Conditions Trip Routes Freight Speeds

NPMRDS National Performance Measure Research Data Set - 6 th Region

West Virginia’s Network

November 2014

Link Level Speed Data Blue = minimum speeds Red = maximum speeds Arrow = direction of travel

Origin-Destination Data

Trips by Day Part

Tim Padgett, P.E., Kimley-Horn Scott Thomson, P.E., KYTC Saleem Salameh, Ph.D., P.E., KYOVA IPC Questions?