Use of Truck GPS Data for Travel Model Improvements Talking Freight Seminar April 21, 2010.

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

Determining the Free-Flow Speeds in a Regional Travel Demand Model based on the Highway Capacity Manual Chao Wang Joseph Huegy Institute for Transportation.
GIS at PSRC GIS data collection & travel demand modeling ESRM 250 February 4, 2010.
Ohio Department of Transportation Leadership Meeting#1 Jun 12, 2012 Steering Committee Meeting #1 WELCOME Bicycle and Pedestrian Travel Pike and Wok Travel.
GREATER NEW YORK A GREENER Travel Demand Modeling for analysis of Congestion Mitigation policies October 24, 2007.
French cities’ urban freight surveys 1 st Scientific and Technical Workshop Bologna 05/11/2013 Presented by: Adrien Beziat, PhD, Paris, France.
Presented to presented by Cambridge Systematics, Inc. Transportation leadership you can trust. Comparison of Activity-Based Model Parameters Between Two.
A Toll Choice Probability Model Application to Examine Travel Demand at Express and Electronic Toll Lanes in Maryland.
What is the Model??? A Primer on Transportation Demand Forecasting Models Shawn Turner Theo Petritsch Keith Lovan Lisa Aultman-Hall.
Status of the SEMCOG E6 Travel Model SEMCOG TMIP Peer Review Panel Meeting December 12, 2011 presented by Liyang Feng, SEMCOG Thomas Rossi, Cambridge Systematics.
SCAG Region Heavy Duty Truck Model Southern California Region Heavy Duty Truck Model.
Development of a New Commercial Vehicle Travel Model for Triangle Region 14 th TRB Planning Applications Conference, Columbus, Ohio May 7, 2013 Bing Mei.
Junction Modelling in a Strategic Transport Model Wee Liang Lim Henry Le Land Transport Authority, Singapore.
Washington State Truck Freight Performance Measure Research Interim Report Dale A Tabat Truck Freight Program and Policy Manager Freight Systems Division.
Analysis of Alternatives for Accommodating Trucks on Urban Freeways in Southern Nevada Vinod Vasudevan, Alexander Paz, Naveen Veeramisti, Pankaj Maheswari.
Sequential Demand Forecasting Models CTC-340. Travel Behavior 1. Decision to travel for a given purpose –People don’t travel without reason 2. The choice.
Archived Data User Services (ADUS). ITS Produce Data The (sensor) data are used for to help take transportation management actions –Traffic control systems.
Norman W. Garrick CTUP. Norman W. Garrick Transportation Forecasting What is it? Transportation Forecasting is used to estimate the number of travelers.
Norman W. Garrick Transportation Forecasting What is it? Transportation Forecasting is used to estimate the number of travelers or vehicles that will use.
NATMEC JUNE 5, 2012 DALLAS, TEXAS Improving MPO Decisions With Better Data: Examples in Dallas-Fort Worth Michael Morris, P.E. Director of Transportation.
Regional Travel Modeling Unit 6: Aggregate Modeling.
Presented to presented by Cambridge Systematics, Inc. Transportation leadership you can trust. Development of a Tour-Based Truck Travel Demand Model using.
COLLABORATE. INNOVATE. EDUCATE. What Smartphone Bicycle GPS Data Can Tell Us About Current Modeling Efforts Katie Kam, The University of Texas at Austin.
THE TRANSPORTATION IMPLICATIONS OF A TERRORIST ATTACK ON SEATTLE’S HIGHWAY NETWORK Chang-Hee Christine Bae (University of Washington) Larry Blain (Puget.
Transportation leadership you can trust. presented to TRB Planning Applications Conference presented by Vamsee Modugula and Maren Outwater Cambridge Systematics,
Analysis of Truck Route Choice using Truck-GPS Data
Transportation leadership you can trust. presented to presented by Cambridge Systematics, Inc. Development of a Hybrid Freight Model from Truck Travel.
Bus Rapid Transit: Chicago’s New Route to Opportunity Josh Ellis, BRT Project Manager Metropolitan Planning Council.
June 15, 2010 For the Missoula Metropolitan Planning Organization Travel Modeling
January Utah Statewide Household Travel Study Study overview and results.
Simpson County Travel Demand Model Mobility Analysis November 7, 2003.
Transportation leadership you can trust. presented to FHWA “Talking Freight” Seminar Series presented by Daniel Beagan Cambridge Systematics, Inc. February.
Cell Phone Traffic Data Technology Demonstration in Minnesota ITS America 2007 Annual Meeting & Exposition Bernie Arseneau, Mn/DOT Rashmi Brewer, Mn/DOT.
Jennifer Murray Traffic Forecasting Section Chief, WisDOT Metropolitan Planning Organization Quarterly Meeting July 28 th, 2015.
Transportation leadership you can trust. presented to Southeast Florida Model Users Group presented by Krishnan Viswanathan Cambridge Systematics, Inc.
Transportation Planning, Transportation Demand Analysis Land Use-Transportation Interaction Transportation Planning Framework Transportation Demand Analysis.
Highway Functional Classification Chapter 16 Dr. TALEB M. AL-ROUSAN.
1 Mississippi River Bridge An Analysis of Alternatives Expert Panel Review Sharon Greene & Associates.
Income-Based Work Trip Stratification within the Puget Sound Regional Council Travel Model Framework 20 th International Emme Users’ Conference Montreal,
Overview Freight Modeling Overview Tianjia Tang, PE., Ph.D FHWA, Office of Freight Management and Operations Phone:
Southwest Washington ITS Traffic Data Collection & Analysis: A Tale of 3 Projects Jill MacKay ITE Traffic Simulation Roundtable October 4, 2012.
Major Transportation Corridor Studies Using an EMME/2 Travel Demand Forecasting Model: The Trans-Lake Washington Study Carlos Espindola, Youssef Dehghani.
Assessing the Marginal Cost of Congestion for Vehicle Fleets Using Passive GPS Data Nick Wood, TTI Randall Guensler, Georgia Tech Presented at the 13 th.
The Stockholm trials – Emme/2 as a tool for designing a congestion charges system 1.The trials and the congestion charges system 2.Observed effects 3.Transportation.
Transportation Forecasting The Four Step Model. Norman W. Garrick Transportation Forecasting What is it? Transportation Forecasting is used to estimate.
Review of the Texas Transportation Institute (TTI) 2007 Urban Mobility Report By Ronald F. Kirby Daivamani Sivasailam TPB Technical Committee October 5,
ANALYSIS TOOL TO PROCESS PASSIVELY- COLLECTED GPS DATA FOR COMMERCIAL VEHICLE DEMAND MODELLING APPLICATIONS Bryce Sharman & Matthew Roorda University of.
A Tour-Based Urban Freight Transportation Model Based on Entropy Maximization Qian Wang, Assistant Professor Department of Civil, Structural and Environmental.
Florida’s First Eco-Sustainable City. 80,000+ Residential Units 10 million s.f. Non-Residential 20 Schools International Clean Technology Center Multi-Modal.
Model Validation of Transit Ridership at the Corridor and Transit Route Level by Mark Charnews October 19, 2006.
Topics Survey data comparisons to “old” model Bluetooth OD data findings Freight model design Q & A.
Client Name Here - In Title Master Slide 2007/2008 Household Travel Survey Presentation of Findings on Weekday Travel Robert E. Griffiths Technical Services.
Best Practices for Collecting Counts and Risk Evaluation for Bicyclists and Pedestrians Krista Nordback, P.E., Ph.D., PSU Taylor Phillips, PSU Mike Sellinger,
The Regional Mobility and Accessibility Study Initial Results of CLRP/CLRP+ Analysis with Round 6.4 Growth Forecasts and Five Alternative Land Use Scenarios.
Chapter 9 Capacity and Level of Service for Highway Segments
Company LOGO Georgia Truck Lane Needs Identification Study Talking Freight Seminar March 19, 2008 Matthew Fowler, P.T.P Assistant State Planning Administrator.
Transit Choices BaltimoreLink Ad-hoc Committee Meeting January 12, 2016.
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.
Reconciliation of regional travel model and passive device tracking data 14 th TRB Planning Applications Conference Leta F. Huntsinger Rick Donnelly.
TRAFFIC STUDY AND FORECASTING SHRI DEVI POLYTECHINIC
Applications in Mobile Technology for Travel Data Collection 2012 Border to Border Transportation Conference South Padre Island, Texas November, 13, 2012.
Kanok Boriboonsomsin, Guoyuan Wu, Peng Hao, and Matthew Barth
Macro / Meso / Micro Framework on I-395 HOT Lane Conversion
Assessing Strengths and Limitations of a Statewide Tour Based Freight Model Using Scenario Analysis in Maryland By Colin Smith, RSG Sabya Mishra, University.
ITTS FEAT Tool Methodology Review ITTS Member States Paula Dowell, PhD
Presented to 2017 TRB Planning Applications Conference
Johnson City MPO Travel Demand Model
Ventura County Traffic Model (VCTM) VCTC Update
Chattanooga Transportation Data Collection Review
Comparison and Analysis of Big Data for a Regional Freeway Study in Washington State Amanda Deering, DKS Associates.
Presentation transcript:

Use of Truck GPS Data for Travel Model Improvements Talking Freight Seminar April 21, 2010

22 Presentation Overview Truck GPS Data Vehicle Speed Comparison Grocery Store Trip Generation Discussion

33 Speed Differences on Freeways Truck GPS Data Truck Speeds Trip Generation Discussion Trucks Travel Slower than Cars Acceleration Deceleration Speed Maintenance Modeling Implications Travel Demand Model Air Quality Model Analysis

44 Motivation Continued Truck GPS Data Truck Speeds within PSRC’s Modeling Framework Trucks speeds are not differentiated Overestimation of trucks on freeways Underestimation of trucks on arterials Facility TypeCountVolumeLocationsDifference Percent Difference Freeways 552, , ,71474% Arterials 679, , (411,585)-61% Total 1,231,767 1,227, (3,871)0% Truck Speeds Trip Generation Discussion

55 GPS Data Truck GPS Data Collected in 2003 and 2004 Nearly ½ million observation points From 8 trucking firms and 25 GPS units 14 routes considered No comparable passenger vehicle data available Truck Speeds Trip Generation Discussion

66 GPS Analysis Results Truck GPS Data Midday General Purpose (GP) and Truck Speeds Truck Speeds Trip Generation Discussion

77 Short and Long Vehicle Comparison Truck GPS Data Data 20-second speed trap data from the Washington State Department of Transportation 4 vehicle length classes Bin 1: < 26’ (short—i.e. cars) Bins 2-4: > 26’ (long—i.e. trucks) Only consider observations where either cars or trucks are present Truck Speeds Trip Generation Discussion

88 Short and Long Vehicle Analysis Truck GPS Data Comparison of Daily Average Truck GPS and Long Vehicle Speeds GPSLong Vehicles SpeedObservationsSpeedObservations Truck Speeds Trip Generation Discussion

99 Short and Long Vehicle Analysis Truck GPS Data TimeShortLong Difference Percent Difference AM Peak % PM Peak % Weighted Average Speed Differences Truck Speeds Trip Generation Discussion

10 Short and Long Vehicle Analysis Truck GPS Data Comparison Over Time Truck Speeds Trip Generation Discussion

11 Short and Long Vehicle Analysis Truck GPS Data Speed and Car Percent at Select Locations Truck Speeds Trip Generation Discussion

12 In and Out Lane Comparison Truck GPS Data Data 5-minute speed trap data from the Washington State Department of Transportation Innermost non-HOV lane as proxy for car speeds Second to outermost lane as proxy for truck speeds Truck Speeds Trip Generation Discussion

13 In and Out Lane Analysis Truck GPS Data TimeShortLong Difference Percent Difference AM Peak % PM Peak % Weighted Average Speed Differences Truck Speeds Trip Generation Discussion

14 In and Out Lane Analysis Truck GPS Data Comparison Over Time Truck Speeds Trip Generation Discussion

15 Discussion Truck GPS Data Method Comparison Truck Speeds Trip Generation Discussion

16 Moving Forward Truck GPS Data The in and out lane method yields the most promising results and is simple to implement In 2006 trucks traveled by an average of 10% slower than cars, virtually unchanged from 2000 Future truck model will account for 10% difference on freeways The short and long vehicle comparison could be useful if disaggregate classification data is available Continued need for truck count information for model validation Truck Speeds Trip Generation Discussion

17 GPS Based Trip Generation Truck GPS Data Truck Speeds Trip Generation Discussion Data from the Washington State Department of Transportation (WSDOT) and University of Washington 2,500 trucks per day 15 minute reads > 3,000,000 records per month Examined data from Fall 2008 Geocode observation to road network

18 Defining Origins and Destinations Truck GPS Data Truck Speeds Trip Generation Discussion Traffic based versus intentional stops Use 3-minute dwell time to differentiate One month of data: 3,000,000 reads 358,000 trips 16 mile average trip distance 21 minute average travel time 34 miles per hour average speed

19 Grocery Store Trip Generation Truck GPS Data Truck Speeds Trip Generation Discussion Truck O/D data related to grocery stores and distribution centers in the Puget Sound Over 91 days: 2,400 trucks 22,000 tours 215,000 individual trips 9 tours per truck 0.1 tours per day 10 trips per tour 2 trips to major grocer

20 Validation Truck GPS Data Truck Speeds Trip Generation Discussion GPS dataset is subset of all trucks Favorable comparison to interview information (10 to 12 daily trucks) But half of observed manual counts

21 Daily Trips by Area Type Truck GPS Data Truck Speeds Trip Generation Discussion Land UseDaily Trucks Metropolitan Cities12.4 Core Cities12.1 Larger Cities8.4 Smaller Cities6.6 Unincorporated Urban Areas7.3 Rural3.9

22 Discussion Truck GPS Data Truck Speeds Trip Generation Discussion Can estimate total trips by referencing truck count data disaggregated by employment sectors, land use types and times of day Average trip and tour lengths Useful for estimating and calibrating aggregate distribution models Calibrate aggregate trip generation models Speed data and route choice Potential for commodity flow model Improving quality of GPS data

23 Thank You Truck GPS Data Alon Bassok Puget Sound Regional Council Truck Speeds Trip Generation Discussion