November 28, 2006 CCOS On-Road Allocation Factors Page 1 Spatial & Temporal Allocation of On-Road Emissions CCOS Technical Committee November 28, 2006.

Slides:

Advertisements

Similar presentations

SE Florida FSUTMS Users Group Meeting FDOT Systems Planning Office

Advertisements

1 Estimating On-Road Vehicle Emissions Using CONCEPT Alison K. Pollack Ralph Morris ENVIRON International Corporation.

Analysis and Multi-Level Modeling of Truck Freight Demand Huili Wang, Kitae Jang, Ching-Yao Chan California PATH, University of California at Berkeley.

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.

Intercity Person, Passenger Car and Truck Travel Patterns Daily Highway Volumes on State Highways and Interstates Ability to Evaluate Major Changes in.

1Chapter 9-4e Chapter 9. Volume Studies & Characteristics Understand that measured volumes may not be true demands if not careful in data collection and.

California Energy Commission Medium and Heavy Vehicles and Movement of Light and Heavy Vehicles Inputs and Assumptions for Transportation Energy Demand.

Session 11: Model Calibration, Validation, and Reasonableness Checks

CE 2710 Transportation Engineering

Advanced Modeling System for Forecasting Regional Development, Travel Behavior, and the Spatial Pattern of Emissions Brian J. Morton Elizabeth Shay Eun.

Lec 29: Ch3.(T&LD): Traffic Analysis – Non-site traffic forecast Understand why estimating non-site traffic forecast is necessary Know three principal.

Norman W. Garrick CTUP. Norman W. Garrick Transportation Forecasting What is it? Transportation Forecasting is used to estimate the number of travelers.

California Energy Commission Transportation Electrification Electricity and Natural Gas Model Inputs Workshop Rosenfeld Hearing Room February 26, 2015.

Design Speed and Design Traffic Concepts

GreenSTEP Statewide Transportation Greenhouse Gas Model Cutting Carbs Conference December 3, 2008 Brian Gregor ODOT Transportation Planning Analysis Unit.

Framework for Model Development General Model Design Highway Network/Traffic Analysis Zones (TAZs) Development of Synthetic Trip Tables Development of.

Regional Travel Modeling Unit 6: Aggregate Modeling.

Using Aggregated Federal Data and Local Shipping Data to Model Freight Alabama Michael Anderson Civil Engineering The University of Alabama in Huntsville.

Transportation leadership you can trust. presented to presented by Cambridge Systematics, Inc. Development of a Truck Model for Memphis 2015 Transportation.

Transportation leadership you can trust. presented to TRB Planning Applications Conference presented by Vamsee Modugula and Maren Outwater Cambridge Systematics,

Source: NHI course on Travel Demand Forecasting (152054A) Session 10 Traffic (Trip) Assignment Trip Generation Trip Distribution Transit Estimation & Mode.

Problem Statement and Motivation Key Achievements and Future Goals Technical Approach Kouros Mohammadian, PhD and Yongping Zhang (PhD Candidate), CME,

ODOT Freight Modeling Presented to the Ohio Conference on Freight Toledo, OH September 18, 2007 By Gregory Giaimo, PE Ohio Department of Transportation.

Using the Oregon Statewide Integrated Model for the Oregon Freight Plan Analysis Prepared for the TRB SHRP2 Symposium: Innovation in Freight Demand Modeling.

©2005,2006 Carolina Environmental Program Sparse Matrix Operator Kernel Emissions SMOKE Modeling System Zac Adelman and Andy Holland Carolina Environmental.

Freight Bottleneck Study Update to the Intermodal, Freight, and Safety Subcommittee of the Regional Transportation Council September 12, 2002 North Central.

Recent Developments in the Community Emissions Model CONCEPT 5 th Annual CMAS Model User Conference Tuesday October 17, 2006 Mark Janssen LADCO.

Kip Billings, P.E. Andy Li, Phd Wasatch Front Regional Council October 14, 2010.

Florida Multimodal Statewide Freight Model

COMPARISON OF LINK-BASED AND SMOKE PROCESSED MOTOR VEHICLE EMISSIONS OVER THE GREATER TORONTO AREA Junhua Zhang 1, Craig Stroud 1, Michael D. Moran 1,

How to Put “Best Practice” into Traffic Assignment Practice Ken Cervenka Federal Transit Administration TRB National Transportation.

Harikishan Perugu, Ph.D. Heng Wei, Ph.D. PE

Norman W. Garrick Transportation Forecasting What is it? Transportation Forecasting is used to estimate the number of travelers or vehicles that will use.

Overview Freight Modeling Overview Tianjia Tang, PE., Ph.D FHWA, Office of Freight Management and Operations Phone:

+ Creating an Operations-Based Travel Forecast Tool for Small Oregon Communities TRB National Transportation Planning Applications Conference May 20, 2009.

Oregon’s Work Zone Traffic Analysis Program FHWA Work Zone Rule Virtual Workshop November 6, 2008 Irene Toews, P.E. Oregon Department of Transportation.

WRAP Fugitive Dust Emission Summary and Evaluation (AoH Phase II/TSS Task 7b) ENVIRON International Corporation 15 November 2005 Tempe, AZ.

HELIOS: Household Employment and Land Impact Outcomes Simulator FLORIDA STATEWIDE IMPLEMENTATION Development & Application Stephen Lawe RSG Michael Doherty.

The Iowa Travel Analysis Model Civil Engineering 451/551 Fall Semester 2009 Presented by: Phil Mescher, AICP Office of Systems Planning, Iowa Department.

Incorporating Traffic Operations into Demand Forecasting Model Daniel Ghile, Stephen Gardner 22 nd international EMME Users’ Conference, Portland September.

MATRIX ADJUSTMENT MACRO (DEMADJ.MAC AND DEMADJT.MAC) APPLICATIONS: SEATTLE EXPERIENCE Murli K. Adury Youssef Dehghani Sujay Davuluri Parsons Brinckerhoff.

Transportation leadership you can trust. presented to TRB 11 th Conference on Transportation Planning Applications presented by Dan Goldfarb, P.E. Cambridge.

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.

Emission Inventories and EI Data Sets Sarah Kelly, ITEP Les Benedict, St. Regis Mohawk Tribe.

Tri-level freight modeling: A simulation of trucks going near and far Rolf Moeckel Parsons Brinckerhoff Sabya Mishra University of Maryland TRB Planning.

Dynamic Tolling Assignment Model for Managed Lanes presented to Advanced Traffic Assignment Sub-Committee presented by Jim Hicks, Parsons Brinckerhoff.

Presented to Model Task Force Model Advancement Committee presented by Thomas Rossi Krishnan Viswanathan Cambridge Systematics Inc. Date November 24, 2008.

Presented to Time of Day Subcommittee May 9, 2011 Time of Day Modeling in FSUTMS.

BUSINESS SENSITIVE 1 Network Assignment of Highway Truck Traffic in FAF3 Maks Alam, PE Research Leader Battelle.

Improvements to the Spatial and Temporal Representativeness of Modeling Emission Estimates: Phase 1 Findings and Recommendations Presented by: Lyle R.

11 th National Planning Applications Conference Topic: Statewide Modeling Validation Measures and Issues Authors: Dave Powers, Anne Reyner, Tom Williams,

Putting the LBRS and other GIS data to Work for Traffic Flow Modeling in Erie County Sam Granato, Ohio DOT Carrie Whitaker, Erie County 2015 Ohio GIS Conference.

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.

Abstract Background Methodology Methods While the project is in the data-collection and background research phase, there are several studies that utilize.

Incorporating Time of Day Modeling into FSUTMS – Phase II Time of Day (Peak Spreading) Model Presentation to FDOT SPO 23 March 2011 Heinrich McBean.

The World Bank Toll Road Revenue Forecast Quality Assurance/Quality Control.

Generated Trips and their Implications for Transport Modelling using EMME/2 Marwan AL-Azzawi Senior Transport Planner PDC Consultants, UK Also at Napier.

November 16, Retrospective Analysis of Ambient and Emissions Data and Refinement of Study Hypotheses –Task 1: Review of Available Emissions Data.

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.

Background  Prior to EPA/ORD effort, no temporally spatially resolved inventory prepared for Fairbanks (focus was on CO and spatial resolution not needed/addressed)

Induced Travel: Definition, Forecasting Process, and A Case Study in the Metropolitan Washington Region A Briefing Paper for the National Capital Region.

Jeff Vukovich, USEPA/OAQPS/AQAD Emissions Inventory and Analysis Group

SMOKE-MOVES Processing

Development of New Supply Models in Maryland Using Big Data

A STATE-WIDE ACTIVITY-BASED

Presented By: George Noel – Volpe Mark Glaze - FHWA 1/13/2014

Presentation transcript:

November 28, 2006 CCOS On-Road Allocation Factors Page 1 Spatial & Temporal Allocation of On-Road Emissions CCOS Technical Committee November 28, 2006 Prepared by: Tom Kear, Ph.D., P.E. Dowling Associates Debbie Niemeier, Ph.D., P.E. UC Davis

November 28, 2006 CCOS On-Road Allocation Factors Page 2 Presentation Overview Preview of key issues On-road proportion & Prior CCOS work Major trends identified in the literature & heavy duty modeling practice Critical assumptions Findings Phase II priority projects

November 28, 2006 CCOS On-Road Allocation Factors Page 3 Preview Of Key Issues The ITN used to develop the base-year (2000) inventory is not applicable to future years Heavy-duty vehicle activity, in general, is not being modeled, but is assigned to roads as a percentage of light duty vehicle activity Speed post-processing has been to shown dramatically affect emission estimates under certain conditions Current modeling techniques are not capturing the spatial distribution of weekend travel

November 28, 2006 CCOS On-Road Allocation Factors Page 4 On-Road Proportion Of Emissions On-Road contributes about 1/3 of the ROG inventory Diesel vehicles are not an important source of ROG

November 28, 2006 CCOS On-Road Allocation Factors Page 5 On-Road Proportion Of Emissions On-Road contributes about 50% of the NOx inventory Trucks account for about 3% of VMT but 30% of on-road NOx

November 28, 2006 CCOS On-Road Allocation Factors Page 6 Prior CCOS Work BURDEN 2002 emissions allocated to grid cells using DTIM4 Integrated Transportation Network (ITN) from individual county (loaded) travel demand model networks Temporal allocations assigned per BURDEN and available traffic counts

November 28, 2006 CCOS On-Road Allocation Factors Page 7 Prior CCOS Work

November 28, 2006 CCOS On-Road Allocation Factors Page 8 Prior CCOS Work

November 28, 2006 CCOS On-Road Allocation Factors Page 9 CCOS: NOx, TOG, HDV NOx

November 28, 2006 CCOS On-Road Allocation Factors Page 10 Critical assumptions CCOS assumes uniform growth of vehicle activity across regions Note the variation in growth forecasts, ranging from none to more than 10x (e.g., 1,000%) ITN needs to be rebuilt using loaded networks for each analysis year (interpolated trip tables) prior to DTIM runs

November 28, 2006 CCOS On-Road Allocation Factors Page 11 Prominent Trends in Literature Light/heavy vehicle ratio differ by day of week Less truck activity on weekends, but the ratio of LDV/HDT increases Ratios vary by geographic location Weekdays (Mon-Thurs) have similar temporal allocation Saturday and Sunday are often very different from each other

November 28, 2006 CCOS On-Road Allocation Factors Page 12 Prominent Trends in Literature Speed post processing has a significant effect on congested emissions

November 28, 2006 CCOS On-Road Allocation Factors Page 13 Prominent Trends in Literature Statewide HVMT accounts for only about 1% of the annual total. The low HVMT suggests that changes in harvest hauling traffic patterns will not dramatically affect emissions for a typical day. Current activity factor for nonagricultural unpaved roads underestimated vehicle activity for Forest and Woodland and Urban Residential areas, but overestimated vehicle activity in Grasslands, Sand dunes and Scrubland and Urban Interface areas. Table 10. Annual unpaved road VMT in California Harvest VMT Nonharvest VMTTotal Statewide VMT 4,945,329468,023,838472,969,167

November 28, 2006 CCOS On-Road Allocation Factors Page 14 Heavy Duty Vehicles Not modeled but captured during calibration by increasing non- home-based-trips to match counts True freight models aggregate trip tables from inter county commodity flow data and regional gravity models. Trucks not well captured by SJV phase II truck model, or any of the 8 RTPA models SJV Phase III truck model forecast is being extrapolated from 1978 commodity flow surveys

November 28, 2006 CCOS On-Road Allocation Factors Page 15 Heavy Duty Vehicles SJV Goods Movement Study Phase II (2004)

November 28, 2006 CCOS On-Road Allocation Factors Page 16 Critical assumptions The current approach assumes weekend and weekday trip distribution is identical, only the number of trips generated changes –Just matching base year creates a forecasting problem because behavioral component is lacking Heavy duty vehicle activity is assumed to be distributed similarly to the light duty vehicle activity on all RTPA networks. Assumes that trip based emission factors are applicable to links Existing and future activity is assumed to follow the same spatial / temporal distributions

November 28, 2006 CCOS On-Road Allocation Factors Page 17 Findings from Phase 1 Areas of uncertainty –Spatial changes between weekday-weekend activity –Where are the trucks? –Spatial mismatch between activity data & emissions rates –Impact of better transportation data (refinement of spatial network, speed post processing, and the treatment of trip ends) –Impact of seasonality on agricultural goods movement

November 28, 2006 CCOS On-Road Allocation Factors Page 18 Findings from Phase 1 Best way to group daily hours of travel? Importance of speed post processing Trucks are not well represented Weekend activity is not well represented

November 28, 2006 CCOS On-Road Allocation Factors Page 19 Phase II priority projects TaskDescriptionCost forecasts Statewide Model, DTIM, spline smoothing. $80 K 2.Improve truck data Model truck activity on highways and arterials, integrate w/ task (1) $75 K ($115 K if counts needed) 3.Speed post- processing Identify best method and implement $45 K 4.Improve weekend data (LDV) Create weekend trip tables, validate/calibrate relative distributions $75 K 5.Link-level EFs Trucks from Lit or E55/E98 data, MOBILE6 for LDVs $ 50 - $75 K High Moderate Low Note: cost assumptions in speaking notes window Very High

November 28, 2006 CCOS On-Road Allocation Factors Page 20 Phase II priority projects 1)2010, 2015, 2020 on-road forecasts. –BURDEN 2007 control totals –Statewide model (rather than ITN) w/DTIM for spatial allocation –Interpolate trip tables for intermediate year assignments. –“Disperse” (via spline interpolation) the on-road allocation to approximate the impact of network elements not explicitly modeled in the Statewide network

November 28, 2006 CCOS On-Road Allocation Factors Page 21 Impact of Spline Function Source: Atm. Env. V.38, issue 2, (2004)

November 28, 2006 CCOS On-Road Allocation Factors Page 22 Phase II priority projects 2)Improve truck activity estimates: –Reverse fit an OD table to observed truck counts, use SJV Phase II goods movement model as an initial condition –Base projections on TAZ employment growth Rational: Heavy-duty truck activity is poorly understood.

November 28, 2006 CCOS On-Road Allocation Factors Page 23 Count Locations from Phase II truck Model

November 28, 2006 CCOS On-Road Allocation Factors Page 24 Phase II priority projects 3)Speed post processing link data –Post process speed data to represent hourly conditions –Research into the sensitivity / appropriateness of different formulations –SAS code to implement –Impacts highly congested links Rational: As shown in the literature review, the impact of speed post processing on estimated emissions can be dramatic for links operating near and over capacity conditions.

November 28, 2006 CCOS On-Road Allocation Factors Page 25 Phase II priority projects 4) Improve weekend spatial allocation –Incorporating behavioral characteristics into the method (e.g., ratio OD tables by trip type and ITE data). – Reverse fit OD tables to observed light duty counts Rational: Trip making patterns change along with trip generation rates for weekend activity. Currently only trip rates are taken into account

November 28, 2006 CCOS On-Road Allocation Factors Page 26 Phase II priority projects 5)Link level emission rates –Use emission rates and activity data with similar spatial specificity –HDV emission rates from models in the literature –Option: use E55/E59 data to construct new rates based on Kear & Niemeier 2006 –Use light duty rates from MOBILE6 –BURDEN 2007 still sets control totals Rational: Link-based emissions rates are based on road segment level activity. BURDEN trip based rates include operation over all facility types

November 28, 2006 CCOS On-Road Allocation Factors Page 27 Q & A What effect will time and resource constraints have on CCOS priorities? How does the on-road inventory uncertainty compare to that in the rest of the inventory? Different projects have different uncertainties and commensurate impacts Extrapolations from an inappropriate set of year 2000 assumptions would have little value. Internal consistency and a scientific/behavioral bases for on- road activity is critical.