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Illinois Statewide Travel Demand Model Technical Approach

Illinois Statewide Travel Demand Model Technical Approach

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Illinois Statewide Travel Demand Model Technical Approach

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  1. Illinois Statewide Travel Demand ModelTechnical Approach Joshua Auld, Behzad Karimi, Zahra Pourabdollahi, Kouros Mohammadian October 10, 2014 Illinois Department of Transportation

  2. Outline • Introduction • Methodology • Long distance travel • Freight • External/Rural • Data Collection • Network Development • Simulation System • Future Tasks 2

  3. Introduction 3

  4. Statewide Models • Statewide models increasingly common as states struggle with complex transportation issues that must be studied from a system perspective • Portion of travel not modeled directly in MPO models: • 34% of all person miles traveled from long-distance trips (NHTS 2009) • Freight trucks account for 10% of vehicle miles traveled (NTS 2011) • Provide the opportunity to evaluate the entire system in an integrated framework • Results in better understanding of travel behavior across the state • Cover area beyond MPO borders - crucial for future land use development • Help with better planning of transportation services for all modes • More efficient infrastructure planning and management for the • Demand management, supply management, safety, economic development, land-use 4

  5. Statewide Models • Applied for projects ranging from local traffic to multi-state corridor studies. The range of applications include: • Statewide Plan: as a common ingredient to many components of a plan including transportation systems analysis, scenario analysis, economic benefits and environmental analysis. • Local Planning: The model could be used for local planning in the smaller rural communities. • Long Distance Corridors: The integration of long distance travel and freight movements can make the comparison of alternatives in a corridor possible. • Support for local and regional models: the statewide model can be used to find external trips, truck trips and other modes to be used in the smaller model. • Our study will focus on items 3 and 4. 5

  6. UIC’s Project • MPOs across the state have developed sophisticated transportation demand models that are used to determine travel demand • Such models could greatly benefit from more complete long-distance personal travel and truck freight movement data. • Researchers at University of Illinois at Chicago (UIC) examine statewide long-distance travel and truck freight movement within the state and parts of the larger region that directly impact our state. • “Long Distance Travel” is defined similar to the National Household Travel Survey as those trips that are at least 100 minutes (approximately 75 miles or more). • MPOs can use the model to modify their existing TDM to more accurately depict travel and freight behavior, thus having better information and more efficiently plan or manage transportation system. 6

  7. Current Status of Statewide Modeling • Current status of statewide models (Alan Horowitz) 7

  8. Current Status of Statewide Modeling • Technical approaches used in statewide: • Traffic count and growth factor (e.g. Montana) • Four-step model • Activity- and Tour-based microsimulation model (e.g. Ohio and Oregon) • Cost to develop the model highly depends on the technical approach: • $25,000 for South Carolina to millions of dollars for states of Ohio and Oregon • considerable portion of spent costs and time in tour- and activity-based models goes to data collection efforts • It costs $3,500,000 for Ohio to collect needed data for the being revised model 8

  9. Current Status of Statewide Modeling • Time to develop the model also depends on the technical approach: • 6 months for Traffic count and growth factor model • 8 years for integrated tour-based model • Statewide models are moving toward a more detailed zone systems and networks. • The second generation of Oregon statewide model, called SWIM2, has over 3,000 zones and over 53,000 links and it is while SWIM1 had only 125 zones and 2,000 links. 9

  10. Model Development Through Collaboration • Given the size / scope of this work collaboration necessary • Collaboration on model development: • Argonne National Laboratory computing resources, network editing and simulation software • UIC: freight modeling, activity-based modeling survey implementation • MPOs: network and land-use information, demand model results • Result of this work should be useful to many agencies • IDOT for long distance planning purposes • External and freight trips for local MPO models 10

  11. Current Status of Statewide Modeling • Market Segmentation is crucial to deal with heterogeneity: • Short- and Long-distance trips • Trip purposes • Combination of trip purposes in short- and long-distance trips can be very different • Freight and Passenger 11

  12. Current Status of Statewide Modeling • Threshold distance between long and short distance trips varies from 50 miles (e.g. Oregon) to 100 miles (e.g. California) • Based on the following chart, 75 miles for Chicago and 50 miles for other part of Illinois was selected as the distance threshold Data Source: NHTS 2001 12

  13. methodology A World-Class Education, A World-Class City

  14. ILSTDM Development Methodology • Four primary components: • Long-distance passenger travel model • Freight model • Local travel demand • MPO results where available • Default activity-based model for other areas • Visitor & pass-through trips A World-Class Education, A World-Class City

  15. ILSTDM Methodology 1. Long-Distance Travel Model 2. Freight Model 3. MPO and External 4. Other Local Population Synthesis FirmSynthesis OD Tables Population Synthesis Trip Frequency (Generation) Supplier Selection Diurnal Curves Activity Generation Trip Distribution (Location Choice) Shipment Size Transims Convert Trips routine Destination Choice Mode Choice Mode Choice Mode Choice Long-distance trips Freight trips Local/Visitor trips Local trips 5. Network Simulation 15

  16. Long-distance travel modeling A World-Class Education, A World-Class City

  17. Long-Distance Travel Modeling • Simulation of trips over 50/75 miles • Important for statewide planning • Accounts for a significant portion of trips on interstates and state highways • All trips on intercity bus, rail and air • Long distance travel simulated for all residents of Illinois and neighboring counties • Estimated using econometric activity-based model covering all primary modes A World-Class Education, A World-Class City

  18. Long-Distance Travel Model Framework • Primary inputs: • Census data (ACS and 2010 SF1 • TAZ Land use data from MPOs • Congested Network skims • Person and intercept survey results • Five inter-related models • Generation, Distribution, Mode choice connected through logsums. • Conditional time-of-day choice • Population synthesis using PopSyn program developed for CMAP Time of day choice Long-distance Travel

  19. Trip Generation • ZINB count regression models: • Gives annual work/non-work trips • Utilizes logsums from destination choice models • Estimated using weighted person travel survey results • Party size choice model • Ordered logit model for 1, 2, 3+ • Annual trip counts by household then used as input to daily trip realization model

  20. Trip Generation Discussion • Factors associated with higher trip rates: • Males, whites and high-income (all) • More vehicles (all) • More children (non-work trips) • Employment accessibility (work) • Destination log-sum (non-work) • Decreased trip rates: • Larger households (work ) • Cultural accessibility (non-work) • Factors associated with higher zero trips • Low income (work) • Factors associated with lower zero trip probability: • Larger households and households with children (all) • College educated and male (work) • Employed and married individuals (non-work)

  21. Destination Choice Models • Two-level destination choice model: • Region-choice utilizes TAZ choice logsum • 20 regions (including external regions) • TAZ choice in region • Sample of regional TAZs • Uses mode choice logsum for TAZ

  22. Destination Choice TAZ results • Factors increasing TAZ utility: • Increased population and employment • Cultural and recreational opportunities • Nearby employment • Access to university and recreational areas • Higher mode choice logsum • Factors decreasing utility: • Higher surrounding population • Higher zonal average income

  23. Mode Choice Models • Two levels of MNL models: • Main mode choice – depends on access/egress logsums • Access / egress mode choice • Estimated using weighted SP/RP survey data • Modal constants calibrated to observed survey distribution

  24. Time of Day Choice Model • Moving to daily travel model makes TOD component significant • Estimate segmented time-of-day choice for each long distance trip • Implemented using multinomial logit conditional on other choices • Uses data collected from household travel survey 24

  25. Freight modeling 25

  26. Freight Model Methodology Firm Synthesis Introducing individual decision-makers Supplier Selection Determining trade relationships/supply chains ShipmentSize Using an iterative proportional fitting model ModeChoice Modal split between truck and rail • FAME Framework • Firm Synthesis • Supply Chain Formation • Logistics Decisions • Shipments Forecasting • Network Analysis 26

  27. Freight Model Framework • Geographical Scale • National Scale: Domestic freight flows • Zone System (333 zone) • Township level zones in the Chicago area (118 zone) • County level zones in rest of Illinois (95 zone) • FAF zones in the rest of US (120 zone)

  28. Freight Model Framework • Decision-making agents • Firms : the decision-maker units • Producer/Receiver of goods • Form supply chains • Specify logistics choices • Firm-types : a group of firms with the same • industry type • employee size • geographic location in the zoning system

  29. Freight Modeling Framework National Agent-Based Freight Model Framework Economic Activity Zone System Firm Synthesis Model CBP Data Zoning System IO Accounts Industry- Commodity Crosswalk Socio- Economic Factors Freight Generation Model List of Firms with Their Characteristics Economic Activity Data Commodity Production Consumption Rates IO accounts/ Industry-commodity crosswalk Establishment Freight Survey Supplier Selection Model Supplier Evaluation Model Annual Commodity Flow (firm-to-firm) Logistics Choices Main Mode Choice Model Shipment Size / Frequency Choice Model Establishment Survey Shipping Chain Configuration (direct/non-direct shipping chains) GPS data gathering Interview Survey (specialists) Number of Stops per Chain Model Stop Type Model Access/Egress Mode Choice Model Vehicle Choice Model Simulated Individual Shipments Network Analysis Transportation Performance Measures Network Assignment Empty Trucks / Backhauling

  30. Economic Activity Overview Zone System CBP Data Economic Activity Firm Synthesis Model IO Accounts Industry- Commodity Crosswalk Zoning System Socio- Economic Factors Freight Generation Model List of Firms with Their Characteristics Economic Activity Data Commodity Production Consumption Rates

  31. Firm Synthesis and Freight Generation • Firm Synthesis: • 7,687,522 business establishments Classified into 70,116 firm-type groups Firm-Type: 1302361 (17) Zone Number of Establishments Menard County NAICS Construction of Buildings Employee Size 1-19 employee • Freight Generation Model • Commodity-industry crosswalk • Firm level production/consumption rates • Make-Use commodity-industry crosswalks • Number of establishments in zone • Size of establishments (employee size) • Data • Input-Output Accounts (BEA, 2013) • Freight Analysis Framework (FAF) • Commodity Flow Survey (CFS) • Synthesized Firm-types

  32. Logistics Choice Modeling Overview List of Firms with Their Characteristics Commodity Production Consumption Rates IO accounts/ Industry-commodity crosswalk Supplier Selection Model Supplier Evaluation Model Establishment Freight Survey Annual Commodity Flow (firm-to-firm) Logistics Choices Main Mode Choice Model Shipment Size / Frequency Choice Model Establishment Survey Shipping Chain Configuration (direct/non-direct shipping chains) GPS data gathering Interview Survey (specialists) Number of Stops per Chain Model Stop Type Model Access/Egress Mode Choice Model Vehicle Choice Model Simulated Individual Shipments

  33. Supplier Evaluation and Selection Model • A two-step modeling framework • Multi-criteria supplier evaluation model • To take into account decision makers’ opinions • To calculate suitability score for each potential supplier • Multi-criteria supplier selection optimization model • Maximize total suitability score of selected suppliers • Minimize total logistics costs • Meet the production capacity of suppliers and cover total demand of buyers

  34. Shipping Chain Configuration Model Chemical manufacturing Shipping Chain: One stop at a Distribution Center Mode :Truck Shipment size: 4K ~ 30K lbs Actual weight: 29400 lbs Annual frequency: 204 3 KT Chemical and Pharmaceutical Products 218 Nonmetallic mineral product manufacturing • Shipping chain/Distribution channel/Transport chain • Modeling Approach • Rule-based decision tree clustering method • Growth method: Exhaustive CHAID algorithm • Number of intermediate stops in a chain & type of facility at each stop

  35. Network Analysis Framework Logistics Choice Models Simulated Individual Shipments Network Analysis Transportation Performance Measures Network Assignment Empty Trucks / Backhauling

  36. External trips 36

  37. External / MPO Trip Models • 1995 American Travel Survey (ATS) used to compute base year long-distance trip distribution for U.S. • Iterative proportional fitting procedure updates base trip distribution to 2010 using Census data • Generate OD table for model zoning system • Combined with MPO OD tables • Converted to individual trips using TransimsConvertTrips utility + diurnal distribution assumptions • External trips in Gravity Model formulation to include sensitivity to network changes 37

  38. Local travel 38

  39. 4. Local Travel Model: ADAPTS ABM • Activity-based scheduling process model: • Bottom-up approach to activity-travel pattern formation • Activities generated, planned and scheduled dynamically • Planning process is explicitly modeled • Operationalized using multiple scheduling process surveys • Integrated activity-travel microsimulation • Dynamic, multi-day activity-travel simulation • Activities planned, scheduled and executed in single framework • Fully agent-based: all aspects implemented as individual agent behaviors – including routing and travel simulation • Currently Implemented in POLARIS model framework • Estimated based on Chicago-region data – not adjusted to rural area

  40. Local Travel Model Overview Preprocessing Each Planning Time-step(5-min intervals) In continuous time GenerationModel Household ActivityGeneration Destination Choice Read Data and Scenario Population Synthesis Individual ActivityGeneration Timing Choices Planning order model Routine and PreplannedActivity Scheduling Check Activity Schedule Activity Scheduling Mode Choices Gather Pre-trip info Party Choice Modify plans Get Route Schedule Departures Simulation

  41. Data collection 41

  42. Household Survey • Collects trip data for long-distance trips at household level • Similar to American Travel Survey 1995 – conducted as part of NHTS • Collects: • Trip frequency • Travel modes • Trip type • Party composition • Used to estimate trip frequency models • Dependent on household characteristics • Destination characteristics • Mode characteristics • Approximated logsums (accessibility-based) • Can be combined statistically with NHTS and ATS to extend sample 42

  43. Instrument Design • Introduction • Verify correct contact information • Household roster and demographic information • Demographics for respondent • Demographics for other household members • Housing information • Trip screening questions • Number of trips in the past 12 months • Trip count by quarter, mode, purpose, party size • Commuting trips • Trip detail for last trips (work and non-work) • Start date, duration • Origin, destination, station access • Travel Modes • Purpose

  44. Household Survey • Long distance trip purpose • Long distance Mode Choice 44

  45. Household Survey • Departure Time-of-day choice Start time 45

  46. Household Survey • HH Income HH Income 46

  47. Freight Data Sources • Publicly Available Data • County Business Patterns • Industry input-output accounts • Commodity Flow Survey • Freight Analysis Framework • Survey Data • UIC establishment survey, 1st wave (2009) • UIC establishment survey, 2nd and 3rd waves (2010-2011) 47

  48. UIC Establishment Survey (2010-2011) • Data Collection Method • telephone introductions • e-mail blast campaigns • web crawling

  49. UIC Establishment Survey (2010-2011) • Survey Design • Participants: logistics or shipping managers of firms • Three major parts • Characteristics of the business establishment • Attributes of five most recent shipments • Contact information

  50. UIC Establishment Survey (2010-2011) 1st wave 2nd wave • Survey Results • Approximately 219,000 contacts nationwide • 657 establishment surveys • 970 useable shipment survey forms