Illinois Statewide Travel Demand Model Technical Approach

Joshua Auld, Behzad Karimi, Zahra Pourabdollahi, Kouros Mohammadian

October 10, 2014

Illinois Statewide Travel Demand ModelTechnical Approach

Illinois Department of Transportation

Outline

• Introduction• Methodology

• Long distance travel• Freight• External/Rural

• Data Collection• Network Development• Simulation System• Future Tasks

2

INTRODUCTION

3

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

Statewide Models

• Applied for projects ranging from local traffic to multi-state corridor studies. The range of applications include:1. Statewide Plan: as a common ingredient to many components of a

plan including transportation systems analysis, scenario analysis, economic benefits and environmental analysis.

2. Local Planning: The model could be used for local planning in the smaller rural communities.

3. Long Distance Corridors: The integration of long distance travel and freight movements can make the comparison of alternatives in a corridor possible.

4. 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

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

Current Status of Statewide Modeling

• Current status of statewide models (Alan Horowitz)

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• 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


• 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

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

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• 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

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• 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

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5000

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20

30

40

50

60

70

80

90

100

Chicago MSA

IL_OtherMSAs

OR

OH

CA

Trip Distance (Mile)

Cu

mu

lati

ve D

isti

bu

tio

n o

f V

MT

s

Data Source: NHTS 2001

METHODOLOGY

A World-Class Education, A World-Class City

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


ILSTDM Methodology

15

Population Synthesis

Trip Frequency(Generation)

Trip Distribution (Location Choice)

Mode Choice

1. Long-Distance Travel Model

2. Freight Model

Long-distance trips

Freight trips

3. MPO and External

OD Tables

Diurnal Curves

Transims Convert Trips routine

Local/Visitor trips

4. Other Local

Local trips


Activity Generation

Destination Choice

Mode Choice

5. Network Simulation

FirmSynthesis

Supplier Selection

Shipment Size

Mode Choice

LONG-DISTANCE TRAVEL MODELING


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


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

Census Land Use

Synthetic Population

Long Distance Trip Generation

Trip Distribution

Mode Choice

Ridership

HH Survey

Intercept Surveys

Long-distance Travel

Time of day choice

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

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)

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

50.0%

0 1 2 3 4 5 6 7 8 9 10

SIM-Vacation ATS-Vacation HHSurvey - Vacation

Other trips: AverageSimulated 3.06 ATS 2.29 Survey 3.08

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 Nested Logit Destination Choice

Region Choice

Chi Stl . . . External

TAZ1 TAZ2 TAZ N

LogsumLogsumLogsum

HH Survey

Intercept Surveys

Destination Choice TAZ results

Variable Coefficient t-stat p-value

Population 0.117 4.20 0

Employment 0.597 18.32 0Cultural Area 1.63 1.54 0.12 *

Major Recreational Area 1.21 3.40 0

Avg. Household Income -0.094 -4.45 0

Population Accessiblity -0.287 -2.02 0.04

Recreational Accessibility 0.024 1.68 0.09Retail Accessibility 0.103 1.49 0.14 *Total Employment Accessibility 0.177 6.93 0

University Accessibility 0.035 4.18 0

Mode Choice Logsum 0.951 4.00 0

Model Fit StatisticsNumber of observations: 828Likelihood ratio test: 866.6

Rho-square: 0.176

• 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

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

ModeChoice

Auto Air BUS RAIL HSR

Access Mode

TransitAuto Taxi

Access LOGSUM

Egress Mode

TransitAuto Taxi

Egress LOGSUM

Intercept Surveys

ModeChoice

Auto Air BUS RAIL HSR

Access Mode

TransitAuto Taxi

Access LOGSUM

Egress Mode

TransitAuto Taxi

Egress LOGSUM

Intercept Surveys

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

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FREIGHT MODELING

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Freight Model Methodology

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FAME Framework

• Firm Synthesis

• Supply Chain Formation

• Logistics Decisions

• Shipments Forecasting

• Network Analysis

Firm SynthesisIntroducing individual decision-makers

Supplier SelectionDetermining trade relationships/supply chains

Shipment SizeUsing an iterative proportional fitting model

Mode ChoiceModal split between truck and rail

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)

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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

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Zone SystemZone System

Eco

no

mic

Act

ivit

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og

isti

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ho

ices

Zoning System

Zoning System

Socio- EconomicFactors


Economic Activity Data

Economic Activity Data

Freight Generation Model

Commodity Production Consumption Rates

Supplier Selection Model

Network Assignment

Establishment Survey Establishment Survey

Annual Commodity Flow (firm-to-firm)

GPS data gatheringGPS data gathering

Supplier Evaluation Model

IO accounts/ Industry-commodity crosswalk


Establishment Freight Survey


Transportation Performance Measures

Interview Survey (specialists)


CBP DataCBP Data

IO AccountsIO AccountsIndustry-

CommodityCrosswalk

Industry-CommodityCrosswalk

Firm Synthesis Model

List of Firms with Their Characteristics

Empty Trucks / Backhauling

Shipping Chain Configuration(direct/non-direct shipping chains)

Shipment Size / Frequency Choice Model

Main Mode Choice Model

Vehicle Choice Model

Number of Stops per Chain Model

Stop Type ModelAccess/Egress Mode

Choice Model

Simulated Individual Shipments

Net

wo

rk

An

alys

isNational Agent-Based Freight Model FrameworkFreight Modeling Framework

Economic Activity Overview

Zone SystemZone System

Eco

no

mic

Act

ivity Zoning

SystemZoningSystem



Economic Activity DataEconomic

Activity Data

Freight Generation Model


CBP DataCBP Data

IO AccountsIO Accounts



Firm Synthesis Model

List of Firms with Their

Characteristics

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Firm Synthesis and Freight Generation

• Firm Synthesis:• 7,687,522 business establishments

Classified into 70,116 firm-type groups

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Firm-Type: 130 236 1 (17)

Zone

NAICS

Employee Size

Number of EstablishmentsMenard County

Construction of Buildings

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

Logistics Choice Modeling OverviewLo

gist

ics

Cho

ices


Supplier Selection Model

Establishment Survey Establishment Survey

Annual Commodity Flow

(firm-to-firm)

GPS data gatheringGPS data gathering

Supplier Evaluation Model







List of Firms with Their

Characteristics

Shipping Chain Configuration(direct/non-direct shipping chains)

Shipment Size / Frequency Choice Model

Main Mode Choice Model

Vehicle Choice Model

Number of Stops per Chain Model

Stop Type Model

Access/Egress Mode Choice Model


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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

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Shipping Chain Configuration Model

• 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

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Chemical manufacturing

Nonmetallic mineral product manufacturing218

3 KT Chemical and Pharmaceutical Products

One stop at a DistributionShipping Chain:

Center

Mode :TruckShipment size: 4K ~ 30K lbsActual weight: 29400 lbsAnnual frequency: 204

Network Analysis Framework

Network Assignment

Logistics Choice Models

Transportation Performance Measures

Empty Trucks / Backhauling


Ne

two

rk A

na

lysi

s

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EXTERNAL TRIPS

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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 Transims ConvertTrips utility + diurnal distribution assumptions

• External trips in Gravity Model formulation to include sensitivity to network changes

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LOCAL TRAVEL

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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

Local Travel Model Overview

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Household ActivityGeneration

Individual ActivityGeneration

Each Planning Time-step(5-min intervals)

Modify plans

Schedule Departures

Destination Choice

Timing Choices

In continuous time

Mode Choices

Party Choice

Get Route

Check Activity Schedule

Planning order model

Simulation

Activity Scheduling

GenerationModel

Preprocessing

Gather Pre-trip info

Read Data and Scenario


Routine and PreplannedActivity Scheduling

DATA COLLECTION

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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

Instrument Design

1. Introduction • Verify correct contact

information

2. Household roster and demographic information • Demographics for

respondent• Demographics for other

household members• Housing information

3. Trip screening questions• Number of trips in the past 12 months• Trip count by quarter, mode, purpose,

party size• Commuting trips

4. Trip detail for last trips (work and non-work)• Start date, duration• Origin, destination, station access• Travel Modes• Purpose

Household Survey

• Long distance trip purpose

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• Long distance Mode Choice

Household Survey

• Departure Time-of-day choice

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2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 220%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Start time

Household Survey

• HH Income

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HH Income

Less than $25,000

$25,000-$49,999

$50,000-$74,999

$75,000-$99,999

$100,000-$149,000

$150,000 or more

0%

5%

10%

15%

20%

25%

30%

11%

26%

22%

18%16%

6%

Freight Data Sources

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• Publicly Available Data1. County Business Patterns

2. Industry input-output accounts

3. Commodity Flow Survey

4. Freight Analysis Framework

• Survey Data1. UIC establishment survey, 1st wave (2009)

2. UIC establishment survey, 2nd and 3rd waves (2010-2011)

UIC Establishment Survey (2010-2011)

• Data Collection Method

• telephone introductions

• e-mail blast campaigns

• web crawling

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• 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

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• Survey Results• Approximately 219,000 contacts nationwide• 657 establishment surveys • 970 useable shipment survey forms

1st wave 2nd wave

0 5 10 15 20 25 30 35 400

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100

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300

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450

Days Since Survey Released

Num

ber

of P

arti

cipa

nts

0 10 20 30 400

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40

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100

120

140

160

180

200

Days Since Survey Released

Num

ber

of P

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Data Acquisition from MPOs

MPO Network Zoning Land Use TDM ResultsChicago ü ü ü üSpringfield ü ü üChampaign ü ü üSt. Louis ü ü ü üBloomington ü ü ü üPeoria ü ü üQuad-Cities ü ü üDanville ü üDecaturKankakeeDekalbRockford

Dubuque

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NETWORK DEVELOPMENT

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ILSTDM Network Development

• Approximately 90,000 links

• Data sources:• MPO models• FAF2 Network• Illinois HPMS (IRIS)• Argonne Chicago network

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Combining networks

• Widely varying networks depending on source:• 1-way vs 2-way links• Missing capacity, lanes, speeds• Disconnects (especially in HPMS)• Very little traffic control information

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• Develop custom Python scripts to combine networks• Generate estimates for speed, capacity, etc. when not provided• Import all networks into a common database format• Sqlite open-source DBMS with Spatialite extensions• Compatible with Argonne Network Editing software

Network Editor

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Adding a missing link

Correcting connectivity

Synthesized Intersection Controls

• Generate signal/stop information using Transims IntControl program• Estimates signal/sign warrants given link connectivity, link capacities,

link type and area type• Different outcomes by area type (Chicago, St.Louis, Other urban or

rural) and primary/secondary street• Timing/phasing are then estimated using the warrants and a given

signal type, cycle length, etc.

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ILSTDM Zone System Development

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Region (Superzone) OrganizationTAZs Census TractsChicago Rest of Northern IllinoisChicago Suburbs Rest of Central IllinoisSt. Louis Rest of Southern IllinoisSt. Louis Suburbs CountiesChampaign Iowa - NeighboringSpringfield Wisconsin - NeighboringQuad Cities Indiana - NeighboringPeoria Missouri - NeighboringBloomington Kentucky - NeighboringDecatur StatesDanville Northeast (states)

Northwest (states)Southeast (states)Southwest (states)

• 5800 zones in 20 regions

Socio-Economic and Land Use Data Collection

• Many areas lack required zone data• Process to generate employment and land use:

• Extract employment by Zip Code from Zip Code Business Patterns• Plot Illinois Land coverage for ILGS, showing developed areas• Transfer zip code employment to developed areas through overlay

(assumes equal distribution of jobs throughout the developed area in each zip code• Transfer employment from developed

area shapes to zones through overlay• Aggregate to county level and perform

IPF to match county totals• Follow same process for Census of

Governments data• Land use overlay from ESRI points of

interest/landmarks dataset

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SIMULATION SYSTEM

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Simulation-BasedDynamic Traffic Assignment Model

• Trips enter traffic simulation either:• Directly: Long-distance model, Freight model, ADAPTS• Indirectly from OD tables converted to trips by Transims

• Network simulation contains:• Route Choice Model• En-route Switching Model• Traffic Control Model• Mesoscopic Traffic Simulation Model

• Currently implemented in POLARIS agent-based simulation framework

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Route choice model

• One-Shot Assignment using prevailing travel information• Averaged experienced travel times in last interval (e.g. 5 minutes)• Travel times are output from traffic simulation model• Current implemented route choice model is pre-trip route choice model

with enroute replanning• Pre-trip route choice model is for pre-trip users who use the travel time

information based on current traffic conditions to find a shortest path from his/her origin to destination. (e.g. using google map to compute shortest path considering traffic at that time)

• Shortest path algorithm: individual link-based A-Star Algorithm that takes care of delay at turn movement

• Enroute replanning: travelers can switch routes at decision points based on experienced travel times using bounded rationality model

• This solution maintains an approximation of an instantaneous equilibrium

Traffic Simulation Model

• Newell’s Simplified Kinematic Wave model• Using cumulative curves• Capturing queue formation, spillback, and dispersion• Capturing shock wave• Adhering to the fundamental diagrams

• Output:• Network flow pattern

• Cumulative vehicles at upstream and downstream of a link• Vehicle trajectory (enter time and exit time of each link)

• Network performance• Time-dependent link travel time by turn movement

POLARIS Simulation Environment

63

Simulation Model Process

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Trip Frequency(Generation)

Trip Distribution (Location Choice)

Mode Choice

Long-distance trips

Freight trips

OD Tables

Diurnal Curves

Transims Convert Trips routine

Local/Visitor trips Local trips


Activity Generation

Destination Choice

Mode Choice

Network Simulation

FirmSynthesis

Supplier Selection

Shipment Size

Mode Choice

Network Skims

Check Convergence

Results

NEXT STEPS


Next Steps

• Estimate models using statewide data• Trip Generation, destination choice with new regions/zones, mode choice• Develop and estimate Time-of-day Models / diurnal distributions

• Implement models in POLARIS simulation framework• Local travel ABM already implemented• Integrate long-distance travel, freight travel models

• Model calibration and validation• Run model in calibration iteration scheme• Match to ground counts, survey observations where possible

• Policy Scenario Analysis• Work with stakeholders to implement scenarios of interest for testing

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THANK YOU!

Illinois Department of Transportation

Documents

Illinois Statewide Travel Demand Model Technical Approach