Transcript Status of the SEMCOG E6 Travel Model presented to SEMCOG TMIP Peer Review Panel Meeting presented by Liyang Feng, SEMCOG Thomas Rossi, Cambridge Systematics December 12, 2011 Objectives Improve.

Status of the SEMCOG E6 Travel
Model
presented to
SEMCOG TMIP Peer Review Panel Meeting
presented by
Liyang Feng, SEMCOG
Thomas Rossi, Cambridge Systematics
December 12, 2011
Objectives
Improve key modeling components as needed to analyze
key projects and policies
Reflect the most recent available data
Implement 2004 TMIP peer review recommendations
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E6 Model Components
1. Trip Generation
4. Transit Model
2. Trip Distribution
5. Mode Choice
3. Time of Day
6. Commercial Vehicle Model
Data Sources
2004-2005 household survey (SEMCOG, MI Travel
Counts)
2010-2011 transit on-board survey
SEMCOG traffic count database
Information from transit providers (ridership counts,
schedules)
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Work to Be Done for All Model Components
Model estimation
Application programming (TransCAD)
Validation at component level
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E6 Status
Model
Objectives
Component
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Status
Trip
generation
Rates reflect most recent survey, explain
differences in subareas
Awaiting final
validation
Trip
distribution
Parameters reflect most recent survey, test
destination choice formulation
Gravity model
complete
Time of day
Consistency with most recent survey and
analysis needs for highway and transit
Awaiting final
validation
Transit
model
Consistency of parameters throughout process,
reflect recent transit survey
Awaiting final
validation
Mode
choice
Appropriate for New Starts analysis, capability
to analyze proposed new transit services
Estimation
nearly complete
Commercial
vehicle
Better reflect current commercial vehicle/truck
movements in region
Estimation
nearly complete
Trip Generation
Identified ways to improve the trip generation rates
Home based university trip purpose added
Parameters updated using household survey data
Factors used to separate non-motorized travel
Air passenger model updated
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Trip Generation
Income segmentation by quartiles for HBW, HBShop,
HBO – primarily for environmental justice analysis
HBSchool not sensitive to income – persons x children
HBU – Trip rates/person to 25 largest colleges by type by
distance
Attractions – reclassified employment types (Basic,
TCUW, Retail, Service, Educational, and Government)
HBU Attractions – based on total enrollment minus group
quarters population
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Trip Generation Validation
Initial validation showed trip productions in Monroe and
Livingston Counties substantially overpredicted
Calibrated area type adjustment factor (rural/non-rural)
Further adjustments regarding external travel to be
performed during system calibration
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Trip Distribution
Gravity model parameters recalibrated by trip purpose
(income segmentation for HBW, HBShop, HBO)
Logit destination choice model to be estimated
» Using the most recent data, test whether destination choice
model produces better results
» If so, implement and validate logit destination choice model
» If not, revalidate existing gravity model using recent data
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Time of Day
New time periods defined…
» Definitions useful for both highway and transit analysis
Period
Definition
AM
6:30-9:00 a.m.
MD
9:00 a.m.-3:00 p.m.
PM
3:00-6:30 p.m.
Evening
6:30-10:00 p.m.
Overnight
10:00 p.m.-6:30 a.m.
Factors reestimated using household survey data
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Time of Day Factors
AM
1HBW
1HBW
2HBO
2HBO
3HBSH
3HBSH
4HBSCH
4HBSCH
5HBU
5HBU
6NHBW
6NHBW
7NHBO
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From home
To home
From home
To home
From home
To home
From home
To home
From home
To home
From work
To work
All
MD
29.9%
1.2%
13.7%
3.9%
3.2%
0.7%
54.2%
0.1%
18.2%
0.0%
2.1%
12.2%
7.8%
PM
11.0%
7.4%
18.5%
12.0%
19.7%
22.6%
3.1%
16.3%
21.3%
16.4%
30.3%
18.2%
40.9%
3.4%
25.5%
13.7%
14.2%
10.7%
20.7%
0.7%
23.9%
9.7%
13.1%
26.6%
3.2%
33.1%
Evening Overnight
1.4%
8.9%
6.8%
4.5%
7.2%
1.4%
12.8%
2.8%
6.6%
1.0%
13.5%
1.3%
0.3%
0.5%
0.8%
0.1%
1.9%
0.8%
16.9%
1.8%
4.2%
1.3%
0.8%
1.1%
16.3%
1.9%
Transit Model
Focus on transit network parameters and path building
processes
Parameters:
» Times
» Fares
» Maximum access times
» Bus speeds
» Transfer rules
» Mode choice related
parameters
Used new on-board survey data
» Compared paths between survey and model
» Adjusted path building settings to improve match
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Transit Model Speed Definition
Using 2010 data, SEMCOG did a comparison between
model auto time and scheduled bus time for 145 routes
for AATA, DDOT, and SMART
Initial analysis adjusted to account for systemic
differences
Scheduled_bus_time = 0.917 * (Model_autotime) + .318 * (Model_stops)
Stop (dwell) time adjustments by operator
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Transit Walk Access Time
E5 model – Walk access capped at 18 minutes
Examined on-board survey data
Recommended increase to 36 minutes (about 90% of
observations after eliminating outliers)
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Transit Network and Path-Building Procedure
Checks
Reviewed survey data boardings and determine prevalence of
reported multipath transit use
Checked that all transit routes have non-zero flow
Constructed aggregate prediction success table of the reported
boardings per passenger trip with modeled boardings of paths
(prediction success rate = 73%)
Modified path building parameters to improve the path building
prediction success outcome
Recommended allowing park-and-ride in off-peak to better
balance daily O-D
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Mode Choice
Existing mode choice model needs to be evaluated:
»
»
»
»
»
»
»
Range of current and potential transit services
FTA New Starts analysis
Project impacts on population segments
Incorporation of transit model improvements
Use of recent data (counts, surveys)
Efficiency of model structure and procedures
Validity of results
Recommendations for structure, parameters of mode
choice model to be implemented
Reestimate/revalidate
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Mode Choice
Nesting Structure Tests
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Mode Choice
Nesting Structure Tests (continued)
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Handling New Modes in Mode Choice
Application
Arterial Rapid Transit (ART)
Bus Rapid Transit (BRT)
Light Rail (LRT), including on Woodward
Commuter rail (CRT) from Detroit to Ann Arbor
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Commercial Vehicle Model
Three-step model – generation, distribution, assignment
Prepared vehicle classification count data – adjusted for
growth/decline in region
Adjusting parameters to reflect current data
Adjustments to reflect changes in external station
volumes
Revalidating
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System Calibration
Validate individual components as they are developed
Use recent data to see “what has changed”
» Enhance short-term forecast capability
Get the “big picture” correct
Examine “trouble spots” from previous model versions
Make sure forecasts make sense
Expected completion – March 2012
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