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 presented to

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 2

E6 Model Components 1.Trip Generation 2.Trip Distribution 3.Time of Day 4.Transit Model 5.Mode Choice 6.Commercial Vehicle Model

Data Sources household survey (SEMCOG, MI Travel Counts) transit on-board survey SEMCOG traffic count database Information from transit providers (ridership counts, schedules) 4

Work to Be Done for All Model Components Model estimation Application programming (TransCAD) Validation at component level 5

E6 Status 6 Model Component ObjectivesStatus 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 7

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 8

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 9

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 10

Time of Day New time periods defined… »Definitions useful for both highway and transit analysis Factors reestimated using household survey data 11 PeriodDefinition AM6:30-9:00 a.m. MD9:00 a.m.-3:00 p.m. PM3:00-6:30 p.m. Evening6:30-10:00 p.m. Overnight10:00 p.m.-6:30 a.m.

Time of Day Factors 12

Transit Model Focus on transit network parameters and path building processes Parameters: 13 »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

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 Stop (dwell) time adjustments by operator 14 Scheduled_bus_time = * (Model_autotime) * (Model_stops)

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) 15

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 16

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 17

Mode Choice Nesting Structure Tests 18

Mode Choice Nesting Structure Tests (continued) 19

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 20

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 21

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