Justifying Rail Bias Factor for Houston METRO’s Transit Model

Presentation by

Vijay Mahal, HDR Inc

Vincent Sanders, Houston METRO

May 18, 2009

TRB Applications Conference

Houston, Texas

Outline of Presentation Houston’s Long range Transit Plan- MetroSolutions Houston’s first Light Rail Line- Phase I

Description Ridership profile

Houston METRO’s travel model – earlier version Need for Rail Bias factor Incorporation of Rail Bias Model application / Conclusions

Houston METRO’s Long Range Plan

Development of METROSolutions Voter referendum Different Phases The Redline (Phase 1)

Houston’s first LRT opens January 2004

Houston’s first Light Rail

Starter line is 7.5 miles long 16 stations Serves very important activity

centers (CBD, TMC, UH, HCC, Rice)

High frequency Uses German-made Siemens cars Top speeds achievable: 66 MPH

Stations a few blocks apart in CBD and about one mile apart outside of CBD

Uses Clean electric power Moves large volumes of people Operates faster than local buses

LRT Ridership Profile

Actual Ridership (Average Weekday Boardings, 2004 to 2007)

-

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

Jan-04 Mar Jun Sep Jan-05 Mar Jun Sep Jan-06 Mar Jun Sep Jan-07 Mar Jun Sep

Forecasts from Earlier Models

LRT ridership forecasts made during the 1990s Opening year (2004) forecast= 22,000 2020 forecast = 40,000 Actual ridership in September 2004 = 33,000 Actual current ridership = 44,000 The Red Line is considered the most successful line in the

country. Nov. 2007 Breaks 40 Million boardings

Sept. 2008 Breaks 50 Million boardings

So, what factors make this line so successful?

Travel Surveys

1st Survey in Sept 2004 (elaborate OD survey) 2nd Survey in November 2004 (Supplemental survey to

collect data on new riders) 3rd Survey in 2007 (OD survey to update model

parameters)

The 2004 O/D survey format

19 Question survey Basic demographics question were asked

age, gender, income, ethnicity, available vehicles Access-Egress patterns, type of trips, transfer, types of

fare media, how long & often have you used transit One question not asked: Were you a METRO

user before METRORail?

Supplemental Survey Instrument

Highlights from SurveySurvey conducted from November 8th 2004  to November 17th 2004

Total surveys handed out = 13,000Valid surveys returned = 5,940  (very high response)

Percent of new riders (Question 1 of survey) was 37 %

Were you a METRO bus/shuttle rider before METRORail opened?

Houstonians Offer Positive Comments

Survey response:• 90 % comments were positive• 3 % neutral, 7 % negative• 36 % wanted immediate system expansionMost cited reasons for positive response• reliability• comfort / smoothness of ride• faster than bus

Houston METRO’s original Mode Choice Model

Light Rail data was never a part of Houston METRO’s mode

choice model estimation Primary Data Sources

1985 Home Interview Survey 1990 Journey to Work Census data 1995 and 2001 On-board OD surveys Periodic ride check data

LRT came into existence in January 2004 None of the data set contained the rail attributes

Earlier Planning Studies

Major Planning Studies for this region began in the mid 1990s

For the Red line, Alternative Analysis & Environmental documents in progress Travel forecasts were needed urgently for a mode that didn’t exist Modifications made to the mode choice model to accommodate LRT

(quick fix)

Structure of the original Mode Choice Model

Structure of the revised Mode Choice model

Mode-Specific Constants

Utility equationsOriginal Model U LB = C1 x Inveh TT + C2 x Out-of-veh TT + C3 x Fare

U CB = K 1 + C1 x Inveh TT + C2 x Out-of-veh TT + C3 x Fare

Revised Model U LB = C1 x Inveh TT + C2 x Out-of-veh TT + C3 x Fare

U CB = K 1 + C1 x Inveh TT + C2 x Out-of-veh TT + C3 x Fare

U EX = K 2 + C1 x Inveh TT + C2 x Out-of-veh TT + C3 x Fare

U LRT = C1 x Inveh TT + C2 x Out-of-veh TT + C3 x Fare

Purpose of Mode Specific constant

Most models consider travel time, travel costs and some measurable service characteristics (# of Xfers)

Visibility, comfort of ride, reliability, passenger amenities etc. – are non-quantifiable and not considered

In theory, mode specific constant is supposed to represent the non-quantifiable factors

Misuse of Mode Specific constant : correction factors (for calibration purposes)

Underestimation of LRT forecasts

LRT forecasts were significantly underestimated when its mode bias constant was zero

Opening year (2004) forecasts = 22,000 daily boardings Horizon year (2020) forecasts = 40,000 daily boardings Current (2009) actual ridership = 44,000 daily boardings What was the most likely reason for the underestimation? Survey results clearly demonstrated Houstonians perceive LRT

as a Premium mode. So, a mode specific constant was introduced to represent the Rail Bias.

Incorporating Rail Bias Factor

Methodology Started with a rail bias factor of 0.33 used in the old

Minneapolis and San Diego models. In terms of IVTT, this equals 6.73 min in our models.

Run model with and without LRT network Analyze ridership and new trips and compare them to

survey results Increase bias factor if necessary and repeat the process Final factor was 0.50 (equal to 10.2 minutes of IVTT)

Calibration Results (Yr 2004)

Iteration Rail Bias factor

Value of Rail Bias in terms of in-vehicle

time

Estimated LRT

ridership

Increase in system

wide linked transit trips (new trips)

Portion of rail trips that are

new to the transit system

1 0 0 22,000 4,600 20 % 2 0.33 6.73 29,400 8,200 28 % 3 0.40 8.16 30,700 9,800 32 % 4 0.45 9.18 31,500 11,000 35 % 5 0.50 10.2 33,000 11,900 36 %

Model Application

Application results

Observed rail boardings in October 2008 (from APC System)

41,000

Estimated rail boardings from the model for 2008 (with Rail Bias

42,000

Projected rail boardings from the model for 2020 (with Rail Bias)

67,000

Projected rail boardings from the model for 2030 (with Rail Bias)

81,000

Rail Bias Impacts on New Starts projects

The North Corridor Extension

Rail Bias Impacts

Ridership projections increased by about 20 to 25 percent

User Benefits increased by about 15 percent Distribution of user benefits improved Most diagnostic statistics from SUMMIT reasonable

Conclusions

Travel surveys reveal Houstonians perceive LRT as a premium mode. Its ride quality, reliability, station amenities are as important to them as its travel times and frequency. They have a positive Bias towards rail.

The rail bias factor in Houston is worth approximately 10 min of IVTT in comparison to local bus mode

Rail Bias can improve User Benefits and ridership- in our case about 15 to 20 percent