qz qc mrt ridership

MRT Ridership (and Revenue) Forecast Quality Assurance/Control: 

MRT Ridership (and Revenue) Forecast Quality Assurance/Control

What’s the Problem?: 

What’s the Problem? Consistent, world-wide record of ridership (and revenue) forecasts made at time of “go” decision for MRT projects being far too high Not a random process of an equal number of “actuals” being over and under forecasts

Slide3: 

Is anti-democratic and unethical Skews decision-making May result in over-investment, in wrong system, in wrong place Can create unexpected financial burden for local and national governments May prevent investments with greater return from being made May not mean projects are necessarily “bad,” but situation:

Actual (adjusted**) Vs. Predicted Ridership for U.S. MRT Projects*: 

Actual (adjusted**) Vs. Predicted Ridership for U.S. MRT Projects* *Source: USDOT/FTA, 2005 ** “Actuals” adjusted to 2005

Actual (adjusted**) Vs. Predicted Ridership for U.S. MRT Projects *: 

Actual (adjusted**) Vs. Predicted Ridership for U.S. MRT Projects *

Not Just an Issue in USA: 

Not Just an Issue in USA * Prof. Phil. Sayeg, University of Queensland, “Review of Patronage Ramp Up for Recent Asia-Pacific Mass Rapid Transit Systems,” SMART Urban Transport Conference Proceedings, November 2001 ** Prof. Phil. Sayeg, University of Queensland, David Bray, Consultant, Integrating Mass Rapid Transit in Bangkok: Options Report (ADB TA), 12/2005 ++ Has grown significantly since

Not Just an Issue in USA: 

Not Just an Issue in USA “How (In)Accurate are Demand Forecasts in Public Works Projects;” Fyvberg, Holm, Buhl; Journal of Amer. Planning Assoc. Spring 2005 50 25 15 10 5 20 0 - 40 40 80 160 -80 Frequency % Inaccuracy Of MRT Ridership Forecasts 120

What are the Causes?: 

What are the Causes? Not the lack of fundamental technical knowledge; Fifty+ year knowledge base, including 2000 Nobel Economics Prize-winning work by Dan McFadden of U. Cal. Berkeley Not unexpected “acts of G-d”

What are the Causes?: 

What are the Causes? Compound “optimism” in virtually every part of forecasting process Input assumptions Structure, development and application of models

Compound optimism: Input assumptions : 

Compound optimism: Input assumptions Population, employment Totals (forecasts too high) Allocation within regions to sub-areas (forecasts too focused on CBD and other nodes) Representation of land use, densities Assumed pedestrian orientation too great Assumed development too dense and too clustered near stations

Compound optimism: Input assumptions : 

Compound optimism: Input assumptions MRT levels of service, capacity Walking distances Availability of feeder services Fares, parking charges, gasoline prices Public transport competition Auto competition

Slide12: 

To understand methodological issues, must understand forecasting process.

One Common Structure:Four-Step Travel Model: 

One Common Structure: Four-Step Travel Model Trip Generation (Trip Frequency) How many Trips? Distribution (Destination Choice) O/D Volumes Mode Choice Network Description P.T. Highway Pub. Transport Assignment (Path Choice) Link, Line Volumes Highway Assignment (Path Choice) Link Volumes Land Use Urban Activity Demographics

Slide14: 

QA/QC

First, Review Methodological Issues: 

First, Review Methodological Issues Treatment of: Walk access Transit system capacity Park and ride lot capacity Impact of transfer requirements Impact of size of traveling group (auto occupancy) Are models properly structured and validated?

Second, Analyze Inputs and Results: 

Second, Analyze Inputs and Results Check inputs and results from every stage of process Are expected/forecast changes reasonable? Are forecasts reasonable, in the absolute, when compared to current “actuals” elsewhere in city (e.g., another MRT line) or in other, analogue cities?

Parameters to Focus on:: 

Parameters to Focus on: Input Assumptions Population, employment, motorization Densities, allocation of growth to sub-areas, site plan assumptions Extent and capacity of whole system; Is everything assumed to be there (e.g., feeder bus routes) actually going to be? Public transport, taxi and auto “competition;” i.e., levels of service, out of pocket costs

Analyze More than Final Volumes:: 

Analyze More than Final Volumes: Review all results Aggregate trip rates Trip lengths Transfer Requirements Mode shares Regional Sub-area Daily and peak hour travel volumes Regional totals Corridor (screen line, cut line) Accumulations (cordon line) Comparisons of demand forecasts and capacity

Slide19: 

Example: Is This Reasonable???

Sensitivity Analyses: 

Sensitivity Analyses Focus on key parameters whose future values are uncertain Fares Fuel, parking prices Pop., employment totals and sub-regional allocations Public transport and highway levels of service Perform analyses of changes in individual parameters and comprehensive “best/worst/likely case” scenarios or use Monte-Carlo approach Evaluate changes and calculate implicit elasticity's where appropriate

Compare Implicit Elasticity's Against Historic Records.: 

Compare Implicit Elasticity's Against Historic Records. From same city; From other cities using secondary resources TCRP Report 12, Traveler’s Response to Transportation System Changes, Pratt et al TRL Report 593, Demand for public transport; a practical guide.

Is this Reasonable???: 

Is this Reasonable??? *TRL, TCRP

Need for Better Q/A – Q/C is not Unique to Ridership: 

Need for Better Q/A – Q/C is not Unique to Ridership * “Underestimating Costs in Public Works Projects;” Flyvberg, Holm, Buhl; Journal of American Planning Association, Summer 2002, Cost Escalation% Frequency %

Possible Policy Changes: 

Possible Policy Changes Require providers to perform and document explicit Q/A – Q/C process, including analysis by totally independent reviewer(s); Require proponents to perform and document explicit sensitivity analyses, especially with all uncertain inputs consistently pessimistic; Make initial planning consultants ineligible for subsequent project development work.