MIT1_258JS10_lec11

MIT1_258JS10_lec11 - RIDERSHIP PREDICTION Outline 1....

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- - - Outline 1. Introduction: route ridership prediction needs and issues. 2. Alternative approaches to route ridership prediction. rofessional dgement Professional judgement Survey-based methods Cross-sectional models Time-series models 3. Examples of route ridership prediction methods: TC lasticity ethod TTC elasticity method "Direct Demand" models 4. IS ased Simultaneous quations Route evel Model GIS Based, Simultaneous Equations, Route Level Model 5. Network-Based Forecasting Methods TransCAD EMME/2 Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 1 RIDERSHIP PREDICTION
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1. as a result of fare aeeastct cacuato 1. Predicting ridership/revenue as a result of fare changes Predicting ridership/revenue changes systemwide prediction usually required -- f re elasticity calculation -- time-series econometric model -- best methods use two-stage, market segment model 2. Predicting ridership/revenue for general agency planning and budgeting purposes: systemwide prediction required -- trend projection -- time-series econometric model Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 2 Roles for Ridership/Revenue Prediction
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3. Predicting ridership/revenue as a result of service changes route-level prediction usually required service changes of interest include changes in: -- period(s) of operation -- eadway headway -- route configuration -- stop spacing -- service type (e.g., local versus express) e will focus on (short- n) route- vel prediction methods. We will focus on (short run) route level prediction methods. Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 3 Roles for Ridership/Revenue Prediction
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1. EXOGENOUS (uncontrollable): Auto ownership/availability & operating costs Fuel prices & availability Demographics (age, gender, etc.) Activity system (population & employment distributions, etc.) Usually can be assumed to be "fixed" in the short-run. Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 4 Factors Affecting Transit Ridership
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2. ENDOGENOUS (controllable): •F a r e • Headway (wait time) • Route structure (walk time; ride time) •C r o w d i n g * • Reliability* * Usually not explicitly accounted for in ridership prediction methods. Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 5 Factors Affecting Transit Ridership
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Traditional Approach: Exogenous Change: monitor ridership change Endogenous Change: modify system accordingly Reactive -- does not attempt to anticipate impacts prior to the exogenous/endogenous change occurring. Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 6 Route Ridership Prediction
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Current Practice: Little attention given to the problem in many agencies, except for fare changes and major capital projects Traditional urban transport planning models inappropriate and ineffective (generally not detailed enough and too complex to run repeatedly) • Ad-hoc, judgemental methods dominate Nigel Wilson 1.258J/11.541J/ESD.226J Spring 2010, Lecture 11 7 Route Ridership Prediction
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1. Professional judgement 2. Non-committal survey techniques 3. Cross-sectional data models Time-series data models 4.
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This note was uploaded on 12/06/2011 for the course ESD 11.380j taught by Professor Fredsalvucci during the Fall '02 term at MIT.

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MIT1_258JS10_lec11 - RIDERSHIP PREDICTION Outline 1....

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