Materials developed by K. Watkins, J. LaMondia and C. Brakewood Understanding Changes in Ridership Unit 7: Forecasting & Encouraging Ridership

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Operators Strive For Increased Ridership More Passengers = Better Service – Good measure of performance? Challenge with Costs – Need to improve and support additional demand for service

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Reoccurring Questions If we make changes in transit service, how will it impact ridership? Defining travel behavior and quantifying travel demand. How do we proactively address both of these developments?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Goal of Understanding Ridership Tells us which changes are worth the time and monetary investments Helps us anticipate how transit system operations will evolve Improves our planning process for selecting the best alternative

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Class Question… What factors of a transit system affect ridership? Do these factors vary for......travel during different times of day?...different types of riders?...different trip purposes?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Describing these Relationships Important to characterize exactly how changes in the transit system will affect how passengers use the transit system – Varies by region – Based on many combined factors

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Regional: Ridership by Neighborhood

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Location: Park & Ride Users

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Transit Travel Behavior …is defined as: the direct and indirect ways in which patrons use the transit system, including: – Number of trips – Destinations – Activities – Trip patterns – Timing

Materials developed by K. Watkins, J. LaMondia and C. Brakewood TRAVEL DEMAND is an Important Topic …is defined as: the size, composition and distribution of transit ridership Often the predominant focus of transit planners

Materials developed by K. Watkins, J. LaMondia and C. Brakewood A Future-based Process Travel behavior today – Look outside! Travel behavior tomorrow – Need to guess at: Potential changes How people will react How Do We Do This Accurately?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Future is Based on Behavioral Considerations Estimation of future travel behavior is only as accurate as the estimates of future development Estimates are based on current conditions and behavior; assume people act the same in the future Estimates are determined by external factors as well as the type of system, so need to consider both Behavior is inherently individual, so should consider personal preferences/ biases

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Behavior Review Process Collect Information on Past Trends Develop Relationship Trends Estimate Future System Changes Predict Future Travel Behavior

Materials developed by K. Watkins, J. LaMondia and C. Brakewood General Factors Affecting Travel Behavior Transport System Context Transit Service Characteristics Transport Policies/ Perception Region-level Individual-level

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Transport System Context Population characteristics Economic conditions Cost & availability of alternative modes Land use & development patterns Travel conditions

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Transit Service Characteristics Service adjustments/ improvements Partnerships & coordination Marketing, promotion and information initiatives Fare collection & fare structure initiatives

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Transport Policies/ Perception Price & availability of modes Quality of service of modes Characteristics of desired trips Traveler motivation/ bias

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Relationships are not always clear Confounding factors – Additional factor that is the cause of behavior but is highly correlated with other factors Lurking factors – Additional factor that is the cause of behavior but is missed in analysis

Materials developed by K. Watkins, J. LaMondia and C. Brakewood What is the Gas Price Tipping Point?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood TCRP Report 128 National review of transit travel behavior based on TOD characteristics Found factors most influential: – Station proximity – Transit speed, frequency, comfort – Parking policies (high prices, constrained supply)

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Behavior Trends

Materials developed by K. Watkins, J. LaMondia and C. Brakewood TOD Characteristics & Sensitivities

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Methods for Analyzing Behavior Transfer of Past Trends Supply/ Demand Elasticities Demand Forecasting

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Transfer Trends: Time Use & Scheduling Think about the last time you used transit... – When during the day was your trip? – How did it fit into your day? – How far did you travel? – Did you have to plan in advance? – Was it faster than driving?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Time Use & Scheduling

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Time Use & Scheduling

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Insert scan Vuchic page 35 Time Use & Scheduling

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Insert scan Vuchic page 35 Time Use & Scheduling

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Collecting Schedule Data

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Analysis Methods Many ways to measure this – Timing of Transit Trips Discrete choice models – Length of Transit Trips Hazard duration regression – Chaining of Transit Trips Microsimulation techniques

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Time Use/ Scheduling Insert scan Vuchic page 36

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Supply & Demand Supply: – Number of patrons able to be served by transit at a given price/ travel time Demand: – Number of patrons interested in using transit at a given price/ travel time

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Quantifying Functions V – Transit Passenger Demand – Units: Passengers C – Generalized Cost – Units: Dollars (or minutes) – Includes actual and perceived costs

Materials developed by K. Watkins, J. LaMondia and C. Brakewood The Equilibrium Point…

Materials developed by K. Watkins, J. LaMondia and C. Brakewood …Constantly Moves!

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example 1 The travel time on a transit route from a major neighborhood to the downtown area has been observed to follow a supply trend of: The demand trend for travel between the two areas is: Determine the equilibrium travel time and passenger volume that will result on this route. Assume time is in generalized minutes and volume is in passengers per hour.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example 2 If the transit agency wants to add in a 5 minute layover at a stop & ride between the neighborhood and the city center, what impact will that have on ridership?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Elasticities Demand (and price) sensitivities are measured using elasticities Demand elasticity is… the percentage change in the number of riders as a result of a one-percent change in price (or some other factor) Simpson-Curtin rule, 3% fare increase reduces ridership by 1%

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Elasticity Equation

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Elastic/ Inelastic Threshold Range from -∞ to +∞ + indicates an increase in ridership due to the 1% factor change -indicates a reduction in ridership due to the 1% factor change <|1| indicates inelasticity, with little impact >|1| indicates elasticity, with high impact

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Cross-Elasticities

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example 1 Estimate the ridership elasticity for a park-ride shuttle service based on results from a cost- change experiment. Is it elastic? The shuttle currently operates with 15 minute trips and serves 45 people during the peak hour. When these people were asked if they would continue using the service if trips were made every 20 minutes, 36 said they would.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example 2 Estimate the ridership cross-elasticity for a park- ride shuttle service based on results from a cost- change experiment. Is it elastic? When the same people were asked if they would continue using the service if the time it took to drive directly to the terminal was reduced from the current 30 minutes to 20 minutes, 25 agreed to stay with the park-ride shuttle service.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Demand Forecasting: The 4-Step Method Trip Generation (Frequency) Trip Distribution (Destination) Mode Split (Mode) Traffic Assignment (Route)

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Collect Data Describing a Specific Area Predict % of Transit Riders Predict Total # Travelers Determine the Total # of Transit Riders per Timeframe Predict # Transit Riders Methods for Forecasting Demand

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Collect Data Describing a Specific Area Predict % of Transit Riders Predict Total # Travelers Determine the Total # of Transit Riders per Timeframe Predict # Transit Riders Methods for Forecasting Demand

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Two Approaches System Demand Approach Individual/ Aggregate Large Spatial Scale – Typically a city or region Policy/ Choice Analysis – Mode choices – Influence on congestion Route Level Demand Approach Aggregate Local Spatial Scale – Typically a route or stop Volume Analysis – Stop arrivals – Rail system studies – Ridership along route

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Collect Current/ Past Behavior & Characteristic Data Collect/Project Future Characteristic Data Predict Future Behavior Apply Demand Model to Future Characteristic Data Estimate Relationships Between Characteristics & Behavior (DEMAND MODEL) General Demand Forecasting Process

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Data Collection System Demand Approach O-D Survey Region level estimates and assumptions for the future growth Route Level Demand Approach Route Opinion Survey Route only estimates and assumptions for future growth

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Factors Affecting Demand Service related variables tend to overwhelm demographic and employment factors – Fare costs – Travel times – Wait times – Access/ egress distances

Materials developed by K. Watkins, J. LaMondia and C. Brakewood SDA: Predicting Total # of Travelers Use information about a region to predict the number of total travelers from that region – Considers all modes

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Cross-Classification & ITE Trip Generation Identify land uses and predict trips generated from these land uses Three techniques: – Rate, Regression, Match

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example of ITE We have a beachside townhome resort for 800 people being developed. The community will be served by rail, ferry and bus service, and we expect all the residents to use these modes. Calculate the total number of travelers we can expect during the AM peak period.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example of ITE

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example of ITE

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example of ITE

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example of ITE

Materials developed by K. Watkins, J. LaMondia and C. Brakewood SDA: Predicting % of Transit Riders Use information about modes to predict the share of travelers that will take transit – Considers CHOICE among all modes

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Discrete Choice Models Based on utility of each option: NOTE: Always uses a base case, with a relative utility of zero

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Discrete Choice Models Predict share based on relative utility of each mode:

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example Calculate the shares of travelers that choose Rail, Bus, or Ferry.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood SDA: Predicting # of Transit Riders Combine number of travelers and share using transit to generate total number of travelers on a route

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example Calculate the number of people that we can expect on each of the transit options from the beachside community.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood RLDA: Predicting # of Transit Riders Use information about a region to predict the exact number of transit riders on a given route – Gets straight to the number we’re looking for – Requires detailed route estimation – Usually site specific

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Example Calculate the number of riders we expect to take the bus during the AM peak, using the ridership forecast equation: Interpret the coefficients in this demand model

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Which Method for Understanding Ridership is BEST? It all depends... – How much information do you have? – How accurate is your analysis? – What is the scale of your analysis? – How will it be used?

Materials developed by K. Watkins, J. LaMondia and C. Brakewood An EXAMPLE

Materials developed by K. Watkins, J. LaMondia and C. Brakewood

Conclusion Incentives to ridership depend and the population, the geographic area, and the time of day. It is important to determine travel demand to plan for future service. The elasticity of demand is the marginal increase in ridership as a function of improved service or decreased price.

Materials developed by K. Watkins, J. LaMondia and C. Brakewood Reference Materials in this lecture were taken from: National Transit Database, U.S. Energy Information Administration's Gas Pump Data History, and Bureau of Labor Statistics' Employment Data. Arrington, G. B., and Robert Cervero. "TCRP Report 128: Effects of TOD on Housing, Parking, and Travel." Transportation Research Board of the National Academies, Washington, DC 3 (2008). ITE, “Trip Generation Manual”