Business Logistics 420 Public Transportation Lecture 18: Demand Forecasting

Lecture Objectives Provide understanding of the role of demand forecasting in transit planning and operations Outline basic techniques for short range demand forecasting Explain long-range 3C Planning Process demand forecasting techniques

Demand Forecasting Needed for all types of transportation planning to determine the number of persons or vehicles using a particular service, route or corridor. Demand vs. need –Demand is the actual use of a facility or service –Need is the potential demand that may not turn into actual demand

Three Aspects of Demand Must be Forecast Spatial arrangement of demand – where do people want to travel – origins and destinations (OD) Temporal arrangement – when do people want to travel – peak/off-peak Absolute level of demand number of passenger or vehicle trips per time period

Uses for Transit Demand Forecasts Design routes – spatial arrangement of demand Determine schedules – absolute level and temporal arrangement of demand determines number vehicles required per time period

Uses of Transit Demand Forecasts (Continued) Vehicle size and number – absolute level and temporal arrangement of demand determine number and size of vehicles required Financial budgets – ridership and revenue estimates based on absolute level forecasts

Common Forecasting Methods Used for Short-Range Demand Forecasts Judgmental method – "expert" opinion based on supply (service) factors and demand factors Comparative Demand method – forecasting by analogy – estimating demand based upon similar routes

Example of a Demand Forecast Using the Comparative Demand Method Ridership = number of residents within 1/4 mile of the route times the transit response rate (trips/capita) If population within 1/4 mile of route is, say, 2,000 and for a similar service area (demographics) and service type (frequency of service, fare, etc) the number of passengers trips per year per resident is 4.3, then an estimate of annual ridership would be 2,000 x 4.3 or 8,600 annual passenger trips.

Non-Committal Survey Method Ask potential riders if they would use new service. Commonly used but not very accurate. Almost always overestimates actual ridership

Reasons for overestimate using non- committal survey methods No commitment required – people give answer without serious consideration of what would be required to use the service Actual service different from that described or imagined – different frequency, convenience, price How to overcome bias – reduce non- commitment response by a factor as high as 1/7th or 1/10th

Example of Non-commitment Response Method A survey of residents in an area being considered for transit service gives the following response: –Would use the service daily (10 times/week) 10% –Would use the service once or twice a week (assume 4 trips/wk) 15% –Would use the service once a month (assume 2 trips per month) 10% –Would never use the service 65%

Example of Non-commitment Response Method The area being considered has 2,000 residents so, based on the questionnaire responses the annual ridership would be: Daily users – 10 per week x 52 weeks x 10% x 2, ,000 trips Weekly users – 4 per week x 52 weeks x 15% x 2,000 62,400 Monthly users – 2 per month x 12 x 10% x 2,000 4,800 Total Non-committal demand 171,200

Example of Non-commitment Response Method Total Non-committal demand 171,200 Non-committal bias adjustment – 1/7th (.14) Best estimate of demand.14 x 171,200 23,968, say 24,000

Successive Overlay Technique A method of identifying spatial demand for transit Mapping of key socioeconomic and demographic factors can help with route design by identifying areas most likely to generate transit ridership

Successive Overlay Technique (Continued) By mapping data from census and land use plans, the transit analyst can identify areas of highest demand potential – use Census tracts or enumeration districts as areal unit for mapping. Map key transit use-related factors such as auto ownership, income, age, and also key transit generators such as shopping centers, major employers, low income and senior citizen housing, multi-family housing, hospitals, clinics, etc.

Chicago Multi-family housing and household income by Census Tract

Chicago 1990 Population Density Chicago Transit Use and Population Density 1990 by Census Tract

Approaches to Long-Range Planning Demand Forecasts Try to answer the question of what will be the travel patterns in 20 years, what modes will people use, and what volume of traffic will be on each highway or transit route A four-step “3C” Planning Model approach is usually used

The “3C” Modeling Process Consists of Four Steps Trip Generation – using socioeconomic information models estimate total number of trip origins and destinations for analysis zones Trip Distribution – these models distribute work and non-work trips between zones based on a number of different factors

The “3C” Modeling Process (Continued) Mode split – a mode split model divides total trips by mode based primarily on relative costs and time for each mode Trip Assignment – trips by each mode are assigned to specific routes or highway links based on minimum time or distance routing algorithms.

The “3C” Modeling Process (Continued) The 3C process is most often used for long range highway and transit plans – too costly to collect data, and not accurate enough for route-level transit demand forecasts. Requires many projections of land use, population, trip-making behavior Self-fulfilling prophesy charge is often made -- by projecting demand and building facilities, the demand will materialize

Transit Demand Forecasting Summary Demand forecasts are necessary for a number of short and long range planning needs The methods currently available are often too imprecise or inaccurate to meet our needs Sophisticated techniques require extensive data, take a long time to implement and are costly Often a very crude forecast using comparable systems is the best approach

Study Questions If you were asked to estimate ridership for a new route that CATA was considering between State College and Philipsburg, what method or methods might you consider? Why? What data would you need? One way to judge how much effort/money you should spend to estimate demand for transit is to consider the cost of an error if you make an incorrect forecast. In the case of the proposed Philipsburg bus service, how much (not in exact dollars, rather, very little, moderate, significant amount) would you be willing to spend on a demand study? Why?

Study Questions If past market research surveys were not judged to be a very accurate way to gauge potential for new services, do you think there are ways that the methodology could be improved (Ask better questions, get a new survey firm, etc) or do you think there will always be problems with asking people about future intentions?

Study Questions Describe the 3C demand forecasting process What are some of the key assumptions that are made as part of this modeling? What are some of the problems with this approach?