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 noncommittal 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 noncommitment 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
104,000 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 -- 1990
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?