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WorldBank Workshop and Training on Urban Transport Planning
and Reform. Baku, April 14-16, 2009
Market Factors and Demand Analysis
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WorldBank 2
Market Factors
The market for Public Transport is affected by a variety of factors
No two cities or even neighborhoods are the same in terms of these factors
Different combinations of factors generate the need for different types and levels of Public Transport service
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Factors Affecting Market for Public Transport
Travel characteristics
Land use
Trip maker numbers and demographics
Public Transport service parameters
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Why is It Important to Understand Market Factors?
Helps in estimating Public Transport
ridership
linked to performance, revenue, financial
sustainability
measure of benefits
Essential for planning and design
Facilitates performance analysis
?
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Travel Characteristics
Purpose
Time-of-Day
Origin/Destination
Time of Day
Pa
ss
en
ge
rs o
r S
ea
ts
Demand (Passengers)Supply (Seats)
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Trip Purpose Impacts PT Use
Non-Work
Shopping, personal business, medical, recreational, religious
Occasional trips — 1-3 times/week
Discretionary trips means users can forgo them, change timing or combine them
People often travel as group, e.g., family
Work/School trips
Recurring (e.g., 5 days/week)
Not-discretionary, more tightly scheduled
Workers/students travel as individuals
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Travel Time-of-Day
Peak — Morning/Afternoon Commuting Hours Higher demand/unit time
High percentage of work trips
More individual travel
Choice and captive riders
Off-Peak — Midday, Evening, Weekend Hours Lower demand
More non-work travel
More group travel
Captive riders
Safety, security issues
Time of Day
Pa
ss
en
ge
rs o
r S
ea
ts
Demand (Passengers)Supply (Seats)
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Time-of-Day Demand Affects Bus and Facility Utilization
Time of Day
Pa
ss
en
ge
rs o
r S
ea
ts
Demand (Passengers)Supply (Seats)
• More peak, less off-peak service operated
• Inefficient use of buses and facilities• Low service hours/bus
• Low passengers/bus
• Unused capacity during off-peak periods
• There are strategies to address this problem
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Some Areas Have “Flat” Demand
Time of Day
Pa
ss
en
ge
rs o
r S
ea
ts
Demand (Passengers)Supply (Seats)
• Relatively constant service operated•e.g., Casablanca
• Efficient use of buses and facilities•High service hours/bus• High passengers/bus• Capacity efficiently used
during all periods
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Urumqi, China2006 O/D Survey Results
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Urumqi, China
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Manila EDSA Bus Users
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Distance from Origin to Destination
Extremely short trips (2 km) mostly made by walking
Bicycles viable option up to 8-10 km
Conventional bus trip lengths generally 5-10 km in developing cities
Suburban rail trip length average over 10 km
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Urumqi, China Trip Times2006 O/D Survey
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Land Use
Intensity/Density Residential (Origin)
Activity Center (Destination)
Availability of safe, secure walking environment
Residential Activity Center
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Origin/Destination
Public transport works best for trips between: High density, “walkable” residential and High density “walkable” non-residential areas
(e.g., traditional central business districts) That are arranged linearly along major
arterials
Traditional public transport does not serve well trips between: Low density residential areas and Low density employment areas That are arranged randomly
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Land Use Variations in Manila
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Land Use Variations in Beijing
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Important Demographic Characteristics
Income
Gender
Age
Labor force/student population
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Income Is Most Important Demographic Factor
Low Income Affordability
A problem when fares > 10% to 20% of income
Concessionary fares sometimes help
Alternatives are walking, bicycling
Medium Income Affordability is 3% to 5% of income
Taxis, two-wheelers and sometimes autos are alternatives
High Income Autos are an alternative
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Bogota Travel by Income Group
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Gender
Men are a larger proportion of PT riders in developing (not developed) cities
Lower proportion of women working
Higher proportion of women on weekends when non-work trips increase
Religious rules
Women’s safety/security concerns
Lighting at stops
To stop/from stop
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GenderManila Edsa Bus Users
62.3%59.4%
37.7%40.6%
0%
10%
20%
30%
40%
50%
60%
70%
Weekday Weekend
Male Female
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Age
Majority of PT users between 16-40
Workers
Students
Fewer older workers, students
They may have money for taxis and other forms of private transport
More younger travelers on weekends
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Age Profile Manila Edsa Bus Users
0%
5%
10%
15%
20%
25%
10-15
16-20
21-25
26-30
31-35
36-40
41-45
46-50
51-55
56-60
61-65
66-70
70+
Age
Weekday Weekend
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Public Transport System Factors
Levels and quality of PT service
Travel times, reliability
Comfort, amenities
PT Fares
Availability of safe, secure non-motorized access
If affordable, availability of other options
2-, 3- and 4-wheeled taxis
Private motor vehicles two-wheelers, autos
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All travel time not the same
Waiting, transferring and walking time much more onerous
Reliability may be more important than average travel time
Crowding a key quality factor, particularly for:
Women
Older people
Higher income travelers with choices
Levels and Quality of PT Service
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Availability of Safe, Secure Non-Motorized Access
Pedestrian access conditions
Sidewalk coverage and repair
Crossings
Bicycle facilities
Bikeways
Bicycle parking
Beijing
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Availability and management of safe, secure access and waiting facilities are important determinant of PT use (particularly for women and senior citizens)
Why? Pedestrians and bicycle users
Large % of traffic injuries and deaths
People going to/from/waiting for PT
Large % of non-motorized travel deaths
Perception and reality of crime
Safety, Security and Traffic Management
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Passenger Information a Key Service Quality Parameter
People need to be aware of options Routing
Schedules
Fares
Many trips are non-recurring, making PT use difficult Non work
Visitors
Tourists
A big issue in developing cities for local bus and small bus services
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Why is Demand Estimation Needed?
Ridership critical planning and design parameter
Assess the passenger and revenue impacts of new services and facilities
Assess the passenger and revenue impacts of service changes
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Demand Estimation Techniques for Short-Medium Term Service Changes
Similar routes method
Apply existing service experience to a service change
Statistical models
Develop formula relating existing demand to existing service parameters
Elasticity models
Apply percent change to current ridership based on change in a fare or service parameter
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Similar Routes
Method Ridership on proposed service will reflect ridership on an existing service
Estimation
1. Select similar service based on (typical):
Population density
Generators served
Service design (e.g., intervals, span)
2. Adjust ridership for differences
Service levels
Rider potential
≈
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Example of Similar Routes
Problem Estimate ridership for a new route that will provide bus service between La Source (an edge town) and Orleans.
Solution
1. Collect data for a similar route
≈
New
Route Route 12
Population/Square Kilometer 15000 17000
Daily Kilometers 1600 1800
Daily Passengers ? 3125
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2. Calculate ridership rate for Route 12
Ridership rate = Daily passengers / Daily kilometers
= 3125 / 1800
= 1.74 passengers/KM
3. Calculate potential users for new route as a percent of Route 12 population density
Potential (%) = Population density (New route)/
Population density (Route 12)
= 15000/17000
= 88.2%
Example of Similar Routes≈
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4. Estimate ridership rate for the new route
Ridership rate = Route 12 ridership rate x Potential %
= 1.74 passengers/KM x 88.2%
= 1.53 passengers/KM
5. Estimate daily ridership rate for the new route
Ridership rate = New route ridership rate x daily kilometers
= 1.53 passengers/KM x 1600 KM
= 2448 passengers (or 2400)
Example of Similar Routes≈
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Key Issues Similar Routes Method
1. Identification of key differences between existing and new route
2. Approach used to adjust for differences
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Statistical Models
Method Based on ridership on existing routes and key service and demographic variables
Ridership = B + A1X1 + A2X2 + ... +A3X3
Estimation
1. Collect data on existing routes• Socioeconomic variables — e.g., income
• Land use variables — e.g., population
• Service variables — e.g., headway
• Daily ridership
2. Statistically “calibrate” model, develop
mathematical parameters
3. Apply model
r2 = 0.74
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Example of Linear Regression
Y
B
XPopulation within 300 Meters of the Route
Rid
ers
hip
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Elasticity Models
Method Elasticity is the ratio of the percent change in ridership to the percent change in a transit service parameter (e.g., fares, service levels)
RBefore
RAfter
FAfter
Fares
Rid
ers
FBefore
Demand Curve
e
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Summary
Discussed factors that affect public transport demand
Described simple demand estimation approaches.
Remember, understanding the market factors that influence public transport use is critical to Public Transport service planning