Norman W. Garrick Mode Choice The introduction of congestion charging in London in 2003 is one...
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Transcript of Norman W. Garrick Mode Choice The introduction of congestion charging in London in 2003 is one...
Norman W. Garrick
Mode Choice
The introduction of congestion charging in London in 2003 is one example of a situation where mode choice modeling is needed.
The fundamental question is this case was “Will charging work to reduce congestion?”
The expectation was that people would switch modes – mode choice modes would be used to predict how many people would switch
Norman W. Garrick
Mode Choice
The introduction of the new AVE service between Madrid and Barcelona is another example of a situation were model choice modeling would be needed.
How many people would switch from plane or driving to train?
http://www.renfe.es/video.html
Norman W. Garrick
Factors affecting Mode Choice
In lecture 2 we talked about some of the factors affecting mode choice. These factors include
1. Type and purpose of trip 2. Ownership status 3. Cost (mostly out of pocket cost) 4. Door-to-door travel time 5. Convenience/service/comfort 6. Prestige 7. Availability 8. Accessibility of mode 9. Land use characteristics of start and end point
Obviously, not all of these factors can be effectively incorporated into a quantitative model of mode choice. We need to be cognizant of those factors that are important in influencing choice but are not fully accounted for in mode choice models.
Norman W. Garrick
Mode Split or Modal Choice Models
Mode Choice Models are used to try to predict travelers mode choice
Contemporary models are based on using UTILITY or DISUTILITY functions
These functions are meant to express the level of satisfaction (for utility functions) or dissatisfaction (for disutility functions) with a given mode
One the utility function is calculated for each mode, the probability that a given mode will be chosen can be calculated
Norman W. Garrick
Utility Function
A utility function takes the following form
uk = ak + a1 X1 + a2 X2 + ….. ar Xr + ε0
uk – utility function for mode k
ak – modal constant
Xr – variables measuring modal attributes such as cost or time of travel
ar – coefficient associated with each attribute
Where
ε0 – error term
Norman W. Garrick
Multinomial Logit Model
If utility function, uk, is assumed to be a Weibull Probability Distribution then the Multinomial Logit Model is used to calculate the probability that a traveler will chose a given mode
Multinomial Logit Model
p(k) = euk / Σ eux
Norman W. Garrick
Example
The mode available between Zone I and J are i) Passenger car (A), ii) Bus (B)
Find the market share for each mode given the following attribute table for the modes.
The utility function is
uk = ak – 0.025 X1 – 0.032 X2 – 0.015 X3– 0.002 X4
wherex1 – access plus egress time (min)x2 – waiting time (min)x3 – line haul time (min)x4 – out of pocket cost (cents)