1. 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

2. 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

3. 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.

4. 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

5. Norman W. Garrick Utility Function A utility function takes the following form u k = a k + a 1 X 1 + a 2 X 2 + ….. a r X r + ε 0 u k – utility function for mode k a k – modal constant X r – variables measuring modal attributes such as cost or time of travel a r – coefficient associated with each attribute Where ε 0 – error term

6. Norman W. Garrick Multinomial Logit Model If utility function, u k, 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) = e uk / Σ e ux ε 0 – error term

7. 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 u k = a k – 0.025 X 1 – 0.032 X 2 – 0.015 X 3 – 0.002 X 4 where x1 – access plus egress time (min) x2 – waiting time (min) x3 – line haul time (min) x4 – out of pocket cost (cents) ε 0 – error term

8. Norman W. Garrick Example … continue The attribute table for each mode is given below And a a = -0.10 a b = +0.00 Therefore u(A) = -0.625 u(B) = -1.530 Probability of selecting PC, p(A) = e (-0.625) / [e (-0.625) +e (-1.530) ] = 0.71 x1x2x3x4 PC5020100 Bus10154050