14 lightings by station. The consultant also identified the main sources of uncertainty which could invalidate the forecast.

The stated preference survey had a crucial role. It generated data on the preferences, recent trips and demographic characteristics of 800 potential commuter rail users in the corridorincluding quotas for time-of-day, age and gender of users. The respondents were recruited at 16 bus stops in or near the corridor from 5 am to 8.30 pm and were offered a fee of up to 200 pesos to participate in the interview. They were offered the option of being interviewed at their home or and an interview site next to the bus station. On the interview day, they were guided though a 30 minutes computer-based interview.

The questionnaire was designed to estimate the trade-off between time, cost ad other service factors. It essentially asked the user to pick among of alternatives differentiated according to walking time, waiting time, time in mode (bus, rail, subway), total trip time, total tip cost and total number of transfers. The experiments were customized based on the attributes of the intercepted tip to ensure realism. The choice experiments clearly show that potential users place different values on walking time, waiting time, in-vehicle time and transfers between modes. They value their time at 3 to 9 pesos per hour (roughly UScts25 to 75) depending on the use of time. In contrast to other locations where in-vehicle time seems to be preferred to walking time, Mexican seem to generally perceive time spent walking comparable to time spent traveling. Mexicans prefer to spend time walking to travelling than waiting (as anywhere else). They see transfers as equivalent to 13-15 minutes of traveling. This means that the speed advantage of subway or commuter rail transfers is offset by 15 to 30 minutes for 1 to 2 transfers. Overall, they prefer the commuter rail to bus or metro (but this may be biased by the perception of additional security).

The network data is used to assign each trip in the total market to a specific mode or combination of modes based on the user trade-offs between travel time, cost and modes obtained from the survey and on the characteristics of each model. This is done for the proportion of trips for each Origin-Destination pair that will travel by each mode of transportation. The specification of the modal choice model is a nested logit structure with the following mode combinations: (1) bus only, (2) metro only, (3) metro and bus, (4) commuter rail only, (5) Commuter rail and bus) and (6) a commuter rail, metro and bus option. The choice of mode is estimated separately for each trip, taking into account the distinct preferences of the person making the trip with respect to age, gender and other factor.

The model is calibrated to allow the reproduction of time and cost in 1998 for buses and metro and generates a commuter rail demand for each average week day assuming that commuter rail fares are set equal to the bus fares of 0.28 pesos/km. More specifically, the demand model estimates the probability that a person will choose a particular mode for their trip _for each of more than 90,000 public transportation trips._ This information is then used to estimate demand for each transportation mode for weekday demand. So for any pair of origin and destination, it computes the total trip time, the cost and the number of transfers and on that basis it estimated that the user will pick any one of the 6 travel options identified earlier. This week day demand is discounted for weekends. For the subway, this is done by applying a factor of 0.58 based on traffic counts. Annual demand is equal to weekday demand multiplied by a conversion factor for each mode as well based on a traffic count. Prospects are quite good and are likely to grow even better with an annual weekday demand for commuter rail services of about 150 million passenger in 2005 getting to over 200 in 2030.

There are some serious caveats also quite important to assess from the viewpoint of a regulator. There may be a certain degree of diversion from expected traffic due to increased use of vehicle due to changes in travel habits. On the other hand, congestion may get worse and