# How many passengers use car/bus at a station? Using traffic data and passenger stats

I want to investigate how people travel to various stations in the UK. I have found data on both passengers per year, and traffic by mode along the main roads leading to those stations, also per year.

With this data I should be able to get some estimate of how many passengers use each mode to get to the station eg)

change in passengers = A+ Bchange in cars + Cchange in bus +...

although this would give the amount of additional passengers resulting from additional traffic, not the amount of additional cars/buses per x new passengers.

Can anyone think of a functional form that answers my question with the available data? Many thanks.

• Well the stations I'm looking at are parkway stations which tend to be accessed heavily by road. The DfT traffic count data splits traffic by type so the bus/coach travel is part of this. – Chris Jul 16 '15 at 14:53
• Yes those are certainly obvious flaws in the approach but I guess I'm interested in a broad brush approach based on a large sample of stations. I have not seen any such direct observations except one small pilot survey. I would love any input into alternate approaches if you have experience though – Chris Jul 16 '15 at 15:06
• I'm looking at the impact of location characteristics on method of travel to a station, specifically parkway vs city centre/suburban. Thanks for the ideas and effort, I've taken a look at the NTS but it doesn't seem to give the individual stations from the survey, will try with the ATOC – Chris Jul 16 '15 at 15:30
• – EnergyNumbers Jul 16 '15 at 15:39

Given that, as per your comments, you're "looking at the impact of location characteristics on method of travel to a station, specifically parkway vs city centre/suburban", one popular method is to estimate a multinomial logit model of modal choice for access to and egress from the rail station, where the probability of an individual trip $y_i$ having access mode $j$ is given by:
$$P(y_i = j) = \frac{e^{x_i\beta_j}}{\sum_{j\in J}e^{x_i\beta_j}}$$
where you'd estimate the $\beta_j$ coefficients from your observations: each $x_i$ representing some observed aspect of the level of service.