This repository contains the midterm report analysis for the Urban Transportation Planning and Analysis (UTPA) course at Tokyo University.
The project assesses the potential impact and demand for a major, hypothetical transport innovation: the Swissmetro SA, an underground mag-lev (magnetic levitation) system designed to connect major Swiss cities at speeds up to 500 km/h.
| Feature | Details |
|---|---|
| Data Source | Stated Choice Survey Data |
| Survey Period | March 1998, Switzerland |
| Sample Size | 1191 respondents |
| Transport Alternatives | Rail (Train), Swissmetro (SM), Car (for car owners only) |
| Key Variables | Cost (CO), Travel Time (TT), Demographics, General Abonnement (GA) pass ownership |
- The core modeling technique is the Multinomial Logit Model (MNL).
- MNL is used to analyze discrete choice (selecting one alternative from a set) by estimating parameters (
$\beta$ ) of a utility function explaining why a traveler chooses a specific mode.
- GA pass allows unlimited travel on most Swiss train lines.
- Its inclusion helps understand how a pre-paid, non-recoverable travel cost influences the choice between public transport (Rail/SM) and private transport (Car).
- Measure the sensitivity of demand (probability of choosing a mode) to changes in variables like cost or travel time.
-
Direct Elasticity (
$\epsilon^D$ ): Effect of a change in a mode's attribute on its own choice probability. -
Cross Elasticity (
$\epsilon^C$ ): Effect of a change in one mode's attribute on another mode’s choice probability.
- Calculated as the ratio of the travel time coefficient to the travel cost coefficient:
VTTS (Value of Travel Time Saving) =β_TT / β_CO - Represents the monetary amount travelers are willing to pay to save one unit of travel time.
- The initial MNL model demonstrated a good fit, with aggregate choice probabilities closely matching observed sample ratios (approx. 57% for Swissmetro choosing SM).
- Including the GA pass variable was necessary; excluding it resulted in counter-intuitive, positive cost coefficients for rail and Swissmetro.
| Metric | Result | Interpretation |
|---|---|---|
| Direct Elasticity for SM Travel Time | Approx. -0.55% | A 1% increase in SM travel time decreases the probability of choosing SM by 0.55%. |
| Cross Elasticity (SM TT → Car) | Approx. +0.95% | A 1% increase in SM travel time increases the probability of choosing Car by 0.95%, showing a substitution effect. |
- Rail (Train): Highest VTTS (~14.54)
- Swissmetro (SM): High VTTS (~13.65)
- Car: Lowest VTTS (~2.97)
- Including the LUGGAGE variable improved model goodness-of-fit (
$\rho^2$ increased). - The Car coefficient with luggage had the highest negative value, suggesting luggage strongly reduces Car choice probability compared to Train or Swissmetro.
The analysis confirms a strong potential demand for the Swissmetro, especially among current public transport users.
Advanced econometric models like MNL are essential for accurately forecasting demand by accounting for pre-paid passes, trip-specific needs (e.g., luggage), and other complex factors.