py-np4vtt 1.0.5

Python library providing NonParametric models for Value of Travel Time analysis

NP4VTT

NP4VTT is a Python package that enables researchers to estimate and compare nonparametric models in a fast and convenient way. It comprises five nonparametric models for estimating the VTT distribution from data coming from two-attribute-two-alternative stated choice experiments:

• Local constant model (Fosgerau, 2006, 2007)
• Local logit (Fosgerau, 2007)
• Rouwendal model (Rouwendal et al., 2010)
• Artificial Neural Network (ANN) based VTT model (van Cranenburgh & Kouwenhoven, 2021)
• Logistic Regression based VTT model (van Cranenburgh & Kouwenhoven, 2021)

Additionally, a Random Valuation model (Ojeda-Cabral, 2006) is included for benchmarking purposes

Installation steps

• Use pip to install the py-np4vtt library normally:
  • python3 -m pip install py-np4vtt

Examples

We provide Jupyter Notebooks that show how to configure and estimate each model included in NP4VTT:

• Local constant model: link
• Local logit: link
• Rouwendal model: link
• ANN-based VTT model: link
• Logistic Regression-based VTT model: link

These examples guide the user through the process of loading a dataset, estimating a nonparametric model, and visualising the VTT distribution using scatter and histogram plots. We use the Norwegian 2009 VTT data to illustrate each example.

Take, for example, the VTT distribution from the Rouwendal model using NP4VTT:

References

• Fosgerau, M. (2006). Investigating the distribution of the value of travel time savings. Transportation Research Part B: Methodological, 40(8), 688–707. https://doi.org/10.1016/j.trb.2005.09.007
• Fosgerau, M. (2007). Using nonparametrics to specify a model to measure the value of travel time. Transportation Research Part A: Policy and Practice, 41(9), 842–856. https://doi.org/10.1016/j.tra.2006.10.004
• Rouwendal, J., de Blaeij, A., Rietveld, P., & Verhoef, E. (2010). The information content of a stated choice experiment: A new method and its application to the value of a statistical life. Transportation Research Part B: Methodological, 44(1), 136–151. https://doi.org/10.1016/j.trb.2009.04.006
• Ojeda-Cabral, M., Batley, R., & Hess, S. (2016). The value of travel time: Random utility versus random valuation. Transportmetrica A: Transport Science, 12(3), 230–248. https://doi.org/10.1080/23249935.2015.1125398
• van Cranenburgh, S., & Kouwenhoven, M. (2021). An artificial neural network based method to uncover the value-of-travel-time distribution. Transportation, 48(5), 2545–2583. https://doi.org/10.1007/s11116-020-10139-3