A Game Theory Approach to Trip Distribution Model: Game Distribution Model

In Transport planning, the four-step travel demand model is one of the most popular macro scale planning tools since the 1970s. The second step of this traditional model, i.e., trip distribution, plays an essential role in the model and at the same time, it is the most controversial step.

Various alternative approaches are available in the literature for modelling trip distribution, such as the Gravity Model, intervening opportunities model, logit models etc. However, none of these models is universally accepted. Although the gravity model is the most common and well-known trip distribution model, it is often subject to the same criticisms as other alternative models. In this study, the trip distribution step of the four-step model is designed as a rational game to replicate preferences in a more realistic way. The usual assumption in the traditional distribution model is that decision-makers are influenced by only origin/destination-based travel attributes and/or impedance of the trip to the destination. However, other probable destination circumstances are not included in this procedure. In order to incorporate the actual determinants of trip making, Traffic Analysis Zones (TAZ) are considered to be gamers in the proposed model, and the generalized trip cost was considered to be the cost of gamers. Each TAZ has a utility that attracts individuals from other TAZs. Trips are distributed by comparing the utility of a TAZ with the associated cost. Empirical verification of the model is performed by using the household survey data for Eskişehir/Turkey.

The evaluation was performed with different goodness of fit statistics for 5 different structured O/D matrices representing various demand settings. The results indicate that the proposed model demonstrates strong performance according to selected micro- and macro-level goodness-of-fit statistics, with all R² values exceeding 0.80.

When compared to the classic Gravity Model, these goodness-of-fit measures yield better results in general.