Route choice modeling is one of the most important parts of traffic assignment problem. Recently, this model is used to describe the reactions of drivers to Traveler Information Systems in order to develop accurate Advanced Traffic Management and Information System (ATMIS). Therefore accurate model is necessary. In this project we proposed a new model based on Type-2 fuzzy logic to model route choice problem. This model can take account of the imprecision, uncertainties and vagueness lying in the dynamic choice process and makes more accurate modeling of drivers’ behavior than deterministic, stochastic and Type-1 fuzzy models.
Traditional methods use numerical techniques where perceived costs are treated as crisp numbers. However, costs can barely be strictly ordered. To solve these problems, Random Utility models were presented.
Since much of human reasoning is based on imprecise, vague and subjective values, it has been argued that these types of modeling techniques ignore the presence of vagueness and ambiguity in drivers’ perceptions. The other way of dealing with imperfect ordering of alternatives given to drivers is to use fuzzy logic theory.
The framework of fuzzy sets, systems, and relations is very useful to deal with the dynamic nature of traffic flow. However, there is much uncertainty and vagueness in driver route choice, which is very difficult to handle with Type-1 fuzzy sets. These fuzzy sets are not able to model such uncertainties directly because their membership functions are crisp. On the other hand, Type-2 fuzzy sets are able to model such uncertainties as their membership functions are themselves fuzzy. Type-2 fuzzy set, on the other hand, is able to successfully model these uncertainties.
The travel time and average speed of a link in the network is a function of traffic flow volume. By varying traffic flow rates in diffract time periods, the travel time and average speed would be changed. A link has its maximum travel time and minimum average speed at the maximum traffic flow and minimum travel time at minimum traffic flow in which drivers cross it with free flow speed. The travel time and average speed vary between these two boundaries. In Type-1 fuzzy sets, the membership function assigns a crisp value to a certain travel time. However, this certain travel time in different periods has different level of desirability. If a driver knows that he would cross a link at maximum travel time at non pick hour period, he would not choose that link. In other words, drivers can endure more travel time and lower average speed in pick hours that they would not endure in off pick hours. For example, consider a link on a network which has a maximum 60min travel time and minimum travel time of 15 min at off pick period. If a driver crosses the link in 30 min, In Type-1 fuzzy sets we assign 0.8 as the membership value to the cluster of good traveling time, regardless of if he is travelling in pick hour on not. But In viewpoint of a driver, if he crosses the link in 30 min in off pick hours, he will think that he is experiencing long travel time for this link. So the Membership value is not 0.8 anymore. But if the driver crosses this link in 30 min at pick hour, the driver will experience a good travel time. So Type-2 fuzzy sets can consider driver’s desires and behavior more realistic and accurate than Type-1 fuzzy sets.
In our proposed model we consider average speed and cost separately and use Type-2 fuzzy reasoning method to consider both elements. We obtain the initial membership functions of the average speed and the cost by using Elicitation and Heuristic Selection method. A Type-2 fuzzy rule base using standard operators is defined. The results show that the proposed model is more reliable and more accurate in modeling real drivers’ behavior and could handle the uncertainties more efficient.
To find more about this project, please read the related Article.
