Abstract:
Identifying the travelers’ route choice pattern is one of the important tasks of the
transport planning. It is an important element for travel demand management of the
existing transportation system and depends totally upon the individual’s decision.
Such decision is uncertain in nature and depends on each user’s perception of
different factors influencing route choice.
The main objective of this research is to identify the bus users’ route choice pattern
through two model approaches- Multinomial Logit Model and Fuzzy Logic Model.
Multinomial Logit Model is one of the popular approaches for studying the route
choice behavior. But as such decision of choosing any route involves uncertainty,
Fuzzy Logic approach has been selected as the alternative method for addressing
such uncertainty while modeling the route choice pattern. Two major bus stoppages
of Dhaka city have been selected for conducting the study and user opinion survey
namely Mirpur Sector 1 (origin point) and Motijheel Commercial Area (destination
point). The factors which influence the route choice decision have been identified
through users’ opinion survey and have been used as the independent variables in
developing the models. The identified input variables are travel time, waiting time,
travel cost, distance, comfort, safety, security, regularity, age, gender, and income
level. The models have been developed for both weekday and weekend. It is to be
noted that not all identified variables are statistically significant for developing the
models. As such, numbers of models have been developed for different combination
of input variables for both model approaches to identify the final models (for both
weekday and weekend). In case of Multinomial Logit Model, the goodness-of-fit
measures (Chi-Square Distribution, Log Likelihood results, Psedu R² value) have
been compared for selecting the final model. As the notion of fuzzy logic is nonstatistical
in nature and does not provide any goodness-of-fit measures, the output of
each model (for different combination of input variables) have been compared with
the actual field data for selecting the final models for both weekday and weekend.
It has been found from the study that both model approaches (Multinomial Logit
Model and Fuzzy Logic Model) have identified travel time and waiting time to be
the significant factors for choosing the routes for both weekday and weekend. It has
also been found that except Fuzzy Logic weekend model, the other models identified
comfort, safety, security and regularity as important factors for choosing the routes.
On the other hand, the comparison of the output results of both model approaches
shows that Fuzzy Logic models can predict the route choice pattern (route share)
more accurately than the Multinomial Logit Model for both weekday and weekend.
This study proposes to include more origin and destination points to develop more
precise and realistic model. The preciseness of the Fuzzy Logic approach depends
on the quality of the field data. Therefore, it is recommended that more data needs to
train the Fuzzy Logic models in Neuro-Fuzzy training which will result in more
accurate results. This study concludes that Fuzzy Logic approach can be a better
way for predicting the route share for its strength to address the uncertainty and
impreciseness relationship between the input and output variables.
