Strategic Transit Route Recommendation Considering Multi-Trip Feature Desirability Using Logit Model with Optimal Travel Time Analysis

Abstract

Route recommendation continues to manifest noteworthy contributions to the intelligent transportation system field of research as it evolves through time. Early related studies helped passengers and tourists experience a more convenient travel. At the same time, these helped transport planners analyze people’s trip preferences and its correlation with the region-specific economic status in a more time-relevant data. Majority, however, require historical data and heavy data collection methods. For user quantified metrics such as route cost in terms of travel time and distance, the complexity and sparsity of preferences between travelers are persistent challenges. The strategic transit route recommendation proposed in this study takes into account multiple trip features (both quantitative and qualitative) desirability using logit model and the optimal travel time with respect to a given road traffic condition, headway, and passenger demand. The chosen area of study is the Western Visayas region of the Philippines specific to the public utility bus (PUB) and jeepney (PUJ) transit routes. The results of the research exhibited the feasibility of an optimal and strategic recommendation of public transportation route for passengers considering present time relevant trip conditions rather than relying on the historical data which are difficult to obtain, or worse, non-existent.

Tourist Attraction Recommendation Model Based on RFPAP-NNPAP Algorithm

“…For this purpose, the study adopts two machine learning algorithms, RF and ANN, to integrate and construct a prediction model for tourist preference for scenic spots. RF is an ensemble learning method that creates multiple decision trees and combines their outputs to obtain accurate and stable prediction results [18]. ANN can address nonlinear problems and learn and extract deep level features from data.…”