# Adaptive Optimal Scheduling of Public Utility Buses in Metro Manila Using Fuzzy Logic Controller

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2 The rise of population and large volume of vehicles lead to the exigency for faster and more efficient transportation system (Lye, 2012). Intelligent public transportation system is the effective integration of transportation, communications, computing and sensor technologies incorporated into the transportation system s infrastructure to relieve congestion, improve safety and enhance productivity (Chunli, 2012). Fuzzy logic is an extension of Boolean logic by Lotfi Zadeh in 1965 based on the mathematical theory of fuzzy sets, which is a generalization of the classical set theory (Dadios & Maravillas, 2001). By introducing the notion of degree in the verification of a condition, thus enabling a condition to be in a state other than true or false, fuzzy logic provides a very valuable flexibility for reasoning, which makes it possible to take into account inaccuracies and uncertainties (Dernoncourt, 2009). Fuzzy logic based controller is developed in this study. The system is designed to find the best dispatch interval for a given set of parameters such as number of passengers aboard, rate of boarding passengers and vehicles headway to the preceeding vehicle. 2 BUS SCHEDULING SYSTEM The main goal of most transit agencies is to offer to the population a service of good quality that allows passengers to travel easily (Mo, 2009). Tactical planning problems concern the decisions related to the service offered to the public, namely the frequencies of service along the routes and the timetables. These problems are usually solved on a seasonal basis, with occasional updates (Corcuran & Wainwright, 1995). One of the main reasons of traffic congestion in Metro Manila is the large volume of buses all the time. Relatively short bus headways results in congestion of the highway (Land Transportation Franchising and Regulatory Board, 2012). The presence of multiple overlapping routes contributes to the overcrowding and slow vehicular flow. Congestion in urban city yields to the uneven arrival of public transport vehicles at station causing delays and un-productivity. Multiple bus lines having multiple overlapping routes implement different dispatch and scheduling timetables. Unfortunately, there is no coordination between different bus lines that leads to congestion of the road. A strategic timetable setting of bus dispatch is one of the solutions to the congestion problem in EDSA. Departure intervals will be computed based on passenger demand and actual headway relative to the followed bus. 3 FUZZY LOGIC CONTROLLER The objective of this paper is to create a fuzzy logic controller that can limit the vehicular volume in EDSA by setting different dispatch interval based on different input parameters. The controller will set the time when the bus company should dispatch another bus depending on figures given by the preceding bus. Figure 1 is the Mamdami inference system of the fuzzy logic controller. In Mamdami system, crisp output is generated using defuzzification method (Dernoncourt, 2009). Figure 1: Mamdami inference system of the controller

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