Green Light Please: An Algorithm-Based Public Motility System for Batangas City

It was proclaimed a city on July 23, 1969, and was later classified as a regional growth center and identified as a regional agro-industrial center and special economic zone as mandated by the Medium Term Philippine Development Plan and the Ecozone Act of 1995. The city's rapid population and economic growth after its cityhood in 1969 resulted in heavy congestion on roadways, especially during peak hours in the morning and afternoon. The city's transportation system consists of public utility jeepneys (PUJs) and tricycles, which operate through a color-coding system. PUJs provide point-to-point service, while tricycles offer point-to-point service and are not allowed on main roads. In 2013, there were 2,164 tricycles operating in the city, but only 1,394 units obtained the mayor's permit and paid the necessary fees. The city government, together with the management information systems department, had installed traffic signals at seven major intersections, which were upgraded with LED lights and electronic timers in 2010. The city's traffic problems are exacerbated by the presence of private vehicles, which occupy most of the narrow roads. This has created many problems, including traffic congestion, air and noise pollution, long travel times, and greenhouse gas emissions. The city's mayor had a chance to meet the ecology office director in Suseonggu, South Korea, and discussed his concerns over air pollution. He realized that many small changes could make a big difference to provide a good quality of life for every Batangas City citizen. Vera, the transportation development regulatory officer, was tasked with finding solutions to the city's traffic problems. She decided to use algorithms to find ways to ease the city's traffic situation, which had worsened with the collapse of the western portion of the Bridge of Promise that spanned the Calumpang River and served as the main conduit for going to SM Mall, the biggest mall in Batangas City. Vera recalled from memory the different algorithms she learned in college and in her postgraduate studies, knowing these would help her. The ant colony algorithm, an algorithm for finding optimal paths based on the behavior of ants searching for food, was considered as a potential solution. The algorithm works by having ants wander randomly, then return to the colony leaving "markers" or pheromones that show the path with food. Other ants follow the path with a certain probability, then populate the path with their own markers as they bring food back. As more ants find the path, a couple of streams of ants traveling to various food sources near the colony are formed. Shorter paths are more likely to be discovered when the ants drop pheromones every time they bring food. The Rank Based Ant System (RBAS) is a variation of the ant colony algorithm that uses a ranking system to select the best paths. The algorithm generates a route, evaluates it, and then verifies if it is a global or local optimum. If it is, the algorithm evaporates pheromone in all trails, generates an elite from the current colony, and deposits pheromone on elite trails. The algorithm also deposits pheromone on the best global route. Dijkstra's algorithm, which determines the shortest path, was also considered as a potential solution. The algorithm finds a path between two vertices (nodes) on a given graph, such that the sum of the weights on its constituent edges is minimized. The algorithm was developed by Edsger Dijkstra in 1959 to solve the shortest path problem for the positive linking graph. The genetic algorithm, which mimics the survival of the fittest process to generate an increasingly better path, was also considered as a potential solution. The algorithm works by generating a population of paths, then selecting the best paths based on their fitness. The algorithm then uses crossover and mutation to generate new paths, and repeats the process until a satisfactory solution is found.