The Philippines has one of the biggest automobile industries in the Asia-Pacific area, with an approximate average of 10,000 to 15,000 vehicle volumes in the National Capital Region (NCR) alone in 2023 [1] and roughly 11.9 thousand gasoline stations in the country in 2022 [2]. One of the largest challenges for motorists in the country is navigating through traffic and getting to destinations on time. This challenge is exacerbated when fuel is running low and there is difficulty in deciding which path to take to find the nearest gasoline station location. Factors such as brand preferences, availability of specific fuel types, and fuel costs, as gasoline prices differ across stations, must also be taken into account [3][4].
One existing platform that partly addresses this need is Google Maps Navigation and its feature which displays gas stations along a user’s route. However, the implementation of this feature is currently limited in the Philippines and misses crucial details such as consideration of a vehicle’s remaining range. Moreover, there is insufficient and sometimes outdated information displayed regarding the gasoline stations’ locations. Addressing this gap, the use of an isoline map for routing will be explored to highlight the variations in the distances and fuel prices of nearby stations in proximity to a user.
In navigation and transportation planning, isoline mapping is a method that finds the best routes based on isolines that reflect equal values of a given criterion. Some application programming interfaces (APIs), such as the HERE Isoline Routing API, achieve this using time and distance isolines called Isochrones and Isodistances, respectively. Under the constraint of a vehicle’s remaining range, the Isoline map will be able to define which gasoline stations remain reachable for the user [5].
In recognition of the necessity for time and fuel efficiency on the road, this research proposal aims to create a comprehensive navigation app with efficient route planning capabilities, tailored specifically for the Philippine market. The proposed solution is a map-based and location-aware mobile application that overlays an isoline map and heat map for determining the distances of nearby and reachable gasoline stations with the lowest fuel prices. The app will be able to generate the fastest route to the found stations in relation to a vehicle’s current location and remaining range.
With numerous gasoline stations with varying locations and fuel prices, it can be hard to facilitate proactive refueling decisions while prioritizing its relation to a vehicle’s current position and fuel tank capacity. This poses a hindrance for motorists to find the most time and fuel-efficient gasoline station for when they are low on gas but still need to take into account the best possible route and fuel costs. Thus, creating a centralized application where users can quickly overview the location of nearby gasoline stations within their remaining range, assess fuel costs, compare and contrast the gasoline prices between each station, and obtain the fastest route where they can refuel, will significantly help motorists in the Philippines.
The proposed application will help equip consumers with the tools necessary to optimize their transportation expenditures and travel time to gasoline stations. With an overview and optimized navigation to the nearest gasoline stations within the vehicle’s remaining range, consumers can achieve better time and fuel efficiency for their daily commutes and reduce the likelihood of running out of gas while driving or wasting time searching for stations.
It will enable customers to observe better resource management and efficiently schedule their refueling stops by giving them access to real-time updates on neighboring gasoline stations and estimating their remaining range depending on their current gasoline levels. With the onset of the volatility of fuel prices [6] and price variations per gasoline station [3][4], the need for a centralized platform where fuel prices and gasoline station locations can be tracked and displayed is highlighted. This application will help drivers plan and improve their travel safety and comfort.
Beyond personal advantages, this application will also have an environmental impact as it can encourage environmentally responsible driving practices and help reduce vehicle emissions and fuel use associated with inefficient driving practices, such as driving long distances in search of fuel, by suggesting fuel-efficient routes.
The general objective of this study is to develop a mobile application that provides convenience and assistance to motorists in their daily commute through optimized route navigation to gasoline stations and fuel price information. Specifically, this study intends to achieve the following:
1) To create a centralized platform that tracks the location and prices of gasoline stations.
2) To display user-updated gasoline station prices and determine their reliability.
3) To help users find the nearest gasoline station with the lowest fuel price and generate the fastest and most fuel-efficient route towards it.
4) To evaluate the usability of the application using the System Usability Scale (SUS).
This study will focus on the production of a scalable user-friendly and accessible application that allows motorists to view the location and current fuel prices in gasoline stations near them and be able to get the fastest route to the station/s in question. It will also involve the evaluation of the application using the System Usability Scale (SUS) and an analysis of the effectiveness of the application through quantitative assessments. For the purpose of this study, the areas or scope of testing will be within Calamba to Los Baños, Laguna only. This study will be limited to generating routes to the nearest gasoline station and will not include routing to other destinations. It will also be limited according to gasoline stations’ availability and users’ willingness to update the retail prices on a corresponding server. This study will also be subject to resource and time constraints that may restrict the amount of data that can be used for training.