Contextual Route Recommendation System in Heterogeneous Traffic Flow

Surya Michrandi Nasution, Emir Husni, Kuspriyanto Kuspriyanto, Rahadian Yusuf and Bernardo Nugroho Yahya
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Surya Michrandi Nasution: School of Electrical Engineering, Institut Teknologi Bandung, Jl. Ganesa 10, Bandung 40132, Indonesia
Emir Husni: School of Electrical Engineering, Institut Teknologi Bandung, Jl. Ganesa 10, Bandung 40132, Indonesia
Kuspriyanto Kuspriyanto: School of Electrical Engineering, Institut Teknologi Bandung, Jl. Ganesa 10, Bandung 40132, Indonesia
Rahadian Yusuf: School of Electrical Engineering, Institut Teknologi Bandung, Jl. Ganesa 10, Bandung 40132, Indonesia
Bernardo Nugroho Yahya: Department of Industrial & Management Engineering, Hankuk University of Foreign Studies Global Campus, Oedae-ro 81, Mohyeon-eup, Cheoin-gu, Yongin 17035, Gyeonggi, Korea

Sustainability, 2021, vol. 13, issue 23, 1-21

Abstract: The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

Keywords: route recommendation; heterogeneous traffic flow; traffic prediction; Knowledge Growing System; shortest path; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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