Contextual Route Recommendation System in Heterogeneous Traffic Flow

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).

Dijkstra-based Official Motorcycle Repair Shop Application for Determining the Shortest Route

“…Dijkstra's algorithm is a form of greedy or greedy algorithm. This algorithm includes an algorithm that completes the search for the shortest path on a graph with one source on a weighted graph that has a negative side cost, and produces a shortest path tree [9], [12]- [14]. In the search for the shortest path, Djikstra's Algorithm can work by finding an efficient route and having a minimum length into a weighted graph, negative nodes cannot pass through weights that have positive numbers [11], [15], [16].…”

Personalized Route Recommendation Using F-AHP-Express

“…The road network illustrates the connection between several intersections (nodes) and road segments (edges), with the arrangement of direction, as in the directed graph [17][18][19][20]. To reach a destination in the road network, vehicles need to pass several nodes whether directly connected or not by its edges.…”

“…[31]. Nasution et al, create a new point of view in calculating the road network's weight by using Road Capacity Value (RCV) [32].…”

“…The advantage of RCV is not only the compatibility value that can compare the size of the road and vehicles, but also considering the driving preferences of a driver. Driving preference is another criterion that must be taken into consideration [26,28,[32][33][34][35]. There is no point to recommends the best route if the drivers refuse to pass it.…”

“…The creation of RCV needs driver preferences. The driver preferences in Nasution et al are manually determined by using priority order [32]. This paper discussed the procedure of determining the preferences of the driver, so later it could be used as the basis of personalized route recommendations.…”