Vehicle Routing Trifecta: Data-Driven Route Recommendation System

Abstract

In recent years, driving route recommendation has attracted growing interest from researchers and industries. However, previously proposed route recommendation systems cannot jointly consider different factors (e.g., fuel consumption, travel time, air quality) in parallel with different weights entered by users. In addition, as users set the weights based on their own evaluation (e.g., much higher weight on air quality than fuel consumption), which may lead to a very unbalanced route (e.g., worst travel time and worst fuel consumption) that actually is not what the users desire. To handle these issues, in this paper, we propose a routing recommendation system, called Vehicle Routing Trifecta (VRT), which can jointly blend different considered factors with different weights entered by users while still producing well-balanced routes that conform user normal desire. VRT consists of two innovative components. First, we establish three different predictors for air quality, travel time, and fuel consumption estimations of each road segment in the road network. Second, we design an optimal route selector, which consists of the solution of a multi-criteria optimization problem based on the given user preference on three different aspects (e.g., air quality, travel time, and fuel consumption). We conduct extensively simulation studies based on the real-world, geo-tagged datasets to evaluate VRT. The comparative studies with other existing routing recommendation systems show the superior performance of VRT in terms of recommending routes that meet user entered preference.