An Optimal Ride Sharing Recommendation Framework for Carpooling Services

Abstract

Carpooling services allow drivers to share rides with other passengers. This helps in reducing the passengers' fares and time, as well as traffic congestion and increases the income for drivers. In recent years, several carpooling-based recommendation systems have been proposed. However, most of the existing systems do no effectively balance the conflicting objectives of drivers and passengers. We propose a highest aggregated score vehicular recommendation (HASVR) framework that recommends a vehicle with highest aggregated score to the requesting passenger. The aggregated score is based on parameters, namely: a) average time delay; b) vehicle's capacity; c) fare reduction; d) driving distance; and e) profit increment. We propose a heuristic that balances the incentives of both drivers and passengers keeping in consideration their constraints and the real-time traffic conditions. We evaluated HASVR with a real-world data set that contains GPS trace data of 61,136 taxicabs. Evaluation results confirm the effectiveness of HASVR compared with existing scheme in reducing the total mileage used to deliver all passengers, reducing the passengers' fare, increasing the profit of drivers, and increasing the percentage of satisfied ride requests.

Keywords

• Transportation system
• vehicular recommendation
• information filtering