A Real-Time Taxicab Recommendation System Using Big Trajectories Data

Abstract

Carpooling is becoming a more and more significant traffic choice, because it can provide additional service options, ease traffic congestion, and reduce total vehicle exhaust emissions. Although some recommendation systems have proposed taxicab carpooling services recently, they cannot fully utilize and understand the known information and essence of carpooling. This study proposes a novel recommendation algorithm, which provides either a vacant or an occupied taxicab in response to a passenger's request, called VOT. VOT recommends the closest vacant taxicab to passengers. Otherwise, VOT infers destinations of occupied taxicabs by similarity comparison and clustering algorithms and then recommends the occupied taxicab heading to a close destination to passengers. Using an efficient large data-processing framework, Spark, we greatly improve the efficiency of large data processing. This study evaluates VOT with a real-world dataset that contains 14747 taxicabs' GPS data. Results show that the ratio of range (between forecasted and actual destinations) of less than 900 M can reach 90.29%. The total mileage to deliver all passengers is significantly reduced (47.84% on average). Specifically, the reduced total mileage of nonrush hours outperforms other systems by 35%. VOT and others have similar performances in actual detour ratio, even better in rush hours.

Taxi Demand Prediction using L-CNN

“…Chen et al [13] proposed a novel suggestion calculation, which gives either an empty or an involved cab because of a traveler's solicitation, called VOT. VOT prescribes the nearest empty cab to travelers.…”

Coalitional Game Based Carpooling Algorithms for Quality of Experience

“…Carpooling service is a beneficial solution to solve the problem. By allowing multiple passengers to share a vehicle, carpooling can reduce the total mileage to deliver passengers, therefore alleviating traffic congestion and reducing excessive emissions effectively [3]- [5].…”

“…Many works on carpooling service focus on designing recommendation systems [5], [7]- [9], solving vehicle routing problems [9]- [12] and inventing security protocols [13]- [16] to improve availability, convenience and security of carpooling. However, only a few works have focused on designing incentive mechanisms to motivate both passengers and drivers to participate in carpooling.…”