Inferring Trip Occupancies in the Rise of Ride-Hailing Services

Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Tuan-Anh Hoang

Abstract:

The knowledge of all occupied and unoccupied trips made by selfemployed
drivers are essential for optimized vehicle dispatch by
ride-hailing services (e.g., Didi Dache, Uber, Lyft, Grab, etc.). However,
vehicles’ occupancy status is not always known to service
operators due to adoption of multiple ride-hailing apps. In this paper,
we propose a novel framework, Learning to INfer Trips (LINT), to
infer occupancy of car trips by exploring characteristics of observed
occupied trips. Two main research steps, stop point classification
and structural segmentation, are included in LINT. In the first step,
we represent a vehicle trajectory as a sequence of stop points, and
assign stop points with pick-up, drop-off, and intermediate labels
thus producing a stop point label sequence. In the second step, for
structural segmentation, we further propose several segmentation
algorithms, including greedy segmentation (GS), efficient greedy segmentation
(EGS), and dynamic programming-based segmentation
(DP) to infer occupied trip from stop point label sequences. Our
comprehensive experiments on real vehicle trajectories from selfemployed
drivers show that (1) the proposed stop point classifier
predicts stop point labels with high accuracy, and (2) the proposed
segmentation algorithm GS delivers the best accuracy performance
with efficient running time.