Izvorni znanstveni članak
https://doi.org/10.7307/ptt.v38i5.1263
Real-Time Scheduling Model for Shared Autonomous Vehicles in Ride-Sharing Mode
Shaoling Rong
; College of Architecture and Transportation Engineering, Guilin University of Electronic Science and Technology, Guilin, China
Longxin Zeng
; College of Architecture and Transportation Engineering, Guilin University of Electronic Science and Technology, Guilin, China
*
Fujian Chen
; College of Architecture and Transportation Engineering, Guilin University of Electronic Science and Technology, Guilin, China
* Dopisni autor.
Sažetak
Numerous studies have demonstrated that ride-sharing optimisation for shared autonomous vehicles (SAVs) can effectively address the efficiency limitations of conventional shared mobility systems, realising the sustainable urban transportation vision of “serving more passengers with fewer vehicles”. However, current shared autonomous vehicle (SAV) ride-sharing models exhibit inefficiencies. To overcome this, we propose a real-time SAV scheduling model with dynamic detour ride-sharing, minimising total passenger travel time while maximising ride-sharing distance ratios, thus fulfilling ride-sharing demands within defined spatial ranges. To solve this model, a two-stage ride-sharing scheduling matching approach was implemented, which integrates feasible matching pair acquisition and vehicle scheduling optimisation, with robustness and opportunism evaluated via Information Gap Decision Theory. MATLAB simulations validate the model by comparing dynamic detour ride-sharing with traditional overlapping modes. Results show the dynamic detour mode achieves a 64% ride-sharing rate using 68 vehicles, outperforming conventional modes with a 22% higher ride-sharing rate and 11 fewer vehicles required. This approach enhances ride-sharing adoption, alleviates congestion, lowers travel costs and maximises passenger service efficiency with minimal fleet size.
Ključne riječi
shared autonomous vehicles; dynamic detour ride-sharing; real-time scheduling; information gap decision theory
Hrčak ID:
347514
URI
Datum izdavanja:
27.5.2026.
Posjeta: 0 *