2 research outputs found
A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization
Ride-sharing is a modern urban-mobility paradigm with tremendous potential in
reducing congestion and pollution. Demand-aware design is a promising avenue
for addressing a critical challenge in ride-sharing systems, namely joint
optimization of request-vehicle assignment and routing for a fleet of vehicles.
In this paper, we develop a probabilistic demand-aware framework to tackle the
challenge. We focus on maximizing the expected number of passenger pickups,
given the probability distributions of future demands. The key idea of our
approach is to assign requests to vehicles in a probabilistic manner. It
differentiates our work from existing ones and allows us to explore a richer
design space to tackle the request-vehicle assignment puzzle with a performance
guarantee but still keeping the final solution practically implementable. The
optimization problem is non-convex, combinatorial, and NP-hard in nature. As a
key contribution, we explore the problem structure and propose an elegant
approximation of the objective function to develop a dual-subgradient
heuristic. We characterize a condition under which the heuristic generates a
approximation solution. Our solution is simple and
scalable, amendable for practical implementation. Results of numerical
experiments based on real-world traces in Manhattan show that, as compared to a
conventional demand-oblivious scheme, our demand-aware solution improves the
passenger pickups by up to 46%. The results also show that joint optimization
at the fleet level leads to 19% more pickups than that by separate
optimizations at individual vehicles
Spatio-Temporal Hierarchical Adaptive Dispatching for Ridesharing Systems
Nowadays, ridesharing has become one of the most popular services offered by
online ride-hailing platforms (e.g., Uber and Didi Chuxing). Existing
ridesharing platforms adopt the strategy that dispatches orders over the entire
city at a uniform time interval. However, the uneven spatio-temporal order
distributions in real-world ridesharing systems indicate that such an approach
is suboptimal in practice. Thus, in this paper, we exploit adaptive dispatching
intervals to boost the platform's profit under a guarantee of the maximum
passenger waiting time. Specifically, we propose a hierarchical approach, which
generates clusters of geographical areas suitable to share the same dispatching
intervals, and then makes online decisions of selecting the appropriate time
instances for order dispatch within each spatial cluster. Technically, we prove
the impossibility of designing constant-competitive-ratio algorithms for the
online adaptive interval problem, and propose online algorithms under partial
or even zero future order knowledge that significantly improve the platform's
profit over existing approaches. We conduct extensive experiments with a
large-scale ridesharing order dataset, which contains all of the over 3.5
million ridesharing orders in Beijing, China, received by Didi Chuxing from
October 1st to October 31st, 2018. The experimental results demonstrate that
our proposed algorithms outperform existing approaches