19 research outputs found
Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours
Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, \lpalg, that allows the platform designer to control the profit and fairness of the system via parameters and respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use \lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using \lpalg, the competitive ratios for profit and fairness measures would be no worse than and respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that \lpalg under some choice of can beat two natural heuristics, Greedy and Uniform, on \emph{both} fairness and profit
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
In this paper, we develop a reinforcement learning (RL) based system to learn
an effective policy for carpooling that maximizes transportation efficiency so
that fewer cars are required to fulfill the given amount of trip demand. For
this purpose, first, we develop a deep neural network model, called ST-NN
(Spatio-Temporal Neural Network), to predict taxi trip time from the raw GPS
trip data. Secondly, we develop a carpooling simulation environment for RL
training, with the output of ST-NN and using the NYC taxi trip dataset. In
order to maximize transportation efficiency and minimize traffic congestion, we
choose the effective distance covered by the driver on a carpool trip as the
reward. Therefore, the more effective distance a driver achieves over a trip
(i.e. to satisfy more trip demand) the higher the efficiency and the less will
be the traffic congestion. We compared the performance of RL learned policy to
a fixed policy (which always accepts carpool) as a baseline and obtained
promising results that are interpretable and demonstrate the advantage of our
RL approach. We also compare the performance of ST-NN to that of
state-of-the-art travel time estimation methods and observe that ST-NN
significantly improves the prediction performance and is more robust to
outliers.Comment: Accepted at IEEE International Conference on Big Data 2018. arXiv
admin note: text overlap with arXiv:1710.0435
Preference-aware task assignment in on-demand taxi dispatching: An online stable matching approach
A central issue in on-demand taxi dispatching platforms is task assignment, which designs matching policies among dynamically arrived drivers (workers) and passengers (tasks). Previous matching policies maximize the profit of the platform without considering the preferences of workers and tasks (e.g., workers may prefer high-rewarding tasks while tasks may prefer nearby workers). Such ignorance of preferences impairs user experience and will decrease the profit of the platform in the long run. To address this problem, we propose preference-aware task assignment using online stable matching. Specifically, we define a new model, Online Stable Matching under Known Identical Independent Distributions (OSM-KIID). It not only maximizes the expected total profits (OBJ-1), but also tries to satisfy the preferences among workers and tasks by minimizing the expected total number of blocking pairs (OBJ-2). The model also features a practical arrival assumption validated on real-world dataset. Furthermore, we present a linear program based online algorithm LP-ALG, which achieves an online ratio of at least 1−1/e on OBJ-1 and has at most 0.6·|E| blocking pairs expectedly, where |E| is the total number of edges in the compatible graph. We also show that a natural Greedy can have an arbitrarily bad performance on OBJ-1 while maintaining around 0.5·|E| blocking pairs. Evaluations on both synthetic and real datasets confirm our theoretical analysis and demonstrate that LP-ALG strictly dominates all the baselines on both objectives when tasks notably outnumber workers
Inductive Graph Transformer for Delivery Time Estimation
Providing accurate estimated time of package delivery on users' purchasing
pages for e-commerce platforms is of great importance to their purchasing
decisions and post-purchase experiences. Although this problem shares some
common issues with the conventional estimated time of arrival (ETA), it is more
challenging with the following aspects: 1) Inductive inference. Models are
required to predict ETA for orders with unseen retailers and addresses; 2)
High-order interaction of order semantic information. Apart from the
spatio-temporal features, the estimated time also varies greatly with other
factors, such as the packaging efficiency of retailers, as well as the
high-order interaction of these factors. In this paper, we propose an inductive
graph transformer (IGT) that leverages raw feature information and structural
graph data to estimate package delivery time. Different from previous graph
transformer architectures, IGT adopts a decoupled pipeline and trains
transformer as a regression function that can capture the multiplex information
from both raw feature and dense embeddings encoded by a graph neural network
(GNN). In addition, we further simplify the GNN structure by removing its
non-linear activation and the learnable linear transformation matrix. The
reduced parameter search space and linear information propagation in the
simplified GNN enable the IGT to be applied in large-scale industrial
scenarios. Experiments on real-world logistics datasets show that our proposed
model can significantly outperform the state-of-the-art methods on estimation
of delivery time. The source code is available at:
https://github.com/enoche/IGT-WSDM23.Comment: 9 pages, accepted to WSDM 202