A new ride-hailing subsidy system uses deep causal networks to estimate consumer elasticity.
problem Estimating consumer elasticity with subsidies in ride-hailing industry.
method Introduces a consumer subsidizing system using deep causal networks to address confounding effects.
result Effective in estimating the uplift effect of subsidies without confounding.
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the metho…
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
problem Repositioning idle supply before future demand is observed in ride-hailing.
method A predict-then-optimize approach using calibrated demand regimes, a similarity gate, and spatial queue-regret decomposition.
result The spatial gate reduces mean wait time to 82.3s compared to 85.3s for a hand-tuned similarity gate and 85.8s for a distributional-only baseline.
Method uses pseudo-samples to improve RCT data in ride-hailing pricing studies.
problem Small and biased RCT data leads to significant bias when generalizing to broader user base.
method Pseudo-sample matching to expand and match RCT data with observational data.
result 0.41% improvement in profit through pseudo-sample matching.
This study predicts parking availability using multi-source data and a self-supervised learning enhanced transformer.
problem Accurate parking availability prediction to support urban planning and management.
method Proposes SST-iTransformer, a self-supervised learning enhanced spatio-temporal inverted transformer, integrating multi-source data.
result SST-iTransformer achieves state-of-the-art performance in parking availability prediction.
Spatial and time-dependent data is of interest in many applications. This task is difficult due to its complex spatial dependency, long-range temporal dependency, data non-stationarity, and data heterogeneity. To address these challenges, we propose Forecaster, a graph Transformer architecture. Specifically, we start b…
New estimator reduces bias-variance tradeoff in Markovian interference experiments.
problem Estimating impact of interventions in systems with limited resources.
method Differences-In-Q (DQ) estimator for on-policy policy evaluation.
result DQ estimator has exponentially smaller variance than off-policy methods.
New approach uses deep reinforcement learning for vehicle dispatching, reducing waiting times.
problem Dynamic vehicle dispatching problem in various contexts.
method Event-based semi-Markov decision process with deep q-learning.
result Deep reinforcement learning policies outperform heuristic methods in New York City data.
Road Network Metric Learning improves ETA prediction accuracy by addressing data sparsity.
problem Data sparsity in road network embedding vectors affects ETA prediction accuracy.
method Proposes Road Network Metric Learning (RNML-ETA) framework with an auxiliary metric learning task and triangle loss.
result RNML-ETA outperforms state-of-the-art models and improves prediction accuracy for cold links.
Study examines ridesourcing patterns in Chicago using K-prototypes segmentation.
problem Limited data on ridesourcing trips hinders research on urban mobility.
method Utilized K-prototypes algorithm for mixed data types segmentation.
result Six ridesourcing user segments identified based on various factors.
PESCAL uses mediators to learn from confounded offline data.
problem Learning from confounded observational data in reinforcement learning.
method PESCAL uses mediator variables and the pessimistic principle to address confounding bias and distributional shift.
result It is sufficient to learn a lower bound of the mediator distribution function to mitigate distributional shift.