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Found 14 papers, 8 papers with code
Synergizing Spatial Optimization with Large Language Models for Open-Domain Urban Itinerary Planning
In this paper, we for the first time propose the task of Open-domain Urban Itinerary Planning (OUIP) for citywalk, which directly generates itineraries based on users' requests described in natural language.
Time Series Supplier Allocation via Deep Black-Litterman Model
Time Series Supplier Allocation (TSSA) poses a complex NP-hard challenge, aimed at refining future order dispatching strategies to satisfy order demands with maximum supply efficiency fully.
Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System
The results suggest that our proposed method enhances both accuracy and fairness in forecasting ride-hailing demand, ultimately resulting in more equitable vehicle rebalancing in subsequent operations.
Large Language Models for Travel Behavior Prediction
Travel behavior prediction is a fundamental task in transportation demand management.
Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction
This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity.
Uncertainty Quantification via Spatial-Temporal Tweedie Model for Zero-inflated and Long-tail Travel Demand Prediction
Understanding Origin-Destination (O-D) travel demand is crucial for transportation management.
ST-GIN: An Uncertainty Quantification Approach in Traffic Data Imputation with Spatio-temporal Graph Attention and Bidirectional Recurrent United Neural Networks
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems.
Fairness-enhancing deep learning for ride-hailing demand prediction
When coupled with the bias mitigation regularization method, the de-biasing SA-Net effectively bridges the mean percentage prediction error gap between the disadvantaged and privileged groups, and also protects the disadvantaged regions against systematic underestimation of TNC demand.
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networks
This Prob-GNN framework is substantiated by deterministic and probabilistic assumptions, and empirically applied to the task of predicting the transit and ridesharing demand in Chicago.
Uncertainty Quantification of Sparse Travel Demand Prediction with Spatial-Temporal Graph Neural Networks
Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy.
The Braess Paradox in Dynamic Traffic
The Braess's Paradox (BP) is the observation that adding one or more roads to the existing road network will counter-intuitively increase traffic congestion and slow down the overall traffic flow.
Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging
Specifically, we propose a novel spatial aggregation network (SAN) inspired by Principal Neighborhood Aggregation, which uses multiple aggregation functions to help one node gather diverse information from its neighbors.
Low-Rank Hankel Tensor Completion for Traffic Speed Estimation
This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors.
Inductive Graph Neural Networks for Spatiotemporal Kriging
Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.
