Search Results for author: Dingyi Zhuang

Found 15 papers, 7 papers with code

Advancing Transportation Mode Share Analysis with Built Environment: Deep Hybrid Models with Urban Road Network

no code implementations23 May 2024 Dingyi Zhuang, Qingyi Wang, Yunhan Zheng, Xiaotong Guo, Shenhao Wang, Haris N Koutsopoulos, Jinhua Zhao

Transportation mode share analysis is important to various real-world transportation tasks as it helps researchers understand the travel behaviors and choices of passengers.

Feature Engineering Graph Embedding

Time Series Supplier Allocation via Deep Black-Litterman Model

no code implementations30 Jan 2024 Jiayuan Luo, Wentao Zhang, Yuchen Fang, Xiaowei Gao, Dingyi Zhuang, Hao Chen, Xinke Jiang

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.

Navigate Time Series

Fairness-Enhancing Vehicle Rebalancing in the Ride-hailing System

no code implementations29 Dec 2023 Xiaotong Guo, Hanyong Xu, Dingyi Zhuang, Yunhan Zheng, Jinhua Zhao

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.


Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction

1 code implementation10 Sep 2023 Xiaowei Gao, Xinke Jiang, Dingyi Zhuang, Huanfa Chen, Shenhao Wang, James Haworth

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

Fairness-enhancing deep learning for ride-hailing demand prediction

no code implementations10 Mar 2023 Yunhan Zheng, Qingyi Wang, Dingyi Zhuang, Shenhao Wang, Jinhua Zhao

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

1 code implementation7 Mar 2023 Qingyi Wang, Shenhao Wang, Dingyi Zhuang, Haris Koutsopoulos, Jinhua Zhao

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

The Braess Paradox in Dynamic Traffic

no code implementations7 Mar 2022 Dingyi Zhuang, Yuzhu Huang, Vindula Jayawardana, Jinhua Zhao, Dajiang Suo, Cathy Wu

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

1 code implementation24 Sep 2021 Yuankai Wu, Dingyi Zhuang, MengYing Lei, Aurelie Labbe, Lijun Sun

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

1 code implementation21 May 2021 Xudong Wang, Yuankai Wu, Dingyi Zhuang, Lijun Sun

This paper studies the traffic state estimation (TSE) problem using sparse observations from mobile sensors.

Matrix Completion

Inductive Graph Neural Networks for Spatiotemporal Kriging

1 code implementation13 Jun 2020 Yuankai Wu, Dingyi Zhuang, Aurelie Labbe, Lijun Sun

Time series forecasting and spatiotemporal kriging are the two most important tasks in spatiotemporal data analysis.

Graph Neural Network Time Series +1

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