Search Results for author: Yunhan Zheng

Found 6 papers, 1 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

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.

Fairness

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.

Fairness

Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey

no code implementations25 Jan 2023 Xuan Jiang, Yuhan Tang, Junzhe Cao, Vishwanath Bulusu, Hao, Yang, Xin Peng, Yunhan Zheng, Jinhua Zhao, Raja Sengupta

Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve accessibility.

Equality of opportunity in travel behavior prediction with deep neural networks and discrete choice models

1 code implementation25 Sep 2021 Yunhan Zheng, Shenhao Wang, Jinhua Zhao

Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms.

Discrete Choice Models Fairness

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