no code implementations • 4 Mar 2024 • Jinhua Zhao, Lili Wu, Xiaoan Yang, Zhilaing Gao, Hong Deng
Patients were categorized into stage 1 or stage 2 based on a baseline eGFR of less than 90 ml/min/m^2 Results: 125 CHB patients were matched 1:1 in both the combined treatment and cured groups.
no code implementations • 11 Feb 2024 • Yihong Tang, Zhaokai Wang, Ao Qu, Yihao Yan, Kebing Hou, Dingyi Zhuang, Xiaotong Guo, Jinhua Zhao, Zhan Zhao, Wei Ma
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.
no code implementations • 29 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.
no code implementations • 30 Nov 2023 • Baichuan Mo, Hanyong Xu, Dingyi Zhuang, Ruoyun Ma, Xiaotong Guo, Jinhua Zhao
Travel behavior prediction is a fundamental task in transportation demand management.
no code implementations • 9 Aug 2023 • Michael Leong, Awad Abdelhalim, Jude Ha, Dianne Patterson, Gabriel L. Pincus, Anthony B. Harris, Michael Eichler, Jinhua Zhao
Transit riders' feedback provided in ridership surveys, customer relationship management (CRM) channels, and in more recent times, through social media is key for transit agencies to better gauge the efficacy of their services and initiatives.
no code implementations • 10 May 2023 • Zepu Wang, Dingyi Zhuang, Yankai Li, Jinhua Zhao, Peng Sun, Shenhao Wang, Yulin Hu
Traffic data serves as a fundamental component in both research and applications within intelligent transportation systems.
no code implementations • 10 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.
1 code implementation • 7 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.
no code implementations • 7 Mar 2023 • Qingyi Wang, Shenhao Wang, Yunhan Zheng, Hongzhou Lin, Xiaohu Zhang, Jinhua Zhao, Joan Walker
The latent space in deep hybrid models can be interpreted, because it reveals meaningful spatial and social patterns.
no code implementations • 10 Jan 2023 • Baichuan Mo, Qing Yi Wang, Xiaotong Guo, Matthias Winkenbach, Jinhua Zhao
To further capture the global efficiency of the route, we propose a new iterative sequence generation algorithm that is used after model training to identify the first stop of a route that yields the lowest operational cost.
no code implementations • 22 Nov 2022 • Awad Abdelhalim, Jinhua Zhao
We propose and evaluate an end-to-end framework integrating traditional transit data sources with a roadside camera for automated roadside image data acquisition, labeling, and model training to predict transit travel times across a segment of interest.
1 code implementation • 11 Aug 2022 • Dingyi Zhuang, Shenhao Wang, Haris N. Koutsopoulos, Jinhua Zhao
Recently, spatial-temporal deep learning models demonstrate the tremendous potential to enhance prediction accuracy.
no code implementations • 7 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.
no code implementations • 4 Jan 2022 • Nicholas S. Caros, Jinhua Zhao
A gradual growth in flexible work over many decades has been suddenly and dramatically accelerated by the COVID-19 pandemic.
1 code implementation • 25 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.
no code implementations • 1 Feb 2021 • Shenhao Wang, Baichuan Mo, Stephane Hess, Jinhua Zhao
The relative ranking of the ML and DCM classifiers is highly stable, while the absolute values of the prediction accuracy and computational time have large variations.
no code implementations • 11 Jan 2021 • Baichuan Mo, Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao
Individual mobility is driven by demand for activities with diverse spatiotemporal patterns, but existing methods for mobility prediction often overlook the underlying activity patterns.
no code implementations • 22 Oct 2020 • Shenhao Wang, Baichuan Mo, Jinhua Zhao
However, the two methods are highly complementary because data-driven methods are more predictive but less interpretable and robust, while theory-driven methods are more interpretable and robust but less predictive.
no code implementations • 16 Sep 2019 • Shenhao Wang, Baichuan Mo, Jinhua Zhao
Overall, this study demonstrates that prior behavioral knowledge could be used to guide the architecture design of DNN, to function as an effective domain-knowledge-based regularization method, and to improve both the interpretability and predictive power of DNN in choice analysis.
2 code implementations • 9 Sep 2019 • Dániel Kondor, Xiaohu Zhang, Malika Meghjani, Paolo Santi, Jinhua Zhao, Carlo Ratti
Recent technological developments have shown significant potential for transforming urban mobility.
Computers and Society
no code implementations • 2 Jan 2019 • Shenhao Wang, Qingyi Wang, Jinhua Zhao
This study presents a framework of multitask learning deep neural networks (MTLDNNs) for this question, and demonstrates that MTLDNNs are more generic than the traditional nested logit (NL) method, due to its capacity of automatic feature learning and soft constraints.
no code implementations • 11 Dec 2018 • Shenhao Wang, Qingyi Wang, Jinhua Zhao
To demonstrate the strength and challenges of DNNs, we estimated the DNNs using a stated preference survey, extracted the full list of economic information from the DNNs, and compared them with those from the DCMs.
no code implementations • 5 Aug 2018 • Xiaojiang Li, Bill Yang Cai, Waishan Qiu, Jinhua Zhao, Carlo Ratti
GSV images have view sight similar to drivers, which would make GSV images suitable for estimating the visibility of sun glare to drivers.