Jishun Ou

Systematic Performance Assessment and Hybrid Strategy Development for Reliable Traffic Flow Imputation

Jishun Ou, Jingyuan Li, Abolfazl Karimpour, YANG CANG XU et autres

Traffic flow imputation is crucial for reliable traffic data analysis and modeling. While existing studies have focused on developing advanced imputation models, a few studies have systematically evaluated these models by considering a range of factors. Additionally, although simple imputation models can …

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Understanding Congestion Evolution in Urban Traffic Systems Across Multiple Spatiotemporal Scales: A Causal Emergence Perspective

Jishun Ou, W. X. Zhang, Pengxiang Yue, Qinghui Nie

Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic …

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Systematic inference of online urban travel demand: decomposition, observability, and error correction

Jishun Ou, Qinghui Nie, Abolfazl Karimpour, He Yang et autres

Origin-destination (OD) demand matrix plays an essential role in performance assessment and traffic management of road networks. While existing real-time models for time-varying OD estimation offer promising solutions, their applicability could be constrained by prior OD database development, insufficient system observability modelling, …

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A robust integrated multi-strategy bus control system via deep reinforcement learning

Qinghui Nie, Jishun Ou, Haiyang Zhang, Jiawei Lu et autres

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Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Jishun Ou, Jing Li, Chen Wang, Yun Wang et autres

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems (ITSs). Numerous studies have been conducted for traffic flow forecasting during the past decades. However, most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy. …

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DSIL4IMR: A dual step-based initial localization method for industrial mobile robot

Wenguang Li, Jishun Ou, Jiying Ren, Panling Huang et autres

This paper introduces a dual-step based initial localization method for industrial mobile robots (DSIL4IMR) that operates without user intervention at start-up. Utilizing a differential chassis equipped with a radar sensor, the method combines feature extraction and spatial information matching steps to locate …

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A Robust Integrated Multi-Strategy Bus Control System via Deep Reinforcement Learning

Qinghui Nie, Jishun Ou, Haiyang Zhang, Jiawei Lu et autres

An efficient urban bus control system has the potential to significantly reduce travel delays and streamline the allocation of transportation resources, thereby offering enhanced and user-friendly transit services to passengers. However, bus operation efficiency can be impacted by bus bunching. This problem …

A 2D‐connected automated vehicle car‐following control algorithm

Ran Yi, Yang Zhou, Jishun Ou, Xin Wang et autres

Car-following prevails in daily traffic under various scenarios. However, most connected automated vehicle (CAV) car-following algorithms in the literature only apply to one-dimensional CAV control, which limits the application scenarios. This paper presents a two-dimensional (2D) CAVs car-following strategy on curved roads …

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Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach

Jiawei Lu, Qinghui Nie, Yuqing Wang, Jingxin Xia et autres

Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially …

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Rich arc routing problem in city logistics: Models and solution algorithms using a fluid queue-based time-dependent travel time representation

Jiawei Lu, Qinghui Nie, Monirehalsadat Mahmoudi, Jishun Ou et autres

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A general dynamic sequential learning framework for vehicle trajectory reconstruction using automatic vehicle location or identification data

Yinpu Wang, Chengchuan An, Jishun Ou, Zhenbo Lu et autres

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Traffic Volatility Forecasting Using an Omnibus Family GARCH Modeling Framework

Jishun Ou, Xiangmei Huang, Yang Zhou, Zhigang Zhou et autres

Traffic volatility modeling has been highly valued in recent years because of its advantages in describing the uncertainty of traffic flow during the short-term forecasting process. A few generalized autoregressive conditional heteroscedastic (GARCH) models have been developed to capture and hence forecast …

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