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Journal Article 3

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OD 1

OD demand prediction 1

PAG-STAN 1

Pandemic 1

Urban rail transit 1

anaerobic-anoxic (A²/O) 1

biological phosphorus removal 1

denitrifying phosphorus removal 1

network travel time reliability 1

on-demand ride services 1

oxidation ditch (OD) 1

travel time rate 1

wastewater treatment 1

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Understanding network travel time reliability with on-demand ride service data

Xiqun (Michael) CHEN, Xiaowei CHEN, Hongyu ZHENG, Chuqiao CHEN

Frontiers of Engineering Management 2017, Volume 4, Issue 4, Pages 388-398 doi: 10.15302/J-FEM-2017046

Abstract: ., travel time spent for the unit distance, in min/km) of each origin-destination (OD) pair in the road

Keywords： network travel time reliability on-demand ride services travel time rate OD

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Anoxic phosphorus removal in a pilot scale anaerobic-anoxic oxidation ditch process

Hongxun HOU, Shuying WANG, Yongzhen PENG, Zhiguo YUAN, Fangfang YIN, Wang GAN

Frontiers of Environmental Science & Engineering 2009, Volume 3, Issue 1, Pages 106-111 doi: 10.1007/s11783-009-0005-8

Abstract: The anaerobic-anoxic oxidation ditch (A /O OD) process is popularly used to eliminate nutrients fromof DPB to biological nutrient removal, and enhance the denitrifying phosphorus removal in the A /O ODprocess, a pilot-scale A /O OD plant (375 L) was conducted.TN were 88.2%, 92.6%, 87.8%, and 73.1%, respectively, when the steady state of the pilot-scale A /O OD

Keywords： wastewater treatment anaerobic-anoxic (A²/O) oxidation ditch (OD) biological phosphorus removal

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Physics Guided Deep Learning-based Model for Short-term Origin-Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic

Shuxin Zhang,Jinlei Zhang,Lixing Yang,Feng Chen,Shukai Li,Ziyou Gao,

Engineering doi: 10.1016/j.eng.2024.04.020

Abstract: Accurate origin–destination (OD) demand prediction is crucial for the efficient operation and managementSpecifically, PAG-STAN introduces a real-time OD estimation module to estimate real-time complete ODSubsequently, a novel dynamic OD demand matrix compression module is proposed to generate dense real-timeOD demand matrices.Thereafter, PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD

Keywords： OD demand prediction Urban rail transit PAG-STAN Pandemic