Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression

Tang, Zhihua; Yin, Hua; Yang, Caiyun; Yu, Junyan; Guo, Hai‐Yang

doi:10.1016/j.scs.2020.102690

Cited by 23 publications

(10 citation statements)

References 28 publications

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“…Each line of the urban railway has a different passenger flow and distribution because of its different urban passenger traffic and different land uses along the line [15,16].…”

Section: Spatial Distribution Of Passenger Flowmentioning

confidence: 99%

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Zhang

2022

Mathematical Problems in Engineering

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Based on spatial data dynamic analysis technology, the characteristics of urban rail transit passenger flow are analyzed. This study analyzes the passenger flow characteristics of urban rail transit from the perspective of operation and planning by using the dynamic analysis technology of spatial data, combined with social and economic development. Based on the analysis of research data from the study of passenger traffic and passenger traffic data measurement, the characteristics of passenger traffic from urban railways are derived from the time distribution, spatial distribution, and transfer of passengers waiting on the platform. The final length of line 3 is 39.7 m, the passenger volume is 102, with 11 people/day, the passenger flow density is 1.8/2.3, and the high section in peak hours is 2.4/3.2. By considering the various influencing factors, efforts should be made to develop urban rail transit projects to promote the growth of social and economic benefits.

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“…Each line of the urban railway has a different passenger flow and distribution because of its different urban passenger traffic and different land uses along the line [15,16].…”

Section: Spatial Distribution Of Passenger Flowmentioning

confidence: 99%

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Zhang

2022

Mathematical Problems in Engineering

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“…Several research efforts have investigated models to reduce system-wide energy consumption in URTs. Generally, URT system energy consumption is affected by several variables, such as ridership, temperature, and train schedules, among others (4)(5)(6)(7). A comprehensive systems analysis of European URTs identified various dimensions of energy consumption and showed that energy savings of up to 35% could be realized in a URT by optimizing timetables, implementing efficient driving techniques, and installing energy-saving infrastructure (8).…”

Section: Related Workmentioning

confidence: 99%

Line-Specific Energy Modeling Framework for Urban Rail Transit Systems: A Case Study of Boston

Han

Gonzales

Christofa

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Urban rail transit systems play an essential role in improving mobility and efficiency. A complex rail transit network serves the Boston metropolitan area, U.S., which costs $38 million for the 422 GWh of system electricity consumed annually. With the aim of developing a tool for energy and cost reduction decision support, we propose a comprehensive machine learning framework to investigate line-specific contributions to energy. This effort builds on prior work in estimating a system-wide energy model for the Boston network. By introducing line-specific train movement and operation variables, we obtain a higher-performing model [Formula: see text]. Furthermore, the model better explains the relationship between energy and train movement, ridership, and weather variables. Most importantly, the model facilitates analyses of how each line contributes to system consumption at the hour level. We found that the non-line-specific variables made a contribution of −2.7% to the average hourly energy of consumption of −5.4 MWh with a baseline energy consumption of 39 MWh. The Red Line dominates the energy consumption among line-specific variables, contributing 2.3% to the hourly average. Our model could be further enhanced to evaluate the energy and cost impacts of line-specific strategies that may be required for future planning and disaster response, as well as for real-time energy monitoring by line.

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“…As one of the SVM applications, SVR is used for regression, producing a linear mapping between input and target variables, and function estimation. Solving regression problems is the main function of SVR [111], [112]. Among SVR models, the classical model (ε-SVR) is a version of SVR basically considered in engineering and also used in this work.…”

Section: ) Support Vector Regression (Svr)mentioning

confidence: 99%

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

et al. 2022

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Predicting the electricity consumption of urban rail transit based on binary nonlinear fitting regression and support vector regression

Cited by 23 publications

References 28 publications

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Analysis of Passenger Flow Characteristics of Urban Rail Transit Based on Spatial Data Dynamic Analysis Technology

Line-Specific Energy Modeling Framework for Urban Rail Transit Systems: A Case Study of Boston

Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis

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