Two‐step method to reduce metro transit energy consumption by optimising speed profile and timetable

Jin, Bo; Sun, Pengfei; Wang, Hongtao; Feng, Xiaoyun

doi:10.1049/iet-its.2019.0103

Cited by 13 publications

(16 citation statements)

References 29 publications

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“…ITS is able to assist smart city saving power in the subway and the trucking. Due to the massive transportation capacity and excellent convenience, the metro systems have been expanding rapidly in recent years [9]. For this reason, it can be supposed that the subway system may have a high possibility also acting as an essential part of public transportation in a smart city.…”

Section: Energy Reductionmentioning

confidence: 99%

“…For this reason, it can be supposed that the subway system may have a high possibility also acting as an essential part of public transportation in a smart city. However, with the increment in energy price and the public awareness of the environment protection, the massive energy consumption of the metro transit system becomes an urgent issue [9]. Moreover, a smart city concentrates on creating an efficient region for residents (mentioned in the definition of smart city), which involves improving the energy utilization level.…”

Section: Energy Reductionmentioning

confidence: 99%

“…Therefore, it's essential to reduce energy consumption for metro systems in a smart city. In Jin's study, researchers are able to save power in two ways with the application of ITS [9]. The first is establishing a more efficient timetable for departing and stopping time in the station based on the traffic volume need because ITS have the ability to monitor the real-time driving condition and location of the train [9].…”

Section: Energy Reductionmentioning

confidence: 99%

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The Study of the Roles that Intelligent Transportation System Playing in the Development of a Smart City

Pu¹,

Zhou²

2023

HSET

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With the acceleration of urbanization, cities in the future will encounter various kinds of problems such as providing adequate living space, water and transportation. To address these issues which cities may face in the future, the concepts of Intelligent Transportation System (ITS) and Smart City come into being. Since both two areas aim to address possible urban issues in the future, it’s essential to study the relationship between ITS and Smart city. In this article, researchers will initially introduce serval reasons to conduct the study. Later, in the main body part, 3 possible roles are presented to illustrate the possible relationship between ITS and Smart City through reviewing other researchers’ articles. Finally, this article will give serval evaluations and suggestions about those 3 roles above. The whole article aims to state that ITS is an essential part in the future development of smart city, though it is not mature now and may encounter many problems in the process of development.

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Section: Energy Reductionmentioning

confidence: 99%

Section: Energy Reductionmentioning

confidence: 99%

Section: Energy Reductionmentioning

confidence: 99%

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The Study of the Roles that Intelligent Transportation System Playing in the Development of a Smart City

Pu¹,

Zhou²

2023

HSET

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“…The first stage is to optimize a desired speed trajectory as the tracking target, which can ensure the operation requirements for high-speed train, including safety, arrival on time, energy conservation etc. [3][4][5][6]. Based on the first stage, the second stage of ATO is to control the high-speed train to track the desired trajectory.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy sliding mode control for high‐speed train using multi‐body dynamics model

Guo

Sun

Feng

et al. 2022

IET Intelligent Trans Sys

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In this paper, an equivalent multi‐body dynamics model of high‐speed train with uncertain parameter is established. Based on this detailed model and neural network minimum parameter learning algorithm, an adaptive sliding mode control method is designed for speed and position tracking control of high‐speed train, and the stability of the proposed control method is proved strictly. Then an adaptive fuzzy sliding mode control method (AFSMC) is presented to avoid chattering caused by excessive robust switching gain. Finally, numerical tests are carried out on the proposed AFSMC under different scenarios and operation strategies, and model reference adaptive control (MRAC), PID control and sliding mode control are also tested under the same condition for performance comparison. The results show that the proposed control method is superior to the currently used method. More specifically, compared with MRAC and PID, the average speed tracking accuracy of AFSMC is improved by 0.25 km/h and 0.18 km/h, and the average position tracking accuracy is improved by 0.68 m and 0.7 m, respectively.

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“…Su et al ( 6 ) proposed an approach for target speed optimization based on the automatic train operation (ATO) control principles to reduce the traction energy consumption. Jin et al ( 7 ) proposed adding coasting phases to the no-coasting phase in the energy-efficient speed profile in the conditions of speed limit or varying gradient, so as to reduce energy. Chen et al ( 8 ) proposed searching for the switching speed to switch the mode from traction to coasting for the single-train speed profile to reduce the energy consumption in the interval, and this was the basis for optimizing the multi-train speed profile.…”

mentioning

confidence: 99%

Train Operation Curve Optimization for an Urban Rail Interval With Multi-Parameter Adjustment

Deng

Zhong

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This paper proposes an energy consumption optimization method of train operation by controlling multiple parameters for an urban rail interval. The parameters include the maximum operation speed, the ratio of the minimum coasting speed to the maximum speed, the maximum traction, and the braking force. Based on the mass belt model, an optimization model of train operation is built with these parameters as decision variables. The objective function is to minimize the energy consumption and the penalty on the running time delays for the observed rail interval. A simulated annealing algorithm is designed to solve the model, which adopts synergic adjustment of multiple parameters based on the influence of the running time with variation of each parameter. A numerical example of Guangzhou Metro Line 8 is adopted to test the method. The results show that the maximum operation speed has a decisive influence on the optimal train operation strategy, interval running time, and energy consumption; the train traction has a direct effect on energy consumption; the train braking force has multiple optimal solutions; moreover, the energy-saving strategy has a significant effect on energy saving in a longer running time.

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Two‐step method to reduce metro transit energy consumption by optimising speed profile and timetable

Cited by 13 publications

References 29 publications

The Study of the Roles that Intelligent Transportation System Playing in the Development of a Smart City

The Study of the Roles that Intelligent Transportation System Playing in the Development of a Smart City

Adaptive fuzzy sliding mode control for high‐speed train using multi‐body dynamics model

Train Operation Curve Optimization for an Urban Rail Interval With Multi-Parameter Adjustment

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