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Calculation Method of Switch Machine Health Index Based on Long-Term and Short-Term Neural Network 1

5th International Symposium for Intelligent transportation and Smart City, ITASC 2022

作者： Shen, Tuo Zheng, Zhi Zeng, Xiaoqing Ying, Peiran Zhang, Xuanxiong School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety School of Transportation Engineering Tongji University Shanghai China The Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University No. 4800 Cao’an Road Shanghai201804 China

ISBN: (数字)9789819922529

ISBN: (纸本)9789819922512

With the continuous development of urban rail, it has become one of the main ways for people to travel. How to ensure the safe and efficient operation of urban rail transit is the focus of attention. The switch machine is one of the important equipment of the signal system. During the long-term service, the performance of the device gradually deteriorates or sudden failure, which will have a significant impact on the operation of the train. For this reason, it is necessary to evaluate the health status of the point machine to predict the life of the point machine. Therefore, a continuously distributed health index (HI) is proposed to characterize the health status of the switch machine in service throughout its life cycle. The operation state of Switch machine is related to the characteristics of its working current. Based on this assumption, this paper monitor the operation state of Switch machine by applying artificial intelligence technology. In this paper, Long-Short Term Memory is trained to predict the health index of the Switch machine. The result shows that the model used by this paper can achieve high accuracy and the prediction accuracy rate reaches 84.9%.It is reliable in predicting the health index of Switch machine. Finally, the HI can be used not only to evaluate the health status of switch machines, but also to predict the health status of other key rail equipment, and promote the development of smart rail transit operation and maintenance. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Life cycle

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Increasing the Accuracy of Classification Models with a Scaler for Bus Rapid Transit (BRT) Reliability Values 4

Increasing the Accuracy of Classification Models with a Scal...

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4th International Conference on Electrical engineering and Computer Science, ICECOS 2024

作者： Nur Hakim, Muhammad Iman Siswanto, Joko Qalban, Anas Azhimi Nuryono, Aninditya Anggari Automotive Engineering Technology Politeknik Keselamatan Transportasi Jalan Tegal Indonesia Road Transportation System Engineering Politeknik Keselamatan Transportasi Jalan Tegal Indonesia Universitas Islam Negeri Profesor Kiai Haji Saifuddin Zuhri Informatics Faculty of Dakwah Banyumas Indonesia Institut Teknologi Kalimantan Department of Informatics Balikpapan Indonesia

ISBN: (纸本)9798350368253

Classification of reliability values for Bus Rapid Transit (BRT) is very important to ensure services run smoothly and meet passenger expectations. Comparison of scaler variants is used in the KNN and DT algorithms to find a proposed classification model that is reliable and consistent. The DT and KNN (K=3 and Euclidean metric) algorithms use default parameters with 75% training data and 25% testing data, built with the Python programming language, and experiments using Jupyter Lab on macOS Venture 13.5. The purpose of this study is to find a classification model with the right scaler to classify the reliability of bus rapid transit. The use of a scaler changes the values of boarding, boarding per service hour, and punctuality in the dataset with large values in the scheme without a scaler, but the use of a scaler causes the values to decrease to a smaller extent. The DT algorithm with a standard scaler was found to be the most reliable and consistent classification model for BRT reliability values based on accuracy values. The use of appropriate algorithms and scalers is a finding to avoid classification data errors as seen from the data distribution and increase accuracy values. © 2024 IEEE.

关键词： Data reliability

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Discovering periodic frequent travel patterns of individual metro passengers considering different time granularities and station attributes

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International Journal of transportation Science and Technology 2024年第2期14卷 12-26页

作者： Zhibin Jiang Yan Tang Jinjing Gu Zhiqing Zhang Wei Liu College of Transportation Engineering Tongji UniversityShanghai 201804China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji UniversityShanghai 201804China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Tongji UniversityShanghai 201804China School of Information Science and Engineering Yunnan UniversityKunming 650500China The Key Laboratory of Internet of Things Technology and Application in Yunnan Province Kunming 650500China Technical Center of Shanghai Shentong Metro Group Co. Ltd.Shanghai 201103China

Periodic frequent pattern discovery is a non-trivial task to discover frequent patterns based on user interests using a periodicity *** conventional algorithms for periodic frequent pattern detection have numerous applications,there is still little research on periodic frequent pattern detection of individual passengers in the *** travel behavior of individual passengers has complex spatio-temporal characteristics in the metro network,which may pose new challenges in discovering periodic frequent patterns of individual metro passengers and developing mining algorithms based on real-world smart card *** study addresses these issues by proposing a novel pattern for metro passenger travel pattern called periodic frequent passenger traffic patterns with time granularities and station attributes(PFPTS).This discovered pattern can automatically capture the features of the temporal dimension(morning and evening peak hours,week)and the spatial dimension(entering and leaving stations).The corresponding complete mining algorithm with the PFPTS-tree structure has been *** evaluate the performance of PFPTS-tree,several experiments are conducted on one-year real-world smart card data collected by an automatic fare collection system in a certain large metro *** results show that PFPTS-Tree is efficient and can discover numerous interesting periodic frequent patterns of metro passengers in the real-world dataset.

关键词： Metro passenger travel pattern Spatio-temporal characteristics Periodic frequent pattern PFPTS-tree structure Smart card data

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction

Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakar...

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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Joko Siswanto Hendry Untung Rahardja Irwan Sembiring Erick Alfons Lisangan Muhammad Iman Nur Hakim Feri Wibowo Road Transportation System Engineering Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Information Technology Satya Wacana Christian University Indonesia Faculty of Science and Technology University of Raharja Tangerang Indonesia Faculty of Computer Science Satya Wacana Christian University Indonesia Computer Science and Software Engineering The University of Western Australia Perth Australia Automotive Engineering Technology Polytechnic of Road Transportation Safety Tegal Indonesia Faculty of Engineering and Science Muhammadiyah University Purwokerto Banyumas Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

A crucial role in the BRT transportation system’s planning, development, and operation is the prediction of passenger numbers. Using time-series data, it is necessary to develop careful prediction models, appropriate techniques, and an indepth understanding of the number of BRT Transjakarta passengers. A prediction model is sought based on comparing combined LSTM and BiLSTM experiments using three evaluation matrices and time. Historical data used from daily passenger data for 13 BRT Transjakarta corridors (1/01/2021-31/12/2023). The best prediction model was obtained from the BiLSTM-CNN combination with the lowest MSLE (0.0425), MAPE (0.1598), and SMAPE (15.9387) evaluation matrix values. However, it required a longer time (00:02:14). Predictions of passenger numbers on the 13 Transjakarta BRT corridors for the next 12 months can be made simultaneously by predicting fluctuations occurring simultaneously. The strongest positive correlation is in corridor 9-6, while the strongest negative correlation is in corridor 12-5. The prediction results must be understood by stakeholders, managers, and technopreneurs to develop and support appropriate strategies to increase the number of BRT passengers.

关键词： Deep learning Training Correlation Accuracy system performance Predictive models Data models Planning Stakeholders Long short term memory

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Spend More But Get Less: Unraveling the Causes of Bus Ridership Decline Using a Data-Driven Machine Learning Approach

SSRN

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SSRN 2024年

作者： Zhang, Yuliang Dong, Wentao Yu, Chengcheng Yang, Chao Intelligent Transportation System Research Center Hangzhou City University Hangzhou310015 China College of Transportation Engineering Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Caoan Road Shanghai201804 China Urban Mobility Institute Tongji University 1239 Siping Road Shanghai200082 China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji University 4800 Caoan Road Shanghai201804 China

Bus ridership experiences a critical decline. However, the complex causal interactions that impact ridership haven't been well understood because the correlation analysis cannot handle the inherent causal relationship between the related variables. To address this gap, we propose a data-driven causal machine-learning framework employing empirical panel data from 197 cities in China before COVID-19, which allows us to explore the heterogeneous causal effects on ridership and its relevant factors, rather than just focusing on correlation relationships. Our results indicate that the potential gains in bus ridership may be cancelled by the negative causal effect of increasing income, the operation of urban rail transit, ride-hailing, and bike-sharing services. The heterogeneous causal effects and counterfactual inference results further reveal the diminishing marginal utility of improving bus services on regaining ridership and the increasing marginal utility of bus ridership decline due to the inevitable growth of income and private car ownership. Based on our causal analysis, we propose a policy matrix that suggests policy combinations for cities in different situations. © 2024, The Authors. All rights reserved.

关键词： Buses

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Effective Finite Time Stability Control for Human-Machine Shared Vehicle Following system

arXiv

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arXiv 2024年

作者： Wang, Zihan Li, Mengran Zhang, Ronghui Zhao, Jing Hu, Chuan Ma, Xiaolei Qiu, Zhijun Guangdong Key Laboratory of Intelligent Transportation System School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510275 China Automotive Engineering Lab Department of Electromechanical Engineering University of Macau China School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Key Laboratory of Intelligent Transportation Technology and System School of Transportation Science and Engineering Beihang University Beijing100191 China Department of Civil and Environmental Engineering University of Alberta EdmontonAB Canada

With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to maintain stability when driver reaction time fluctuates, as these variations require different levels of system intervention. To address this issue, the proposed human-machine shared vehicle following assistance system (HM-VFAS) integrates driver outputs under various states with the assistance system. The system employs an intelligent driver model that accounts for reaction time delays, simulating time-varying driver outputs. A control authority allocation strategy is designed to dynamically adjust the level of intervention based on real-time driver state assessment. To handle instability from driver authority switching, the proposed solution includes a two-layer adaptive finite time sliding mode controller (A-FTSMC). The first layer is an integral sliding mode adaptive controller that ensures robustness by compensating for uncertainties in the driver output. The second layer is a fast non-singular terminal sliding mode controller designed to accelerate convergence for rapid stabilization. Using real driver videos as inputs, the performance of the HM-VFAS was evaluated. Results show that the proposed control strategy maintains a safe distance under time-varying driver states, with the actual acceleration error relative to the target acceleration maintained within ±0.5 m/s2 and the maximum acceleration error reduced by 1.2 m/s2. Compared to traditional controllers, the A-FTSMC controller offers faster convergence and less vibration, reducing the stabilization time by 27.3% . Copyright © 2024, The Authors. All rights reserved.

关键词： Adaptive control systems

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RCS: Automatic Navigation Framework for Legged Robots with Incremental RGB and Consistent Semantics Information

RCS: Automatic Navigation Framework for Legged Robots with I...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Liu, Dayu Chen, Teng Zhang, Guoteng Zhao, Zhicheng Rong, Xuewen Fan, Yong Li, Yibin Shandong Youbaote Intelligent Robotics CO. LTD Jinan250098 China Shandong University School of Control Science and Engineering 17923 Jingshi Road Jinan250061 China Ministry of Education Engineering Research Center of Lntelligent Unmanned System Jinan250061 China Shandong Jiaotong University School of Rail Transportation Jinan250357 China

ISBN: (纸本)9798350325706

Autonomous movement of legged robots in outdoor scenes requires accurate state estimation and environment navigation maps. Different from wheeled and tracked mobile robots, legged robots can adjust parameters such as gait type, step height, gait frequency, torso height, foot friction, and other coefficients to better adapt to the geometric characteristics and physical properties of verstile terrains. To this end, in this paper, we propose a framework for autonomous navigation of legged robots, including the construction of geometric-property RGB consistent semantic map (RCS) and the autonomous navigation method under the RCS map. The method for building RCS map formulates lidar-inertial odometry based on a factor graph. Through end-to-end semantic segmentation of images, terrain information is projected and saved while mapping. For achieving pose and point cloud consistency, local and global lidar boudle adjustment (BA) is introduced to the optimization process. We demonstrated the effectiveness of RCS through outdoor navigation experiments on a quadruped robot. The experiment shows that our terrain detection method can achieve an average detection time of 30 ms on an embedded board. © 2023 IEEE.

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Predictive Resilience Assessment of road Networks Based on Dynamic Multi-Granularity Graph Neural Network

SSRN

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SSRN 2024年

作者： Zang, Di Ding, Yongjie Zhao, Jiayi Tang, Keshuang Zhu, Hong Key Laboratory of Embedded System and Service Computing Ministry of Educaiton Department of Computer Science and Technology Tongji University No. 4800 Cao’an Road Shanghai China Key Laboratory of Road and Traffic Engineering Ministry of Education College of Transportation Engineering Tongji University No. 4800 Cao’an Road Shanghai China

Due to the influence of global climate anomalies, abnormal weather conditions such as heavy rainfall have become more frequent in recent years, posing a significant threat to the operation of transportation systems. An effective assessment of the resilience of the transportation system before and during heavy rain can alert the transportation department to take necessary emergency actions. However, existing methods for assessing the rainfall resilience of transportation networks mostly suffer from the following problems: 1) Simulation methods for modeling rainfall impacts lack realism;2) After-the-fact evaluations of resilience cannot offer advance warning prior to or during a heavy rain event. To address above problems, we present a novel resilience assessment methodology for evaluating the resilience of road networks in real-time during heavy rainfall scenarios. In this methodology, we propose the temporal decomposition-based dynamic multi-granularity graph neural network (TD2MG2NN) for long-term traffic speed forecasting, providing a perspective on the future evolution of traffic states for accurate resilience assessment. In addition, we construct a composite traffic resilience indicator, designed to comprehensively reflect changes in the spatial-temporal resilience of the transportation system during heavy rain. Experimental results on four publicly real datasets indicate that the prediction performance of TD2MG2NN outperforms state-of-the-art models. The assessment results for the transportation road network in California demonstrate that the comprehensive resilience indicator is superior to single functional resilience indicator and the real-time methodology for evaluating resilience can accurately depict and predict the operation of the road network system under heavy rainfall scenarios. © 2024, The Authors. All rights reserved.

关键词： roads and streets

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Collaborative rescheduling of train timetables to relieve passenger congestions in an urban rail transit network: A rolling horizon approach

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International Journal of transportation Science and Technology 2024年

作者： Wang, Fangsheng Wang, Pengling Hao, Xiaoyu Yang, Rudong Xu, Ruihua College of Transportation Tongji University China The Key Laboratory of Road and Traffic Engineering Ministry of Education China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Tongji University 4800 Cao'an Road Shanghai201804 China CASCO Signal Co. Ltd China

At some urban rail transit (URT) stations, large activities, emergencies, or holidays usually lead to a significant surge in passenger flow demand quickly, known as Large Passenger Flow (LPF) events. The passenger congestion will spread quickly via transfer stations and affect other stations and lines in the URT network. This study develops a timetable rescheduling and coordinating method for the URT network under LPF events. Firstly, a collaborative adjustment model of train timetables with an implement backup vehicle strategy is formulated to simultaneously consider rescheduling and coordinating problems, to reduce the congestion influence for an URT network. Then, a rolling horizon approach is developed to divide the whole adjustment problem into several decision-making stages to ensure solution efficiency. In each decision-making stage, the influence of LPF propagation within the URT network is firstly evaluated. Based on the congestion evaluation results, the proposed method determines whether it's necessary to adjust timetables of the LPF Line or other lines. The proposed method is applied to the Xi'an Metro network in China. The results indicate that the proposed method can effectively evaluate and adjust the train timetables for large URT networks under the LPF events. © 2024 Tongji University and Tongji University Press

关键词： Urban transportation

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Evaluating rail transit assignment models in the temporal dimension: The problem and its solution

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International Journal of transportation Science and Technology 2024年

作者： Zhu, Wei Wei, Jin Xu, Changyue College of Transportation Engineering Tongji University Shanghai201804 China The Key Laboratory of Road and Traffic Engineering of the Ministry of Education Tongji University Shanghai201804 China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Tongji University Shanghai201804 China

Passenger flow is the foundation for urban rail transit (URT) operations. However, its calculated results from assignment models may deviate from the actual situation in both spatial and temporal dimensions, which arouses more attention and needs to be evaluated in particular. On the other hand, onboard video data from URT trains provides a potential way for model evaluation. This study defines the evaluation problem and proposes a methodological solution for evaluating rail transit assignment models in the temporal dimension, including qualitative validation and difference quantification. A suitable time granularity is determined for the best effectiveness and onboard video data is used for actual passenger flow extraction. The gap between the actual and the calculated by the model is identified with nonparametric statistical techniques (NPSTs) and quantified with time series similarity measurement (TSSM) methods. A case study on the Shanghai metro demonstrates the performance of the proposed approach, and several practice implications for URT operation agencies are also discussed. © 2024

关键词： Light rail transit

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