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Unveiling the influential factors for customized bus service reopening from naturalistic observations in Shanghai

International Journal of transportation Science and Technology 2024年第1期13卷 106-121页

作者： Yu Shen Chenlong Xu Shengchuan Jiang Zhikang Zhai Yuxiong Ji Yuchuan Du Key Laboratory of Road and Traffic Engineering of the MOE Tongji Unviersity4800 Cao’an Hwy.Shanghai 201804China Department of Transportation System Engineering Business SchoolUniversity of Shanghai for Science and Technology516 Jungong Rd.Shanghai 200093China

This work attempts to understand how a customized bus(CB)operator decides to open or close a CB *** look into the changes in the operation status of CB lines(*** and closure)from one of the largest CB operators in Shanghai,China,with a 22-month con secutive observation ranging from January 2019 to October *** all CB services were totally suspended at the beginning of 2020 due to the COVID-19 travel restriction and then gradually recovered in March 2020,we utilize this study period as a naturalistic observa tion experiment to investigate the changes in the operation status of each CB line before and after the travel *** the operation status at each month as the binary alternatives,the mixed logit models and the tree-based models with explainable machine learning techniques are respectively adopted to explore the factors that influence the decision-making *** findings from both types of models are in general *** results show that the characteristics of each CB line including the ridership,the length of the line,the closeness to charging stations,and the overlap of CB lines significantly impact the *** addition,the land-use types around the CB stops and the market competition from alternative travel modes also play a key role in making the decisions.

关键词： Customized bus Decision-making Naturalistic observations Discrete choice models Explainable machine learning

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Hybrid Deep Learning Model of LSTM and BiLSTM for Transjakarta Passenger Prediction 3

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

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

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

ISBN: (纸本)9798350367492

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. © 2024 IEEE.

关键词： Prediction models

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U75V Rail Wear Research Using a Full-Scale Wheel-Rail Test Rig and 3D Scanning Technology 3

U75V Rail Wear Research Using a Full-Scale Wheel-Rail Test R...

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3rd International Conference on Rail transportation, ICRT 2024

作者： Weng, Zhiyi Zhou, Yu Li, Junpeng Wang, Chi Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety College of Transportation Tongji Univ. Shanghai China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

Based on real wheel-rail system, a full-scale wheel-rail test rig was designed and manufactured to simulate the working conditions of heavy haul railway, taking into account the mechanical and dynamic systems. The test rig was utilized for graded loading in order to investigate the evolution law of surface wear of U75V rail under different load levels. These tests covered axle load levels from 14 to 30 t and were divided into nine groups, with each group undergoing 200,000 rolling contact wear cycles. Each 20,000 rolling cycles were considered as an observation phase, and at the end of each phase, changes in rail surface morphology and wear volume were observed and quantitatively analyzed using an optical microscope and a 3D profilometer. The study revealed that, after controlling for environmental variables, the primary wear mechanisms on the rail surface were adhesive wear and fatigue wear. As the number of wheel cycles increased, there was an alternating dominance between adhesive wear and fatigue wear. With increasing axle load levels, the wear volume under the same number of rolling cycles showed a three-tiered nonlinear increase, with boundaries at 20 and 28 t. © ASCE.

关键词： Axles

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Frequency and Reliability Analysis of Load-Bearing Composite Beams

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Automotive Innovation 2024年第1期7卷 194-207页

作者： Junlei Wei Lingyu Sun Xinli Gao Wenfeng Pan Jiaxin Wang Jinxi Wang School of Transportation Science and Engineering Beihang University37 Xueyuan RoadHaidian DistrictBeijing 100191China Beijing Key Laboratory for High-Efficient Power Transmission and System Control of New Energy Resource Vehicle Beihang University37 Xueyuan RoadHaidian DistrictBeijing 100191China Beijing Hangshu Vehicle Data Research Institute Co. Ltd.Beijing 100062China

The increasing utilization of fiber-reinforced thermoplastics(FRTPs)as a substitute for metal in load-bearing structures poses challenges related to NVH issues arising from frequency variations and reliability concerns stemming from fiber dispersion within the resin *** this study,the steel automobile seat beam serves as a benchmark for *** beams are designed and fabricated using two distinct processes:compression molding and injection ***,their modal frequency and reliability are meticulously *** investigations are conducted to explore the influence of various factors,including the combination of laminates and ribs,as well as the stacking sequence of laminates,on the modal *** findings reveal that the modal frequency and vibration mode are subject to alterations based on the fiber type,beam material,and laminate stacking ***,in comparison to the steel benchmark,the first-order frequency of the FRTP beam in this study experiences a 6.59%increase while simultaneously achieving a weight reduction of 32.42%.To assess reliability,a comprehensive analysis is performed,considering a six-fold standard *** analysis yields the permissible range of fluctuation for material elastic constants,bending performance,and frequency ***,the FRTP beams meet the required reliability *** results provide valuable insights for comprehending the stiffness-dependent response and effectively controlling structural performance when implementing FRTP for weight reduction purposes.

关键词： Automobile Fiber-reinforced thermoplastics Lightweight design Frequency change Reliability analysis

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Train rescheduling and platforming in large high-speed railway stations

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International Journal of transportation Science and Technology 2024年第4期16卷 100-118页

作者： Jing Teng Jinke Gao Pengling Wang Siyuan Qu College of Transportation Engineering Tongji University4800 Cao’an RoadShanghai 201804China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Tongji University4800 Cao’an RoadShanghai 201804China Shanghai Collaborative Innovation Research Center for Multi-Network&Multi-Modal Rail Transit Tongji University4800 Cao’an RoadShanghai 201804China Transport Department China Railway Shanghai Group Co.Ltd80 East Tianmu RoadShanghai 200070China

To deal with train delays in large high-speed railway stations,a multi-objective mixedinteger nonlinear programming(MO-MINLP)optimization model was *** model used the arrival time,departure time,track occupation,and route selection as the decision variables,and fully considered the station infrastructure layout,train operational requirements,and time standards as limiting *** optimization objectives were to minimize train delays and reduce track and to route *** realize the large-scale and rapid solution of the MO-MINLP model,this study proposed a rolling horizon optimization algorithm that used half an hour as a time interval and solved the rescheduling and platforming problem of each time interval *** numerical experiments,227 train movements under delay circumstances in Hangzhoudong station were optimized by using the proposed model and solution *** results show that the proposed MO-MINLP model could resolve route conflicts,compress unnecessary dwell times,and reduce train delays,and the solution algorithm could efficiently increase the computational *** maximum solution time for optimizing the 227 train movements is 15 min 24 s.

关键词： Large high-speed railway station Rescheduling and platforming Multiple objective mixed-integer nonlinear programming(MO-MINLP) Rolling horizon

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A Data-Driven Calibration Method of Short-Term Passenger Flow Prediction Models for Urban Rail Transit systems 24

A Data-Driven Calibration Method of Short-Term Passenger Flo...

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24th COTA International Conference of transportation Professionals: Resilient, Intelligent, Connected, and Lowcarbon Multimodal transportation, CICTP 2024

作者： Miao, Qiqi Xi, Sulu Liu, Zechen Zhu, Wei Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety College of Transportation Engineering Tongji Univ. Shanghai China Nanchang Metro Co. Ltd. Nanchang China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

At present, a variety of models exist for short-term passenger flow prediction, each of which requires parameter calibration in practical applications. However, the parameters for most currently used models are set using empirical values, leading to occasional discrepancies between actual outcomes and model predictions. This paper proposes a data-driven parameter calibration method for short-term passenger flow prediction models of urban rail transit systems with a genetic algorithm. The method’s accuracy and effectiveness are initially verified through a practical case study of the Nanchang Metro. The results indicate that the method can enhance the precision of parameter values and reduce the deviations between actual outcomes and model predictions in urban rail transit systems. © ASCE.

关键词： Prediction models

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Mining motif periodic frequent travel patterns of individual metro passengers considering uncertain disturbances

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International Journal of transportation Science and Technology 2024年第3期15卷 102-121页

作者： Yan Tang Zhibin Jiang Xiaolei Zou Xinkuan Liu Qi Zhang Shenmeihui Liao College of Transportation Engineering Tongji UniversityShanghai 201804China Key Laboratory of Road and Traffic Engineering Ministry of Education Tongji UniversityShanghai 201804China Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Tongji UniversityShanghai 201804China Technical Center of Shanghai Shentong Metro Group Co. Ltd.Shanghai 201103China

Periodic pattern mining is of great significance for understanding passenger travel behav-ior,but the previous works mainly focused on the trajectory data and the dimension of the spot/***,many uncertain factors(severe weather,traffic accident,etc.)may interfere with discovering original and accurate periodic travel *** paper pro-poses a novel type of travel pattern called motif periodic frequent pattern(MPFP),which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics considering,uncertain *** also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data(SCD),and apply the real long-time-span experimental data from a large-scale metro system is *** show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of“week”.Only the top 10 types of all 4624 types account for about 95%of all motifs and the top 5 types constitute about 90%,and the MPFP of the top 3 types of motifs account for nearly 80%of all periodic patterns,in which Mono-MPFP and 2-MPFP are the main *** relatively stable time range of MPFP is three months,and the threshold for the optimal uncertain disturbance factor should be set at 5%.Additionally,several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.

关键词： Metro passenger travel pattern(MPFP) Periodic frequent pattern Temporal motif Smart card data(SCD)mining

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Research on the Adaptability of Train Speed on Kilometer-Span Bridge for High-Speed Railway 3

Research on the Adaptability of Train Speed on Kilometer-Spa...

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3rd International Conference on Rail transportation, ICRT 2024

作者： Sun, Zhiang Yang, Xinwen Zhao, Wenbo Yang, Fei Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety Key Laboratory of Road and Traffic Engineering of the Ministry of Education College of Transportation Engineering Tongji Univ. Shanghai China Infrastructure Inspection Research Institute China Academy of Railway Sciences Corporation Limited Beijing China

ISBN: (数字)9780784485941

ISBN: (纸本)9780784485941

In China, high-speed railway is developing rapidly in recent years, with 45,000 km in operation. The proportion of high-speed railway bridges is increasing, including more and more ultra-span bridges. To explore dynamic performance of train passing the kilometer-span bridge under various speed conditions and systematically analyze the influence of beam-end deformation on the train running safety and stability, this paper carried out co-simulation with FEM and multi-body dynamics calculation. Results show that: (1) For kilometer-span railway bridges in operation in China, the train speed should be lower than 300 km/h;(2) If train speed reaches 350 km/h, vertical acceleration can exceed 2.2 m/s2 based on existing track-bridge system;(3) When the speed is 350 km/h and beam-end angle reaches 2%, the vertical acceleration will exceed 1.5 m/s2 (the limit value) and the vertical Sperling index will reach 3.12 (the limit value is 3), so it is necessary to supplement current limitation about vertical beam-end angle at higher speeds. © ASCE.

关键词： Railroad bridges

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Leveraging Big Data Technologies for Economic Growth in the Digital Era 3

Leveraging Big Data Technologies for Economic Growth in the ...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Mardiana Siswanto, Joko Kamil, Muhammad Farhan Henry, Bintang Nandana Ramadan, Ahmad Komara, Maulana Arif University of Raharja Faculty of Accountancy Tangerang Indonesia Polytechnic of Road Transportation Safety Road Transportation System Engineering Tegal Indonesia Kreatif Desain Faculty of Digital Business Tangerang Indonesia Kreatif Desain Faculty of Information Technology Tangerang Indonesia Pandawan Sejahtera Indonesia Faculty of Information Technology Tangerang Indonesia Cai Sejahtera Indonesia Faculty of Digital Business Tangerang Indonesia

ISBN: (纸本)9798350367492

The digital economy is undergoing rapid transformation driven by the widespread adoption of Big Data technologies, significantly influencing various sectors including retail, finance, and manufacturing. The background of this study explores the impact of Big Data technologies on enhancing profitability and efficiency in these sectors through personalized customer service and predictive analytics. The research objective is to provide a comprehensive review of current literature and case studies to analyze the role of standardized data exchanges and a common definition of Big Data in facilitating industry-wide integration. The method employed in this study involves a systematic literature review and analysis of case studies to evaluate the implementation and impact of Big Data technologies. The results indicate that implementing Big Data technologies leads to improved operational efficiency, significant cost reductions, and enhanced customer experiences. However, challenges such as data privacy concerns, integration complexities, and skill shortages need to be addressed. The conclusion of this study suggests that adopting standardized data governance frameworks, investing in skill development, and leveraging scalable technologies are essential for maximizing the potential of Big Data technologies. These measures are crucial for navigating data-driven environments and promoting sustainable economic growth and innovation. © 2024 IEEE.

关键词： Big Data Digital Economy Digital Transformation Operational Efficiency Profitability

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Number of road Accidents Predicting Using Deep Learning-Based LSTM Development Models 11

Number of Road Accidents Predicting Using Deep Learning-Base...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Siswanto, Joko Hendry Rahardja, Untung Sembiring, Irwan Hartomo, Kristoko Dwi Purnomo, Hindriyanto Dwi Iriani, Ade Road Transportation System Engineering Politeknik Keselamatan Transportasi Jalan Tegal Indonesia Satya Wacana Christian University Faculty of Information Technology Salatiga Indonesia University of Raharja Faculty of Science and Technology Tangerang Indonesia

ISBN: (纸本)9798350305968

road accidents cause chaos and state financial losses every year. road accidents cannot be stopped but can be controlled with predictions. The existing road accident prediction model accuracy for the medium and long term still has many discrepancies, and it is rarely done. A comparison of the deep learning algorithms of the LSTM, Bi-LSTM, ConvLSTM, and Att-LSTM models with hidden layer variations is used to select the suitable model to make predictions. Use data from the road accident dataset in Thailand in 2011-2022, which can be accessed freely on Kaggle. The results show that the BiLSTM model with 4 hidden layers can predict the number of road accidents in the medium and long term with the highest accuracy. MSLE is used to measure the accuracy of the model with variations without hidden layers, 2 hidden layers, and 4 hidden layers to find the best model for predicting the number of road accidents for the medium and long term. © 2023 IEEE.

关键词： Forecasting

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