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17 pages, 4720 KiB

Open AccessArticle

Exploration of the Mountainous Urban Rail Transit Resilience Under Extreme Rainfalls: A Case Study in Chongqing, China

by Chenhui Liu, Xue Su, Zhichun Wu, Yingjun Zhang, Cuizhu Zhou, Xiangguo Wu and Yong Huang

Appl. Sci. 2025, 15(2), 735; https://doi.org/10.3390/app15020735 - 13 Jan 2025

Cited by 1 | Viewed by 815

Abstract

Extreme rainfalls could greatly affect operations of urban rail transit systems of mountainous cities, which are prone to have landslides and floods under rainfalls. Therefore, it is essential to assess and enhance the resilience of mountainous urban rail transit networks under heavy rainfalls. [...] Read more.

Extreme rainfalls could greatly affect operations of urban rail transit systems of mountainous cities, which are prone to have landslides and floods under rainfalls. Therefore, it is essential to assess and enhance the resilience of mountainous urban rail transit networks under heavy rainfalls. Taking the metro network of Chongqing, the largest mountainous city in China, as an example, this study establishes a network topology model to identify the high-risk nodes under rainfalls and find the effective recovery strategies. By introducing the metro ridership and topological shortest distances, a network service efficiency function is developed, and the importance of nodes is quantified using service efficiency index and topological importance index. The resilience assessment model based on service efficiency is constructed using the resilience triangle theory. Additionally, risk levels for landslide and flood-prone areas are classified using the K-means algorithm, based on rainfall, elevation, and slope data, identifying high-risk stations. Finally, the node recovery sequence and strategies for high-risk nodes affected by landslides and floods are examined. The results indicate that in extreme rainfall scenarios, two transfer stations (Daping and Fuhua Road) are among the high-risk landslide stations, while most other nodes have a service efficiency index of less than 0.2. High-risk flood stations are located on non-transfer lines and mostly on metro lines with high traffic flow, with service efficiency index generally high, with some stations, like Bijin Station, exceeding 0.3. When all affected nodes fail, network service efficiency decreases by 84.0% and 75.2% under landslide and flood disasters, respectively. Compared with the random recovery strategy, recovery strategies based on topological importance and service efficiency index, the optimal recovery strategy based on genetic algorithm performs much better. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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26 pages, 9971 KiB

Open AccessArticle

Data-Driven Modular Vehicle Scheduling in Scenic Areas

by Yilin Hong, Min Xu, Yong Jin and Shuaian Wang

Appl. Sci. 2025, 15(1), 205; https://doi.org/10.3390/app15010205 - 29 Dec 2024

Viewed by 691

Abstract

As tourism demand continues to grow and fluctuate, the problems of increasing empty capacity and high operating costs for tourist shuttle buses have become more acute. Modular vehicles, an emerging transport technology, offer flexible length adjustments and provide innovative solutions to address these [...] Read more.

As tourism demand continues to grow and fluctuate, the problems of increasing empty capacity and high operating costs for tourist shuttle buses have become more acute. Modular vehicles, an emerging transport technology, offer flexible length adjustments and provide innovative solutions to address these challenges. This paper develops a data-driven method to address the problem of scheduling modular vehicles in scenic areas with dynamic passenger demand. The aim is to minimize operating costs and maximize vehicle utilization by exploiting the adjustable capacity of modular vehicles. This approach is applied to tourist shuttle scenarios, and a sensitivity analysis is conducted by varying parameters such as individual vehicle capacity and waiting penalties. Then, we investigate the optimization performance gap between the proposed model and the theoretical global optimum model. The results show that increasing vehicle capacity and varying penalties improve the performance of the data-driven model, and the optimization rate of this model can reach 70.2% of the theoretical optimum, quantifying the effectiveness of the model. The method proposed in this study can effectively reduce the operating cost of shuttle vehicles for scenic areas and meet the challenge of unpredictable passenger demand, which serves as a good reference for fleet management in scenic areas. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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19 pages, 19517 KiB

Open AccessArticle

Design and Implementation of the Python-Driven Digital Horn System: A Novel Approach for Electric Vehicle Sound Systems

by Hakan Tekin, Hikmet Karşıyaka and Davut Ertekin

Appl. Sci. 2024, 14(23), 10977; https://doi.org/10.3390/app142310977 - 26 Nov 2024

Viewed by 856

Abstract

Electric and hybrid vehicles are known for their significant reduction in road noise. However, concerns have emerged regarding their silent operation, potentially increasing risks for other road users. To mitigate this, the Acoustic Vehicle Alert System (AVAS) has been mandated by regulations such [...] Read more.

Electric and hybrid vehicles are known for their significant reduction in road noise. However, concerns have emerged regarding their silent operation, potentially increasing risks for other road users. To mitigate this, the Acoustic Vehicle Alert System (AVAS) has been mandated by regulations such as R138 by UNECE in the USA and Europe. This regulation dictates the generation of sound in electric vehicles of categories M and N1 during normal, reverse, and forward motion without the internal combustion engine engaged. Compliance involves meeting specific sound requirements based on vehicle mode and condition. This paper introduces a Python-based approach to designing digital horn sounds, leveraging music theory and signal processing techniques to replace traditional mechanical horns in electric vehicles equipped with AVAS devices. The aim is to offer a practical and efficient means of generating digital horn sounds using this software. The software includes an application capable of producing and customizing horn sounds, with the HornSoundGeneratorGUI class providing a user-friendly interface built with the Tkinter library. To validate the digital horn produced sounds by the software and ensure compliance with AVAS regulations, comprehensive electrical and acoustic tests were conducted in a fully equipped quality laboratory. The results demonstrated that the sound levels achieved met the required 105–107 dB/2 m standard specified by the regulation. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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14 pages, 3271 KiB

Open AccessArticle

A MSA-YOLO Obstacle Detection Algorithm for Rail Transit in Foggy Weather

by Jian Chen, Donghui Li, Weiqiang Qu and Zhiwei Wang

Appl. Sci. 2024, 14(16), 7322; https://doi.org/10.3390/app14167322 - 20 Aug 2024

Cited by 1 | Viewed by 1242

Abstract

Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, [...] Read more.

Obstacles on rail transit significantly compromise operational safety, particularly under dense fog conditions. To address missed and false detections in traditional rail transit detection methods, this paper proposes a multi-scale adaptive YOLO (MSA-YOLO) algorithm. The algorithm incorporates six filters: defog, white balance, gamma, contrast, tone, and sharpen, to remove fog and enhance image quality. However, determining the hyperparameters of these filters is challenging. We employ a multi-scale adaptive module to optimize filter hyperparameters, enhancing fog removal and image quality. Subsequently, YOLO is utilized to detect obstacles on rail transit tracks. The experimental results are encouraging, demonstrating the effectiveness of our proposed method in foggy scenarios. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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22 pages, 6626 KiB

Open AccessArticle

An Efficient GPS Algorithm for Maximizing Electric Vehicle Range

by Karim Aboelsoud, Hatem Y. Diab, Mahmoud Abdelsalam and Moutaz M. Hegaze

Appl. Sci. 2024, 14(11), 4858; https://doi.org/10.3390/app14114858 - 4 Jun 2024

Cited by 1 | Viewed by 1495

Abstract

Although the main purpose of conventional geographical positioning systems (GPSs) is to determine either the fastest path or the shortest distance to a destination, this function may not be enough for electric vehicles (EVs). This is simply because the fastest/shortest path may consume [...] Read more.

Although the main purpose of conventional geographical positioning systems (GPSs) is to determine either the fastest path or the shortest distance to a destination, this function may not be enough for electric vehicles (EVs). This is simply because the fastest/shortest path may consume relatively higher energy when compared to other paths depending on the nature, speed limit, and topography of the road. This means that the driving range of the EV per charge decreases dramatically. This paper aims to develop a new algorithm and model dedicated for EV GPS which not only selects shortest/fastest routes, but also focuses on the most energy efficient route. This is achieved by considering many factors including aerodynamics, wind speed, topology of roads, with a clear objective of reducing the energy consumed from the battery. A MATLAB Simulink model is developed and validated with real-life case studies to ensure the results are realistic and accurate. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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17 pages, 10699 KiB

Open AccessArticle

Improved Long-Term Forecasting of Passenger Flow at Rail Transit Stations Based on an Artificial Neural Network

by Zitao Du, Wenbo Yang, Yuna Yin, Xinwei Ma and Jiacheng Gong

Appl. Sci. 2024, 14(7), 3100; https://doi.org/10.3390/app14073100 - 7 Apr 2024

Cited by 1 | Viewed by 1480

Abstract

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this [...] Read more.

When new rail stations or lines are planned, long-term planning for decades to come is required. The short-term passenger flow prediction is no longer of practical significance, as it only takes a few factors that affect passenger flow into consideration. To overcome this problem, we propose several long-term factors affecting the passenger flow of rail transit in this paper. We also create a visual analysis of these factors using ArcGIS and construct a long-term passenger flow prediction model for rail transit based on a class neural network using an SPSS Modeler. After optimizing relevant parameters, the prediction accuracy reaches 94.6%. We compare the results with other models and find that the neural network model has a good performance in predicting long-term rail transit passenger flow. Finally, the factors affecting passenger flow are ranked in terms of importance. It is found that among these factors, bicycles available for connection have the biggest influence on the passenger flow of rail stations. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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16 pages, 3674 KiB

Open AccessArticle

A New Indicator for Measuring Efficiency in Urban Freight Transportation: Defining and Implementing the OEEM (Overall Equipment Effectiveness for Mobility)

by Adrián Les, Paula Morella, María Pilar Lambán, Jesús Royo and Juan Carlos Sánchez

Appl. Sci. 2024, 14(2), 779; https://doi.org/10.3390/app14020779 - 16 Jan 2024

Viewed by 1534

Abstract

Urban freight transportation is the activity that has the greatest impact on urban areas in terms of sustainability and livability, and it is, therefore, necessary to reduce its impact. Currently, there is a lack of methodologies to validate the methods proposed by companies [...] Read more.

Urban freight transportation is the activity that has the greatest impact on urban areas in terms of sustainability and livability, and it is, therefore, necessary to reduce its impact. Currently, there is a lack of methodologies to validate the methods proposed by companies to reduce their impacts. The proposed methodology presents the implementation of a KPI (Key Performance Indicator) based on the triple bottom line approach: economic, social and environmental, since a company with good results on the “triple bottom line” will experience an increase in its economic profitability and its environmental commitment while reducing the impacts that generate negative perceptions of it. This KPI is the OEEM (Overall Equipment Effectiveness for Mobility), a redesign of the well-known OEE (Overall Equipment Effectiveness), but adapted to the needs of urban freight transportation since this indicator provides a quick overview of the efficiency or performance of the activity according to five components: quality of deliveries, vehicle utilization, availability of the vehicle–driver tandem and efficiency (result of traffic and efficiency of delivery stops). The methodology developed will be implemented in a case study where the KPI will be calculated on the basis of real-time data and visualized on a control panel; thanks to this KPI, the company will be able to validate whether the measures taken have a positive or negative impact. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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22 pages, 2585 KiB

Open AccessArticle

An Access Control Framework for Multilayer Rail Transit Systems Based on Trust and Sensitivity Attributes

by Xin Geng, Yinghong Wen, Zhisong Mo and Yu Liu

Appl. Sci. 2023, 13(23), 12904; https://doi.org/10.3390/app132312904 - 1 Dec 2023

Cited by 1 | Viewed by 1221

Abstract

The construction of multilayer rail transit systems is a necessary way to realize “modern metropolitan areas on rail”, improve resource sharing, and increase travel services, where data integration is of utmost importance. To break data silos and realize data flow between different rail [...] Read more.

The construction of multilayer rail transit systems is a necessary way to realize “modern metropolitan areas on rail”, improve resource sharing, and increase travel services, where data integration is of utmost importance. To break data silos and realize data flow between different rail systems, a fine-grained access control framework is proposed in this paper. Through categorical and hierarchical schemes, a universal security scale is established for cross-domain data resources. Based on this, a trust and sensitivity attribute-based access control (TSABAC) model is put forward to describe the characteristics of the access control process. Furthermore, the method of policy integration is discussed, as well as the solution to the policy incompatibility problem, due to cross-domain interaction. As shown in practical application and simulation analysis, this framework can meet the requirements of security and granularity. This research is of great significance for promoting the high-quality development of urban agglomerations and metropolitan areas, and improving the quality and efficiency of rail transit. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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Review

Jump to: Research

17 pages, 2573 KiB

Open AccessReview

Remote Sensing and Machine Learning for Safer Railways: A Review

by Wesam Helmi, Raj Bridgelall and Taraneh Askarzadeh

Appl. Sci. 2024, 14(9), 3573; https://doi.org/10.3390/app14093573 - 24 Apr 2024

Cited by 7 | Viewed by 2096

Abstract

Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of [...] Read more.

Regular railway inspections are crucial for maintaining their safety and efficiency. However, traditional inspection methods are complex and expensive. Consequently, there has been a significant shift toward combining remote sensing (RS) and machine learning (ML) techniques to enhance the efficiency and accuracy of railway defect monitoring while reducing costs. The advantages of RS-ML techniques include their ability to automate and refine inspection processes and address challenges such as image quality and methodological limitations. However, the integration of RS and ML in railway monitoring is an emerging field, with diverse methodologies and outcomes that the research has not yet synthesized. To fill this gap, this study conducted a systematic literature review (SLR) to consolidate the existing research on RS-ML applications in railway inspection. The SLR meticulously compiled and analyzed relevant studies, evaluating the evolution of research trends, methodological approaches, and the geographic distribution of contributions. The findings showed a notable increase in relevant research activity over the last five years, highlighting the growing interest in this realm. The key methodological patterns emphasize the predominance of approaches based on convolutional neural networks, a variant of artificial neural networks, in achieving high levels of precision. These findings serve as a foundational resource for academics, researchers, and practitioners in the fields of computer science, engineering, and transportation to help guide future research directions and foster the development of more efficient, accurate, and cost-effective railway inspection methods. Full article

(This article belongs to the Special Issue Application of New Technology and New Ideas in Intelligent Transportation System)

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