Pub Date : 2024-02-02DOI: 10.1109/OJITS.2024.3361842
He Yuesheng;Wang Tao;Chen Long;Zhuang Hanyang;Yang Ming
Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras’ extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.
{"title":"An Extrinsic Calibration Method for Multiple Infrastructure RGB-D Camera Networks With Small FOV","authors":"He Yuesheng;Wang Tao;Chen Long;Zhuang Hanyang;Yang Ming","doi":"10.1109/OJITS.2024.3361842","DOIUrl":"10.1109/OJITS.2024.3361842","url":null,"abstract":"Multiple infrastructure RGB-D cameras can be used for localizing autonomous vehicles in Automated Valet Parking. The accurate calibration of these cameras’ extrinsic parameters is crucial. However, due to the sparse and distributed placement of the cameras, the field of view (FOV) between them is very small. This makes the calibration process complex and dependent on human expertise. To address this, this paper proposes an automatic extrinsic calibration method for multiple infrastructure cameras with a small FOV. The method introduces an auxiliary camera to enhance the association between the multiple infrastructure cameras. A moving checkerboard placed within the public FOV is utilized as a reference for calibration. The optimization method involves constructing a pose graph to store the poses of the cameras and checkerboard, and it solves the pose graph by calculating the reprojection errors of the checkerboard. The experimental results demonstrate that the proposed method achieves a calibration accuracy of two centimeters. It outperforms other calibration methods when applied to a constructed multiple RGB-D camera system. Furthermore, the proposed method is simple and efficient in the real calibration procedure.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"617-628"},"PeriodicalIF":4.6,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10418976","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-02DOI: 10.1109/OJITS.2024.3361473
Md Saifuddin;Mahdi Zaman;Yaser P. Fallah;Jayanthi Rao
Cellular Vehicle-to-Everything (C-V2X) has become one of the most anticipated technologies for vehicular safety network. In LTE C-V2X Basic Safety Messages (BSMs) are transmitted on radio resources that are allocated with a periodic resource reusability. This allocation is based on a semi persistent sensing-based scheduling scheme (SPS) algorithm. But, due to this reuse of periodic resources, the possibility of loss of consecutive packets between the same vehicle pair is significant. This study discusses different approaches proposed to solve this consecutive loss problem. Based on this investigation, this article suggests an efficient One-Shot based solution with a new control parameter, that performs superior to the state-of-the-art solution that is standardized in SAE J3161/1 which this article analyzes and shows to have limitation in case of high-density scenario.
{"title":"Addressing Rare Outages in C-V2X With Time-Controlled One-Shot Resource Scheduling","authors":"Md Saifuddin;Mahdi Zaman;Yaser P. Fallah;Jayanthi Rao","doi":"10.1109/OJITS.2024.3361473","DOIUrl":"10.1109/OJITS.2024.3361473","url":null,"abstract":"Cellular Vehicle-to-Everything (C-V2X) has become one of the most anticipated technologies for vehicular safety network. In LTE C-V2X Basic Safety Messages (BSMs) are transmitted on radio resources that are allocated with a periodic resource reusability. This allocation is based on a semi persistent sensing-based scheduling scheme (SPS) algorithm. But, due to this reuse of periodic resources, the possibility of loss of consecutive packets between the same vehicle pair is significant. This study discusses different approaches proposed to solve this consecutive loss problem. Based on this investigation, this article suggests an efficient One-Shot based solution with a new control parameter, that performs superior to the state-of-the-art solution that is standardized in SAE J3161/1 which this article analyzes and shows to have limitation in case of high-density scenario.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"208-222"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419018","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139947722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1109/OJITS.2023.3347484
Salah Zidi;Bechir Alaya;Tarek Moulahi;Amal Al-Shargabi;Salim El Khediri
The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.
{"title":"Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment","authors":"Salah Zidi;Bechir Alaya;Tarek Moulahi;Amal Al-Shargabi;Salim El Khediri","doi":"10.1109/OJITS.2023.3347484","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3347484","url":null,"abstract":"The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"132-145"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10403965","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1109/OJITS.2023.3348988
Jiaqi Ma
Dear Authors and Readers, Welcome to the 2024 Volume of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). This marks my second year serving as the Editor-in-Chief (EiC) of OJ-ITS. First and foremost, I would like to express my gratitude to all the active associate editors and reviewers who have devoted their valuable time to OJ-ITS and enabled the journal’s rapid growth. I also want to thank the IEEE staff and the ITS society for their efforts in publishing each article and promoting the journal.
亲爱的作者和读者，欢迎阅读《IEEE 智能交通系统开放期刊》（OJ-ITS）2024 卷。今年是我担任《OJ-ITS》主编（EiC）的第二年。首先，我要感谢所有活跃的副主编和审稿人，他们为 OJ-ITS 奉献了宝贵的时间，使期刊得以快速发展。我还要感谢 IEEE 工作人员和 ITS 协会为发表每篇文章和推广期刊所做的努力。
{"title":"2024 Editorial IEEE Open Journal of Intelligent Transportation Systems","authors":"Jiaqi Ma","doi":"10.1109/OJITS.2023.3348988","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3348988","url":null,"abstract":"Dear Authors and Readers, Welcome to the 2024 Volume of the IEEE Open Journal of Intelligent Transportation Systems (OJ-ITS). This marks my second year serving as the Editor-in-Chief (EiC) of OJ-ITS. First and foremost, I would like to express my gratitude to all the active associate editors and reviewers who have devoted their valuable time to OJ-ITS and enabled the journal’s rapid growth. I also want to thank the IEEE staff and the ITS society for their efforts in publishing each article and promoting the journal.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10382254","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1109/OJITS.2024.3350213
Dinh Viet Cuong;Vuong M. Ngo;Paolo Cappellari;Mark Roantree
Bike sharing schemes can be used both to improve mobility around busy city routes but also to contribute to the fight against climate change. Optimization of the network in terms of station locations and routes is a focus for researchers, where usage can highlight the precise times at which bike availability is high in some areas and low in others. Locations for new stations are important for the expansion of the network, but spatio-temporal pattern analysis is required to accurately identify those locations. In other words, one cannot rely on spatial information nor temporal information in isolation, when making interpretations for the purpose of optimizing or expanding the network. In this research, a solution based on graph networks was developed to model activity in transport networks by exploiting properties and functions specific to graph databases. This generic approach adopts a broad series of analyses, comprising different levels of granularity and complexity, to enable better interpretation of network dynamics at a suitably granular level to help the optimization of transport networks. A large dataset provided by an electric bike company is used to address key research questions in both interpreting activity patterns and supporting network optimization.
{"title":"Analyzing Shared Bike Usage Through Graph-Based Spatio-Temporal Modeling","authors":"Dinh Viet Cuong;Vuong M. Ngo;Paolo Cappellari;Mark Roantree","doi":"10.1109/OJITS.2024.3350213","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3350213","url":null,"abstract":"Bike sharing schemes can be used both to improve mobility around busy city routes but also to contribute to the fight against climate change. Optimization of the network in terms of station locations and routes is a focus for researchers, where usage can highlight the precise times at which bike availability is high in some areas and low in others. Locations for new stations are important for the expansion of the network, but spatio-temporal pattern analysis is required to accurately identify those locations. In other words, one cannot rely on spatial information nor temporal information in isolation, when making interpretations for the purpose of optimizing or expanding the network. In this research, a solution based on graph networks was developed to model activity in transport networks by exploiting properties and functions specific to graph databases. This generic approach adopts a broad series of analyses, comprising different levels of granularity and complexity, to enable better interpretation of network dynamics at a suitably granular level to help the optimization of transport networks. A large dataset provided by an electric bike company is used to address key research questions in both interpreting activity patterns and supporting network optimization.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"115-131"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10382155","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1109/OJITS.2023.3339042
{"title":"IEEE OPEN JOURNAL OF THE INTELLIGENT TRANSPORTATION SYSTEMS SOCIETY","authors":"","doi":"10.1109/OJITS.2023.3339042","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3339042","url":null,"abstract":"","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10382239","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-05DOI: 10.1109/OJITS.2023.3339044
{"title":"IEEE Open Journal of Intelligent Transportation Systems Instructions for Authors","authors":"","doi":"10.1109/OJITS.2023.3339044","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3339044","url":null,"abstract":"","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10382253","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-04DOI: 10.1109/OJITS.2024.3349635
Olger Siebinga;Arkady Zgonnikov;David A. Abbink
Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimise their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour.
{"title":"Human Merging Behavior in a Coupled Driving Simulator: How Do We Resolve Conflicts?","authors":"Olger Siebinga;Arkady Zgonnikov;David A. Abbink","doi":"10.1109/OJITS.2024.3349635","DOIUrl":"https://doi.org/10.1109/OJITS.2024.3349635","url":null,"abstract":"Traffic interactions between merging and highway vehicles are a major topic of research, yielding many empirical studies and models of driver behaviour. Most of these studies on merging use naturalistic data. Although this provides insight into human gap acceptance and traffic flow effects, it obscures the operational inputs of interacting drivers. Besides that, researchers have no control over the vehicle kinematics (i.e., positions and velocities) at the start of the interactions. Therefore the relationship between initial kinematics and the outcome of the interaction is difficult to investigate. To address these gaps, we conducted an experiment in a coupled driving simulator with a simplified, top-down view, merging scenario with two vehicles. We found that kinematics can explain the outcome (i.e., which driver merges first) and the duration of the merging conflict. Furthermore, our results show that drivers use key decision moments combined with constant acceleration inputs (intermittent piecewise-constant control) during merging. This indicates that they do not continuously optimise their expected utility. Therefore, these results advocate the development of interaction models based on intermittent piecewise-constant control. We hope our work can contribute to this development and to the fundamental knowledge of interactive driver behaviour.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"103-114"},"PeriodicalIF":0.0,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10380755","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139572601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-25DOI: 10.1109/OJITS.2023.3346477
Ilias E. Panagiotopoulos;George J. Dimitrakopoulos;Gabriele Keraite
Highly automated passenger vehicles hold great potential to alleviate traffic congestion, enhance road safety, and revolutionize the travel journey. However, while much attention has been given to the technical aspects of this technology, the investigation of public acceptance remains crucial for successful implementation in the global market. To address this gap, this paper introduces innovative research that explores the predictors influencing consumers’ intention to adopt highly automated passenger vehicles. Through an online questionnaire-based survey conducted among European adults, we extend the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to incorporate three additional constructs: perceived reliability/trust, perceived financial cost, and perceived driving enjoyment. The key findings of this study underscore the significance of driving enjoyment, financial cost, social influences, and reliability/trust as influential predictors of consumers’ intention to adopt highly automated passenger vehicles. By considering these factors, automotive stakeholders can gain valuable insights to develop effective strategies and approaches for the successful implementation of highly automated passenger vehicles in the near future. Last, its innovations pave the way for a transformative shift in transportation, enabling the realization of safer, more efficient, and enjoyable travel experiences for individuals and society as a whole.
{"title":"On Modelling and Investigating User Acceptance of Highly Automated Passenger Vehicles","authors":"Ilias E. Panagiotopoulos;George J. Dimitrakopoulos;Gabriele Keraite","doi":"10.1109/OJITS.2023.3346477","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3346477","url":null,"abstract":"Highly automated passenger vehicles hold great potential to alleviate traffic congestion, enhance road safety, and revolutionize the travel journey. However, while much attention has been given to the technical aspects of this technology, the investigation of public acceptance remains crucial for successful implementation in the global market. To address this gap, this paper introduces innovative research that explores the predictors influencing consumers’ intention to adopt highly automated passenger vehicles. Through an online questionnaire-based survey conducted among European adults, we extend the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to incorporate three additional constructs: perceived reliability/trust, perceived financial cost, and perceived driving enjoyment. The key findings of this study underscore the significance of driving enjoyment, financial cost, social influences, and reliability/trust as influential predictors of consumers’ intention to adopt highly automated passenger vehicles. By considering these factors, automotive stakeholders can gain valuable insights to develop effective strategies and approaches for the successful implementation of highly automated passenger vehicles in the near future. Last, its innovations pave the way for a transformative shift in transportation, enabling the realization of safer, more efficient, and enjoyable travel experiences for individuals and society as a whole.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"70-84"},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10373556","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139434817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-25DOI: 10.1109/OJITS.2023.3346534
Michael Nold;Francesco Corman
The railway system can fulfil society’s current and future transportation goals; compared to other transport modes, it does that with high energy, space and resource efficiency. It can deliver high-quality transport services, superior speed, safety and comfort to most competing modes. Nevertheless, its share of the total traffic is often relatively small. This study examines new technologies, their challenges and opportunities for the railway system to understand possible futures of the railway systems, allowing it to prepare ahead of time to prepare and exploit its competitive strengths and possible technological developments. In this paper, we report on multi-stage interviews of 30 experts concerning a holistic technological view of the railway system. The surveyed experts reported on perspectives from the railway operator, industry and research from Switzerland and Europe. The outcomes were categorized into supply, operation and technology aspects and evaluated by their potential for improvement, system impact of the changes, time horizon of possible implementation, and effects on modal shift. The results show that many aspects contribute to the further development of the technologies, but no single game changer could be identified. Developments are expected in automation; revolutionary changes are perceived as unlikely.
{"title":"How Will the Railway Look Like in 2050? A Survey of Experts on Technologies, Challenges and Opportunities for the Railway System","authors":"Michael Nold;Francesco Corman","doi":"10.1109/OJITS.2023.3346534","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3346534","url":null,"abstract":"The railway system can fulfil society’s current and future transportation goals; compared to other transport modes, it does that with high energy, space and resource efficiency. It can deliver high-quality transport services, superior speed, safety and comfort to most competing modes. Nevertheless, its share of the total traffic is often relatively small. This study examines new technologies, their challenges and opportunities for the railway system to understand possible futures of the railway systems, allowing it to prepare ahead of time to prepare and exploit its competitive strengths and possible technological developments. In this paper, we report on multi-stage interviews of 30 experts concerning a holistic technological view of the railway system. The surveyed experts reported on perspectives from the railway operator, industry and research from Switzerland and Europe. The outcomes were categorized into supply, operation and technology aspects and evaluated by their potential for improvement, system impact of the changes, time horizon of possible implementation, and effects on modal shift. The results show that many aspects contribute to the further development of the technologies, but no single game changer could be identified. Developments are expected in automation; revolutionary changes are perceived as unlikely.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"85-102"},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10373408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139473768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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