IEEE Open Journal of Intelligent Transportation Systems最新文献

{"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}

{"title":"Theoretical Trade-Off Between Fairness and Efficiency in the Cooperative Driving Problem for CAVs at On-Ramps","authors":"Zimin He;Huaxin Pei;Yuqing Guo;Danya Yao;Li Li","doi":"10.1109/OJITS.2023.3344216","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3344216","url":null,"abstract":"Cooperative driving is crucial for improving traffic efficiency and safety for connected and automated vehicles (CAVs), especially in traffic bottlenecks. However, most of the state-of-the-art cooperative driving strategies neglect the issue of fairness. Fairness is essential to properly allocate road resources and improve the travel experience. In this paper, we focus on the fairness concerns in the on-ramp cooperative driving problem. First, we note that enhancing traffic efficiency usually leads to unfairness, but we propose solutions to balance both aspects. Using the fundamental relation in traffic flow theory, we illustrate the existence of the trade-off at congested on-ramps. We then make some modifications to the cooperative driving strategies to incorporate fairness considerations. Simulation results show that the modified strategies achieve trade-offs in agreement with the theoretical one, laying the foundation for implementing the trade-off in real-world scenarios. These findings are enlightening for the increasing research on fairness issues in cooperative driving, and contribute to the optimization of traffic management strategies.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"41-54"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365497","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406730","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}

{"title":"Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies","authors":"Lisa Kessler;Klaus Bogenberger","doi":"10.1109/OJITS.2023.3341631","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3341631","url":null,"abstract":"This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and severe congestion scenarios like Wide Jam. We analyze multiple traffic data sets, including speed data from loop detectors, travel time measurements from Bluetooth sensors, and floating car data (FCD) collected from probe vehicles. Each combination of congestion pattern and detection technology is thoroughly examined and evaluated in terms of its capability and suitability for identifying specific traffic congestion patterns. For our experimental site, we selected the freeway A9 in Germany, which spans a length of \u0000<inline-formula> <tex-math>$mathrm {157~km}$ </tex-math></inline-formula>\u0000. Our findings reveal that Bluetooth sensors, which record travel times between two locations, are barely suited for detecting short traffic incidents such as Jam Waves due to their downstream detection direction, contrasting with the upstream congestion propagation. Segment-based speed calculations prove more effective in identifying significant congestion events. FCD tend to recognize Stop and Go patterns more frequently than loop detectors but often underestimate severe congestion due to their sensitivity to penetration rates and data availability.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"29-40"},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10356725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406710","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}

{"title":"Using Connected Vehicle Trajectory Data to Evaluate the Effects of Speeding","authors":"Jorge Ugan;Mohamed Abdel-Aty;Zubayer Islam","doi":"10.1109/OJITS.2023.3341962","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3341962","url":null,"abstract":"Speeding remains a key factor in traffic fatalities, prompting transportation agencies to propose speed management solutions. While studies have examined speeding percentages above limits, few address its impact on individual journeys. Most studies rely on detector speed data, lacking route insights. This research employs connected vehicle trajectory data to analyze driver paths and variables, predicting speeding levels with various learning models. Extreme Gradient Boosting performed best, achieving 75.6% accuracy. This model elucidates how journey factors influence speeding and forecasts high-speed zones. Results reveal a driver’s total travel time significantly affects speeding, along with environmental features like residential area proportions. These findings aid transportation agencies in understanding trip-specific speeding factors, potentially informing better road safety measures.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"16-28"},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10354062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139399678","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}

{"title":"Fast Bidirectional Motion Planning for Self-Driving General N-Trailers Vehicle Maneuvering in Narrow Space","authors":"Hanyang Zhuang;Qiyue Shen;Yeqiang Qian;Wei Yuan;Chunxiang Wang;Ming Yang","doi":"10.1109/OJITS.2023.3340174","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3340174","url":null,"abstract":"Self-driving General N-trailers (GNT) vehicles are one of the future solutions to build intelligent factory due to its flexibility and large load. Maneuvering of GNT vehicle to its destination requires accurate and robust motion planning. But the narrow operating environment causes nonlinear nonconvex constraints which are challenging. Furthermore, the nonholonomic constraints in GNT kinematics elevate the complexity in state space. Therefore, motion planning of GNT vehicle maneuvering in narrow space within a reasonable time and high success rate is a critical problem. This paper proposes a fast bidirectional motion planning algorithm to generate trajectories for GNT vehicles to maneuver in a narrow space. A coarse-to-fine motion planning paradigm has been proposed to balance the robustness and time. In the coarse step, an initial guess is generated through a bidirectional-sampled closed-loop Rapidly-exploring Random Tree, and a spatial-temporal safety corridor has been constructed to convert nonlinear nonconvex constraints to linear convex constraints. In the fine step, an optimal control problem is defined accordingly and solved to obtain feasible trajectory. Four different scenarios have been conducted with forward and reverse GNT vehicle maneuvering in a narrow environment. The results show that the proposed method outperforms state-of-the-art sampling-based and optimization-based motion planning methods.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"989-999"},"PeriodicalIF":0.0,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10347483","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060212","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}

{"title":"Digital Twinning From Vehicle Usage Statistics for Customer-Centric Automotive Systems Engineering","authors":"Kunxiong Ling","doi":"10.1109/OJITS.2023.3339430","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3339430","url":null,"abstract":"Towards customer-centric automotive systems engineering, it is essential to incorporate physical models and vehicle usage behavior into decision support systems (DSSs). Such DSSs tend to apply digital twin concepts, where simulations are parameterized with fine-grained time-series data acquired from customer fleets. However, logging vast amounts of data from customer fleets is costly and raises privacy concerns. Alternatively, these time-series data can be aggregated into vehicle usage statistics. The feasibility of creating digital twins from these vehicle usage statistics and the corresponding DSSs for systems engineering is yet to be established. This paper aims to demonstrate this feasibility by proposing a DSS framework that integrates four key elements of digital twinning: aggregate usage statistics from customer fleets, logging data from testing fleets, physical models for vehicle simulation, and evaluation models to derive decision support metrics. The digital twinning involves a four-step process: pre-processing, profiling, simulation, and post-processing. Based on a real-world fleet of 57110 vehicles and four evaluation metrics, a proof of concept is conducted. Results show that the digital twin covers the evaluation metrics of 99% of the vehicles and reaches an average fleet twinning accuracy of 91.09%, which indicates the feasibility and plausibility of the proposed DSS framework.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"966-978"},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10342795","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139034193","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}

{"title":"Transformer-Conformer Ensemble for Crash Prediction Using Connected Vehicle Trajectory Data","authors":"Zubayer Islam;Mohamed Abdel-Aty;B M Tazbiul Hassan Anik","doi":"10.1109/OJITS.2023.3339016","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3339016","url":null,"abstract":"Crash prediction is one of the important elements of real time traffic management strategies. Previous studies have demonstrated the use of infrastructure-based detector data and UAV video to predict a crash in the near future. The main limitation of such data is limited coverage. In this work, we have used connected vehicle trajectory data that can have wide coverage as well as provide insight into the trajectory that might lead to a crash. The trajectory data was provided by Wejo which collects data from the manufacturer and was spaced at 3 seconds. GPS locations and their associated time series features such as speed, acceleration and yaw rate were used to feed into an ensembled Transformer and Conformer model. A voting classifier was used to obtain the output of the final model which achieved a recall of 76% and the false alarm rate of 30%. This study showed how connected vehicle trajectory data can aid in getting insight into crashes. While most previous studies focus on using aggregated data to estimate crashes, the proposed work shows that trajectory data mining can also provide competitive results.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"979-988"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10339651","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060211","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}

{"title":"Optimal Conflict Resolution for Vehicles With Intersecting and Overlapping Paths","authors":"Johan Karlsson;Nikolce Murgovski;Jonas Sjöberg","doi":"10.1109/OJITS.2023.3336533","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3336533","url":null,"abstract":"A collaborative centralized model predictive controller solving the problem of autonomous vehicles safely crossing an intersection is presented. The solution gives optimal speed trajectories for each vehicle while considering collision avoidance constraints between vehicles traveling on the same path before, after and/or within the intersection. This extends earlier results, where collision avoidance was only considered for vehicles with intersecting paths, with the possibility of vehicles on the same path and by this, the controller is not only one step closer to handling complex traffic intersections but can now be used for merging and splitting of roads, roundabouts and intersection networks. The proficiency of the extended controller is demonstrated by applying it to a four-way intersection. It is shown that the controller provides smooth, collision free trajectories in scenarios with and without vehicles traveling in the same lane. Further, it is evaluated how the solutions differ when using various cost functions and how the controller handles disturbances in the form of a sudden lane blockage. Lastly, it is discussed how the presented controller could also be extended to handle mixed-traffic scenarios and how soft constraints can be used to avoid infeasibility in the case of missing or noisy traffic data.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"146-159"},"PeriodicalIF":0.0,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10335957","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139676132","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}

{"title":"Characterization and Selection of WiFi Channel State Information Features for Human Activity Detection in a Smart Public Transportation System","authors":"Roya Alizadeh;Yvon Savaria;Chahé Nerguizian","doi":"10.1109/OJITS.2023.3336795","DOIUrl":"https://doi.org/10.1109/OJITS.2023.3336795","url":null,"abstract":"Robust methods are needed to detect how people are moving in smart public transportation systems. This paper proposes and characterizes effective means to accurately detect passengers. We analyze a public WiFi-based activity recognition (WiAR) dataset to extract human activity features from Channel State Information (CSI) data. To do so, CSI power changes caused by nearby human activity are analyzed. Our method first extracts multi-dimensional features using a Short-Time Fourier Transform (STFT) of CSI data to capture the relevant signal features. Since the environment of a transportation system changes dynamically and non-deterministically, we propose analyzing these changes with a heuristic algorithm that leverages a decision tree to automate a decision-making solution for feature selection. Principal Component Analysis (PCA) is performed before the decision tree algorithm. Reported results are compared with those obtained from the existing methods. Based on these results, we explore the effectiveness of various features such as the chirp rate, delta band power, spectral flux, and frequency of movement. This allows identifying and recommending the most effective features for the explored detection task according to observed variability, information gain, and correlation between features. The reported classification results show that using only the chirp rate estimated from CSI information as a feature, we achieve precision = 83%, True Positive \u0000<inline-formula> <tex-math>$(TP)=94%$ </tex-math></inline-formula>\u0000, True Negative \u0000<inline-formula> <tex-math>$(TN)= 91%$ </tex-math></inline-formula>\u0000 and F1-score = 87%. Considering delta band power as an additional feature adds more information and allows getting higher performance with precision = 100%, \u0000<inline-formula> <tex-math>$TP=97%$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$TN = 95%$ </tex-math></inline-formula>\u0000 and F1-score = 95%.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"5 ","pages":"55-69"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10332939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139406652","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}

{"title":"A Multi-Task Vision Transformer for Segmentation and Monocular Depth Estimation for Autonomous Vehicles","authors":"Durga Prasad Bavirisetti;Herman Ryen Martinsen;Gabriel Hanssen Kiss;Frank Lindseth","doi":"10.1109/OJITS.2023.3335648","DOIUrl":"10.1109/OJITS.2023.3335648","url":null,"abstract":"In this paper, we investigate the use of Vision Transformers for processing and understanding visual data in an autonomous driving setting. Specifically, we explore the use of Vision Transformers for semantic segmentation and monocular depth estimation using only a single image as input. We present state-of-the-art Vision Transformers for these tasks and combine them into a multitask model. Through multiple experiments on four different street image datasets, we demonstrate that the multitask approach significantly reduces inference time while maintaining high accuracy for both tasks. Additionally, we show that changing the size of the Transformer-based backbone can be used as a trade-off between inference speed and accuracy. Furthermore, we investigate the use of synthetic data for pre-training and show that it effectively increases the accuracy of the model when real-world data is limited.","PeriodicalId":100631,"journal":{"name":"IEEE Open Journal of Intelligent Transportation Systems","volume":"4 ","pages":"909-928"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10330677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138576854","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}