Selected Online Reading on Intelligent Transport Systems Applied to Road Transport

• Data-Driven Diffraction Loss Estimation for Future Intelligent Transportation Systems in 6G Networks; Pattanaik Sambit, Imoize Agbotiname Lucky, Li Chun Ta, Francis Sharmila Anand John, Lee, Cheng Chi, Roy Diptendu Sinha; Mathematics (Basel); 2023; Vol.11 (13); p.3004; Article 3004

Abstract by the author: The advancement of 6G networks is driven by the need for customer-centric communication and network control, particularly in applications such as intelligent transport systems. These applications rely on outdoor communication in extremely high-frequency (EHF) bands, including millimeter wave (mmWave) frequencies exceeding 30 GHz. However, EHF signals face challenges such as higher attenuation, diffraction, and reflective losses caused by obstacles in outdoor environments. To overcome these challenges, 6G networks must focus on system designs that enhance propagation characteristics by predicting and mitigating diffraction, reflection, and scattering losses. Strategies such as proper handovers, antenna orientation, and link adaptation techniques based on losses can optimize the propagation environment. Among the network components, aerial networks, including unmanned aerial vehicles (UAVs) and electric vertical take-off and landing aircraft (eVTOL), are particularly susceptible to diffraction losses due to surrounding buildings in urban and suburban areas. Traditional statistical models for estimating the height of tall objects like buildings or trees are insufficient for accurately calculating diffraction losses due to the dynamic nature of user mobility, resulting in increased latency unsuitable for ultra-low latency applications. To address these challenges, this paper proposes a deep learning framework that utilizes easily accessible Google Street View imagery to estimate building heights and predict diffraction losses across various locations. The framework enables real-time decision-making to improve the propagation environment based on users’ locations. The proposed approach achieves high accuracy rates, with an accuracy of 39% for relative error below 2%, 83% for relative error below 4%, and 96% for both relative errors below 7% and 10%. Compared to traditional statistical methods, the proposed deep learning approach offers significant advantages in height prediction accuracy, demonstrating its efficacy in supporting the development of 6G networks. The ability to accurately estimate heights and map diffraction losses before network deployment enables proactive optimization and ensures real-time decision-making, enhancing the overall performance of 6G systems.

• Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment; Chen Jian, Qian Li‐Jun, Xuan Liang, Chen; Chen IET intelligent transport systems; 2023; Vol.17 (7); p.1312-1330

Abstract by the author: In recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%. A hierarchical eco‐driving control strategy for hybrid electric vehicle platoons is developed to improve fuel economy at signalized intersections under partially connected and automated vehicle environment. The performance of the proposed strategy is evaluated in mixed traffic scenarios and under different CAV penetration rates.

• Safe, Efficient, and Comfortable Reinforcement-Learning-Based Car-Following for AVs with an Analytic Safety Guarantee and Dynamic Target Speed; ElSamadisy Omar, Shi Tianyu, Smirnov Ilia, Abdulhai Baher; Transportation research record; 2024; Vol.2678 (1); p.643-661

Abstract by the author: Over the last decade, there has been rising interest in automated driving systems and adaptive cruise control (ACC). Controllers based on reinforcement learning (RL) are particularly promising for autonomous driving, being able to optimize a combination of criteria such as efficiency, stability, and comfort. However, RL-based controllers typically offer no safety guarantees. In this paper, we propose SECRM (the Safe, Efficient, and Comfortable RL-based car-following Model) for autonomous car-following that balances traffic efficiency maximization and jerk minimization, subject to a hard analytic safety constraint on acceleration. The acceleration constraint is derived from the criterion that the follower vehicle must have sufficient headway to be able to avoid a crash if the leader vehicle brakes suddenly. We critique safety criteria based on the time-to-collision (TTC) threshold (commonly used for RL controllers), and confirm in simulator experiments that a representative previous TTC-threshold-based RL autonomous-vehicle controller may crash (in both training and testing). In contrast, we verify that our controller SECRM is safe, in training scenarios with a wide range of leader behaviors, and in both regular-driving and emergency-braking test scenarios. We find that SECRM compares favorably in efficiency, comfort, and speed-following to both classical (non-learned) car-following controllers (intelligent driver model, Shladover, Gipps) and a representative RL-based car-following controller.

• The effect of visibility on road traffic during foggy weather conditions; Ali Faryal, Khan Zawar Hussain, Khattak Khurram Shehzad, Gulliver Thomas; Aaron IET intelligent transport systems; 2024; Vol.18 (1); p.47-57

Abstract by the author: The impact of fog on visibility is a major factor affecting traffic congestion and safety. This paper proposes a microscopic traffic model that captures the features of traffic in foggy weather and characterizes it based on visibility. The intelligent driver (ID) model is based on a constant acceleration exponent and produces similar traffic behaviour for all conditions, which is unrealistic. The performance of the ID and proposed models is evaluated on a 2.2 km ring road for 250 s with a platoon of 51 vehicles. Results are presented which show that the proposed model characterizes traffic realistically with lower acceleration and deceleration compared to the ID model. Further, it does not create stop‐and‐go waves and is stable even during foggy weather. The proposed model can be used to reduce fuel consumption and pollution resulting from traffic congestion. This study proposes a new microscopic traffic model, which realistically characterizes the traffic flow during foggy weather based on visibility unlike the existing intelligent driver model, which results in unrealistic traffic flow behaviour.

• Efficient Fusion Decision System for Predicting Road Crash Events: A Comparative Simulator Study for Imbalance Class Handling; Elamrani Abou Elassad Zouhair, Ameksa Mohammed, Elamrani Abou Elassad Dauha, Mousannif Hajar; Transportation research record; 2023

Abstract by the author: Road crash events are a fact of life. Although significant progress have been made in adopting machine learning techniques for analyzing road crashes, there has been limited emphasis on evaluating crash events within data fusion systems. The primary purpose of this study is to outline and validate a comparative safety analysis of an ensemble fusion system founded on the use of different base classifiers and a meta-classifier to procure more efficient crash prediction. Three categories of features namely vehicle-telemetry, driver-inputs and environmental-conditions have been collected using a driving-simulator in order to identify the crash strongest precursors through feature extraction technique. Furthermore, optimized strategies using AdaBoost, XGBoost, RF, GBM, LightGBM, CatBoost and KNN techniques were implemented to establish effective predictions within a fusion-based system. To ensure that the proposed system provide superior decisions given the infrequent nature of crash events, an imbalance-learning approach was conducted based on three resampling strategies: over-sampling, under-sampling and SMOTE-Tomek-Links. The findings depict that the superior performance has been attained when adopting LightGBM, CatBoost and KNN as base classifiers along with SMOTE-TL as balancing technique and XGBoost as meta-classifier with 89.19% precision, 96.77% recall and 92.83% f1-score. To our knowledge, there has been a limited interest, if not at all, at endorsing a fusion-based system examining the impact of real-time features' combinations on the prediction of road crashes while providing a critical analysis of class-imbalance. Overall, the findings emphasized the relevance of the explanatory features and can be endorsed in designing efficient intelligent transportation systems.

• Analytical Model for Information Flow Management in Intelligent Transport Systems; Terentyev Alexey, Marusin Alexey, Evtyukov Sergey, Marusin Aleksandr, Shevtsova, Anastasia, Zelenov Vladimir; Mathematics (Basel); 2023; Vol.11 (15); p.3371; Article 3371

Abstract by the author: The performance of this study involves the use of the zoning method based on the principle of the hierarchical relationship between probabilities. This paper proposes an analytical model allowing for the design of information and analysis platforms in intelligent transport systems. The proposed model uses a synthesis of methods for managing complex systems’ structural dynamics and solves the problem of achieving the optimal balance between the information situations existing for the object and the subject under analysis. A series of principles are formulated that govern the mathematical modeling of information and analysis platforms. Specifically, these include the use of an object-oriented approach to forming the information space of possible decisions and the division into levels and subsystems based on the principles of technology homogeneity and information state heterogeneity. Using the proposed approach, an information and analysis platform is developed for sustainable transportation system management, that allows for the objective, multivariate forecasting-based record of changes in the system’s variables over time for a particular process, and where decision-making simulation models can be adjusted in relation to a particular process based on an information situation existing for a particular process within a complex transport system. The study demonstrates a mathematical model that solves the optimal balance problem in organizationally and technically complex management systems and is based on vector optimization techniques for the most optimal decision-making management. The analysis involves classical mathematical functions with an unlimited number of variables including traffic volume, cargo turnover, safety status, environmental performance, and related variables associated with the movement of objects within a transport network. The study has produced a routing protocol prescribing the optimal vehicle trajectories within an organizationally and technically complex system exposed to a substantial number of external factors of uncertain nature.

• Overall architecture and system design of shuttle unmanned ground vehicle with road verification in intelligent transportation system zone; Li Yuanzhe, Ni Jun, Hu Jibin; IET intelligent transport systems; 2023; Vol;17 (7); p.1275-1287

Abstract by the author: The shuttle unmanned ground vehicle (UGV) is an important element in the future intelligent transportation system (ITS), which will be widely used for public transportation in various scenarios. It has been widely accepted that the shuttle UGV will significantly improve the transportation efficiency and intelligent level of ITS in the near future. This paper presents an overall architecture and system of shuttle UGV for the future ITS. The hardware architecture includes sensor, actuator and computing platform, and is built on an X‐by‐wire chassis. The software architecture includes environment perception, decision making and dynamics control. Reliable and accurate autonomous driving algorithms are applied to the software system, which enable the shuttle UGV to drive intelligently and perform task stably. In addition, to address the large‐scale deployment of shuttle UGVs in the future, this paper also introduces the concept of cloud brain system and discusses the application and prospect of the cloud brain system in ITS. The cloud brain system can store, monitor and mine the big data generated by the shuttle UGVs, which will further improve the operational safety and overall performance of the shuttle UGV group. An open road test in an ITS test zone is conducted to validate the performance of the proposed overall architecture and system for the shuttle UGV. The shuttle UGV can replace human to conduct personnel transportation tasks in ITS. It has been widely expected that the shuttle UGV will improve the intelligent level and transportation efficiency of ITS significantly. In this paper, we propose an overall architecture and system of shuttle UGV. And the cloud brain system is designed and deployed in ITS, to further improve the operational security and comprehensive performance of shuttle UGV.

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