Learning Dynamic Spatial-Temporal Dependence in Traffic Forecasting by Chaoyu Ren, Yuezhu Li

“…Accurate traffic forecasting is a key part of intelligent transport systems, facilitating a variety of urban application services such as trip alerting, route planning and traffic management. …”
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Prediction of Ship Traffic Flow and Congestion Based on Extreme Learning Machine with Whale Optimization Algorithm and Fuzzy c-Means Clustering by Yongjun Chen, Ming Huang, Kaixuan Song, Tengfei Wang

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Joint Resource Allocation for V2X Sensing and Communication Based on MADDPG by Zhiyong Zhong, Zhangyou Peng

“…Vehicle-to-Everything (V2X) communication is expected to play a critical role in enabling Intelligent Transportation Systems (ITS) within sixth-generation (6G) networks. …”
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sAMDGCN: sLSTM-Attention-Based Multi-Head Dynamic Graph Convolutional Network for Traffic Flow Forecasting by Shiyuan Zhang, Yanni Ju, Weishan Kong, Hong Qu, Liwei Huang

“…Accurate traffic flow prediction plays a vital role in intelligent transportation systems, helping traffic management departments maintain stable traffic order, reduce traffic congestion, and improve road safety. …”
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A Cyber-ITS Framework for Massive Traffic Data Analysis Using Cyber Infrastructure by Yingjie Xia, Jia Hu, Michael D. Fontaine

“…This brings up a new research topic, data-driven intelligent transportation systems (ITSs), which means to integrate heterogeneous traffic data from different kinds of sensors and apply it for ITS applications. …”
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Predicted Anchor Region Proposal with Balanced Feature Pyramid for License Plate Detection in Traffic Scene Images by Hoanh Nguyen

“…License plate detection is a key problem in intelligent transportation systems. Recently, many deep learning-based networks have been proposed and achieved incredible success in general object detection, such as faster R-CNN, SSD, and R-FCN. …”
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Model-enhanced spatial-temporal attention networks for traffic density prediction by Qi Guo, Qi Tan, Yue Peng, Long Xiao, Miao Liu, Benyun Shi

“…However, accurately predicting real-time traffic density has been a significant challenge in Intelligent Transportation Systems (ITS) due to the nonlinear and spatial-temporal dynamic complexity of traffic density. …”
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The Application of Reinforcement Learning in Traffic Flow Prediction: Advantages, Problems, and Prospects by Li Minghui, Zhou Decheng, Zhang Shiqi

“…Accurate TFP has great significance for traffic management, urban planning, road design, and the development of intelligent transportation systems (ITS). This article summarizes three traditional methods of TFP: parameter-based prediction, shallow machine learning-based prediction, and deep learning (DL)-based prediction. …”
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Optimizing Hybrid Electric Vehicle Performance: A Detailed Overview of Energy Management Strategies by Álvaro Gómez-Barroso, Iban Vicente Makazaga, Ekaitz Zulueta

“…Our analysis also revealed the importance of leveraging vehicle connectivity and intelligent transportation systems for future energy management developments, which will contribute to broader sustainability goals in the automotive sector.…”
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Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities by Muhammad Alam, Davide Moroni, Gabriele Pieri, Marco Tampucci, Miguel Gomes, José Fonseca, Joaquim Ferreira

“…Intelligent Transportation Systems (ITS) have evolved as a key research topic in recent years, revolutionizing the overall traffic and travel experience by providing a set of advanced services and applications. …”
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A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations by Yaofang Zhang, Jian Chen, Jianying Rao

“…The rapid development of highways greatly affects the flow of people, finance, goods, and information between cities, and monitoring the OD flow of travel has become a very important task for intelligent transportation systems (ITS). The temporal dynamics and complex spatial correlations of OD traffic distribution, as well as the sparsity and incompleteness of data caused by uneven traffic distribution, make OD traffic prediction complex and challenging. …”
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Data Imputation for Detected Traffic Volume of Freeway Using Regression of Multilayer Perceptron by Xiang Wang, Yingying Ma, Shengwen Huang, Yu Xu

“…Traffic volume data are the important part of research and application of intelligent transportation systems (ITS). However, data loss often happens due to various factors in the real world, which may cause large deviations in prediction or bad accuracy of optimizations. …”
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TAD: A Large-Scale Benchmark for Traffic Accidents Detection From Video Surveillance by Yajun Xu, Huan Hu, Chuwen Huang, Yibing Nan, Yuyao Liu, Kai Wang, Zhaoxiang Liu, Shiguo Lian

“…Automatic traffic accident detection has attracted the attention of the machine vision community for the rapid development of autonomous intelligent transportation systems (ITS). However, previous studies in this domain have been constrained by small-scale datasets with limited scope, impeding their effectiveness and applicability. …”
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INITIATING SMART PUBLIC TRANSPORTATION IN LAGOS: SETTING THE TONE FOR AFRICAN CITIES by Desmond AMIEGBEBHOR, Boluwatife POPOOLA

“…For African cities, however, there is need to domesticate our public transportation requirements for implementation and gradually ramp up with the deployment of mobility application technology like Intelligent Transport Systems comprising mobile apps, electronic ticketing amongst others. …”
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Spatiotemporal Traffic Flow Prediction with KNN and LSTM by Xianglong Luo, Danyang Li, Yu Yang, Shengrui Zhang

“…The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. …”
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An Efficient Cluster-Based Mutual Authentication and Key Update Protocol for Secure Internet of Vehicles in 5G Sensor Networks by Xinzhong Su, Youyun Xu

“…This work contributes to the development of secure, resilient, and efficient intelligent transportation systems, ensuring robust communication and protection in sensor-based IoV environments.…”
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Vehicle Flow Detection and Tracking Based on an Improved YOLOv8n and ByteTrack Framework by Jinjiang Liu, Yonghua Xie, Yu Zhang, Haoming Li

“…Vehicle flow detection and tracking are crucial components of intelligent transportation systems. However, traditional methods often struggle with challenges such as the poor detection of small objects and low efficiency when processing large-scale data. …”
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Lightweight and secure vehicle track verification scheme via broadcast communication channels by Zhiqiang NING, Yuanyuan WANG, Chi ZHANG, Lingbo WEI, Nenghai YU, Yue HAO

“…In intelligent transportation systems, it is crucial for smart vehicles to broadcast real-time vehicle track messages to coordinate driving decisions and ensure driving safety.However, attackers can manipulate vehicle tracks by modifying timestamps or manipulating signal frequencies, posing a threat to security.To address this problem, a lightweight vehicle track verification scheme was proposed, utilizing the broadcast communication channels to achieve secure verification of vehicle tracks without any special hardware support.Without time synchronization, each verifier calculated the time interval between the reception time of the message and the sending timestamp.Spatial position constraint equations are formulated by combining these time intervals between any two verifiers, effectively defending against timestamp forgery attacks.Additionally, each verifier calculates the Doppler frequency shift between the arrival frequency and the scheduled transmit frequency.Velocity vector constraint equations were formulated by combining these frequency shifts between any two verifiers, providing defense against carrier frequency manipulation attacks.Formal analysis shows that increasing the number of verifiers improves the accuracy of the proposed verification scheme.Experimental results in a real-world environment further validate that the proposed verification scheme exhibits the best performance when the number of verifiers is set to 3.Compared to the existing solution, the proposed verification scheme has a higher accuracy, lower false rejection rate, and lower false acceptance rate when validating true vehicle track and false vehicle track separately.…”
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Cloud-Based License Plate Recognition: A Comparative Approach Using You Only Look Once Versions 5, 7, 8, and 9 Object Detection by Christine Bukola Asaju, Pius Adewale Owolawi, Chuling Tu, Etienne Van Wyk

“…This research contributes to advancing the field of intelligent transportation systems by offering a detailed comparison that can guide future implementations and optimizations of LPR systems in cloud environments.…”
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R-CNN Based Vehicle Object Detection via Segmentation Capabilities in Road Scenes by Bisma Riaz Chughtai, Haifa F. Alhasson, Mohammed Alnusayri, Mohammed Alatiyyah, Hanan Aljuaid, Ahmad Jalal, Jeongmin Park

“…In the realm of intelligent transportation systems, vehicle detection and classification stand as pivotal tasks. …”
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