Intelligent Transportation Research Articles

Short-term Traffic Flow Prediction Based on KELM Optimized By Improved Slime Mould Algorithm

Traffic flow prediction plays a crucial role in improving transportation efficiency and enhancing Intelligent Transportation Systems (ITS). However, the temporal, spatial, and nonlinear nature of traffic flow data presents challenges for accurate short-term prediction. We propose a short-term traffic flow prediction model based on Kernel Extreme Learning Machine (KELM) optimized by the improved Slime Mould Algorithm (SMA). KELM is an improved version of Extreme Learning Machine (ELM) that incorporates kernel functions for improved generalization and stability. SMA is a meta-heuristic algorithm inspired by the behavior of slime mould in foraging, known for its strong global searching ability. For better performance, three strategies are introduced: the Good Point Set method for optimizing the initial population, the combination of Opposition Based Learning (OBL) and Differential Evolution (DE) to improve the slime mould generation mechanism, and the use of adaptive t distribution mutation to enhance convergence speed. After comparing the performance of these improved SMAs on twelve test functions, the ISMA improved by integrating three strategies as mentioned above is best. Then the ISMA is applied to search for the optimal parameters of KELM model. Finally, the optimized KELM with optimal parameters is applied to predict the short-term traffic flow on given traffic data set. Experimental results demonstrate that the proposed model, KELM optimized by ISMA namely ISMA-KELM, outperforms existing models such as Random Forest (RF), Least Squares Support Vector Machine (LSSVM), KELM optimized by Tuna Swarm Optimization Algorithm (TSO-KELM), and KELM optimized by SMA (SMA-KELM) in terms of traffic flow prediction accuracy. The proposed model ISMA-KELM provides a promising approach for addressing the challenges of traffic flow prediction, offering improved accuracy and efficiency in real-time traffic management systems.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

AI-powered decision making for road safety optimization under probabilistic linguistic Sugeno-Weber aggregation information

Traffic accidents, a global concern, pose threats to lives and carry substantial economic and societal burdens. This paper explores the innovative integration of artificial intelligence (AI) into the ambiance of Multi Criteria Decision Making (MCDM) to address the challenge. We investigate AI's potential to enhance road safety globally, utilizing data analysis, predictive modeling, and intelligent traffic management systems. Our goal is to revolutionize accident prevention by optimizing decision-making processes and promoting safer and more efficient road environments. In the realm of information aggregation and fusion, growing interest among researchers has centered on probabilistic linguistic expression sets, which adeptly aggregate uncertain data. This article's primary aim is to explore methodologies for aggregating information within a probabilistic linguistic environment. To achieve this goal, we have introduced procedural laws based on the Sugeno-Weber (SW) framework for handling probabilistic linguistic term elements (PLTEs), rooted in both the product and sum of SW operations. Consequently, we have crafted a range of probabilistic linguistic aggregation techniques, encompassing the probabilistic linguistic SW Average (PLSWA) and Geometric (PLSWG), followed by weighted as well as ordered aggregation operators like PLSWWA and PLSWWG, PLSWOWA and PLSWOWG. By harnessing SW τ-norm and τ-conorm, we have developed versatile aggregation tools that facilitate information reinforcement. Through the utilization of proposed operators, we have presented strategies for effectively integrating probabilistic linguistic term sets (PLTs) in the context of MCDM. We have compared our suggested procedures with the TOPSIS approach and elucidated the diverse features inherent to these operators.

Estrategias de Gestión para la Optimización del Tránsito Urbano en el Cantón La Concordia: Desafíos y Oportunidades

This article analyzes the impact of traffic congestion in La Concordia, Ecuador, from an administrative perspective, and proposes solutions based on a review of existing literature. The study is based on the growing problem of urbanization and insufficient transportation planning, which has caused significant problems in urban traffic, affecting both the quality of life of citizens and the economic productivity of the region. The objective of the study is to propose viable administrative strategies that can reduce vehicular congestion, based on previous experiences in Latin American cities with similar characteristics. In methodological terms, a qualitative literature review was carried out to analyze successful cases in the implementation of sustainable mobility policies, prioritizing those studies that present solutions for medium-sized developing cities. The results of the analysis highlight the importance of implementing intelligent traffic management systems, improving public transport and encouraging the use of non-motorized means of transport, such as bicycles and pedestrian routes. The discussion highlights that, despite technological and administrative advances, there are significant challenges such as lack of funding and social resistance to the use of public transport. The conclusions suggest that the implementation of these solutions must be accompanied by a cultural change and a greater commitment from local authorities to ensure their long-term effectiveness.

Estimating the Expected Change in Safety for a Potential Application of Three Intelligent Transportation System Treatments

This paper documents and illustrates a practice-ready procedure for estimating changes in crash frequency for specific application circumstances for three intelligent transportation system treatments—Closed Circuit Television Cameras (CCTV), Dynamic Message Signs (DMS), and Road Weather Information Systems (RWIS). The procedure will allow an agency to directly evaluate the change in safety that may be associated with a contemplated treatment. In effect, the approach mimics the application of a Crash Modification Function (CMFunction) in that each potential application will, in principle, have its own Crash Modification Factor (CMF). The procedure uses an empirical Bayes framework with safety performance functions (SPFs) for treatment and non-treatment reference sites. The paper also presents those SPFs, which were developed from Pennsylvania freeway data. In principle, this cross-sectional approach can be applied, as it has been, for other safety treatments where safety effects vary with application circumstance and where that variability cannot be captured with conventional before–after studies.

Urban Traffic Identification by Comparing Machine Learning Algorithms

The Internet of Things (IoT) applied to intelligent transport systems has become a key element for understanding the way traffic flow behaves in cities, which helps in decision-making to improve the management of the transport system by monitoring and analyzing network traffic in real time, all with the aim of daily benefiting users of the city’s road infrastructure. Traffic volume estimation in real time, with high effectiveness, may help mobility management and improve traffic flow. Moreover, machine-learning algorithms have shown effectiveness in various scientific fields and have provided a significant platform for achieving intelligent applications. Therefore, we applied various machine learning algorithms to classify the vehicular traffic status in the traffic network of two cities with more than 2 million inhabitants. It was first necessary to establish, from the attributes provided by the datasets, the object class from the LOS (Level of Services) thresholds proposed by the National Academies of Sciences, Engineering, and Medicine, for the basic segments of highways in an urban area. We then selected the attributes of interest using the Recursive Feature Elimination Method (RFE) to reduce the dimensionality of the data, and applied the DT, RF, ET, KNN, and MLP algorithms to train and classify the level of vehicular congestion, defining various volumes of training and validation data. The results show the high effectiveness of the algorithms, highlighting the MLP algorithm as the one that provides the highest effectiveness on average for the evaluated datasets, with a mean precision of 99.5%.

МОДЕЛЬ НЕПРЕРЫВНОГО ТРАНСПОРТНОГО ПОТОКА БЕСПИЛОТНЫХ АВТОМОБИЛЕЙ ПРИ ДВИЖЕНИИ НА СЕРПАНТИНЕ С ВИРАЖАМИ С УЧЕТОМ СРЕДНЕЙ СКОРОСТИ ДВИЖУЩИХСЯ ВПЕРЕДИ АВТОМОБИЛЕЙ

The paper proposes a new model of continuous traffic flow of unmanned autonomous cars when travelling on a serpentine with curves, taking into accountthe average speed of the cars ahead. The review of existing traffic flow models, in particular, the models of following the leader, such as the optimal speed model, generalised traffic model, model of total velocity difference is carried out. It is noted that these models do not take into account the peculiarities of traffic on serpentines with turns. On the basis of the existing models, a new model of continuous traffic flow has been developed, the principal difference of which is the consideration of the transverse slope of the road (turn) when travelling on a serpentine. The model takes into account the action of forces on the vehicle, including gravity, driving force and centripetal force. The prospects of using the developed model in the conditions of using intelligent transport systems, when the information about the average speed of vehicles moving ahead is transmitted over the network, are shown. Th proposed model allows to take into account the transverse slope of the road on curvilinear serpentine sections when calculating speed modes, to adequately assess the carrying capacity and identify potentially dangerous sections for optimisation of design decisions when creating unmanned transport.

XAI-SALPAD: Explainable deep learning techniques for Saudi Arabia license plate automatic detection

In recent decades, automatic license plate recognition (ALPR) has emerged as a critical application of artificial intelligence in intelligent transportation systems (ITS), addressing a multitude of existing issues. Several algorithms have been developed to improve ALPR accuracy under a variety of conditions, each with its own set of strengths and limitations. The KSA intends to digitize traditional services as part of its Vision 2030 initiative. In this methodology, the ALPR system for accurately recognizing KSA's license plates (LP) leverages deep neural networks (DNNs) as a powerful technique, capable of performing effectively in unconstrained environments with images taken under various conditions, even when the training data is limited. The approach employs YOLOv8 to detect LPs and alphanumeric characters in real-time, followed by using a convolutional neural network (CNN) for character recognition. According to our finding, the YOLOv8 model outperforms other models in the identification of LPs, achieving remarkable accuracy and F1 scores (mAP@0.5 = 0.96 and mAP@0.95 = 0.97). As a result, YOLOv8 was chosen for SLP character recognition. This strategy is appropriate for ITS since it tackles a number of issues, like decreasing car theft and improving public safety. Furthermore, a locally interpretable agnostic model (LIME) offers justifications that help users better understand the model. The system's provision of clear explanations for its decisions not only satisfies performance standards but also corresponds with the increasing need for explainable AI, guaranteeing an efficient and responsible implementation.

Applications of Blockchain and Smart Contracts to Address Challenges of Cooperative, Connected, and Automated Mobility

Population growth and environmental burden have turned the efforts of cities globally toward smarter and greener mobility. Cooperative and Connected Automated Mobility (CCAM) serves as a concept with the power and potential to help achieve these goals building on technological fields like Internet of Things, computer vision, and distributed computing. However, its implementation is hindered by various challenges covering technical parameters such as performance and reliability in tandem with other issues, such as safety, accountability, and trust. To overcome these issues, new distributed and decentralized approaches like blockchain and smart contracts are needed. This paper identifies a comprehensive inventory of CCAM challenges including technical, social, and ethical challenges. It then describes the most prominent methodologies using blockchain and smart contracts to address them. A comparative analysis of the findings follows, to draw useful conclusions and discuss future directions in CCAM and relevant blockchain applications. The paper contributes to intelligent transportation systems’ research by offering an integrated view of the difficulties in substantiating CCAM and providing insights on the most popular blockchain and smart contract technologies that tackle them.

Enhancing Cooperative Lane-Changing for Intelligent Connected Vehicles: A DDPG-Based Approach

Current research on lane-changing for intelligent connected vehicles (ICVs) primarily focuses on the control of a single ICV. Even studies on cooperative lane-changing among multiple ICVs often use fixed control algorithms, which are difficult to adapt to constantly changing traffic conditions. This paper proposes a method based on the deep deterministic policy gradient (DDPG) model. The DDPG algorithm has strong generalization capabilities, enabling it to handle complex traffic scenarios. Combined with the design of innovative reward functions, it successfully achieves the control of cooperative lane-changing for multiple ICVs. The novelty of this model is mainly reflected in the implementation of multiple ICVs control and cooperative strategies through the design of a speed reward function. Specifically, when two or more ICVs come within a 15-m range, a collaborative strategy is implemented. In this strategy, the original speeds of the vehicles involved are replaced with their average speed. This average speed is then used to compute the velocity reward. Moreover, an additional headway reward is introduced when two vehicles are on the same lane with a distance of less than 10[Formula: see text]m between them, further optimizing lane-changing decisions and enhancing driving efficiency. The innovative approach described in this paper has been validated through simulations in the highway-env simulation environment. The model’s effectiveness was tested under various conditions, including different road densities and numbers of ICVs. Results indicate that with an increasing number of episodes, the model consistently enhances both the fleet’s and each individual vehicle’s average speed and steps. It also ensures that the headway is maintained within a desired range of 10–20[Formula: see text]m. This paper showcases the potential applications of a new DDPG-based multi-networked vehicular cooperative lane-changing decision model across different scenarios. These findings provide important reference values for future research and development in intelligent transportation systems (ITS), particularly in optimizing vehicle cooperative behaviors and enhancing traffic efficiency. This paper emphasizes the model’s potential to significantly impact the management and operation of future vehicular networks, paving the way for more sophisticated and efficient transportation systems.

Investigation on car-following heterogeneity and its impacts on traffic safety and sustainability

This study proposes a general framework to investigate car-following heterogeneity and its impacts on traffic safety and sustainability. The framework incorporates rigorous driving style classification using a semi-supervised learning technique and a micro-simulation process that includes 66 fine-grained traffic scenarios exhibiting varying degrees of heterogeneity. Validated using two distinct real-world datasets reveals the superiority of S3VM-based classifiers over traditional SVM classifiers in driving style classification. Simulation results show that an increase in driving aggressiveness is correlated with higher safety issues and greater environmental impacts. Further elucidation of these impacts from the mechanism of underlying characteristics of driving behaviour and traffic flow dynamics indicates that less aggressive drivers can lead to the formation of vehicular platoons, thereby encouraging more aggressive drivers to adopt a milder driving style. Importantly, the formation of these platoons is influenced by both the proportion and spatial distribution of less aggressive vehicles. The proposed approach promises advantages in reducing the negative impacts of driving heterogeneity, thus benefiting Intelligent Transportation Systems (ITS) by improving traffic safety and sustainability.

Nighttime Pothole Detection: A Benchmark

In the field of computer vision, the detection of road potholes at night represents a critical challenge in enhancing the safety of intelligent transportation systems. Ensuring road safety is of paramount importance, particularly in promptly repairing pothole issues. These abrupt road depressions can easily lead to vehicle skidding, loss of control, and even traffic accidents, especially when water has pooled in or submerged the potholes. Therefore, the detection and recognition of road potholes can significantly reduce vehicle damage and the incidence of safety incidents. However, research on road pothole detection lacks high-quality annotated datasets, particularly under low-light conditions at night. To address this issue, this study introduces a novel Nighttime Pothole Dataset (NPD), independently collected and comprising 3831 images that capture diverse scene variations. The construction of this dataset aims to counteract the insufficiency of existing data resources and strives to provide a richer and more realistic benchmark. Additionally, we develop a baseline detector, termed WT-YOLOv8, for the proposed dataset, based on YOLOv8. We also evaluate the performance of the improved WT-YOLOv8 method and eight state-of-the-art object detection methods on the NPD and the COCO dataset. The experimental results on the NPD demonstrate that WT-YOLOv8 achieves a 2.3% improvement in mean Average Precision (mAP) over YOLOv8. In terms of the key metrics—AP@0.5 and AP@0.75—it shows enhancements of 1.5% and 2.8%, respectively, compared to YOLOv8. The experimental results provide valuable insights into each method’s strengths and weaknesses under low-light conditions. This analysis highlights the importance of a specialized dataset for nighttime pothole detection and shows variations in accuracy and robustness among methods, emphasizing the need for improved nighttime pothole detection techniques. The introduction of the NPD is expected to stimulate further research, encouraging the development of advanced algorithms for nighttime pothole detection, ultimately leading to more flexible and reliable road maintenance and road safety.

CNN MODEL FOR TRAFFIC SIGN RECOGNITION

The traffic sign recognition framework (TSRS) is an important component of intelligent transportation systems (ITS). Accurately identifying traffic signs can improve driving safety. This research provides a traffic sign recognition approach based on profound learning. It primarily focuses on the location and order of roundabout signs. First, a photograph undergoes pre-processing to highlight important facts. Hough Transform is used to distinguish and find regions. Finally, the unique street traffic signs are analyzed for further understanding. This paper proposes a photo handling-based traffic sign discovery and distinguishing proof technique that is combined with convolutional brain organization (CNN) to sort traffic signs. CNN is useful for recognizing many PC vision tasks due to its high recognition rate. TensorFlow is used to implement CNN. We have a recognition accuracy of over 98.2% for the roundabout image in the German informative collections. KEYWORDS——traffic sign recognition, traffic sign detection, deep learning, convolutional neural network

Synchronous End-to-End Vehicle Pedestrian Detection Algorithm Based on Improved YOLOv8 in Complex Scenarios.

In modern urban traffic, vehicles and pedestrians are fundamental elements in the study of traffic dynamics. Vehicle and pedestrian detection have significant practical value in fields like autonomous driving, traffic management, and public security. However, traditional detection methods struggle in complex environments due to challenges such as varying scales, target occlusion, and high computational costs, leading to lower detection accuracy and slower performance. To address these challenges, this paper proposes an improved vehicle and pedestrian detection algorithm based on YOLOv8, with the aim of enhancing detection in complex traffic scenes. The motivation behind our design is twofold: first, to address the limitations of traditional methods in handling targets of different scales and severe occlusions, and second, to improve the efficiency and accuracy of real-time detection. The new generation of dense pedestrian detection technology requires higher accuracy, less computing overhead, faster detection speed, and more convenient deployment. Based on the above background, this paper proposes a synchronous end-to-end vehicle pedestrian detection algorithm based on improved YOLOv8, aiming to solve the detection problem in complex scenes. First of all, we have improved YOLOv8 by designing a deformable convolutional improved backbone network and attention mechanism, optimized the network structure, and improved the detection accuracy and speed. Secondly, we introduced an end-to-end target search algorithm to make the algorithm more stable and accurate in vehicle and pedestrian detection. The experimental results show that, using the algorithm designed in this paper, our model achieves an 11.76% increase in precision and a 6.27% boost in mAP. In addition, the model maintains a real-time detection speed of 41.46 FPS, ensuring robust performance even in complex scenarios. These optimizations significantly enhance both the efficiency and robustness of vehicle and pedestrian detection, particularly in crowded urban environments. We further apply our improved YOLOv8 model for real-time detection in intelligent transportation systems and achieve exceptional performance with a mAP of 95.23%, outperforming state-of-the-art models like YOLOv5, YOLOv7, and Faster R-CNN.

Modeling impacts of different data transmission delays on traffic jam, fuel consumption and emissions on curved road

Under the intelligent transportation system, the use of autonomous vehicles can ease traffic jam, and reduce fuel consumption and emissions. The effects of different data transmission delays on traffic jam, fuel consumption and emissions on curved road are studied by constructing an extended multi-vehicle curve following model considering different data transmission delays to describe the curve following behavior of multiple autonomous vehicles. Subsequently, the stability of the novel model is derived by using linear stability theory. And then, numerical simulation is executed. Finally, some influence factors including curvature radius, backward looking effect, position signal time delay, and velocity signal time delay are discussed. Numerical simulation results show that reducing curvature radius and increasing backward looking effect can reduce fuel consumption and emissions, decrease density fluctuations, thereby gradually eliminating traffic jam. In addition, as difference between position signal time delay and velocity signal time delay decreases, the improvement degree in fuel consumption and emissions is limited. Meanwhile, it also makes density fluctuation decreases gradually, enhances traffic stream stability, and alleviates traffic jam, which is keeping with theoretical analysis. The finding has theoretical implications for designing autonomous vehicle control algorithms to reduce the negative effects of data transmission delays in traffic flow.

Harmonizing Urban Futures: Integrating Smart and Sustainable City Principles

In an era marked by rapid urbanization and evolving urban challenges, understanding the intricate relationship between sustainable and smart city principles is paramount for shaping resilient and future- ready cities. This study delves into the integration of sustainable and smart city principles, aiming to illuminate their interconnectedness in contemporary urban contexts. By analyzing the latest academic understanding of this integration, the research contributes to the development of comprehensive frameworks guiding urban development strategies. Through bibliometric analysis using VOSviewer, key factors such as air quality, intelligent transportation systems, internet connectivity, urban growth, solar energy, and learning processes emerge as central to understanding the interplay between smart and sustainable cities. This analysis informs efforts towards integrated urban development and resilience. The study hypothesizes that smart city principles build upon sustainable city foundations, as evidenced by interrelated indicators across urban domains. Recognizing the significance of this integration, urban planners and policymakers can craft strategies that prioritize sustainability, innovation, and inclusivity, thus shaping resilient and future-ready cities. By elucidating the symbiotic relationship between smart and sustainable city concepts, this research offers insights crucial for addressing contemporary urban challenges and fostering sustainable urban development globally.

Smart transportation solutions for faster emergency medical services response using an enhanced whale optimization algorithm

Smart transportation solutions for faster emergency medical services response using an enhanced whale optimization algorithm

Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

Spatio-temporal deep learning framework for pedestrian intention prediction in urban traffic scenes

Pedestrian intent prediction is an essential task for ensuring the safety of pedestrians and vehicles on the road. This task involves predicting whether a pedestrian intends to cross a road or not based on their behavior and surrounding environment. Previous studies have explored feature-based machine learning and vision-based deep learning models for this task but these methods have limitations in capturing the global spatio-temporal context and fusing different features of data effectively. To address these issues, we propose a novel hybrid framework HSTGCN for pedestrian intent prediction that combines spatio-temporal graph convolutional neural networks (STGCN) and long short-term memory (LSTM) networks. The proposed framework utilizes the strengths of both models by fusing multiple features, including skeleton pose, trajectory, height, orientation, and ego-vehicle speed, to predict their intentions accurately. The framework’s performance have been evaluated on the JAAD benchmark dataset and the results show that it outperforms the state-of-the-art methods. The proposed framework has potential applications in developing intelligent transportation systems, autonomous vehicles, and pedestrian safety technologies. The utilization of multiple features can significantly improve the performance of the pedestrian intent prediction task.

A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems.

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring.