Intelligent Transportation Management Research Articles

Short-term freeway traffic speed multistep prediction using an iTransformer model

Accurate prediction of the freeway traffic speed is important for enhancing intelligent transportation management and assisting with route planning. Current traffic speed prediction studies usually neglect predictions extending to an hour or longer. Thus, to address this gap, the inverted Transformer (iTransformer) model is utilized to predict freeway speeds across intervals from 5 to 150 minutes. The iTransformer model provides a global view by encapsulating the entire time series from each detector into variate tokens, allowing it to capture the complex patterns and dependencies that change over time. Additionally, a multi-head attention mechanism is employed to identify short-term and long-term speed patterns. This study validates the prediction performance of iTransformer by using the traffic speed data from an interstate freeway in Minnesota, comparing it against traditional traffic prediction methods and two other Transformer models. Results indicate that the iTransformer model outperforms these benchmark approaches, particularly when predicting speed over 60 minutes for peak hour periods.

AI-enhanced fault-tolerant control and security in transportation and logistics systems: addressing physical and cyber threats

Transportation and logistics systems are becoming increasingly complex and critical to modern infrastructure. This paper proposes a novel AI-enhanced fault-tolerant control framework to address the dual challenges of physical malfunctions and cyber threats. By leveraging advanced machine learning algorithms and real-time data analytics, the proposed methodology aims to enhance the reliability, safety, and security of transportation and logistics systems. This research explores the foundations and practical implementations of AI-driven anomaly detection, predictive maintenance, and autonomous response systems. The findings demonstrate significant improvements in system resilience and robustness, making a substantial contribution to the field of intelligent transportation management.

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Factors influencing taxi entrepreneurs to adopt intelligent transport management systems

Background: Transporting the urban population in South African cities is chaotic because of traffic congestion, and one possible solution is the adoption of transport management systems to manage the timely picking and dropping of passengers by metered taxi operators.Objectives: This study examined factors that influence the South African metered taxi entrepreneurs’ willingness to adopt transport management systems (TWTMS).Method: This is a quantitative study that collected data from 253 metered taxi entrepreneurs from Sandton in the Johannesburg metropolitan area, South Africa. This study tested 14 hypotheses using regression analysis.Results: Attitude towards use had an insignificant relation with behavioural intention (BI); however, BI had a significant relationship with TWTMS. There were four factors that directly determined BI, which are perceived usefulness, perceived safety, perceived convenience and perceived trust.Conclusion: The results of this study suggest that taxi entrepreneurs will adopt transport management systems if they find the system useful, safe to use, convenient and trustworthy.Contribution: This study uncovered factors that influence the adoption of transport management systems by metered taxi entrepreneurs. Therefore, software developers should incorporate these factors when designing transport management systems so that acceptance can be improved.

Revolutionizing Urban Mobility: Smart Transportation & Vehicle-to-Infrastructure Communication – A Review

The burgeoning popularity of IoT technology has significantly altered transportation, with an extensive variety of repercussions. Intelligent Transportation Systems (ITS) are at the vanguard of this change, which has the potential to fundamentally change urban transportation for both individuals and businesses. ITS has expanded considerably in recent years, owing to global urbanization and industrialization advancements. It is regarded as a game-changing technology that improves road safety, improves traffic flow, and improves the entire driving experience. Despite many research efforts on ITS applications and simulation platforms, formal semantic descriptions for intelligent transportation systems have been noticeably lacking. Rising rates of accidents suggest the necessity for a robust Smart Transport System (STS) that prioritizes improving mobility, and reliability, as well as reducing carbon dioxide and methane emissions from automobiles beyond dispersing gains throughout everywhere. Multiple traffic signal management approaches have been designed to enhance real-time traffic movement at intersections, but none have achieved seamless, congestion-free traffic movements. The creation of an interconnected unified framework that integrates and links vehicles with VANET, IoT, and AI technologies will significantly affect the establishment of an innovative reliable mobility network. Internet of Vehicles (IoV) systems will connect several automobiles to share vital information through an IoT-enabled network. Vehicle-to-infrastructure (V2I) communication is essential in developing intelligent transportation and rail traffic management systems, significantly improving road traffic safety and efficiency. A V2I system integrates transmission and analysis capabilities into vehicles and corresponding infrastructure. Wireless sensors are strategically positioned along highways or driving zones in this system. The significance of safe and effective V2X communication between automobiles and facilities expands substantially as intelligent modes of transport progress.

Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

ЦИФРОВА ТРАНСФОРМАЦІЯ РЕГІОНІВ УКРАЇНИ ЯК ПЕРЕДУМОВА ІМПЛЕМЕНТАЦІЇ КОНЦЕПЦІЇ РОЗУМНОГО МІСТА

The article is devoted to researching the processes of digital transformation of the regions of Ukraine through the implementation of the "smart city" concept, which is one of the key aspects of the modern development of urban areas. In the context of global trends towards digitization and the introduction of innovations, the concept of a smart city is becoming an important part of the strategic planning of both large megacities and medium and small cities. This is due to the need to adapt cities to the rapidly changing socio-economic environment, which requires more effective solutions for managing resources, improving the quality of life of residents and increasing competitiveness in the international arena. The article examines the relevance of the transition to "smart cities" in light of the challenges facing modern Ukraine, including economic, environmental and social aspects. The main challenges that stand in the way of implementing the concept of a smart city in Ukrainian regions are analyzed. Such challenges include an insufficient level of digital infrastructure, limited financial resources, a low level of digital literacy of the population, an incomplete legal framework, and an insufficient level of interaction between the public and private sectors. The article analyzes the current state of digital infrastructure in different regions of Ukraine, in particular, the level of access to broadband Internet and the implementation of intelligent transport management systems. In addition, a comparison of the implementation of smart city projects in Ukrainian cities with similar initiatives in Europe is presented, which emphasizes the need to accelerate digital transformation in the face of global competition.

The application of CNN in Smart city management and supervision under the background of Smart city

Intelligent transportation management, as an important component of intelligent city supervision, has practical value in improving the effectiveness of urban management. To improve the efficiency of intelligent city traffic management, this study proposes to use convolutional neural networks to improve the performance of navigation and intelligent transportation networks in intelligent transportation. This network is used for vehicle recognition in navigation to predict road congestion and determine the optimal driving path. This structure is applied in the intelligent road network to monitor the driving status of vehicles on the road. It is combined with the vehicle networking system to locate abnormal vehicles to achieve intelligent traffic management and supervision. These results confirm that this algorithm achieves a vehicle recognition accuracy of 91.2 % after 96 iterations and completes the recognition process within 163 ms. This algorithm converges to a vehicle positioning accuracy of 82.4 % after 93 iterations and achieves positioning within 277 ms. This algorithm can not only improve the optimal path planning effect of navigation, but also identify abnormal vehicles within 77 ms. Therefore, the proposed algorithm has high practical value in intelligent transportation management.

Intelligent Network-Connected New Energy Vehicle Technology and Application Under the Dual-Carbon Strategy

In responding to the “dual carbon” strategy, intelligent networked new energy vehicle technology plays a crucial role. This type of vehicle combines the advantages of new energy technology and intelligent network technology, effectively reduces carbon emissions in the transportation sector, improves energy utilization efficiency, and contributes to the green transportation system through intelligent transportation management and collaborative work between vehicles, making significant contributions. This article aims to explore the development of intelligent network-connected new energy vehicle technology and applications under the dual-carbon strategy and lay the foundation for the future development direction of the automotive industry.

Ultra-low detection limit self-sensing nanocomposites with self-assembled conductive microsphere arrays for asphalt pavement health monitoring

Accurately monitoring micro-level strain within pavements poses a significant challenge in contemporary road engineering, limiting the effectiveness of early-stage pavement health diagnostics. Addressing this critical gap, this study introduces an innovative ultra-low detection limit sensor, specifically designed for intricate pavement health monitoring. It pivots on the unique structural design of a well-ordered 3D conductive network, where the self-assembly effect of microsphere arrays ingeniously mitigates the prevalent issue of nanofiller agglomeration within polymer matrices. The study highlights the significant role of carbon nanotubes (CNTs) length in shaping the structure of conductive networks, revealing that while longer CNTs lead to less uniform microsphere assemblies, they significantly enhance the bridging effect crucial for conductive pathways. To balance these traits, surface chemical modifications were strategically applied, effectively enhancing microsphere organization and dispersion, thereby elevating the conductive and sensing performance. The optimized sensor stands out for the ultra-low detection limit of 0.0005% (5 uε), significantly surpassing those of current strain sensors. Moreover, it exhibits remarkable sensitivity, evidenced by a gauge factor of 13.2, along with swift response and recovery times of 19 ms and 26 ms respectively, and impressive durability, enduring over 100,000 loading-unloading cycles. Field tests under diverse vehicular loads and speeds further validate the sensor's accuracy in capturing dynamic responses within pavement structures, a key factor in proactive pavement maintenance and management. Hence, this advancement shows promise in contributing to the development of safer and more resilient road networks, playing a beneficial role in the significant progress of intelligent transportation and sustainable infrastructure management.

OSTM-NET: Joint scale variation and occlusion handling deep network for real-time vehicle counting and volume estimation

Vehicle counting and traffic volume estimation using videos are difficult tasks crucial for efficient traffic control in smart cities. Several existing techniques rely in tracking and detecting mechanisms of the vehicles. These methods are ineffective for detecting occluded and small vehicles. Also, contextual information loss occurs in deep counting networks. An innovative Internet-of-Things (IoT)- driven Intelligent Transportation Management (ITM) system is proposed to address these issues. Initially, traffic videos are converted to temporal-spatial images instead of using complex detection and tracking methods. The density map for the temporal spatial image is estimated using an occlusion-aware spatio-temporal multi-scale network (OSTM-Net). It consists of two sub-networks for capturing occluded and small vehicles simultaneously. The scale-aware column network (SCNet) accurately captures small vehicles and preserves contextual information through enhanced scale representation. At the same time, the occlusion management network (OM-Net) uses position-sensitive regions of interest (PSRoI) deformable pooling to address the occlusion issues. Finally, volume estimation and counting are calculated in accordance with the density map obtained from OSTM-Net. Every path in the videos is processed separately using OSTM-Net to calculate the vehicle count in every path for effective traffic control in this proposed approach. Furthermore, the effectiveness of the sub-networks (SCNet and OM-Net) is validated using ablation experiments. The proposed IoT-based ITM achieves high performance in counting vehicles and estimating traffic volume compared to other existing approaches.

Sustainable Smart City Planning: Advances in Intelligent Transportation and E-Transportation Management

Sustainable Smart City Planning: Advances in Intelligent Transportation and E-Transportation Management

ADAPTATION OF VEHICLE OPERATION TO CHANGING REQUIREMENTS AND TRENDS IN THE TRANSPORT INDUSTRY

In the contemporary era, as the transportation industry undergoes significant transformations, the imperative of refining vehicular operation methodologies gains prominence. This scholarly pursuit is aimed at justifying the necessity of adapting vehicular usage approaches in the context of escalating demands for ecological sustainability, safety, and operational efficacy. To dissect the prevailing landscape and distill principal trends, an exhaustive scrutiny of academic discourses and sector-specific reports encompassing the epoch from 2015 to 2022 was undertaken. This examination delved into statistical delineations of vehicular traffic volumes and the emission of deleterious substances from motor vehicles, both within the Russian Federation and in the international arena. The crux of this inquiry revealed that the contemporary challenges besieging the transportation domain predominantly pertain to curtailing the adverse anthropogenic impact upon the environment, whilst concurrently augmenting both cargo and passenger throughput. In response, a compendium of strategies has been articulated to enhance the environmental rectitude and safety of vehicular operations. These include the pivot to alternative fuel sources, the deployment of sophisticated intelligent transportation management architectures, and the catalyzation of electric mobility's evolution.

DDFL: Dual-Domain Feature Learning for nighttime semantic segmentation

Nighttime semantic segmentation has been playing a critical role in intelligent transportation, building safety and urban management. However, nighttime scenes present some challenges such as complex structures, multiple light sources, uneven lighting and blurry image noise, which severely degrade the segmentation quality of nighttime images. To address these challenges, we propose a Dual-Domain Feature Learning (DDFL) model for nighttime semantic segmentation. Our approach introduces three innovative ideas. First, we establish an exposure correction module to address the impact of lighting differences on the model’s learning, so as to maximally restore the pixel distortion and blurry areas caused by artificial light in nighttime scenes. Second, we incorporate frequency domain information into the nighttime segmentation task to give the model stronger discrimination ability. Finally, we introduce a dual-domain fusion module to complement the information of learning from the spatial and frequency domains in a cross-fusion manner, enabling the network to perceive semantic information while preserving details. The proposed model was experimentally tested on the Nightcity, Nightcity+ and BDD100k datasets. Our results demonstrate that our model outperforms mainstream models, achieving mIoU scores of 56.73%, 57.41% and 28.97%, respectively, under different lighting, image exposure levels, and resolutions. These results show that our model is capable of segmenting nighttime scenes efficiently in a high-quality way.

Pothole detection in bituminous road using convolutional neural network with transfer learning

<p>The challenges of road maintenance, particularly in detecting potholes and cracks, and the proposed method using transfer learning and convolutional neural networks (CNNs) are significant advancements in this domain. Transfer learning is particularly beneficial, as it allows leverage pre-trained models to enhance the performance of the pothole detection system. CNNs, with their ability to capture spatial hierarchies in data, are well-suited for image-based tasks like pothole detection. The potential applications of the suggested method for intelligent transportation systems (ITS) services, such as alerting drivers about real-time potholes, demonstrate we research’s practical implications. This contributes to road safety and aligns with the broader goals of innovative city initiatives and infrastructure management. Achieving a 96% accuracy rate is a significant result, indicating the robustness of the proposed approach. Using this information to assess initial maintenance needs in a road management system is forward-thinking. Overall, we work is a valuable contribution to intelligent transportation and infrastructure management, showcasing the potential of advanced machine-learning techniques for addressing critical issues in road maintenance.</p>

Evaluating the Efficacy of Real-Time Connected Vehicle Basic Safety Messages in Mitigating Aberrant Driving Behaviour and Risk of Vehicle Crashes: Preliminary Insights from Highway Scenarios

Connected vehicle (CV) technology has revolutionised the intelligent transportation management system by providing new perspectives and opportunities. To further improve risk perception and early warning capabilities in intricate traffic scenarios, a comprehensive field test was conducted within a CV framework. Initially, data for basic safety messages (BSM) were systematically gathered within a real-world vehicle test platform. Subsequently, an innovative approach was introduced that combined multimodal interactive filtering with an advanced vehicle dynamics model to integrate BSM vehicle motion data with observations from roadside units. In addition, a driving condition perception methodology was developed, leveraging rough sets and an enhanced support vector machine (SVM), to identify aberrant driver behaviours and potential driving risks effectively. Furthermore, this study integrated BSM data from various scenarios, including car-following, lane changes, and free driving within the CV environment, to formulate multidimensional driving state sequence patterns for short-term predictions (0.5 s) utilising the long short-term memory (LSTM) model framework. The results demonstrated the effectiveness of the proposed approach in accurately identifying potentially hazardous driving conditions and promptly predicting collision risks. The findings from this research hold substantial promise in advancing road traffic safety management.

An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator

A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which includes the acquisition of the images, the labeling of vehicles, pedestrians, and pedestrian crossings as well as a description of the structure and content of the dataset (which amounts to 8694 images including visible images and those corresponding to the semantic segmentation). The images were generated using the CARLA simulator (but were like those that could be obtained with fixed aerial cameras or by using multi-copter drones) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.

Predicting Urban Region Heat via Learning Arrive-Stay-Leave Behaviors of Private Cars

Urban region heat refers to the extent of which people congregate in various regions when they travel to and stay in a specified place. Predicting urban region heat facilitates broad applications ranging from location-based services to intelligent transportation management. The region heat is essentially characterized by the ‘arrive-stay-leave (ASL)’ behaviors, while it is a challenging task to well capture the spatial-temporal evolution of region heat since the following issues remain: i) ASL behaviors of private cars is usually heterogeneous resulting in a hierarchical distribution of region heat. ii) Urban region heat contains complex spatial-temporal correlations hidden in ASL behaviors and how to collaboratively integrate them is challenging. To address these challenges, we propose a Hierarchical Spatial-Temporal Network (HierSTNet) to forecast urban region heat, which contains two representations, namely, grid region from micro perspective and node region from macro perspective. For the grids, three-dimension spatial and temporal convolutional network (3D-STCNN) is proposed to model multi-scale properties in temporal dimension of ASL behaviors. For the nodes, multi-head graph attention networks are utilized to model the periodicity and spatial heterogeneity among macro region. Hierarchical structures are designed for multi-view modeling spatial-temporal distribution of ASL behaviors, by which they capture small-scale features in micro regions and embeds the global representation into graph propagation. Finally, we design an interaction decoder layer to integrate the external factors and aggregate spatial-temporal information across hierarchical structures. Extensive experiments based on real-world private car trajectory dataset demonstrate the superiority and effectiveness of proposed framework.

Investigating machine learning for simulating urban transport patterns: A comparison with traditional macro-models

Predicting passenger flow within a city is crucial for intelligent transportation management systems, especially in the context of urban development, post-pandemic policy changes, and infrastructure improvements. Traditional macro models have limitations in accurately capturing the complex structure of real traffic flows, and recent advancements in machine learning offer promising approaches for improving transportation simulations. This research aims to compare the effectiveness of traditional simulation models with a selective machine learning (ML) model for traffic flow prediction in Oslo, Norway. Sensitivity and scenario analyses are conducted to examine the models’ parameters and derive the city’s characteristics. Results substantiate that the traditional Spatial Interaction model (SIM), although interpretable and requiring fewer parameters, has limitations in accurately capturing real flow structures and exhibits greater variability compared to the ML model. Statistical analyses support these findings and raise questions about the validity of the ML model’s results over the SIM. The research highlights the potential of ML models to identify trends in passenger flows and simulate traffic flows in different scenarios related to city development. Overall, the research presents a decision support system for planners and policymakers to predict traffic flow accurately and efficiently. It highlights the benefits and drawbacks of both the traditional SIM and ML models, contributing to the ongoing discussion of the role of machine learning in transportation modeling.

Vehicle Detection in Multisource Remote Sensing Images Based on Edge-Preserving Super-Resolution Reconstruction

As an essential technology for intelligent transportation management and traffic risk prevention and control, vehicle detection plays a significant role in the comprehensive evaluation of the intelligent transportation system. However, limited by the small size of vehicles in satellite remote sensing images and lack of sufficient texture features, its detection performance is far from satisfactory. In view of the unclear edge structure of small objects in the super-resolution (SR) reconstruction process, deep convolutional neural networks are no longer effective in extracting small-scale feature information. Therefore, a vehicle detection network based on remote sensing images (VDNET-RSI) is constructed in this article. The VDNET-RSI contains a two-stage convolutional neural network for vehicle detection. In the first stage, a partial convolution-based padding adopts the improved Local Implicit Image Function (LIIF) to reconstruct high-resolution remote sensing images. Then, the network associated with the results from the first stage is used in the second stage for vehicle detection. In the second stage, the super-resolution module, detection heads module and convolutional block attention module adopt the increased object detection framework to improve the performance of small object detection in large-scale remote sensing images. The publicly available DIOR dataset is selected as the experimental dataset to compare the performance of VDNET-RSI with that of the state-of-the-art models in vehicle detection based on satellite remote sensing images. The experimental results demonstrated that the overall precision of VDNET-RSI reached 62.9%, about 6.3%, 38.6%, 39.8% higher than that of YOLOv5, Faster-RCNN and FCOS, respectively. The conclusions of this paper can provide a theoretical basis and key technical support for the development of intelligent transportation.