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Intelligent Transportation System (ITS) is becoming essential to modern transportation. Due to rise in the number of intelligent vehicles, memory and computation resource allocation closer to the user is becoming increasingly important. With the advent of Mobile Edge Computing, a new processing site is available that can provide memory and computational resource to lower the burden on the core network. However, handling multiple intelligent vehicles near-site requires fast handling of service requests. This paper proposes OsCoMIT, an Osmotic Computing based task segregation and resource allocation framework to handle services decomposed into macroservices and microservices at the edge network. For resource allocation the Proportional Fairness algorithm considers the requirements of the vehicles and maps them to the resources that result in less internal fragmentation. The effectiveness of the proposed framework is demonstrated on Intelligent Transportation System in 5G network using simulation and numerical analysis. It is found that the Proportional Fairness (PF) algorithm is better than the First Come First Serve (FCFS) and Priority Scheduling (PS) Algorithm in terms for memory and computation resource. It maximizes the system's utility while ensuring fairness and QoS requirements. The performance of OsCoMIT framework is validated statistically using one-way analysis of variance (ANOVA) with 95% confidence interval and the proposed framework shows that the value of F is much greater than F critical in case of memory and computation resources, which shows the significant improvement. [ABSTRACT FROM AUTHOR]

In the domain of Intelligent Transportation Systems (ITS), ensuring reliable travel time predictions is crucial for enhancing the efficiency of transportation management systems and supporting long-term planning. Recent advancements in deep learning have demonstrated the ability to effectively leverage large datasets for accurate travel time predictions. These innovations are particularly vital as they address both short-term and long-term travel demands, which are essential for effective traffic management and scheduled routing planning. Despite advances in deep learning applications for traffic analysis, the dynamic nature of traffic patterns frequently challenges the forecasting capabilities of existing models, especially when forecasting both immediate and future traffic conditions across various time horizons. Additionally, the area of long-term travel time forecasting still remains not fully explored in current research due to these complexities. In response to these challenges, this study introduces the Periodic Transformer Encoder (PTE). PTE is a Transformer-based model designed to enhance traffic time predictions by effectively capturing temporal dependencies across various horizons. Utilizing attention mechanisms, PTE learns from long-range periodic traffic data for handling both short-term and long-term fluctuations. Furthermore, PTE employs a streamlined encoder-only architecture that eliminates the need for a traditional decoder, thus significantly simplifying the model's structure and reducing its computational demands. This architecture enhances both the training efficiency and the performance of direct travel time predictions. With these enhancements, PTE effectively tackles the challenges presented by dynamic traffic patterns, significantly improving prediction performance across multiple time horizons. Comprehensive evaluations on an extensive real-world traffic dataset demonstrate PTE's superior performance in predicting travel times over multiple horizons compared to existing methods. PTE is notably effective in adapting to high-variability road segments and peak traffic hours. These results prove PTE's effectiveness and robustness across diverse traffic environments, indicating its significant contribution to advancing traffic prediction capabilities within ITS. [ABSTRACT FROM AUTHOR]

The logistics industry has witnessed the emergence of digital technology as a central topic of discourse, and terminology associated with digital logistics has gained extensive usage among professionals in the transportation, logistics, and international trade sectors. Digital logistics is highly dependent on the accumulation of data from the logistical activities conducted by organizations. The implementation of a digitalized process presents organizations with novel prospects, such as decreased operational expenses, increased efficiency, and reduced uncertainties associated with order fulfillment. Additionally, digital logistics facilitates the enhancement of distribution methods for businesses. The post-graduation knowledge levels of students become significant factors for potential employers. University education plays a crucial role in imparting academic and technical expertize in the domain of logistics; therefore, it is critical that logistics programs integrate a greater number of digital courses. Academic institutions play a pivotal role in enabling students to gain consciousness and comprehension of emerging patterns and concepts. In light of this, the purpose of this research was to determine "the extent of digital logistics and digitalization conceptual awareness" among university students. The results of the study will be used as a guide for educational institutions like colleges to follow when it comes to incorporating digital courses into their curriculum. It was decided to use questionnaires for the purpose of data collecting. A statistical analysis was conducted on the information gathered from the university students. While most students had a good grasp of the concepts of AI, VR, digital supply chain, and transportation management systems, female students showed less familiarity with these phrases than their male counterparts. It is possible for universities to provide students with the opportunity to acquire knowledge of digital logistics concepts through the implementation of training courses, workshops, programs, and term projects. It is advisable to promote students' acquisition of knowledge regarding digital technologies through the implementation of additional courses. The educational experience and students' overall level of awareness can be greatly enhanced through partnerships between IT firms and institutions. [ABSTRACT FROM AUTHOR]

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system. [ABSTRACT FROM AUTHOR]

In metropolitan regions, the quick increase in vehicles has caused gridlock, pollutants, and problems in the transportation of products. IoT is a breakthrough advancing the development of intelligent management systems and automated processes throughout the universe. For automatic and intelligent cultures, this is a crucial contribution. Effective management and prevention of traffic congestion are reliable strategies that contribute to conserving numerous valuable resources. Autonomous vehicles and smart devices are equipped with an IoT-based ITM system of sensors that can understand, collect, and transfer information. Another method to enhance the transportation system is deep learning (DL). Traffic jams, delays, and a high death rate result from the numerous problems of conventional transportation management systems. An adaptive traffic management system (ATM) built on DL and IoT is designed and implemented in this study's efforts. The suggested system's architecture is built on the automobile, the framework, and the events. To account for all potential problems with the transportation system, the design uses a variety of situations. The proposed methodology uses a Quantum Optical Communication (QOC) system for Traffic monitoring and enhanced data communication. The suggested adaptive traffic management-based QOC model continuously modifies the timing of traffic signals based on traffic volume and anticipated movements from neighbouring junctions. By progressively allowing cars to cross green lights, it considerably reduces the duration of travel. It also reduces congestion in traffic by creating a smoother transition within 38 s, which is less than all other techniques. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition's capacity to discern complex traffic patterns and long short-term memory's proficiency in learning temporal relationships. Firstly, a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model. The second aspect involves predicting traffic volume using the long short-term memory algorithm. Next, the study employs a trial-and-error approach to select a set of optimal hyperparameters, including the lookback window, the number of neurons in the hidden layers, and the gradient descent optimization. Finally, the fusion of the obtained results leads to a final traffic volume prediction. The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures, including mean absolute error, root mean squared error, mean absolute percentage error, and R-squared. The achieved R-squared value reaches an impressive 98%, while the other evaluation indices surpass the competing. These findings highlight the accuracy of traffic pattern prediction. Consequently, this offers promising prospects for enhancing transportation management systems and urban infrastructure planning. [ABSTRACT FROM AUTHOR]

Transportation process reliability is a competitive advantage in the market of cargo delivery services. Transportation customers are interested in shipping cargoes within agreed timeframes and ensuring their safety. These are the two main parameters of transportation process reliability, compliance with which depends on a number of factors. In particular, changes in the external environment where road transportation companies operate inevitably affect the transportation process stability. The purpose of this study is to develop an approach to road transportation reliability and risk mitigation based on the digital twin concept. The variability of business processes is usually the reason for deviations from the transportation terms agreed with the customer. These deviations can be interpreted as failures in delivering cargoes. A prerequisite for ensuring smooth cargo transportation by a road transportation company is the reduction of deviations in the performance of its business processes. The article proposes methods of computer modeling and creating a digital twin of a company reflecting the reference business processes for transportation. As a result, various deviations from the reference digital model that business processes exhibit when there are changes in the external environment can be promptly identified and corrected. [ABSTRACT FROM AUTHOR]

With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and practical approach. Distributed Machine Learning (DML) has been the trend to provide sufficient computing resources and efficiency for MCC tasks to handle massive data and complex models. DML can speed up computation with those computing resources but introduces communication overhead. Gradient collaborative management or gradient aggregation in DML for MCC tasks is a critical task. An efficient managing algorithm of the communication schedules for gradient aggregation in ITS can improve the performance of MCC tasks. However, existing communication schedules typically rely on specific physical connection matrices, which have low robustness when a malfunction occurs. In this article, we propose Robust Searching-based Gradient Collaborative Management (RSGCM) in Intelligent Transportation System, a practical ring-based gradient managing algorithm for communication schedules across devices to deal with ITS malfunction. RSGCM provides solutions of communication schedules to various kinds of connection matrices with an acceptable amount of training time. Our experimental results have shown that RSGCM can deal with more varieties of connection matrices than existing state-of-the-art communication schedules. RSGCM also increases the robustness of ITS since it can restore the system's functionality in an acceptable time when device or connection breakdown happens. [ABSTRACT FROM AUTHOR]

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 carfollowing, 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. [ABSTRACT FROM AUTHOR]

21st century has witnessed a profound metamorphosis in human civilization, primarily driven by the confluence of advanced network technologies and industrial modernization. This transformative period has expanded our understanding of the world, paving the way for innovative concepts such as the "smart city". At its essence, a smart city harnesses the power of artificial intelligence (AI) to revolutionize urban living, presenting a paradigm shift towards more efficient service models and an elevated standard of living for its inhabitants. Integrating AI into the fabric of urban infrastructure marks a monumental leap in societal evolution, underscoring the imperative to cultivate and advance AI technologies. This paper endeavors to elucidate the multifaceted applications of AI within the domains of smart cities, illuminating its pivotal role in shaping and advancing our contemporary era. From intelligent transportation systems and energy management to public safety and healthcare, AI permeates various aspects of urban life, ushering in unprecedented efficiencies and novel solutions to age-old challenges. The symbiotic relationship between AI and smart cities is explored in detail, showcasing how AI technologies are instrumental in optimizing resource allocation, improving decision-making processes, and ultimately enhancing the overall quality of life. Furthermore, this paper delves into the imperative of fostering the development and advancement of AI technologies within the context of smart cities. It underscores the interconnectedness of technological progress and urban development, emphasizing how a concerted effort to cultivate AI capabilities can propel cities into a future marked by sustainable growth, resilience, and innovation. The exploration of challenges and opportunities in deploying AI within urban environments adds a critical dimension to the discourse, encouraging a balanced consideration of ethical, regulatory, and societal implications. In conclusion, this paper seeks to contribute to the ongoing dialogue surrounding smart cities and the transformative impact of AI. By shedding light on the diverse applications of AI within urban landscapes and emphasizing its pivotal role in shaping the trajectory of our era, it underscores the critical importance of advancing AI technology development for the continued progress of smart cities and, by extension, the broader global community. [ABSTRACT FROM AUTHOR]

The bus passenger detection algorithm is a key component of a public transportation bus management system. The detection techniques based on the convolutional neural network have been widely used in bus passenger detection. However, they require high memory and computational requirements, which hinder the deployment of bus passenger detectors in the bus system. In this paper, a lightweight bus passenger detection model based on YOLOv5 is introduced. To make the model more lightweight, the inner and outer cross‐stage bottleneck modules, called ICB and OCB, respectively, are proposed. The proposed module reduces the quantity of parameter and floating point operations and increases the detection speed. In addition, the neighbour feature attention pooling is adopted to improve detection accuracy. The performance of the lightweight model on the bus passenger dataset is empirically demonstrated. The experiment results demonstrate that the proposed model is lightweight and efficient. Compared lightweight YOLOv5n with the original algorithm, the model weight is reduced by 31% to 2.6M, and the detection speed is increased by 6% to 40FPS without an accuracy drop. [ABSTRACT FROM AUTHOR]

In the era of big data, the exponentially increasing data volume and emerging technical tools have put forward new requirements for enterprise information management. Therefore, it is of great significance to enhance the core competitiveness of enterprises to explore how big data can empower the innovation of enterprise information management. Intelligent transportation system combines a variety of technologies and applies them to a large-scale transportation management system, so as to make a reasonable dispatch of traffic conditions. Aiming at the problem of the relatively low accuracy of bus passenger flow forecasting with the existing models, a short-term passenger flow prediction model combining Stacked Denoising Auto Encoder (SDAE) and improved bidirectional Long-short Term Memory network (Bi-LSTM) is proposed. First, the SDAE model is used to fill in the missing bus passenger flow data, the characteristics of the bus passenger flow data are effectively utilized, and the data with rich information is used to predict the missing values with high accuracy. Second, Bi-LSTM model combined with attention mechanism is used for short-term bus passenger flow prediction. Considering that the data sequence of bus passenger flow is relatively long and there is a two-way information flow, the BiLSTM neural network is used for prediction tasks, and the influence of key factors is highlighted through attention weights to mine the internal laws of passenger flow data. The experimental results show that the proposed method achieves the lowest prediction error among all the comparison methods in the task of short-term bus passenger flow prediction on the public transportation dataset, with MAE, MRE, and RMSE values of 6.014, 0.052, and 9.874, respectively. These findings confirmed the effectiveness of the new model in the passenger flow prediction field. [ABSTRACT FROM AUTHOR]

The paper aims to overview the opportunities, approaches and techniques of studying and ensuring functional safety of transportation systems, including those driverless, with the use of Big Data. Examples are provided of machine learning/Big Data application in analysing the functional safety of complex control/management systems in railway transportation. The paper proposes the concept of application of supervised artificial neural networks combined with model checking. The following methods were used in the preparation of the paper: system analysis, logical and comparative analysis and historical principle. Updated requirements are defined for transportation systems using artificial intelligence as part of adaptive train schedule management and autonomous train control. That will ultimately allow developing an entire line of research from AI-based system functional safety estimation and machine learning to safety case preparation of intelligent supervised control/management systems based on formal verification. [ABSTRACT FROM AUTHOR]

The use of cloud computing, big data, IoT, and mobile applications in the public transportation industry has resulted in the generation of vast and complex data, of which the large data volume and data variety have posed several obstacles to effective data sensing and processing with high efficiency in a real-time data-driven public transportation management system. To overcome the above-mentioned challenges and to guarantee optimal data availability for data sensing and processing in public transportation perception, a public transportation sensing platform is proposed to collect, integrate, and organize diverse data from different data sources. The proposed data perception platform connects multiple data systems and some edge intelligent perception devices to enable the collection of various types of data, including traveling information of passengers and transaction data of smart cards. To enable the efficient extraction of precise and detailed traveling behavior, an efficient field-level data lineage exploration method is proposed during logical plan generation and is integrated into the FlinkSQL system seamlessly. Furthermore, a row-level fine-grained permission control mechanism is adopted to support flexible data management. With these two techniques, the proposed data management system can support efficient data processing on large amounts of data and conducts comprehensive analysis and application of business data from numerous different sources to realize the value of the data with high data safety. Through operational testing in real environments, the proposed platform has proven highly efficient and effective in managing organizational operations, data assets, data life cycle, offline development, and backend administration over a large amount of various types of public transportation traffic data. [ABSTRACT FROM AUTHOR]

Indonesia sebagai negara kepulauan memiliki pulau sebanyak 17.504 pulau, dan perairan laut yang luasnya 71% dari keseluruhan wilayah Indonesia. Hal ini menimbulkan masalah bagi rantai pasokan Indonesia karena terkendala pada transportasi dan biaya logistik yang besar. Berangkat dari permasalahan tersebut, Waresix hadir untuk membantu meringankan biaya logistik yang efisien secara end-to-end. Waresix merupakan sebuah perusahaan startup di bidang teknologi logistik yang menyediakan solusi penyimpanan end-to-end yaitu mencakup transportasi hingga kebutuhan pergudangan. Saat ini Waresix sedang melakukan pengembangan produk bernama Transportation Management System (TMS) versi ketiga untuk memperbaiki kekurangan dari versi sebelumnya. Pada penelitian ini, salah satu fitur TMS yang akan dibahas adalah Fleet Management yang berfungsi untuk mengelola truk dan driver milik vendor. Tujuan penelitian ini adalah untuk mengetahui bagaimana perbandingan teori design thinking dengan penerapannya selama mengerjakan fitur tersebut. Metode yang digunakan untuk melakukan penelitian ini adalah metode kualitatif komparasi dengan pendekatan kualitatif, serta menggunakan metode 5W+1H untuk menganalisis data. Hasil dari penelitian ini adalah teori design thinking diterapkan selama pengerjaan fitur Fleet Management. Walaupun ada beberapa hal yang tidak dilakukan dalam tahapannya, tetapi tidak sampai mengganggu proses dari design thinking itu sendiri.

Intelligent Transportation System (ITS) heavily relies on the unique mobile ad-hoc network (MANET) known as the vehicular ad-hoc network (VANET). The convenience that the Location Based Service (LBS), security issues arising from VANETs' great portability. Among the most widely used privacy-preserving techniques, distributed k-anonymity does not consider users' reliability, which results in hostile vehicle tracking. Therefore, Blockchain-enabled, Trust and Location dependent-Privacy Preserving (BTLB-PP) authentication system in VANET to overcome issues. Trust Management (TM) based on Dirichlet distribution, client and member will only collaborate with vehicles they confide by explicitly examining various prerequisites of claimed vehicle and collaborative vehicle during construction of unidentified obfuscation region and integrating attributes of these two functions. Using blockchain, a data structure has been proposed to promptly register trust of vehicles on publicly available blocks, allowing any vehicle to retrieve. In the trials, tests on various datasets have been run. Suggested system is resistant to multiple trust model threats, improving the security of vehicles' confidentiality and privacy. The performance evaluation metrics are precision, recall, f-measure and false positive rate (FPR) are used to evaluate the proposed system. Results from the simulation show that the proposed method is efficient and practical in reality. The suggested method was tested in a simulated traffic situation to verify its effectiveness.

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. [ABSTRACT FROM AUTHOR]

Park-and-Ride (P&R), referring to the transfer between private cars and public transportation, is a public policy and travel mode that advocates green travel to reduce traffic congestion in big cities. Through an analysis on the development and focus of P&R research in China and abroad, this paper examines the research progress and explores the research trend on P&R from the perspective of travel mode choice, transfer demand forecast, location planning, operational management, and benefit evaluation. It points out that, compared with the research abroad, the P&R research in China is insufficient on the subjects of transportation planning and benefit evaluation. It proposes that, in future, research should focus more on planning, management, and benefit evaluation of P&R, so as to enhance the intelligent management of transportation system and promote the implementation of policies supporting P&R development. [ABSTRACT FROM AUTHOR]

The case study of how to govern the boat people can be a typical case on understanding how a nation-state can govern a mobile population. This paper is mainly focusing on how the nation-state’s power penetrated into waterside society. After the People's Republic of China was newly established, Guangzhou Port was flooded with many scattered and poorly managed civilian ships. The Chinese Communist Party and government adopted the Qing management of registering both boat and the owner’s information, aiming at controlling the total supplement and the need, to meet the government’s distribution policy. Due to the mobility of the transportation, the residence information of the boat people could be different from the berth port. The situation gradually changed in the middle 1950s, the place of residence and berth port gradually registered the same. Based on the system of controlling ships and ports, the government had established transportation cooperatives to build transportation management systems. To prevent boat people from leaving the port randomly, the shipping management department further implemented zoning management and redefined the navigation area. This article argues that the Chinese government can reform water transport market structure and invent the system of transport management to complete the construction of state power on waterside society. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN. [ABSTRACT FROM AUTHOR]

As smart transportation systems evolve, secure and efficient V2X communication between vehicles and infrastructure becomes crucial. This paper introduces a Vehicle-to-Blockchain (V2B) communication architecture, leveraging blockchain technology for transparent and decentralized interactions. Our work contributes to the integration of blockchain into V2X and IoT for next-generation transportation systems. We propose several novel blockchain use cases, including a blockchain-based vehicle ownership system based on the multi-token standard, a vehicle scoring system, blockchain–IoT integration, and a decentralized ticket management system for transportation services. The architecture addresses key aspects, such as data integration, validity, and secure messaging, and introduces a decentralized payment system and marketplace for transportation in smart cities. We specifically emphasize the technical implementation of smart contracts for these use cases, underscoring their role in ensuring robust and reliable interactions. Through our decentralized approach, we pave the way for a transformative transportation ecosystem that is adaptable, resilient, and capable of meeting the evolving needs of smart cities. [ABSTRACT FROM AUTHOR]

Real-world route navigation data indicate that nontrivial portion of drivers do not prefer the system-recommended best routes. Current navigation systems have simplified assumptions about drivers' route choice preferences and do not adequately accommodate drivers' heterogeneous route choice preferences, mainly because of: (i) difficulty in acquiring exogenous criteria (e.g., sociodemographic information) that are typically used to differentiate drivers' preferences in behavioral modeling; and (ii) difficulty in capturing preference of individuals due to limited preference data at the individual level. To address these, this paper introduced a human-centric machine learning technique named Multi-Task Linear Classification Model Adaption (MT-LinAdapt). It can capture drivers' common aspects of route choice preferences and yet adapts to each driver's own preference. In addition, any evolvement of individual drivers' preferences can be simultaneously integrated to update the common preference for further individual drivers' preference adaptation. This paper evaluated MT-LinAdapt against two state-of-the-art route recommendation strategies including an aggregate-level and an individual-level data-based strategies, which are categorized based on the data used for modeling. With a real-world dataset containing 30,837 drivers' navigation usage data in Daegu City, South Korea, MT-LinAdapt was compared to existing strategies for its performance at different levels of data availability, and showed at least the same performance with existing strategies when minimum preference data is available and achieves up to 7% higher prediction accuracy as more data becomes available. Higher prediction accuracies are expected to bring better user satisfaction and compliance rates which can further help with transportation system control and management strategies. [ABSTRACT FROM AUTHOR]

The Grand Canal of China, as a traffic artery in ancient China, has exerted profound influence on the development of the cities, towns and rural settlements along it. Now, with China's rapid urbanization, numerous settlements along the canal are confronted with problems like slow cognition of the cultural heritage, loss of the local culture and damage of historical relics. Hence, research on the protection and updating of the settlement space along the canal is in urgent need. This paper, targeting the settlement space along the Grand Canal Tianjin Section, adopts such research methods as the Historical GIS analytical method, the kernel density estimation method and the average nearest-neighbor method to quantitatively analyze the evolution stage and distribution characteristics of the settlement space along the Grand Canal Tianjin Section from the perspective of cultural relics to reveal the in-situ rules of evolution of the settlement space. The research result indicates that the spatial-temporal evolution of the settlement space can be divided into five such periods of the settlements along the canal as the pre-canal period, the rudimentary period, the development period, the mature period and the transformation period. Each period features different cultural relics and settlement distribution patterns. The research shows that the natural geographical environment, the grain transportation projects and management systems, the wasteland opening, grain growing and military defense policies, the agricultural, commercial and economic growth and the migration and folk culture are the influencing factors of the spatial evolution of the settlement space along the Grand Canal Tianjin Section. [ABSTRACT FROM AUTHOR]

License Plate Recognition (LPR) is essential for the Internet of Vehicles (IoV) since license plates are a necessary characteristic for distinguishing vehicles for traffic management. As the number of vehicles on the road continues to grow, managing and controlling traffic has become increasingly complex. Large cities in particular face significant challenges, including concerns around privacy and the consumption of resources. To address these issues, the development of automatic LPR technology within the IoV has emerged as a critical area of research. By detecting and recognizing license plates on roadways, LPR can significantly enhance management and control of the transportation system. However, implementing LPR within automated transportation systems requires careful consideration of privacy and trust issues, particularly in relation to the collection and use of sensitive data. This study recommends a blockchain-based approach for IoV privacy security that makes use of LPR. A system handles the registration of a user's license plate directly on the blockchain, avoiding the gateway. The database controller may crash as the number of vehicles in the system rises. This paper proposes a privacy protection system for the IoV using license plate recognition based on blockchain. When a license plate is captured by the LPR system, the captured image is sent to the gateway responsible for managing all communications. When the user requires the license plate, the registration is done by a system connected directly to the blockchain, without going through the gateway. Moreover, in the traditional IoV system, the central authority has full authority to manage the binding of vehicle identity and public key. As the number of vehicles increases in the system, it may cause the central server to crash. Key revocation is the process in which the blockchain system analyses the behaviour of vehicles to judge malicious users and revoke their public keys. [ABSTRACT FROM AUTHOR]

Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV‐dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed‐integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real‐world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east‐coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights. [ABSTRACT FROM AUTHOR]

Accurate and reliable taxi demand prediction is of great importance for intelligent planning and management in the transportation system. To collectively forecast the taxi demand in all regions of a city, many existing studies focus on the capturing of spatial and temporal correlations among regions but ignore the local statistical differences throughout the geographical layout of a city. This limits the further improvement of prediction accuracy. In this paper, we propose a new deep learning framework, called the locally connected spatial-temporal fully convolutional neural network (LC-ST-FCN), to learn the spatial-temporal correlations and local statistical differences among regions simultaneously. We evaluate the proposed model on a real dataset from a ride-hailing service platform (DiDi Chuxing) and observe significant improvements compared with a bunch of baseline models. Besides, we further explore the working mechanism of the proposed model by visualizing its feature extraction processes. The visualization results showed that our approach can better localize and capture useful features from spatial-related regions. [ABSTRACT FROM AUTHOR]

Vehicle detection and tracking technology plays an important role in intelligent transportation management and control systems. This paper proposes a novel vehicle detection and tracking method for small target vehicles to achieve high detection and tracking accuracy based on the attention mechanism. We first develop a new vehicle detection model (YOLOv5-NAM) by adding the normalization-based attention module (NAM) to the classical YOLOv5s model. By exploiting the YOLOv5-NAM model as the vehicle detector, we then propose a real-time small target vehicle tracking method (JDE-YN), where the feature extraction process is embedded in the prediction head for joint training. Finally, we present extensive experimental results to verify our method on the UA-DETRAC dataset and to demonstrate that the method can effectively detect small target vehicles in real time. It is shown that compared with the original YOLOv5s model, the mAP value of the YOLOv5-NAM vehicle detection model is improved by 1.6%, while the MOTA value of the JDE-YN method improved by 0.9% compared with the original JDE method. [ABSTRACT FROM AUTHOR]

Accurate prediction of the macroscopic traffic stream variables is essential for traffic operation and management in an intelligent transportation system. Adverse weather conditions like fog, rainfall, and snowfall affect the driver's visibility, vehicle mobility, and road capacity. The rainfall effect on traffic is not directly proportional to the distance between the weather station and the road because of terrain feature constraints. The prolonged rainfall weakens the drainage system, affects soil absorption capability, which causes waterlogging. The weather event has a spatiotemporal correlation with traffic stream variables, as waterlogging on the road due to rainfall affects the traffic on adjacent roads. The spatiotemporal and prolonged impact of rainfall is not studied in the literature. In this research, we examine whether the inclusion of the rainfall variable improves the traffic stream variables prediction of a deep learning model or not. We use the RNN and LSTM models to capture the spatiotemporal correlation between traffic and rainfall data using past and current traffic and weather information. To capture the prolonged impact of rainfall more extended past sequence of rainfall data than traffic data is used in this study. The roads prone to waterlogging are more affected due to rainfall compared to freeways. Thus we examine the effect of rain on traffic stream variables prediction for different types of roads. The test experiments show that the inclusion of weather data improves the prediction accuracy of the model. The LSTM outperforms other models to capture the spatiotemporal relationship between the rainfall and traffic stream variables. [ABSTRACT FROM AUTHOR]

An increasing number of devices and their communication with each other generates huge amounts of data. The efficiency of processing such large and heterogeneous data is crucial for extracting the reliable and consistent information that is needed for the effective management of smart cities within the field of transport. Data heterogeneity and volume as well as its integration and analytics are big challenges for decision-makers. The development of urban agglomerations is largely dependent on the proper management of such data. Therefore, this paper explores the role of these data repositories, their acquisition from different sources, and the ways to combine them. The main goal of this paper is to propose a concept of Smart City management based on Big Data Analytics and technology related to UAVs (Unmanned Aerial Vehicle) which may reduce costs and resource consumption. The presented concept includes successive data generation and collection, data type identification, problem and requirement identification, filtering, classification, pre-processing, and data optimization, as well as decision support analysis. A key part of this analysis utilizes computer algorithms, such as Speeded Up Robust Features (SURF) and Thresholding and Blob detection, to develop a multi-camera image recognition system for freight transport management and logistics in smart cities. The objective is to design a system that optimizes the route planning and time of vehicle passage on selected road sections, ultimately leading to the reduction of emissions. During the study, data obtained from multiple sources were compared, and the analysis uncovered different results for the same assumptions. We discuss the reasons for these variances. Overall, the results obtained in the analysis indicated that it is necessary to correct the predictions of the multi-camera image recognition system with additional methods and algorithms. [ABSTRACT FROM AUTHOR]

Introduction : Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks. Method : The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee's two largest urban areas, Nashville and Memphis. Results : The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified. Conclusion : Based on these results, a series of specific, suggested policy changes are presented for implementation consideration. Practical Applications : There is existing guidance in FHWA Lighting Handbook and TDOT's Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested. [ABSTRACT FROM AUTHOR]

Metro passenger flow prediction is an essential part of crowd flow forecasting and intelligent transportation management systems. However, two challenges still need to be addressed to achieve a more accurate prediction: (1) accounting for featural dependence instead of considering only the temporal connection and spatial relations; (2) utilising graph structures to address non-European relationships of spatial and featural dependence. To address these challenges, we developed a novel model called the multi-featured spatial-temporal (MFST) and dynamic multi-graph convolutional network (DMGCN) model. Temporal connections are learned from both the local and global information in a time-series sequence using the combination of a time-trend feature mapping block and a gated recurrent unit block. Spatial relation and featural dependence are separately captured by two DMGCN blocks. Each DMGCN block encodes various relationships by constructing multiple graphs consisting of predefined and non-defined topologies. The results of evaluations conducted of the MFST tensor and the DMGCN on the real-world Beijing subway dataset indicate that the prediction performance of the proposed model is superior to that of the existing baselines. The proposed model thus contributes significantly to the improvement of public safety by providing early warnings of large passenger flow and enabling the smart scheduling of resources. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) create an advanced framework to support the intelligent transportation system and increase road safety by managing traffic flow and avoiding accidents. These networks have specific characteristics, including the high mobility of vehicles, dynamic topology, and frequent link failures. For this reason, providing an efficient and stable routing approach for VANET is a challenging issue. Reinforcement learning (RL) can solve the various challenges and issues of vehicular ad hoc networks, including routing. Most of the existing reinforcement learning-based routing methods are incompatible with the dynamic network environment and cannot prevent congestion in the network. Network congestion can be controlled by managing traffic flow. For this purpose, roadside units (RSUs) must monitor the road status to be informed about traffic conditions. In this paper, an intersection-based routing method using Q-learning (IRQ) is presented for VANETs. IRQ uses both global and local views in the routing process. For this reason, a dissemination mechanism of traffic information is introduced to create these global and local views. According to the global view, a Q-learning-based routing technique is designed for discovering the best routes between intersections. The central server continuously evaluates the created paths between intersections to penalize road segments with high congestion and improve the packet delivery rate. Finally, IRQ uses a greedy strategy based on a local view to find the best next-hop node in each road segment. NS2 software is used for analyzing the performance of the proposed routing approach. Then, IRQ is compared with three methods, including IV2XQ, QGrid, and GPSR. The simulation results demonstrate that IRQ has an acceptable performance in terms of packet delivery rate and delay. However, its communication overhead is higher than IV2XQ. [ABSTRACT FROM AUTHOR]

The use of information technologies such as Transportation Management System (TMS) is crucial to improve the transportation process in a company, offering potential results that include optimization of shipments, an increase in cost savings and customer satisfaction. The TMS, along with data analysis techniques such as Machine Learning (ML) and/or Data Mining (DM) are useful tools for demand forecasting, facilitating strategic, tactical and operational decision making, which ultimately improves the logistics distribution performance. For this, this work proposes a methodology that will compare three prediction methods (traditional statistical method, hybrid method and artificial neural network), which will be tested through the Mean Squared Error (MSE) indicator to determine which one has more accurate results in the forecast of demand for freight transport. The experimental results obtained in this research show that the historical data saved in a TMS can be used for reliably freight transportation demand forecasting. Each of the three methods proposed in this paper has been an effective tool in resolving time series forecasting problems, and when Artificial Neuronal Network (ANN) was used, better results are yielded. [ABSTRACT FROM AUTHOR]

Road safety assessment is one of the most important parts of road transport safety management. When road transportation networks are managed safely, they improve the quality of life for citizens and the economy as a whole. On the one hand, there are many factors that affect road safety. On the other hand, this issue is a dynamic problem, which means that it is always changing. So, there is a dire need for a thorough evaluation of road safety to deal with complex and uncertain problems. For this purpose, two machine learning methods called "feature selection algorithms" are used. These algorithms include a combination of artificial neural network (ANN) with the particle swarm optimization (PSO) algorithm and the differential evolution (DE) algorithm. In this study, two data sets with 202 and 564 accident cases from cities and rural areas in southern Italy are investigated and analyzed based on several factors that affect transportation safety, such as light conditions, weekday, type of accident, location, speed limit, average speed, and annual average daily traffic. When the performance and results of the two models were compared, the results showed that the two models made the same choices. In rural areas, the type of accident and the location were chosen as the highest and lowest priorities, respectively. According to the results, useful suggestions regarding the improvement of road safety on urban and rural roads were provided. The average speed and location were considered the highest and lowest priorities in urban areas, respectively. Finally, there was not a big difference between the results of the two algorithms in terms of how well the algorithm models worked, but the proposed PSO model converged more quickly than the proposed DE model. [ABSTRACT FROM AUTHOR]

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Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management. [ABSTRACT FROM AUTHOR]

Study and assessment of applications of a driver’s trip ticket within the scope of a motor transport company’s digital transformation are of practical interest. One of the functions of a trip ticket is ensuring road traffic safety during transportation, as it document ally confirms that the vehicle is technically sound and the driver has passed a medic al examination. Moreover, the trip ticket also allows monitoring if the driver complies with the work and rest schedule. The trip ticket is a document confirming the reasonableness of the amount spent by the motor transport company. Study of a digital system for a motor transport company management has demonstrated the errors or fraud of employees during trip tickets processing. A conceptual diagram of the Transportation Management System is presented, which provides the application of route optimization programs, the results of vehicles satellite monitoring, the formation of daily tasks, and the preparation of work reports. This is the basis we propose for improving business processes to increase the efficiency of transport activities. Purpose: study of the role of trip tickets and their processing by a digital management system in increasing the efficiency of a motor transport company. Methodology: Process Mining associated with keeping and processing trip tickets. Results: Collected statistics allows improving business processes of the motor transport company to increase the efficiency of its activities. Practical implications: Companies with any form of incorporation and in any economic sector operating motor vehicles.

This paper studies a general facility operational planning problem, which involves managing a network of facilities or infrastructures (such as road sections or tolls) to serve customers or users while considering their decentralized behaviors. The objective is to optimize the service plans for each facility, taking into account that customers aim to minimize their own costs or disutilities. This problem possesses a wide array of practical applications in operations management and transportation systems. Mathematically, it is often formalized as a bilevel programming model. Due to the inherent complexity introduced by the bilevel (sometimes, hidden bilevel) structure, the resulting model is NP-hard in general. As customer demand exhibits spatial–temporal variations in real-world scenarios, service plans often necessitate re-optimization, sometimes on a rather frequent basis, to adapt to changing demand levels. This poses computational challenges due to the complexity of solving the problem, making it difficult for companies to update service plans with high quality under tight time constraints. To address this challenge, we introduce an end-to-end optimization learning framework that combines offline optimization, machine learning techniques, and customized data generation schemes. Once the learning models are developed and trained, they can directly generate near-optimal service plans using demand information as input features, without invoking external solvers/algorithms. Through computational experiments, we demonstrate that this framework delivers outstanding performance. In most cases, it can produce solutions with optimality gaps of less than 0.11% in minimal execution times. We also provide computational insights into the role of learning models during algorithm development and their impacts on different problem classes. • Consider a general facility operational planning with frequent service re-optimization. • Develop end-to-end optimization learning with customized data generation schemes. • Conduct extensive computational studies and produce near-optimal solutions in minimal times. • Provide insights into the role of AI models during offline development and online execution. [ABSTRACT FROM AUTHOR]

Neutrosophic set (NS) is one of the extensive tools to handle unpredictability, vagueness and incompleteness that arise in real-world problems due to its miraculous characteristics. In this study, a generalization of NS, namely Probabilistic Fermatean Neutrosophic Hesitant Set (PFNHS) have been proposed to delineate the randomness and imprecision in a single framework. As a way of enhancing its pragmatic applicability in real-world problems, our set has been examined in terms of its fundamental operations, basic properties, score function, and accuracy function. Also, we have proposed Triangular Probabilistic Fermatean Neutrosophic Hesitant Number (Tr-PFNHN) along with its characteristics as a triangular form of PFNHS to depict the uncertainty by integrating probabilistic information in a fuzzy framework. On the other hand, to safeguard the wellbeing of community and environment from the hazardous impact of biomedical wastes (BMWs), the proper management of these wastes is extremely crucial. In this direction, a three-dimensional transportation model for the sustainable transportation of BMWs has been introduced with an aspiration to optimize the transportation cost, time, carbon emission and job opportunities in PFNH environment. Thereafter, fuzzy and non-fuzzy techniques namely fuzzy programming (FP) and global criterion method (GCM) have been utilized to deal with aforementioned model for the sustainable transportation of BMWs. Moreover, numerical computations, results, as well as the future objectives and restrictions are encompassed. [ABSTRACT FROM AUTHOR]