Development Of Transport System Research Articles

Traffic Sign Recognition Using Deep Learning

Abstract: Traffic sign recognition is a crucial component in advanced driver assistance systems and autonomous vehicles, enhancing road safety and overall transportation efficiency. Deep learning, specifically convolutional neural networks (CNNs), has emerged as a powerful tool for image based recognition tasks. The proposed deep learning-based traffic sign recognition system exhibits promising results, providing a foundation for the development of intelligent transportation systems. The trained model demonstrates remarkable accuracy in recognizing a wide range of traffic signs under different scenarios, including challenging lighting conditions and occlusions. The system's real-time performance is evaluated on both simulated and actual road scenarios, showcasing its feasibility for deployment in practical driving environments. The integration of such technology into vehicles holds great potential for enhancing road safety, reducing accidents, and contributing to the evolution of autonomous driving technology.

Multi-Vehicle Collaborative Planning Technology under Automatic Driving

Autonomous vehicles hold the potential to significantly improve traffic efficiency and advance the development of intelligent transportation systems. With the progression of autonomous driving technology, collaborative planning among multiple vehicles in autonomous driving scenarios has emerged as a pivotal challenge in realizing intelligent transportation systems. Serving as the cornerstone of unmanned mission decision-making, collaborative motion planning algorithms have garnered increasing attention in both theoretical exploration and practical application. These methods often follow a similar paradigm: the system initially discerns the driving intentions of each vehicle, subsequently assesses the surrounding environment, engages in path-planning, and formulates specific behavioral decisions. The paper discusses trajectory prediction, game theory, following behavior, and lane merging issues within the paradigm mentioned above. After briefly introducing the background of multi-vehicle autonomous driving, it provides a detailed description of the technological prerequisites for implementing these techniques. It reviews the main algorithms in motion planning, their functionalities, and applications in road environments, as well as current and future challenges and unresolved issues.

Trans-Caspian International Transport Route: Kazakhstani Perspective on Challenges, Opportunities, and Prospects

The sustainable development of transport and logistics systems and projects is one factor for the successful development of states and an indicator of integration processes in certain regions, including Central Asia. This study is devoted to the analysis of some aspects of the diversification of the external transport and logistics infrastructure; namely, an assessment of the potential of the Trans-Caspian International Transport Route (TITR) from the perspective of the Republic of Kazakhstan. While the route is useful for trade in multiple sectors, this paper will focus on the shipment of petroleum products via the TITR. Despite the absence of restrictions on the transit of goods to Europe through Russia, there are serious challenges to increasing the delivery time. The development of transport and logistics infrastructure is also relevant for Kazakhstan in the context of its new foreign policy role in the international arena to strengthen interregional cooperation. This requires serious attention and research into potentially promising routes. One of these routes is the TITR. Its development can significantly strengthen the economic position of Kazakhstan and strengthen its diplomatic potential. Along with this, there are several challenges and difficulties that need to be analyzed and overcome.

A Lagrangian approach for variable speed limit implementation in C-ITS framework

With the development of Cooperative-Intelligent Transport System (C-ITS) technologies, new strategies based on embedded technologies have emerged to manage road networks. This paper focuses on adapting to this connectivity context a Variable Speed Limit (VSL) system to detect shockwaves and anticipate their propagation based on the kinematic wave theory to dampen them. We provide an alternative framework to adapt the VSL strategy, well-suited for Eulerian approaches, into a Lagrangian context. While the Eulerian approach is based on Loop Detector (LD) and macroscopic traffic indicators (e.g. flow, density), our Lagrangian approach relies on Road Side Units (RSUs) that record the GPS traces shared by Connected Vehicles (CVs). Based on the combination of CV trajectories, Fundamental and Space-Time Diagrams theory, shockwave estimation and prediction processes directly operate on congestion waves, which release the estimation issue for traffic density. The simulation-based analysis reveals that the performance of the Lagrangian approach is comparable to the Eulerian configurations.

Perspectives from military neonatal transport: past, present, and future.

This article traces the historical development of neonatal transport, from ancient Greek mythology to the modern era, with a particular focus on the contributions of U.S. military aviation. The narrative begins with early efforts in thermoregulation through stationary incubators and progresses to the pivotal role of aerial hospitals during World War II. Post-WWII, the establishment of neonatal transport services in New York and advancements in incubator technology set the stage for further innovation. The U.S. military's involvement in neonatal transport, initiated in the 1970s, witnessed significant milestones, including the adaptation of ECMO technology for air transport. The narrative unfolds through the lens of U.S. military neonatology in the Western Pacific, particularly at Clark Air Base. The article concludes with insights into the U.S. Indo-Pacific Command's neonatal transport mission, highlighting challenges faced during the SARS-CoV-2/COVID-19 pandemic and the development of specialized infection containment transport systems.

STBGRN: A Traffic Prediction Model Based on Spatiotemporal Bidirectional Gated Recurrent Units and Graph Convolutional Residual Networks

With the development of society and the advancement of urbanization, the development of intelligent transportation system has attracted much attention. Efficient and accurate traffic flow prediction is one of the core tasks in the research of intelligent transportation system. Most of the existing spatiotemporal traffic flow prediction models do not make full use of various periodic spatiotemporal dynamic characteristics of traffic flow, and it is difficult to effectively capture the complex spatiotemporal changes of traffic flow. To accurately predict the traffic flow of complex urban network, this paper proposes a traffic prediction model (STBGRN) based on spatiotemporal bidirectional gated cycle unit (ST-BiGRU) and graph convolution residual network (GCN-ResNet). The spatiotemporal information in traffic data is learned using the spatiotemporal bidirectional GRU coupled with the forward and backward information of the current time step, and the topology structure of the traffic network is captured by the graph convolution residual network, which is combined to complete the prediction task of the urban traffic network. In this paper, STBGRN was tested on two real data sets, SZ-taxi and Los-loop, and compared with other baseline methods, the RMSE index reached 3.857 and 5.461, and the MAE index reached 2.702 and 3.781, respectively. Accuracy index is 72.66% and 91.35%, R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}^{2}$$\\end{document} index is 0.858 and 0.845, var index is 0.858 and 0.849. The experimental results show that STBGRN model can obtain spatiotemporal dependence from complex traffic data, and can be used in spatiotemporal traffic data processing.

Precise positioning and prediction system for autonomous driving based on generative artificial intelligence

Self-driving systems collect vast amounts of data through a variety of sensors, including cameras, lidar, millimeter-wave radar, and more. This data needs to be processed in real time to identify obstacles such as roads, vehicles, pedestrians and make decisions accordingly. Therefore, this paper discusses the importance of accurate positioning and prediction system in automatic driving technology, and analyzes the performance of various positioning technologies in automatic driving applications.In addition, the paper explores the application potential of AI technology in autonomous driving and the prospect of combining advanced positioning and prediction systems with generative AI. Overall, this study highlights the importance of algorithm performance improvement and artificial intelligence technology in the development of autonomous driving technology, and provides new ideas and directions for the innovation and development of intelligent transportation systems in the future.

Impact of risk perception and trust in autonomous vehicles on pedestrian crossing decision: Navigating the social-technological intersection with the ICLV model

In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians’ tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians’ willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

The Use of Renewable Energy Sources in Road Construction and Public Transport: A Review

The development and advantages of renewable energy technologies mean that their areas of application are constantly expanding. The development of roads, transport systems, and electromobility also increases the demand for electricity. Roads occupy a certain area that could be used to install wind turbines or photovoltaic systems that could be used to power, among others, electric vehicle charging stations and road technical infrastructure facilities (travel service areas, tunnel lighting, road signs). There are many examples around the world where such solutions have been used. This critical review of existing solutions and the possibilities of their application in new places may contribute to further development and research in this area. Particular attention was paid to the possibility of using renewable energy systems in Poland, which can be successfully transferred to other countries with a similar climate.

Leveraging explainable artificial intelligence in understanding public transportation usage rates for sustainable development

Public transportation usage prediction is valuable for the sustainable development of transportation systems, particularly in crowded megacities. Machine learning technologies are of great interest for predicting public transportation usage. While these technologies outperform many other techniques, they suffer from limited interpretability. Explainable artificial intelligence (XAI) tools and techniques that offer post-hoc explanations of the obtained predictions are gaining popularity. This paper proposes an advanced tree-based ensemble algorithm for public transportation usage rate prediction. We aim to explain the predictions both with the most widely used technique of XAI, Shapley additive explanation (SHAP) and in the light of the rules presented. To predict the total public transportation usage, the proposed model combines all types of public transportation, categorized as ferry, railway, and bus, unlike most existing studies focusing on a single kind of public transport. Besides the sort of transportation, the day of the week, whether the day is special, and the daily ratio of passenger types were identified as model features for predicting the daily usage of each type of public transportation. We tested the proposed model using an open data set from Izmir City, Turkey. While the model had superior prediction performance, the explanations showed that the type of public transportation, weekday, and the ratio of full-fare passengers have the highest SHAP values, and the model features have many interactions. We also validated our results using an online data set showing Google search trends.

Малая авиация в Российской Федерации: текущее состояние, проблемы и пути их решения

Today, it is general aviation that influences the development of the regions of the Russian Federation, taking into account its territorial characteristics. General aviation can be used for the development of many fields of activity, such as: training military and civilian pilots, fire monitoring, emergency medical care, environmental monitoring; obtaining weather forecasts; combating poachers, use for the construction of unmanned aircraft and solving many other tasks. Moreover, unlike commercial aviation, general avitation increases transport mobility and increases the attractiveness of regions for investors and entrepreneurs. This article discusses the main problems and methods of their solution for the development of the civil aviation transport system.

Bus Drivers’ Behavioral Intention to Comply with Real-Time Control Instructions: An Empirical Study from China

Developing intelligent bus control systems is crucial for fostering the sustainability of urban transportation. Control instructions are produced in real time by the bus control system; these are important technical commands to stabilize the order in which buses operate and improve service reliability. Understanding the behavioral intention of bus drivers to comply with these instructions will help improve the effectiveness of intelligent bus control system implementation. We have developed a psychological model that incorporates decomposed variables of the theory of planned behavior (TPB) and other influencing variables to explain the micromechanisms that determine bus drivers’ behavioral intention to comply with real-time control instructions during both peak and off-peak-hour scenarios. A total of 258 responses were obtained and verified for analysis. The results showed that the influential factors in the peak- and off-peak-hour scenarios were not identical. Female drivers had greater off-peak-hour behavior intention to comply than male drivers, and there were significant differences in peak-hour behavior intention among drivers of different ages. In both peak and off-peak-hour scenarios, perceived benefit positively and perceived risk negatively affected behavioral intention. Perceived controllability positively affected behavioral intention only during peak hours. Self-efficacy only negatively affected behavioral intention during off-peak hours. Three antecedent variables (i.e., trust, mental workload, and line infrastructure support) influenced drivers’ behavioral intentions indirectly via the decomposed variables of TPB. These results provide profound insights for the improvement and implementation of real-time control technology for bus services, thereby facilitating the development of smart and sustainable urban public transport systems.

Revolutionizing Traffic Management: Advanced Machine Learning Techniques for Accurate Traffic Flow Prediction

In modern urban¬¬¬¬¬ transportation systems, efficient traffic flow prediction is of paramount importance to optimize traffic management, reduce congestion, and improve the overall commuting experience. This research report examines the application of state-of-the-art machine learning algorithms to accurately predict traffic flow patterns. By leveraging historical traffic data, weather conditions, time of day, and various other relevant features, our proposed model exhibits significant predictive capabilities. We study the effectiveness of various machine learning techniques, such as neural networks, decision trees, and ensemble methods, in capturing the complex dynamics of traffic flows. Through extensive experiments and validation using real datasets, we demonstrate the superiority of our approach compared to traditional methods. Ultimately, this research will contribute to the further development of intelligent transportation systems, paving the way for more efficient and sustainable urban mobility solutions

Assessment of the accessibility of public transport in the Tricity (Poland): analytical use of geographical information systems (GIS) in the context of selected public transport measures

ABSTRACT The Tricity consists of three coastal cities – Gdańsk, Gdynia and Sopot – which also form the core of the Tricity agglomeration. They are among the main development centres of the country. Proper access to public transport for residents is crucial for the sustainable development of the agglomeration. Although the cities are connected by functional links in different areas, the further development of a coherent public transport system is problematic. It is therefore crucial to formulate the question of how these cities can develop the public (collective) transport network? Do residents have access to it and does it meet their needs? Are these measures complementary? A proprietary methodological approach using QGIS and ORS tools was used to conduct the survey. The results of the survey show that 83.5% of Tri-City residents have the nearest train, bus, tram or trolleybus stop within a 5-minute walk of their place of residence. This is followed by 14.39% of residents having a stop within 10 min, 1.47% within 15 min and 0.20% within 20 min. Unfavourable time accessibility applies to 0.45% of residents. The findings of the study may help Tricity local authorities to develop a sustainable public transport policy.

ПОСТРОЕНИЕ СИСТЕМЫ ОБЩЕСТВЕННОГО ТРАНСПОРТА, РЕАГИРУЮЩЕЙ НА СПРОС, С УЧЕТОМ КОНЦЕПЦИИ МОБИЛЬНОСТИ

An option has been considered to increase population mobility through the priority integrated devel-opment of a public transport system that responds to demand

Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System

Prioritizing the development of public transport is an effective way to improve the sustainability of the transport system. In recent years, bus passenger flow has been declining in many cities. How to reform the operating mode of the public transportation system is an important issue that needs to be solved. An autonomous modular bus (AMB) is capable of physical coupling and uncoupling to flexibly adjust vehicle capacity as well as provide high-quality service under unbalanced passenger demand conditions. To promote AMB adoption and reduce the operating cost of the bus route, this paper presents a joint optimization method to simultaneously determine the AMB dispatching plan, charging plan, and charging infrastructure configuration scheme. Then, a mixed-integer programming model is formulated to minimize the operating costs of the bus route. A hybrid intelligent algorithm combining enumeration, cloning algorithm, and particle swarm optimization algorithm is designed to resolve the formulated model. Subsequently, an actual bus route is taken as an example to validate the proposed method. Results indicate that the developed method in this paper can reduce the operating costs and operational energy consumption of the route compared with the real route operating plan. Specifically, the reduction ratio of the former is 23.85%, and the reduction ratio of the latter is 5.92%. The results of this study validate the feasibility and advantages of autonomous modular transit service, contributing positively to the sustainable development of the urban public transportation system.

Multisource‐multitarget cooperative positioning using probability hypothesis density filter in internet of vehicles

AbstractAccurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state‐related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource‐multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on‐board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi‐target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM‐PHD‐LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM‐PHD‐LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system.

Sliding Mode Control of an Electric Vehicle Driven by a New Powertrain Technology Based on a Dual-Star Induction Machine

This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system.

Empirical assessment of the impacts of Sino-African cross border relations in the construction industry: a Confirmatory Factor Analysis approach

PurposeIt is no news that Chinese construction companies are highly motivated to invest in Africa in terms of infrastructure and construction. This influx from the beginning of the millennium marked a game-changer for infrastructural development in most African countries. This study, therefore, explores how the partnership between China and Africa has impacted the construction industry in Africa with a focus on Nigeria.Design/methodology/approachA quantitative approach was adapted for the study, which is descriptive in nature, and the primary participants of the study were core construction professionals within the Nigerian construction industry. Data was collected via a structured questionnaire, and multivariate statistics was used to analyse the data.FindingsThe study results revealed that the benefits accrued from Chinese participation in the African construction industry can be classified into three distinct categories: socio-economic development through construction, land transportation system development and construction industry development. The study further revealed that Chinese involvement has been most beneficial to the development of the land transportation system in Nigeria with more investment in the construction and maintenance of roads and railways.Originality/valueThe study will serve as a basis for making informed future decisions on Chinese participation in the Nigerian construction industry as it exposes the impacts of the relationship within the current system. The outcome of this study can be used to refocus the partnership to ensure the optimum development of the local construction industry. The government and other relevant agencies can use the findings from this study to ensure that there is sustainable growth in the local construction industry through Chinese participation.