Machine learning based IoT system for secure traffic management and accident detection in smart cities.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

An intelligent transportation system for smart city applications is regarded as a solution for reducing traffic congestion, fuel consumption, and accidents, and saving lives by levering computing, communication, control, and collected traffic data. Currently, traffic officials are not only facing a significant absence of automated and accurate traffic prediction but also making informed decisions in a real-time manner for traffic management in smart cities. This chapter proposes an automated method for counting vehicles for identifying traffic patterns and offering an adaptive traffic management system by using deep learning. The proposed approach helps to count vehicles in the intersection which helps to make an informed decision about making streets one-way or two-way adaptive traffic light systems depending not only on the number of vehicles in the intersection but also based on the time of the day. Specifically, the proposed approach detects and counts turning vehicles, and pedestrians at a traffic intersection in a real-time manner (as well as in recorded videos in case of offline processing) using deep learning. Then the collected statistics are used to make an informed decision. The proposed approach gives better results (in terms of time and accuracy) compared to state-of-the-art work.

The Intelligent Transportation System (ITS) is the effective and comprehensive application of advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) in transportation, service control, and vehicle manufacturing to strengthen the connection between vehicles, roads, and users. The result is an integrated transport system that ensures safety, enhances efficiency, improves the environment and saves energy. It is an effective means to solve the current urban traffic problems and an important indicator for the construction of a "smart city". Through the construction of an urban traffic collection system, the system solves the existing traffic problems, further integrates all kinds of road traffic resources, builds the urban "traffic brain", mines the huge amount of traffic data, deconstructs the operation law and characteristics of urban traffic, realizes the accurate urban traffic management, scientific management and normal management, and makes people's traffic travel safer. It makes people's traffic travel safer, more convenient and more efficient. This study introduces innovative computational models and algorithms specifically designed for traffic optimisation, as well as data-driven machine learning techniques to improve the operational efficiency of intelligent transport systems. An extensive analysis of case studies on the integration of AI into traffic management systems provides empirical evidence of their impact, focusing on the performance evaluation of these AI applications as well as potential scalability issues that may arise in different urban environments.

Purpose: The aim of the article is to present the results of a study on the perception and evaluation of intelligent communication and transportation systems, as well as their management, by residents of cities in the Silesian Voivodeship who travel by car, bicycle (scooter), or on foot, as factors contributing to safe and fast movement within the city. Design/methodology/approach: The study was conducted using a proprietary survey based on the Consumer Satisfaction Index (CSI) methodology, and the analysis of the survey results was preceded by a brief literature review on the subject. Findings: The study made it possible to determine how road users evaluate the functioning of the intelligent communication and transportation system in their cities, which factors are the most and least satisfactory for them, as well as the importance they assign to these solutions. Research limitations/implications: The study had a limited scope as it was a one-time survey. It is advisable to continue and expand the sample size for future studies to allow for comparative analyses. Practical implications: The article may provide managers of intelligent communication and transportation systems in cities with knowledge about the expected outcomes of their operation for road users. Social implications: Utilizing the research results may enable better adaptation of ITS to the needs of road users and enhance road safety. Originality/value: The study included drivers, cyclists (scooter riders), and pedestrians. In contrast, most similar studies are limited to verifying the opinions of drivers. Keywords: smart city, smart mobility, management intelligent transports system, cyclists, drivers, pedestrians. Category of the paper: research paper.

With the growth of large cities, it becomes necessary to overcome several challenges related to infrastructure. In this context, urban mobility, including transportation systems, is a key topic. It is known that more efficient traffic management makes cities more efficient as a whole, improving the quality of life of their residents and tourists. A common problem that arises concerns traffic jams, encompassing their detection and management. With proper traffic jam detection, it is possible to implement traffic management methods to prevent long-term occurrences or even notify drivers to avoid specific locations. Some traffic jam detection solutions use sensors spread across cities, while others are based on sensors in drivers' smartphones. These approaches have limitations related mainly to the information reliability or to the cost and complexity of implementation. In this work, we propose an approach using Machine Learning techniques applied to public data of transportation systems to detect urban traffic jams. The proposed approach is evaluated using the publicly available GPS data from the buses of the Rio de Janeiro/Brazil transportation system and traffic jam events posted on Twitter by the Rio de Janeiro City Hall.

Transport and transportation systems in pre-colonial Africa were important aspects of Nigeria’s pre-colonial socio-economic history. Prior to the period of colonial rule and the introduction of mechanised transport systems, the movement of goods and services across domestic and national borders was not alien to Nigerian pre-colonial societies. As against the colonial historiography which described pre-colonial transport and transportation systems as primitive, backward, and incapable of supporting economic growth and development, the article demonstrates, through a thorough examination of pre-colonial transport and transportation systems in the Ilorin Emirate, that such arguments are untrue. Ilorin Emirate, like other pre-colonial towns, has a record of different pre-colonial transportation systems in operation to support its economic growth, trade and market systems, and inter-economic relations. Among the pre-colonial transportation systems widely used in the Ilorin Emirate were the Porterage system and the use of animal packs (camel, horses, and donkeys). These identified pre-colonial non-mechanised transport systems contributed significantly to the growth and development of the Ilorin Emirate. Through these transportation systems, revenues were generated through tolls and taxes, and the expansion of trade and markets, among others, were experienced. The paper relies on primary and secondary sources of data to interrogate the contributions of pre-colonial non-mechanised transport systems.

One of the main issues facing contemporary smart cities is urban traffic congestion, which causes delays, fuel waste, and elevated air pollution. This paper describes an intelligent traffic flow management system that combines machine learning models for real-time vehicle detection, classification, and traffic prediction with the YOLO (You Only Look Once) object detection algorithm. In order to predict congestion levels and optimize signal timings, the suggested system uses machine learning regression and clustering techniques, records live traffic video feeds, and uses YOLOv8 to detect and count vehicles. In comparison to conventional sensor-based techniques, experimental results show a detection accuracy of 95.7% and a traffic flow prediction accuracy of 93.2%. For next-generation smart cities, this combination of deep learning and machine learning guarantees effective, scalable, and adaptive traffic control.

Although the number of automobiles on the road currently is more than it was a few years ago, the infrastructure of roads and traffic systems has not kept up with this expansion, which makes handling traffic challenging. This mismatch causes hence a lot of traffic congestion, crowding, and pollution. All throughout the globe, handling increasing traffic is a major challenge. Operations depend on traffic management in great part. It has to do with planning, running, and purchasing the necessary transportation services—cars, trucks, railroads, boats—that carry commodities and vehicles. Intelligent Traffic Systems (ITS) might be a fantastic approach to address these types of issues by means of modern technology. Given this, traffic control philosophy has to change to accommodate smart cities—which use computer vision techniques to run an adaptive traffic management system depending on CCTV image stream analytics. The adaptive traffic management system detects and counts vehicles accurately. In smart cities, where traffic lights must be automated based on vehicle density, this technique is particularly helpful. This study investigated intelligent traffic control, gathering and monitoring traffic data through using CCTV images. It embodies possible risks in the implementation state, benefits, challenges, as well as the future prospect of ITS.

The importance of transportation in the socio-economic development of nations cannot be downplayed. Intermodal transport has become vital concept for ensuring sustainable freight transport in developed economies but less focused on among African scientific community as it attracts fewer researches and developments. This paper aims to review and promote the development of sustainable intermodal freight transport systems in African developing countries with insights from Ghana.The researchers adopted literature review approach for the global intermodal developments and that of developing economies with emphasis on Africa and Ghana.Transport experts and experienced practitioners’ opinions were sought to complement the limited literature on the means to improve intermodal transport and logistics management systems. Authors discovered that some African countries like Ghana potentially posses some relevant multimodal resources such as seaport, waterway, railway and road infrastructures. However, they are not well interconnected to acquire intermodal benefits. Besides, the existing transportation systems were mostly road dominated and frauded with cost-inefficiencies; greenhouse gas emissions, traffic congestion, accidents, high maintenance and service deficiencies.The constraints are lack of skilled labor, limited infrastructure, safety and security problems, limited institutional capacity, poor intermodal transport network and connectivity issues. Some practical measures to improve the lapses in the transport system were highlighted. It was suggested that management of African developing countries must remodify their transport policies to attract investors and transport players. This would strengthen Public Private Partnerships (PPP) collaborations in developing intermodal freight transport and logistics systems.

Real-time traffic management systems with integrated proactive decision support capabilities are expected to operate with ( a) limited prediction accuracy ( b) decision-making latency, and ( c) partial coverage of the managed area. Such deficiencies are difficult to avoid in most real-world traffic network management applications, and there is a need to quantify the effect of these deficiencies on the performance of traffic network management systems. This paper studies the effectiveness of a proactive traffic management system. Various levels of prediction accuracy of the traffic network state, decision-making latency, and partial area coverage are considered. A traffic management system that emulates real-time operations is developed. The system adopts a closed-loop rolling horizon framework, which integrates network state estimation and prediction modules as well as decision support capabilities. A set of simulation experiments considers a hypothetical highway network. The results show that the effectiveness of the traffic management system can be affected by the deficiencies. However, the impact could be smaller if these deficiencies are kept under certain levels.

The fast growing and very variable nature of road traffic presents professional challenges to public safety and effective traffic management in smart city surroundings. Frequent traffic accidents not only result in loss of life but also break urban motility systems, featuring the need for intelligent and automated accident detection explanations. This work presents a complete experimental study of existing traffic accident detection techniques, highlighting the current limitations of the state of the art paths while offering an in-depth analysis of common accident types such as crashes of rear-end, crashes at T-bone, & front accident simpact. To address these objections, the proposed work introduces a novel and lightweight deep learning architecture named AMSE-ADM, specifically designed for traffic accident detection of vision-based in smart city surveillance systems. The model effectively involves RGB video frames with flow of optical information to capture both spatial and temporal motion features of traffic scenes. The architecture influences advanced spatio temporal feature extraction mechanisms along with enhanced temporal dependency modeling across video sequences, enabling accurate detection of accident events. Extensive experimental evaluation demonstrates the effectiveness of the proposed approach, showing that the AMSE-ADM model outperforms other configurations. Furthermore, this study discusses critical challenges such as data imbalance, limited datasets, and constrained road and traffic scenarios, which significantly impact model generalization. Overall, the proposed framework paves the way for an efficient accident detection model in real-time and which is suitable for deployment on edge devices within smart city traffic infrastructures.

Fusion of engineering insights and emerging trends: Intelligent urban traffic management system

The idea of a smart city has evolved in recent years from limiting the city’s physical growth to a comprehensive idea that includes physical, social, information, and knowledge infrastructure. As of right now, many studies indicate the potential advantages of smart cities in the fields of education, transportation, and entertainment to achieve more sustainability, efficiency, optimization, collaboration, and creativity. So, it is necessary to survey some technical knowledge and technology to establish the smart city and digitize its services. Traffic and transportation management, together with other subsystems, is one of the key components of creating a smart city. We specify this research by exploring digital twin (DT) technologies and 3D model information in the context of traffic management as well as the need to acquire them in the modern world. Despite the abundance of research in this field, the majority of them concentrate on the technical aspects of its design in diverse sectors. More details are required on the application of DTs in the creation of intelligent transportation systems. Results from the literature indicate that implementing the Internet of Things (IoT) to the scope of traffic addresses the traffic management issues in densely populated cities and somewhat affects the air pollution reduction caused by transportation systems. Leading countries are moving towards integrated systems and platforms using Building Information Modelling (BIM), IoT, and Spatial Data Infrastructure (SDI) to make cities smarter. There has been limited research on the application of digital twin technology in traffic control. One reason for this could be the complexity of the traffic system, which involves multiple variables and interactions between different components. Developing an accurate digital twin model for traffic control would require a significant amount of data collection and analysis, as well as advanced modeling techniques to account for the dynamic nature of traffic flow. We explore the requirements for the implementation of the digital twin in the traffic control industry and a proper architecture based on 6 main layers is investigated for the deployment of this system. In addition, an emphasis on the particular function of DT in simulating high traffic flow, keeping track of accidents, and choosing the optimal path for vehicles has been reviewed. Furthermore, incorporating user-generated content and volunteered geographic information (VGI), considering the idea of the human as a sensor, together with IoT can be a future direction to provide a more accurate and up-to-date representation of the physical environment, especially for traffic control, according to the literature review. The results show there are some limitations in digital twins for traffic control. The current digital twins are only a 3D representation of the real world. The difficulty of synchronizing real and virtual world information is another challenge. Eventually, in order to employ this technology as effectively as feasible in urban management, the researchers must address these drawbacks.

The expansion of technology in metropolitan centers draws people to cities, which causes excessive traffic on the roadways during peak hours. This exacerbated the traffic situation, resulting in a delay, a loss of resources, and a waste of time. Like any other metropolitan metropolis, Riyadh, Saudi Arabia, has everyday traffic congestion during business hours. The current traffic management has made many attempts to ease traffic congestion in cities; despite these measures, the problem has not been solved adequately. To handle this road congestion, there is a need to appropriately store the big data collected by traffic sensors and utilize it for efficient traffic management employing cutting-edge technology. This study provides an architecture for a smart traffic management system that uses modern technologies such as the Internet of Things (IoT), cloud computing, 5G, and big data to aid conventional traffic management systems and efficiently handle the stated problem. The proposed technique has the potential to reduce traffic congestion significantly. Our proposed solution encourages mobility by using roadside messaging agents to offer real-time traffic information on traffic congestion and unexpected traffic incidents. Citizens will save time by getting these early warning messages, particularly during peak hours. As part of the suggested method, each signal dashboard gets traffic information. A case study is used in the research to evaluate alternative solutions to traffic congestion. The case study results show that the proposed strategy outperforms the present options.

Green Mobility Application (GMAP)