Applications of Computational Learning and IoT in Smart Road Transportation System

Intelligent vehicles (IVs) are an exciting and vital topic for transportation because they can improve roadways’ operational efficiency and safety. Although these expectations may not reflect reality, we should not forget that we still have a long way to go before IVs become commonplace. Research on intelligent cars is a work in progress, where experts continuously deal with old problems and share new ones along its development path. To realise these dreams of integrating IVs into our transportation system infrastructure, it is desirable to cope with existing issues while simultaneously recognising major frontiers that need to be conquered. This paper aims to facilitate researchers’ lives by unveiling them.

As nations become economically stronger and financially capable, more people own their vehicles. Although the road infrastructure has improved, it must cope with the increasing population. With that, more and more road accidents are increasing. Drivers often can’t recognise the appearance of unmarked speed breakers and lose control of the vehicle, causing serious accidents and loss of lives. In many instances, fatalities occur because immediate medical assistance is not readily available due to the absence of a reliable system. With the advancement of technologies like IoT, a pressing need arises to create a system capable of promptly informing relevant authorities with comprehensive data on the incidence of a road accident. The proposed IoT-based speed breaker detection system consists of strategically deployed sensors along roadways equipped with IoT capabilities to detect the presence of vehicles and analyse the vehicle's behaviour to identify potential speed breaker crossings. Upon detection, the system sends real-time alerts to drivers, either through in-vehicle displays, mobile applications, or roadside signage, enabling them to adjust their speed and driving behaviour accordingly. Key components of the system include sensor nodes equipped with accelerometers or distance sensors to detect changes in road surface elevation indicative of speed breakers. These sensor nodes communicate wirelessly with a centralised control system, which processes the collected data and triggers alerts when speed breakers are detected. Along with this, we have incorporated machine learning methods and image processing to accurately identify a road accident. The sensors like accelerometer, Distance sensors, camera, etc., provide data to a microprocessor which matches the sensor data with the machine learning model and determines if there is an accident or not, and if it is, the device sends the related metrics to the server through the internet. Once the data reaches a server, it determines the nearest hospitals, and police stations by looking at the GPS data and sends a notification to them and the registered phone number by the user. The system also incorporates real-time tracking of driver, vehicle, and timing information for speed breaker rule violations, which becomes a life-saving technology.

Smart cities are changing due to machine learning (ML) and the Internet of Things (IoT), advancing liability, sustainability, and increased efficiency. They maximise urban services, from public safety and environmental monitoring to transportation and energy management, using machine learning algorithms and interconnected IoT devices. Through predictive maintenance, anomaly detection, and sophisticated analytics, the integration of ML expands the possibilities of IoT systems. By detecting and notifying authorities of odd activity or possible security risks, incorporating surveillance systems with ML can improve crime prevention and response. ML algorithms examine this data to enhance efforts to create a healthier and cleaner urban environment by offering insights into pollution trends and health hazards. Significant infrastructure and technological investments are also required to deploy and keep these cutting-edge systems. Government agencies, stakeholders in the corporate sector, and society must work together effectively to address these issues and guarantee data’s secure and moral use. In summary, the development of smart cities depends on the fusion of machine learning and the IoT.

An image processing-based smart ambulance detection system provides an enhanced emergency response and traffic management with an all-inclusive solution. Three prominent use cases are integrated into the system to maximise collaboration and communication in urgent circumstances. First, an MIT program allows ambulance drivers to submit messages when the vehicle is far from the camera. The messages are then instantly shown on an LCD screen. This function allows the ambulance crew and traffic management authorities to communicate in real-time, facilitating adequate route clearance and navigation. Second, traffic cops may now remotely regulate LEDs placed along the road thanks to a dedicated app. The technology sounds like a buzzer to inform surrounding cars of an ambulance's impending passing by turning the LED green.

The impact of IoT and ML on smart cities from a city infrastructure development perspective has converged, and data processing, decision-making, and collection have never been more efficient. IoT Sensors are essential for collecting information on environmental parameters, energy consumption, traffic patterns, and public safety. This massive amount of data can provide valuable insights using ML algorithms to predict patterns and enhance local governance. This also aids in projects like waste preservation and traffic provisioning, which work towards efficient energy utilisation within the urbanised spots. Success would require addressing scalability, interoperability, privacy, and data security issues.

This chapter is about how AI is transforming road transportation by improving efficiency, safety, and comfort. By emphasising the importance of AI integration and moving on to main AI technologies such as machine learning (ML) for applications like traffic prediction and autonomous driving. Deep learning techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are essential for vehicle detection and traffic analysis. Also, natural language processing (NLP) enhances traffic planning and customer support by providing real-time information and virtual assistants. We can also know from the chapter that AI applications like self-driving cars that use AI for vision and control, intelligent traffic management systems that optimise signal timings, and predictive maintenance to avoid vehicle problems. It also discusses data privacy, technology, and ethics questions, as well as demonstrates effective real-world AI deployments and future trends. Overall, the chapter emphasises AI’s disruptive impact on road transport and its potential for ongoing innovation and improvement in the pursuit of a smarter, more efficient transportation network.

Urbanisation has significantly changed people’s living standards recently, especially regarding mobility and transportation. As cities develop and expand, reliable transit has become necessary, increasing car ownership. Although increased private and public transportation provides convenience and flexibility to city dwellers, it also adds to substantial traffic congestion and environmental pollution. Therefore, more sophisticated traffic management systems are required to manage high traffic density, optimised signal timing and frequent congestion during peak times. Intelligent transportation systems include diverse technologies to enhance urban transportation networks’ safety, sustainability and quality. Adaptive traffic signal systems play a crucial role in the framework of intelligent transportation systems. Adaptability to changing traffic conditions such as traffic volume, waiting time, delay, congestion level and pedestrians’ movement can result in more efficient traffic management for the urban network. Much work has been done on adaptive traffic signal management. Fuzzy controllers are distinguished among the existing adaptive systems due to their exceptional ability to handle uncertainty, facilitate intuitive rule-based decision-making, and excel in human-like reasoning and adaptability, which is crucial in managing the complexities of urban traffic networks. This study explores designing adaptive rule-based fuzzy controllers for isolated traffic intersections and a city-wide road network, integrating dynamic controllers with smart city infrastructure.

The age of AI for road transport has come to signify a fundamental shift towards smarter, safer, and efficient mobility solutions. With urbanization and population increasing demands on transportation systems, innovative applications of AI can address challenges such as traffic congestion, safety of navigation, energy efficiency, and environmental sustainability concerns. The chapter also intends to examine the current and foreseeable roles of AI in road transport, focusing on prominent applications of traffic management, autonomous vehicles, and fleet optimization. Predictive analytics enable authorities to foretell traffic surges, identify areas of congestion, and offer alternative routes to drivers to minimize delays and emissions. The chapter addresses the layers of vehicle autonomy from allowing driver assistance to full autonomy whilst taking a peek at ethical, regulatory, and technical humps that need to exist for ensuring further propagation.

Road accidents are one of the leading causes of death in India. These road accidents are often caused by rash driving while intoxicated, drowsy or fatigued drivers, road conditions and many more. There are many works and literature for detection systems that aim to mitigate these issues but these systems often fail to be integrated on a large scale either due to high installation/maintenance costs or due to poor reliability because of the high complexity of these systems. This chapter aims to review current existing technologies for the detection of driver drowsiness, alcohol intake, overspeeding/reckless driving etc., by carefully examining their advantages, disadvantages, and methodologies. From this information, we propose a conceptual system that is aimed to overcome the limitations of these currently existing systems and is specially tailored to be effective in the Indian context and for other developing countries.

Optimization is a fundamental factor that drives the efficiency, effectiveness, and precision of structures in synthetic intelligence, device learning, and the Internet of Things (IoT). Employing optimization techniques, including refining fashions with large datasets or improving supply code, can considerably improve performance, accuracy, and dependability. This bankruptcy explores key optimization standards and their essential roles throughout those interconnected fields. We study a spectrum of optimization techniques, starting from conventional strategies like gradient descent to extra superior techniques including evolutionary algorithms and Particle Swarm Optimization (PSO). In Artificial Intelligence (AI), optimization is essential for boosting the decision-making and problem-fixing skills of algorithms. In Machine Learning (ML), it's far vital for reaching excessive prediction accuracy, fine-tuning hyperparameters, and correctly education fashions. In IoT, optimization is essential to control electricity usage, enhance community performance, and decorate real-time processing. The goal of this bankruptcy is to offer researchers, professionals, and college students with an intensive know-how of the importance of optimization in AI, ML, and IoT, allowing them to increase their paintings in those areas.

In the past few years, Big data, machine learning, and artificial intelligence (AI) inclusion have changed the dynamics in various industries, including road transport. This research explores machine learning and artificial intelligence in road transportation, accentuating the advanced image processing methods. We apply these technologies to enhance how traffic is managed, how safe vehicles are operated, and how the most efficient routes are planned. This study also expands to various techniques and methods for analysis, such as image processing, object recognition, and recognition systems, and is effective in implementation. Through extensive experiments and several case studies, we have shown substantial improvements in accuracy and efficiency achieved when image processing in road transport, including machine learning and artificial intelligence. This study points out the tremendous prospects posed by these technologies in shaping the future of transport and suggests further developments and applications.

Smart cities are a concept reshaping how urban environments operate to be more efficient, sustainable, and liveable, especially when machine learning (ML) meets the Internet of Things (IoT). This chapter delves into how these two technologies converging—with urban data being delivered in real-time from IoT and analytics taking place via ML algorithms—can create actionable insights. In this paper, we critically analyse the use cases of most importance, including benefits and challenges, alongside emerging trends from a recent survey that provides an in-depth depiction of what is feasible to create a smart city landscape thanks to IoTs and ML. In this podcast, the speakers will discuss how these disruptive technologies can address challenges related to urbanisation—from energy management, public safety and getting around a congested city to environmental sustainability.

Traffic congestion is one of the unforeseen situations in travel and touring that makes a planned schedule incapable of taking care at the spur of the moment. The problem can even lead to fatal consequences when there is urgency such as fire tendering, healthcare and medical services, criminal investigations, law and order problems, disaster management, etc. The problem is felt more in urban metropolis as there is a sharp increase in the number of vehicles every day and the facilities are always networked among a cluster of roadways as well as their arteries and other transportation services. The proposed study is based on a sensor-based model for the detection of traffic congestion and automated retouring instructions along arterial routes for the commuters keeping the safety and time management as the highest priority goals. The process for resolution of traffic congestion includes automated data display in Wi-Fi connected devices in the vehicles as well as on the traffic control system display giving everybody enough time for decision making on on-the-fly alternate commutation. Furthermore, a prototype of traffic simulation has also been provided to assess the feasibility of the model and its validity using a number of sensor based opto electronic devices. An animated simulation of the prototype has been provided for the veracity of the proposed model.