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2025 | Book

Driving Green Transportation System Through Artificial Intelligence and Automation

Approaches, Technologies and Applications

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About this book

This book is designed to help transportation professionals and construction experts to develop and implement successful smart systems, leveraging the current trends, equipment, and advanced technologies to drive the green transportation system development. Artificial intelligence (AI) is a new direction that has opened a revolution in technology and smart applications, and it is also the basis for creating a green environment in the net-zero era. Therefore, machines, devices, self-driving car, and robots controlled by artificial intelligence-based systems are now the model of a smart transportation ecosystem for which all these technologies are referred to as "green" industries. In past years, the idea of making a green environment has been existing and moving on the society 5.0 being as a country strategy, and today, AI technology continues its development on this prototype. Nowadays, AI has begun actions to resemble a person in a real sense, and the idea of human-liked robotics put forward by scientists has started to be realized and will probably complete its development as living machines in the near future. AI has many subsystems and application in various industries, some of which have automation more accurately and are more integrated in modern industries. This book also targets a mixed audience of specialists, analysts, engineers, scholars, researchers, academics, professionals, and students from different communities to share and contribute new ideas, methodologies, technologies, approaches, models, frameworks, theories, and practices to resolve the challenging issues associated with the leveraging of AI and Industrial Internet of Things (IIoT) in green transportation ecosystem.

Table of Contents

Frontmatter
Artificial Intelligence (AI) and Automation for Driving Green Transportation Systems: A Comprehensive Review

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) have been rapidly transforming and innovating the transportation sector in recent years. This is not only enabling greener, safer and more efficient mobility solutions, but also combating climate change. This comprehensive study explores the current applications, ethical considerations, advantages and disadvantages of AI, machine learning and deep learning in the implementation of green transportation systems. It also highlights the importance of community involvement in promoting ecology in urbanism. Thus, this study examines ML algorithms such as the Genetic Algorithm (GA), Support Vector Machine (SVL), Naive Bayes (NB), k-means clustering, k-Nearest Neighbor (kNN), Classification and Regression Trees (CART), as well as DL algorithms such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Restricted Boltzmann Machine (RBM), and Autoencoder. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT), this review examine the application of AI for green transportation such as Autonomous vehicles, Smart Traffic Management, Mobility-as-a-Service (MaaS), Vehicle-to-Grid technology (V2G), Sustainable Public Transit, Micromobility solutions, and Electric Vehicles (EVs). The findings indicate that while AI technologies offer significant potential for optimizing energy efficiency, reducing emissions and enhancing safety in transport, they also present challenges related to data privacy, algorithmic biases and ethical decision-making. As such, this study highlights the need to develop and implement AI responsibly, reconciling technological advances with ethical considerations and community needs. Finally, this work recommends future research to address these challenges in order to develop more transparent and explainable AI models, and to explore the long-term societal impacts of AI-driven green transportation systems.

Derrick Mirindi, Alex Khang, Frederic Mirindi
Application of Automation and Artificial Intelligence (AI) in Green Transportation System

Automation and artificial intelligence (AI) are becoming important in the development of green transportation systems.This abstract investigates the varied role of AI-driven automation in improving the efficiency, sustainability, and safety of transportation systems. AI application in green transportation includes a variety of technologies such as self-driving cars, smart traffic control systems, predictive maintenance, and energy-efficient logistics. Autonomous cars, outfitted with powerful AI algorithms, are at the vanguard of this transformation, promising to minimize carbon emissions by optimizing driving patterns and energy usage. These cars use machine learning models to read real-time data from sensors, cameras, and Global Positioning System (GPS), allowing them to make more informed judgments that save fuel and minimize traffic congestion. The decrease of human error also helps to improve safety and reduce accident rates. Smart traffic management systems (TMS) utilize artificial intelligence to monitor and analyze traffic flow, reducing idle and emissions. These systems use data from road sensors and linked vehicles to provide a comprehensive view of urban traffic, promoting greener transportation networks. AI-driven automation also plays a crucial role in energy-efficient logistics, optimizing route planning, load allocation, and delivery scheduling to save fuel and reduce greenhouse gas emissions. AI also facilitates the integration of electric vehicles (EVs) into logistics fleets, improving the sustainability of goods transportation. The integration of automation and AI into green transportation systems offers a significant opportunity to improve sustainability and efficiency, reducing the transportation industry's environmental footprint and promoting sustainable growth. The continued progress and use of AI in this sector are crucial for achieving the full potential of green transportation systems.

Sanchita Ghosh, Saptarshi Kumar Sarkar, Piyal Roy
Edge Computing for Enhancing Efficiency and Sustainability in Green Transportation Systems

Given the fast pace of urbanization and increasing environmental concerns, it is imperative to transition transportation networks to more sustainable and efficient models. This chapter examines the impact of edge computing on improving the effectiveness and environmental friendliness of green transportation systems. Edge computing, a technology that moves computational capabilities closer to where data is generated, provides notable benefits in terms of processing speed, data protection, and immediate data analysis. Integrating edge computing with artificial intelligence (AI) and Internet of Things (IoT) technology enables transportation systems to achieve enhanced performance in traffic management, energy consumption, and emissions reduction. This chapter explores different implementations of edge computing in green transportation, such as intelligent traffic light management, live vehicle tracking, and proactive maintenance. The analysis also investigates the implementation of edge-enabled electric vehicle (EV) charging stations and autonomous vehicles, emphasizing their capacity to enhance energy efficiency and diminish carbon emissions. Case studies and practical examples demonstrate the concrete advantages and difficulties of incorporating edge computing into both urban and rural transportation networks. Moreover, the chapter explores the collaboration between edge computing and AI-driven analytics to improve decision-making processes for transportation planners and regulators. The text focuses on the technological, economic, and regulatory elements involved in implementing edge computing infrastructure. It also offers valuable insights into upcoming trends and advancements in this industry. The primary objective of this chapter is to offer a thorough comprehension of how edge computing might facilitate the advancement of sustainable and efficient transportation systems, so aiding in the broader objective of creating greener urban settings and reducing the impact of climate change.

Pankaj Bhambri, Alex Khang
Electric Vehicles: Paving the Way for Sustainable Green Transportation and Environmental Protection

Considering the rise in temperature, regardless of any nation or country, global warming is really becoming a threat to the earth. Many factors are responsible for this global warming, especially the environment, which plays a key role in protecting our mother Earth. Considering the environment and future sustainability, many countries are gradually shifting to zero-emission vehicles. One Hundred Eighty-nine countries agreed to work together on a low-carbon future. It is through lowering greenhouse gas emissions. Transportation is one of the major sectors to lower greenhouse gas emissions. Transportation accounts for 23% energy energy-related greenhouse gas emissions. EVs can be a competitive alternative in the transportation sector, from e-bikes to large trucks. Allowing for a sustainable future and environment, the alternative of electric vehicles (EVs) pops up as a first option. A major chunk of pollution is due to the use of vehicles. With the growing population and customer affordability, the usage of cars has highly increased over the last decade. The extensive use of fuel all over the world forced the automobile industry to develop alternative fuel options or renewable fuel technology. So Electric vehicles are globally promoted by the automobile industry as a sustainable and eco-friendly solution. When you shift to EVs, it brings decarburization and alleviates climate change. As per the projections estimated by the U.S. Energy Information Administration (EIA), the segment of Electric Vehicles is expected to blow up from 0.7 to 31% by the year 2050. According to research conducted by the European Energy Agency, electric cars emit approximately 17–30% less carbon than gasoline and diesel cars. Wide usage of EVs reduces the dependence on non-renewable sources. One of the MIT reports found that a fully electric vehicle emits about 25% less carbon than a comparable hybrid car. To priotarize the environment and sustainability, it is need of an hour to reduce carbon usage, whether it is in production or transportation. This chapter deals with green transportation through electric cars and its effect on the environment, and sustainability. It will be a systematic review of the electronic database of Scopus for the duration of 2020–2024 carried out on electronic vehicles the environment, sustainability, governance (ESG) and its impact on green transportation. It is predicted, that e-vehicles will change the automobile market and provide environmental sustainability.

Dhanashri Sanadkumar Havale, Swati Manoj Yeole, Alex Khang
Industrial Sensors in Smart Transportation System

Issues with traffic, safety, and pollution are just a few of the major transportation-related concerns that modern civilization must contend with. Information and communication technology are playing an increasingly essential role in modern transportation networks. Car companies are working on sensors that may be installed inside vehicles and used for many purposes, such as safety, traffic control, and entertainment systems. Cameras and sensors installed along roadways are helping government agencies monitor traffic and environmental factors. Intelligent transportation systems may be realized by the seamless integration of vehicles and sensing devices, which allows for the exploitation of their sensing and communication capabilities. We discuss the potential benefits of sensor technology for transportation infrastructure connectivity, entertainment applications, and traffic control in an STS, as well as the ways in which a network of sensors embedded in different components of an STS may enhance these areas over time. We wrap up by outlining some of the obstacles that must be overcome before an STS environment can be completely operational and cooperative.

R. Balamurugan, M. Selvakumar, Zahoora Abid, Syed Azahad, Muthu S. Nidhya
Internet of Things (IoT) Smart Sensing Traffic Lights for Revolutionizing Urban Traffic Management

The escalating vehicular population in urban areas has reached critical levels, leading to heightened concerns regarding traffic congestion and safety. In response, this research paper proposes an innovative solution “smart sensing traffic lights” as a transformative measure to overcome the limitations of traditional traffic signal systems. Unlike conventional systems relying on pre-defined signal timing, the smart sensing traffic lights integrate a network of advanced sensors, including cameras, radar, and vehicle-to infrastructure communication, to capture real time traffic data. The core functionality of the smart sensing traffic light system lies in its dynamic adjustment of signal timings and sequences based on the analysis of real-time traffic data. This adaptability enables the system to respond to changes in traffic patterns, congestion levels, and overall urban activity. Notably, the system prioritizes emergency vehicles and provides adaptive crossing times, contributing to a safer and more inclusive urban environment. The simulation-based evaluation methodology follows a meticulous series of steps to ensure a robust analysis. Initially, a detailed model of the urban road network is constructed, considering factors such as road geometry, intersections, and traffic flow patterns. Real-world traffic data, encompassing vehicular density and movement patterns, is seamlessly integrated into the simulation environment to enhance the accuracy and realism of the evaluation. The smart sensing traffic lights system is then implemented within the simulation, incorporating parameters reflective of its real-world capabilities. Diverse scenarios are simulated, including peak traffic hours, emergency vehicle interventions, and unexpected traffic fluctuations. Performance metrics are precisely defined to measure traffic flow efficiency, delay reduction, and the system's adaptability to dynamic conditions. The research paper concludes by engaging in a comprehensive discussion of the implications derived from the simulation results and their practical relevance to real-world urban traffic management. The findings offer valuable insights into the potential of smart sensing traffic lights as a transformative solution for addressing contemporary traffic challenges. By contributing to the development of more efficient, adaptive, and inclusive urban transportation systems, this research lays the groundwork for future advancements in the field of intelligent traffic management.

Alex Khang, Khushwant Singh
Quantum Computing: Revolutionizing Green Transportation Through Advanced Optimization and Simulation

Quantum computing, a nascent paradigm in the field of computation, offers the potential to profoundly transform green transportation through its unparalleled powers in optimization and simulation. Conventional computing approaches frequently face difficulties when dealing with the intricacy and magnitude of problems associated with optimizing transportation systems to achieve environmental sustainability. Quantum computing, utilizing concepts like superposition and entanglement, provides a significant advancement in computational capability, allowing for the effective manipulation of large datasets and intricate models. This chapter explores the ways in which quantum algorithms can improve different areas of environmentally friendly transportation, including optimizing routes, managing traffic flow, designing energy-efficient vehicles, and controlling autonomous systems. Through the examination of practical implementations and theoretical progress, we emphasize the capacity of quantum computing to tackle the pressing issues of carbon emission reduction and operational efficiency improvement in transportation networks. Moreover, this chapter provides a thorough examination of the collaboration between quantum computing and artificial intelligence (AI) within the framework of sustainable mobility. This study investigates the impact of quantum-enhanced artificial intelligence on the creation of prediction models for transportation demand, the optimization of logistics in supply chains, and the enhancement of transportation infrastructure's resilience to environmental effects. By combining quantum computing with AI and automation, we can discover novel approaches to developing more intelligent and environmentally friendly transportation systems. The topic encompasses the analysis of specific instances, prospective advancements, and the moral implications of implementing quantum technologies in transportation. The purpose of this investigation is to offer a clear plan for researchers, practitioners, and policymakers to utilize the potential of quantum computing in facilitating the shift towards a sustainable, environmentally friendly future in transportation.

Pankaj Bhambri, Alex Khang
Role of Human-Centered Design and Technologies in Smart Transportation System

Smart transportation systems are revolutionizing urban mobility by leveraging technology to optimize traffic flow, enhance safety, and improve user experience. Central to the success of these systems is Human-Centered Design, which focuses on the needs, behaviors, and experiences of users. This paper explores the role of Human-Centered Design in smart transportation systems, examining its principles, benefits, and challenges. Additionally, we propose a comprehensive model that integrates Human-Centered Design into the development and implementation of smart transportation solutions, using Helsinki’s Mobility as a Service (MaaS) initiative as a case study.

Babasaheb Jadhav, Mudassar Sayyed, Vikram Barnabas, Alex Khang
Internet of Vehicle Based Quality of Service (QoS) Exploration for Public Transportation

In every country, public transit is an essential for city managers. It is intended that the community will have inexpensive, safe, and convenient transportation for getting about. On the other hand, the development of Internet of Vehicles technology presents an opportunity to design a public transport management system. The shift to electric cars and the existing transportation management system provide the perfect conditions for the development of Internet of cars technologies. Inadequate communication infrastructure makes implementation difficult. A detailed analysis is necessary to set up the enabling infrastructure for optimal growth. To simulate the infrastructure and evaluate its service quality, utilize MATLAB’s Communication Toolbox. Finding the service quality level to determine the least amount of communication infrastructure needed to enable an IoV in public transport is the aim of this study and simulation.

Alex Khang, Khushwant Singh, Kavita Thukral, Ajay Kumar
Intelligent Traffic Management and Accident Prevention System with Vehicle Counting and Distance-Based Brake Control

This chapter proposes a dexterously advancement organization and adversity-shirking system that utilizes vehicle checking and distance-based brake control to optimize the movement stream and improve security. By choosing the number of vehicles on the road and enabling versatile braking based on inter-vehicle restrictions, the system centers on overcoming the obstacles of current action systems. The system comprises vehicle-mounted sensors, a central overseeing unit, and action control contraptions. The sensors recognize adjoining vehicles and transmit this information to the central organizing unit, which businesses calculate to check the total number of vehicles. The sensors engage distance-based brake control, enacting the vehicle's brakes to dodge collisions by always measuring the disconnected to the going a couple of times as of late vehicle. Other than that, the system businesses the vehicle number data to capably modify action light timing at crossing centers, diminishing clogs. On the off chance that effectively executed, this clever action organization system has the potential to insides and redesign security and capability on the way.

Nobhonil Roy Choudhury, Sreeja Bhattacharjee, Saptarsi Ghosh, Shivnath Ghosh, Pranashi Chakraborty
E-Waste and Lithium-ion Battery Recycling Insights for Sustainable Transportation

Electronic gadgets are essential to our lives, yet e-waste is a worldwide problem. Untreated electronic trash contains heavy metals and toxic chemicals that may damage humans and the environment. Recycling lithium-ion rechargeable batteries is essential for green waste management. Electronic trash recycling, extended producer responsibility, sustainable battery recycling, and sustainable municipal solid waste management are covered in this article. This study stresses the need for a comprehensive and coordinated approach to e-waste management throughout the product's lifecycle. The report also discusses how Artificial Intelligence (AI) and automation may promote green transport systems by recycling e-waste and lithium-ion batteries. AI can expedite battery system development, forecast and identify materials, and estimate battery conditions. Automation of disassembly and quality control improves recycling efficiency and safety. Economic benefit analysis methods for lithium battery recycling may accurately assess recycling value using AI. AI and automation may boost the circular economy and sustainable e-waste and lithium-ion battery recycling, enabling green transportation systems.

Alex Khang, Shalom Akhai
Deep Learning (DL)-Powered Drowsiness Detection for Enhanced Driving Safety in Smart Transportation System

Drowsiness or Fatigue is the process of impairing an individual's mental and physical abilities. It can happen as a result of not getting enough sleep, extended periods of inactivity, or other factors like medication, alcohol, or clinical settings. The main consequences of being sleepy are the potential for fatalities and injuries. It distinguishes between awake and sleepy phases by analyzing factors such as frame posture, eye movements, and facial expressions. Drowsiness can find using brainwave patterns, particularly alpha and theta frequencies. The current process includes OpenCV and deep learning model that helps in detecting face and eyes using 68 face land marks. If a person is drowsy then we will alert a person with the help of alarm and we will send mails to their family or friends that the person is drowsy kindly alert him, otherwise accidents may occur. If we alert the drowsy person, we can get rid of accidents and we can avoid financial crisis. The further development which we would like to do is to integrate a Chatbot and that helps in keeping the person alert all the time. The current model will have good accuracy and developments to get rid of accidents. The last purpose is to beautify avenue safety and mitigate injuries due to driving force drowsiness, contributing to the general nicely-being of society.

Pandluri Dhanalakshmi, Golla Hemanth Kumar Yadav, Vidyavathi Kotha, Ravikanth Garladinne, Nayudori Balakrishna
Fuzzy Logic and Integrated Deep Learning (DL) Solution for Precise Vehicle Detection and Classification

In the smart transportation system, vehicle detection is crucial. Additionally, it has a big impact on a lot of other things including advanced driver assistance systems, fleet management, asset tracking, surveillance and security, autonomous cars and robotics, and traffic monitoring and management. Contributes significantly to automation, safety, security, and traffic efficiency, among other facets of contemporary life. This project’s main goal is to investigate the creation and use of neural network models for the prediction of vehicle models and the detection of cars in photos. Several common network models, including CNN (Convolutional Neural Network Features), the Classification Model, and Fuzzy Logic, have been used in these training and classification trials. This approach aims to provide a more accurate vehicle classification.

Khushwant Singh, Mohit Yadav, Yudhvir Singh, Daksh Khurana, Binesh Kumar
Automatic Number Plate Recognition for Motorcyclists Riding Without Helmet

This study focuses on the crucial problem of motorcycle riders circumventing helmet restrictions by introducing an Automatic Number Plate Recognition (ANPR) system specifically designed to detect and penalize riders who do not comply with the regulations. The system employs sophisticated computer vision algorithms to autonomously identify motorcyclists who are not wearing helmets by analyzing photos or video streams in real-time. The proposed ANPR system utilizes advanced image processing algorithms and deep learning approaches to improve law enforcement skills in enforcing road safety and ensuring compliance with helmet laws. The process entails creating a strong Automatic Number Plate Recognition (ANPR) model that is trained on a broad dataset of motorbike riders in different settings. The technology not only identifies license plates but also utilizes advanced helmet detection techniques to identify motorcyclists who do not comply with regulations. The suggested approach aims to enhance public safety efforts by automating the enforcement of helmet rules, thereby decreasing the occurrence of head injuries among motorcycle riders. By employing this ground-breaking method, law enforcement organizations may effectively oversee and uphold adherence to helmet regulations, cultivating a more secure road atmosphere for both motorcyclists and the general populace.

B. Narendra Kumar Rao, Vemula Shalini, Kullai Balaji, Ponthagiri Venkata Siva Kalyan
Application of Internet of Vehicles (IoV) in Smart Transportation System

With the ever-increasing sophistication of modern technology, our society is being graced with an array of smart gadgets that enhance the way we go about our everyday lives. Connecting disparate devices and enabling them to share data in real-time, the Internet of Things (IoT) is a game-changer in terms of modern technology. Researchers are especially interested in smart transportation because of its potential to completely change the way products and people are transported. Among the several advantages that drivers in a smart city may enjoy thanks to the Internet of Things are better logistics, safer driving, more efficient parking, and better traffic management. All of these advantages may be seen in smart transportation apps that are built into transportation networks. Our goal in writing this chapter is to provide a comprehensive analysis of the various smart transportation systems now in use, together with an analysis of the obstacles faced by each. So, we took a look at the frameworks, structures, and communication methods that make these smart transportation apps and systems possible. At last, we discussed the present state of smart transportation research and offered some suggestions for where the subject may go from here.

K. Padmamabhan, S. Geetha, Muthu S. Nidhya, S. Gunasekaran
Advanced Sensor Technologies and Applications for Green Transportation Systems

Advanced sensor technologies are revolutionizing the landscape of green transportation systems, offering unprecedented opportunities for enhancing sustainability, efficiency, and environmental stewardship in the transport sector. This chapter delves into the myriad of sensor applications that are reshaping how we conceptualize and implement eco-friendly transportation solutions. From environmental monitoring to vehicle optimization and intelligent traffic management, sensors are at the forefront of the green transportation revolution. By examining cutting-edge sensor technologies, their integration into transportation infrastructure, and their impact on reducing emissions and improving energy efficiency, this chapter provides a comprehensive overview of the current state and future potential of sensor-driven green transportation. Through a combination of theoretical analysis, experimental data, and real-world case studies, we explore how these technologies are not only addressing current environmental challenges but also paving the way for a more sustainable and intelligent transportation ecosystem.

Ushaa Eswaran, Vivek Eswaran, Keerthna Murali, Vishal Eswaran
Artificial Intelligence (AI)-Driven Traffic Solutions: Enhancing Green Transportation Through Predictive Analytics and Deep Learning

With the surge in vehicle numbers and urbanization, modern cities face escalating traffic management challenges. This research presents AI-driven solutions to enhance green transportation by leveraging predictive analytics and deep learning. A comprehensive framework that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to capture spatial and temporal traffic patterns is proposed. By employing Particle Swarm Optimization (PSO) and Bayesian Optimization, optimal performance is theorized. Additionally, our approach integrates Deep Reinforcement Learning (DRL) to facilitate real-time traffic management, dynamically adjusting to varying conditions to reduce congestion and promote efficient transportation. This research offers innovative strategies for sustainable urban mobility, emphasizing the potential of AI in transforming traffic systems for greener cities. The focus on the intersection of AI and urban mobility underlines how key technologies can help resolve major contemporary challenges in urban transportation.

Ganesh Khekare, Uddhav Khetan, Purav Nirav Doshi
Application of Artificial Intelligence (AI) Techniques for Green Transportation in Smart City

A smart city emerges through the integration of Artificial Intelligence (AI) and the Internet of Things (IoT), allowing data collection from individuals, devices, and buildings. This data is then analyzed to optimize various aspects of urban life, including infrastructure, traffic, and energy management. Smart cities are built on a foundation of Information and Communication Technologies (ICT) and Cloud integration, facilitating seamless interactions. The implementation of smart energy infrastructure within a city to monitor energy consumption, reduce costs, and minimize carbon emissions. The growing emphasis on renewable energy sources underscores the importance of environmental and public health preservation. The abundance of renewable energy sources presents an opportunity to meet the increasing demand for clean, affordable energy while addressing concerns related to cost and climate. AI has ushered in a new era of technological innovation and sustainable development. In this chapter, we explore the application of AI in renewable energy research within smart urban environments. Additionally, we conduct an analytical study that leverages AI and IoT technologies for effective smart energy management in cities. Our primary objective is to assess the efficiency of machine learning and IoT techniques in this context, addressing the dual concerns of sustainability and technological advancement.

Andal Lakshumiah, Anandan Malaiarasan, Rajeswari Packianathan, Suresh Kumar Natarajan, Gobinath Arumugam
Analysis of Wireless Sensor Networks Applications in Intelligent Transportation System

In this chapter, we analyze the application of wireless sensor networks (WSNs) in intelligent transportation systems (ITS). Intelligent transport leverages information technology and intelligent means such as computers, sensors, and self-control technologies to achieve real-time automatic collection of traffic information. The integration of traffic signal control, traffic guidance, and intervention systems ensures orderly traffic operation through real-time supervision. As conventional detection technologies like microwave, video, and ultrasonic fail to meet modern ITS development needs, WSNs emerge as a critical solution. We explore the use of giant magnetoresistive sensors for magnetic-sensitive detection, which enhances the efficiency and intelligence level of ITS. WSNs enable real-time data collection and traffic monitoring, providing traffic managers with essential information to address road conditions promptly, optimize traffic signal control, and improve traffic flow. Additionally, WSNs facilitate intelligent traffic management and emergency responses, increasing safety and emergency handling efficiency. Intelligent navigation systems, based on WSN data, offer accurate road condition information, enabling better route planning and reducing traffic congestion and accidents. This paper highlights the potential of WSNs in transforming ITS and outlines the future research directions necessary for overcoming current challenges.

Alex Khang, Vugar Abdullayev, Yitong Niu
Integrating Industrial Robotics and Internet of Things (IoT) in Smart Transportation System

Smart transportation systems represent a transformative approach to managing urban mobility, leveraging advanced technologies to enhance efficiency, safety, and sustainability. Central to these systems are various types of sensors, which gather real-time data critical for informed decision-making. This chapter explores the diverse applications of sensors within smart transportation, categorizing them into traffic, environmental, vehicle, and infrastructure sensors. It delves into how these sensors are utilized for traffic management and control, public transportation optimization, autonomous and connected vehicle functionality, environmental monitoring, and smart infrastructure management. The integration of Internet of Things (IoT) technology and sophisticated communication protocols such as 5G and Dedicated Short-Range Communications (DSRC) underpins the seamless operation of these systems. Despite the promising advancements, challenges such as sensor accuracy, data security, and privacy concerns persist, necessitating ongoing research and development. Future trends point towards further integration of artificial intelligence and machine learning to enhance predictive capabilities and decision-making processes. Through detailed case studies and real-world implementations, this chapter illustrates the practical benefits and lessons learned from deploying sensor technologies in various urban settings. By synthesizing current research and technological innovations, it provides a comprehensive overview of the critical role sensors play in realizing the vision of smart transportation. The chapter concludes with an outlook on future developments, emphasizing the continuous evolution and potential of sensor applications to revolutionize urban mobility.

Rajeswari Packianathan, Gobinath Arumugam, Anandan Malaiarasan, Suresh Kumar Natarajan
Beyond the Horizon: Exploring the Future of Artificial Intelligence (AI) Powered Sustainable Mobility in Public Transportation System

Recent news highlights the severe impact of traffic congestion, such as in a major Information Technology (IT) city in India where 37 IT companies are relocating due to continual traffic issues. This not only disrupts the daily lives of workers and related businesses but also significantly harms the environment. High temperatures, such as the record 54 degrees Celsius in the capital city, are partly due to pollution, despite efforts to improve mobility with metros, public transport, CNG, and electric vehicles. Imagine driving a quiet vehicle on an empty street. While this may seem like a distant dream now, it can become a reality with Artificial Intelligence (AI)-based automation. Beyond the Horizon: Exploring the Future of AI-powered sustainable mobility aims to discover revolutionary ways to achieve hassle-free travel. By leveraging data science and sophisticated algorithms, AI can identify traffic-free routes, pinpoint the causes of traffic jams, and notify authorities for quick action. This chapter delves into how AI can guide us toward a more sustainable and efficient way to move around the world, envisioning a future where seamless and eco-friendly transportation is within our grasp.

Babasaheb Jadhav, Ashish Kulkarni, Alex Khang, Pooja Kulkarni, Sagar Kulkarni
3D Modelling and Printing in Smart Transportation System

Smart transportation systems that include 3D printing and modeling technology are a game-changer in terms of design and production. In this chapter, we look at how these technologies may be used to make today’s transportation systems more efficient, personalized, and useful. With the use of 3D modeling, transportation systems and components may be accurately shown and simulated, leading to better analysis of performance and new ideas for design. Coupled with 3D printing, which enables rapid prototyping and the production of complex, bespoke parts, this combination offers significant advantages in terms of cost reduction, time efficiency, and adaptability. The study examines various case studies where 3D modeling and printing have been implemented in smart transportation projects, including the development of lightweight vehicle components, customizable infrastructure elements, and advanced traffic management tools. Additionally, it addresses the challenges and limitations of these technologies, such as material constraints and scalability issues, and proposes potential solutions for overcoming these hurdles. Overall, this chapter highlights the transformative potential of 3D modeling and printing in creating more responsive, efficient, and adaptable transportation systems. It offers insights into future directions for research and development in this field, emphasizing the role of these technologies in shaping the future of smart transportation.

R. M. Dilip Charaan, Avinash Mallad, Balajee Maram, Udit Mamodiya, Muthu S. Nidhya
Future-Proofing Green Transportation: Fusing Technology for Safer Roads

At present, the automobile industry is experiencing substantial transformations in the areas of in-vehicle communication and information technologies. This transition is significantly affected by the advancement of advanced driver assistance systems. Nevertheless, this development poses a risk to both vehicles and pedestrians by introducing the potential for information overload. Driver Assistance Systems are critical to guaranteeing vehicle safety in today's world, and they are likely to become even more significant in the future as autonomous vehicles become more common. This technical paper discusses various technologies that can improve the functionality of the Driver Assistance System. These technologies include methods for detecting driver fatigue, precisely defining lanes, tracking vehicles in real time, and correctly recognizing traffic signs. The components were easily merged and synchronized into a web-based platform using the Django framework, resulting in a user-friendly interface. Combining Hyper Text Markup Language and Graphical User Interface components together makes things much easier to access and use. The article also suggests a loT of different ways that the model could be improved in the future. These possible improvements could make it easier and more flexible to make sure the safety and comfort of both pedestrians and cars. The addition of driver-focused technologies to the constantly changing field of Driver Assistance Systems is a big step towards safer and more efficient transportation options.

Venkataramanan Vijendran, Diya Shah, Raj Davawala, Samyak Shah, Mihir Dudhatra, Ishitaa Panda, Vats S. Shah
Technological Features of a Safe Monitoring System Based on the Use of Unmanned Aerial Vehicles

We considers the organization of a subsystem for collecting diagnostic information about railway signaling and interlocking devices using unmanned aerial vehicles (UAVs) with a payload. An expanded architecture for the system of technical diagnosis and monitoring of railway signaling and interlocking equipment has been developed. Principles for ensuring the safety of measurement operations using UAVs have been discussed. A method for forming an overflight over route for UAV is proposed, considering the features of diagnosis objects and the specifics of the arrangement of railway infrastructure. Based on the analysis, work on manual technical diagnosis that is automated using UAVs has been highlighted. Many defects in trackside devices of railway signaling and interlocking have been identified, which distinguished by unmanned aerial vehicles. The use of the solutions proposed by the authors makes it possible in practice to expand many automatically recorded diagnostic events, as well as to reduce the period of diagnosis. This helps to maintain a high level of reliability and readiness of railway signaling and interlocking devices.

Alex Khang, Dmitry V. Efanov, Gasim Mammadov, Vugar Abdullayev, Tatiana S. Pogodina, Abuzarova Vusala Alyar
Battery Health Aware Energy Management Strategy for Hybrid Electric Vehicle Using Artificial Intelligence

Using Artificial Neural Networks (ANN), a battery health aware energy management strategy (EMS) is created for a power-split hybrid electric vehicle (HEV). To acquire a dataset, three distinct speed profiles are used. The highway speed profile is HWFET, the third speed profile is NEDC, and the dynamic, transient speed profile is WLTP. During these driving cycles, the vehicle is run in charge-sustaining mode utilising the Equivalent Consumption Minimisation Strategy (ECMS). In simulations, three distinct beginning State-of-Charge (SOC) values are employed. There are three distinct starting SOC levels for each cycle. The ICE torque and speed are controlled by two ANN controllers. The vehicle’s torque requirement, the state of charge, and the fading of the battery capacity are chosen as the ANN’s inputs. This research aims to investigate fuel usage and battery deterioration via the use of artificial neural networks. According to WLTP results, with the lowest starting SOC value, capacity fading may be decreased by up to 14.85% and fuel consumption can be lowered by 3.83%. Fuel consumption is lowered by 1.84% and capacity fading is decreased by 13.80% for intermediate starting SOC values. With a 5.75% rise in fuel consumption, capacity fading is decreased by 14.70% for the highest starting SOC value. For the other two driving cycles, the outcomes are the same. In HWFET and NEDC, battery deterioration is also decreased.

Alex Khang, Khushwant Singh
The Role of Sensors in Shaping Future Transportation Systems

Smart transportation systems represent a transformative approach to managing urban mobility, leveraging advanced technologies to enhance efficiency, safety, and sustainability. Central to these systems are various types of sensors, which gather real-time data critical for informed decision-making. This chapter explores the diverse applications of sensors within smart transportation, categorizing them into traffic, environmental, vehicle, and infrastructure sensors. It delves into how these sensors are utilized for traffic management and control, public transportation optimization, autonomous and connected vehicle functionality, environmental monitoring, and smart infrastructure management. The integration of Internet of Things (IoT) technology and sophisticated communication protocols such as 5G and Dedicated Short-Range Communications (DSRC) underpins the seamless operation of these systems. Despite the promising advancements, challenges such as sensor accuracy, data security, and privacy concerns persist, necessitating ongoing research and development. Future trends point towards further integration of artificial intelligence and machine learning to enhance predictive capabilities and decision-making processes. Through detailed case studies and real-world implementations, this chapter illustrates the practical benefits and lessons learned from deploying sensor technologies in various urban settings. By synthesizing current research and technological innovations, it provides a comprehensive overview of the critical role sensors play in realizing the vision of smart transportation. The chapter concludes with an outlook on future developments, emphasizing the continuous evolution and potential of sensor applications to revolutionize urban mobility.

Gobinath Arumugam, Rajeswari Packianathan, Anandan Malaiarasan, Suresh Kumar Natarajan
Analyzing Citizen Acceptance of AI-Driven Green Transportation: Mixed-Method Approach of Insights and Strategies for Enhancing Adoption

Artificial Intelligence (AI) and automation hold significant promise for revolutionizing green transportation systems, offering solutions that can enhance efficiency, reduce emissions, and promote sustainable urban mobility. However, the success of these technologies hinges on user acceptance and engagement. Understanding user behavior and acceptance of AI-driven green transportation solutions is crucial for the successful implementation and adoption of these technologies. Despite advancements in AI and automation, there is limited research on how users perceive and engage with these systems. This study addresses this gap by examining the factors influencing user acceptance and behavior towards AI-powered green transportation. Utilizing a mixed-methods approach, the research will collect data through surveys, focus groups, and real-world usage analytics to identify key determinants of user trust, satisfaction, and adoption. The study will explore perceived safety, convenience, environmental impact, and cost-efficiency variables. Additionally, it will investigate demographic differences in acceptance levels and the role of effective communication in fostering user confidence in AI technologies. By understanding these dynamics, the research seeks to develop strategies that enhance user engagement and facilitate the widespread adoption of green transportation systems. The findings will provide valuable insights for policymakers, developers, and stakeholders to design user-centric AI solutions that are not only technologically advanced but also socially accepted and embraced, driving the transition towards sustainable urban mobility.

Sowmya Gopisetti, Rashmitha Sai Chidirala, Pallavi Lanke, Madhu Babu Chunduri
Green Transportation and Moral Licensing: Navigating Ethical Challenges with Artificial Intelligence (AI) and Automation

The integration of artificial intelligence (AI) and automation into green transportation systems holds significant promise for advancing sustainable mobility solutions. However, these efforts are facing ethical challenges associated with moral licensing that can result in undoing the initiatives. This chapter relates travel behavior associated with green transportation initiatives to the psychological effect of moral licensing whereby people justify socially detrimental behaviors because of their engagement in sustainable practices. In that regard, we introduce moral licensing and its relation and effect on green transportation. Secondly, we will be discussing how AI and automation can be both painkillers and pain points for such ethical dilemmas. We borrow lessons from case studies and empirical research to capture how moral licensing affects policy implementation, consumer behavior, and technological adoption in relation to green transportation. Finally, ways of leveraging AI and automation in building robust and ethical frameworks to help make true sustainability a reality are presented. In this respect, this chapter aims to contribute to the process of evolving a more holistic and pragmatic mode of transportation ensuring that green transportation systems drive forward under the thrust of advanced technologies without the drag of moral licensing.

Vilis Pawar, Pravin Chavan, Abhijit Vhatkar, Alex Khang, Siddhi Gawankar
Transformative Impact of Generative Artificial Intelligence (Gen AI) on Smart Transportation System

Transportation, the backbone of our contemporary society, teeters on the edge of an era of digital transformation. Generative Artificial Intelligence (Gen AI), a robust division of artificial intelligence, is emerging as a pivotal catalyst for this revolution, offering the potential to redefine vehicle design, enhance traffic management, and cultivate a safer, more environmentally friendly transportation ecosystem. AI technology in transportation has the potential to transform traffic control by forecasting and alleviating immediate congestion. Picture AI-driven mechanisms that can adapt traffic signals on the fly, redirect vehicles, and anticipate accidents in advance, resulting in a more efficient and secure traffic flow for all. AI technology in the transportation sector enables manufacturers to provide unprecedented levels of personalized vehicle customization. By utilizing Gen AI algorithms that take into account individual preferences, driving behaviors, and lifestyle choices, unique design proposals can be created. This high degree of customization not only meets consumer preferences but also fosters a stronger bond between drivers and their vehicles.

Ipseeta Satpathy, Arpita Nayak, Alex Khang
Intelligent Electronic Ticketing Platform in Smart Transportation Ecosystem

The conductor gives the ticket in the traditional transportation system. The entire process is essentially paper-based and tickets are provided on printed papers. Both the amount of money received and the distance traveled by passengers are manually calculated. The cashless system, which makes use of QR Code, is widely used in several countries. In order to replace the manual fare collecting method and increase the efficiency of fare collection, the Transit Smart Card method, a new and innovative Automatic Fare collecting (AFC) System, is introduced in this work. The bus card or the QR reader can be used by passengers in place of a bus ticket. The QR scanner instructs the travelers on how to create an account, connect their band details to the app, and load funds into their wallet. The allocated and collected ticket fare is based on the user's selected destination. The passenger receives an SMS notification with a confirmation of the ticket payment. When a traveler arrives at their destination, the door will open if they scan their ticket to verify they have a ticket. Additionally, the printed materials will be reduced, and the loss of the card is also eliminated. It would ensure that tedious and financial problems like change are kept to a minimum. The current method of delivering Bus Tickets requires the passenger to wait for a long time before the stage closure and then queue to receive the pass. It also helps India become more digitalize.

Mohit Yadav, Khushwant Singh, Kavita Thukral, Shivani Kwatra, Dheerdhwaj Barak
Enhancing Smart Transportation System: Blockchain Based Integrating Cloud Database Management System

The World is jumping to the next phase of the Transportation System with rapid development of the Internet of Things (IoT) and Intelligent Transportation System. IoT is the most significant technological advancement allowing smart devices and vehicles to communicate with each other and capable of exchanging information with each other. The Scenario mentioned above, will generate so much data that the ability to handle it appropriately is the foundation of Smart Transportation System and to carry out this action we have two best technologies as of now which are Cloud Database Management System (CDBMS) and Blockchain Technology (BT). In this chapter we explore the Benefits, Challenges and Synergies of CDBMS and Blockchain in Smart Transportation System. The enormous amount of data generated by the Smart Transportation Network requires the technology that is Scalable, Flexible and Efficient which is achieved by CDBMS and Decentralized, Transparent and Secure which is fulfilled by BT. This is particularly true for the pivotal plots, which involve several parties and sensitive data, such as Financial Transactions, owner’s information and Vehicles Identification. User’s and Service Providers trust is increased because of the Immutable nature of BT, which guarantees that all transactions and data exchanges are Verifiable and Tamper-proof. Moreover, this chapter also projected over to New Developments and Future Enhancements such as using Edge Computing to reduce Latency and role of AI for the Optimized Transportation Networks. This seeks to give Researchers, Practitioners, and Policymakers involved in the creation and implementation of Smart Transportation System useful insights by giving a thorough overview of the current state of Affairs, Technological developments, and implementation techniques. In conclusion, we can justify that the combination of CDBMS and BT stick with notable promise for the enhancement of Reliability, Security and Efficiency of the Smart Transportation System. But in order to realize a genuine Integrated and Intelligent Transportation environment, these benefits can only be realized by tackling innate difficulties and encouraging collaboration among diverse stakeholders.

Pankaj Pali, Divya Pandey, Mahi Yadav
Cyber Security for Smart Transportation System

Modern transportation infrastructure is starting to include more and more intelligent transportation systems (ITSs), which provide towns and cities safe, effective transportation options. However, ITSs’ dependence on networked technology also raises the possibility of security flaws that hackers may take advantage of. This chapter examines the several kinds of cyber security assaults that may be directed at Information and Communication Technology Systems (ICTSs), including malware, phishing, remote access, denial of service (DoS), physical, insider threat, and social engineering attacks. It also covers the possible fallout from these attacks, which can include everything from bodily harm to fatalities to service interruptions. Intelligent transportation systems (ITS) are intricate, time-sensitive systems where the availability of cyber security directly affects both the effectiveness of transportation services and the physical safety of road users. While ITS standards are being developed, neither the formation of a security strategy nor the implementation of a complete standard have yet to occur. It is necessary to carefully analyze and evaluate the compatibility between the different ITS standards and the interactions with the outside world (Smart Cities, IoT). Intelligent Transportation Systems (ITS) have been created and implemented during the past few decades with the goal of increasing productivity, promoting sustainable transportation development, lowering environmental impact, and improving safety and mobility. ITS blends cutting-edge technology with the traditional realm of transportation infrastructure, using advancements in information systems, communication, sensors, controllers, and sophisticated mathematical techniques. For a rookie researcher, it might be challenging to get a comprehensive view of the entire system because this is an inter-disciplinary subject of study.

Roheen Qamar, Saima Siraj, Baqar Ali Zardari
Metadata
Title
Driving Green Transportation System Through Artificial Intelligence and Automation
Editor
Alex Khang
Copyright Year
2025
Electronic ISBN
978-3-031-72617-0
Print ISBN
978-3-031-72616-3
DOI
https://doi.org/10.1007/978-3-031-72617-0