Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

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.

ABSTRACTIn urban highway networks, some factors may influence the amount of average kilometric vehicle carbon emission (AKVC). Especially in highway networks of a large metropolitan area, the factors could be more diversified. In the present paper, the particular factors in the statistics of the districts in Seoul City that affect the AKVCs were investigated. Individual and combined factors were considered. The individual factors in the present study included the number of intersections, the number of intersections per kilometre, the road portion to the total area, the number of registered vehicles, the number of commuters, the population, the population density, the total land area and the road length. The combined factors considered included the number of intersections per kilometre over the number of registered vehicles, the number of intersections per kilometre over the road portion to the total area, and the road portion to the total area over the number of registered vehicles. Using the stepwise regression, it was demonstrated that the number of intersections per kilometre is the only significant factor affecting the AKVC in the road network of the district. All other factors are regarded as insignificant in affecting the AKVC. Additionally, correlation analysis confirms the result. From the study, as ‘the number of intersections per kilometre’ is the most influencing factor on reduction of vehicle carbon emissions in the road network, it may be implied that the number of intersections should be controlled or reduced in the road network of a city.

Many intelligent transportation systems (ITS) applications require accurate prediction of traffic parameters. Previous studies have shown that data driven machine learning methods like support vector regression (SVR) can effectively and accurately perform this task. However, these studies focus on highways, or a few road segments. We propose a robust and scalable method using n-SVR to tackle the problem of speed prediction of a large heterogenous road network. The traditional performance measures such as mean absolute percentage error (MAPE) and root mean square error (RMSE) provide little insight into spatial and temporal characteristics of prediction methods for a large network. This inadequacy can be a serious hurdle in effective implementation of prediction models for route guidance, congestion avoidance, dynamic traffic assignment and other ITS applications. We propose unsupervised learning techniques by employing k-means clustering, principal component analysis (PCA), and self organizing maps (SOM) to overcome this insufficiency. We establish the effectiveness of the developed methods by evaluation of spatial and temporal characteristics of prediction performance of the proposed variable window n-SVR method.

Smart city initiatives highlight the vital role of Intelligent Transportation Systems (ITS), which remain underexplored with limited AI-driven solutions integration in real-time urban traffic management across African cities. ITS is crucial to enhance urban mobility efficiency and sustainability to address growing mobility challenges in the era of swift African urbanization. This paper proposes an AI-driven predictive model for the Travel Time Index (TTI), a key metric quantifying urban traffic congestion and mobility performance. Using spatio-temporal analysis, neural networks, and advanced machine learning algorithms, the model processes real-time, multimodal traffic data, capturing congestion patterns, TTI fluctuations, and complex urban travel dynamics, focusing on Casablanca, Morocco, as a smart city case study. Five predictive modeling approaches were carefully selected and rigorously evaluated: Multivariate Linear Regression (MLR), Random Forest (RF), Gradient Boosting, Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Their performance was assessed using standard evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2). All models achieved high accuracy, with Random Forest ranking highest (MAE = 0.315, R2 = 0.985). Beyond prediction, the methodology incorporates feature importance analysis and hyperparameter tuning via GridSearchCV to improve operational performance and practical applicability across evolving traffic ecosystems. Hyperparameter optimization further enhanced Random Forest’s accuracy (MAE = 0.220, R2 = 0.988). The findings demonstrate improved travel time estimation and congestion management capabilities, offering a scalable, adaptable framework to guide data-driven mobility strategies in diverse urban settings and provide actionable insights for urban planners, policymakers, and mobility stakeholders.

SummaryShort‐term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio‐temporal‐based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high‐density road networks. Therefore, we propose a spatio‐temporal variable selection‐based support vector regression (VS‐SVR) model fed with the high‐dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two‐stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high‐dimensional spatio‐temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real‐world traffic volume collected from a sub‐area of Shanghai, China, demonstrate that the proposed spatio‐temporal VS‐SVR model outperforms the state‐of‐the‐art. Copyright © 2015 John Wiley & Sons, Ltd.

Genomic selection (GS) offers a promising opportunity for selecting more efficient animals to use consumed energy for maintenance and growth functions, impacting profitability and environmental sustainability. Here, we compared the prediction accuracy of multi-layer neural network (MLNN) and support vector regression (SVR) against single-trait (STGBLUP), multi-trait genomic best linear unbiased prediction (MTGBLUP), and Bayesian regression (BayesA, BayesB, BayesC, BRR, and BLasso) for feed efficiency (FE) traits. FE-related traits were measured in 1156 Nellore cattle from an experimental breeding program genotyped for ~ 300 K markers after quality control. Prediction accuracy (Acc) was evaluated using a forward validation splitting the dataset based on birth year, considering the phenotypes adjusted for the fixed effects and covariates as pseudo-phenotypes. The MLNN and SVR approaches were trained by randomly splitting the training population into fivefold to select the best hyperparameters. The results show that the machine learning methods (MLNN and SVR) and MTGBLUP outperformed STGBLUP and the Bayesian regression approaches, increasing the Acc by approximately 8.9%, 14.6%, and 13.7% using MLNN, SVR, and MTGBLUP, respectively. Acc for SVR and MTGBLUP were slightly different, ranging from 0.62 to 0.69 and 0.62 to 0.68, respectively, with empirically unbiased for both models (0.97 and 1.09). Our results indicated that SVR and MTGBLUBP approaches were more accurate in predicting FE-related traits than Bayesian regression and STGBLUP and seemed competitive for GS of complex phenotypes with various degrees of inheritance.

PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.

The growth of the Smart Cities and rising Big Data have brought about transformational changes in many industry fields, including insurance. Smart Cities use advanced technologies like the Internet of Things, Artificial Intelligence, and real-time data analytics to improve urban living by creating operational efficiencies, safety, and sustainability. The paper highlights the likely impact that Smart City initiatives would create for the insurance industry in Albania. It explores the means through which the incorporation of such smart infrastructures as Connected Cars or intelligent traffic systems, smart homes, or environmental monitoring affords risks more accurate assessments, individual insurance products, and a reduction in claim frequencies. The paper talks about how usage-based and dynamic pricing models are enabled through Big Data so that insurers may be capable of providing those plans, along with the challenges and opportunities arising from data protections, being legally compliant, and having customers that are willing to accept. Utilizing global case studies of Smart Cities, the article provides insights into how Albania could capitalize on these technologies to develop its insurance industry, providing better service delivery and risk management strategies.

With the rapid development of information technology and the advancement of smart city construction, urban management and services increasingly rely on networked intelligent systems. However, this highly interconnected network environment has also brought numerous network security challenges to smart cities. Smart cities involve a large number of Internet of Things (IoT) devices, big data platforms, cloud computing technologies, and smart infrastructure, making network security problems more complex and severe. This paper analyzes the composition and development trends of smart cities, explores the main security risks currently faced, and focuses on the security risks in key areas such as the Internet of Things, cloud computing, big data, and intelligent transportation systems. Furthermore, this paper proposes a multi-layered protection system and strategies for smart city network security, aiming to improve the network security assurance level of smart cities. The research seeks to provide practical protection measures to help smart cities cope with the growing security threats and ensure their sustainable development.

Chapter 25 - Advances in sustainable winter road maintenance and management for future smart cities

In order to solve the shortcoming of support vector regression algorithm, sports economic activities income forecasting based on genetic support vector regression algorithm is presented in this paper. As the kernel parameter, insensitive loss parameter and penalty parameter have a great influence on the forecasting performance of support vector regression algorithm, genetic algorithm is used to perform the parameters optimization of support vector regression algorithm simultaneously in this study. The experimental results show that the sports economic activities income forecasting results of genetic support vector regression model trained by the four-dimension training samples are more excellent than those of genetic support vector regression model trained by the three-dimension training samples, and the sports economic activities income forecasting ability of genetic support vector regression model trained by the four- dimension training samples is better than that of support vector regression model trained by the four-dimension training samples. Thus, genetic support vector regression algorithm has a good application prospect in sports economic activities income forecasting.

To improve the accuracy of short-term traffic state prediction, this paper proposes a short-term traffic state prediction method based on the fusion of Gravitational search algorithm (GSA) and Support Vector Regression (SVR) to predict the average speed and traffic on urban roads. The average speed is predicted. This method combines the working mechanism of SVR in prediction and uses the GSA algorithm to optimize the relevant parameters in the SVR kernel function to improve the accuracy of the SVR kernel function, thereby obtaining more accurate prediction accuracy. Taking 4 road sections in a road network in Xuanwu District of Nanjing City as an example, using the measured traffic flow and speed data of urban roads to test the performance of the GSA-SVR model. Using 12 historical average data from 17:00-17:55 are recorded as training sample to predict road traffic flow and speed data from 18:00-18:25, the analysis results show that from the overall prediction rate, the GSA-SVR algorithm reduces the prediction error to less than 40% of the SVM algorithm, which verifies the GSA-SVR mentioned in this article. The effectiveness and practicality of the algorithm can provide a reliable basis for decision-making for the good management of urban roads.

Abstract: As urban populations grow and cities face increasing challenges in traffic congestion, energy management, and environmental sustainability, Quantum Artificial Intelligence (Quantum AI) is emerging as a transformative technology to optimize urban systems. This book, "Quantum AI for Smart Cities: Optimizing Traffic and Urban Sustainability," explores how the convergence of quantum computing and AI-driven analytics can revolutionize traffic flow management, smart grids, and sustainable urban planning. It delves into quantum-enhanced predictive modeling, real-time optimization algorithms, and autonomous mobility solutions, enabling cities to reduce congestion, lower emissions, and improve resource allocation. The book also examines Quantum AI’s role in energy-efficient infrastructure, climate adaptation strategies, and smart governance models, demonstrating its potential to create data-driven, intelligent, and eco-friendly urban environments. Additionally, it addresses ethical, security, and regulatory considerations, providing insights into quantum cybersecurity, AI governance frameworks, and policy recommendations for responsible adoption. Designed for urban planners, AI researchers, policymakers, and technology leaders, this book serves as a comprehensive guide to leveraging Quantum AI for the next-generation evolution of smart, sustainable, and efficient cities. Keywords: Quantum AI, smart cities, traffic optimization, urban sustainability, quantum computing, artificial intelligence, machine learning, predictive analytics, smart mobility, autonomous vehicles, intelligent transportation, energy management, smart grids, renewable energy, climate adaptation, IoT, big data analytics, sustainable urban planning, ethical AI, AI governance, cybersecurity, quantum encryption, urban infrastructure, smart governance, environmental sustainability, digital transformation.

This article is single-source introduction to the emerging concept of smart cities. It can be used for familiarizing researchers with the vast scope of research possible in this application domain. The smart is primarily concept, and there is still not clear and consistent definition among practitioners and academia. As simplistic explanation, smart is place where traditional networks and services are made more flexible, efficient, and sustainable with the use of information, digital, and telecommunication technologies to improve the city's operations for the benefit of its inhabitants. Smart cities are greener, safer, faster, and friendlier. The different components of smart include smart infrastructure, smart transportation, smart energy, smart health care, and smart technology. These components are what make the cities smart and efficient. Information and communication technology (ICT) are enabling keys for transforming traditional cities into smart cities. Two closely related emerging technology frameworks, the Internet of Things (IoT) and big data (BD), make smart cities efficient and responsive. The technology has matured enough to allow smart cities to emerge. However, there is much needed in terms of physical infrastructure, smart city, the digital technologies translate into better public services for inhabitants and better use of resources while reducing environmental impacts. One of the formal definitions of the smart is the following: connecting the physical infrastructure, the information-technology infrastructure, the social infrastructure, and the business infrastructure to leverage the collective intelligence of the city [4]. Another formal and comprehensive definition is a smart sustainable is an innovative that uses information and communication technologies (ICTs) and other means to improve quality of life, efficiency of urban operations and services, and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social and environmental aspects [12]. A broad overview of various components needed in smart is depicted in Figure 1. Any combination of various smart components can make cities smart. A need not have all the components to be labeled as smart. The number of smart components depends on the cost and available technology.

Building data-driven intelligent transportation is a significant task for establishing data-centric smart cities, and exceptionally efficient and accurate traffic flow prediction (TFP) is a crucial technology in constructing intelligent transportation systems (ITSs). To address the computation and storage problems of processing traffic flow big data with the centralized model on a traditional mining platform, we propose a distributed long short-term memory weighted model combined with a time window and normal distribution based on a MapReduce parallel processing framework in this paper, named as WND-LSTM. More specifically, under the Hadoop distributed computing platform, a distributed modeling framework of forecasting traffic flow on MapReduce is developed to solve the existing issues of storage and calculation in handling large-scale traffic flow data with the stand-alone learning model. Moreover, a distributed WND-LSTM model is presented on the MapReduce-based distributed modeling framework to enhance the accuracy, efficiency, and scalability of short-term TFP. Finally, we forecast the traffic flow on the Sanlihe East Road of Beijing in China using the proposed WND-LSTM model with the real-world taxi trajectory big data. In particular, the extensively experimental results from a case study demonstrate that the MAPE value of WND-LSTM is 88.48%, 65.79%, 70.46%, 68.21%, 66.95%, 68.43%, and 70.41% lower than that of the autoregressive integrated moving average (ARIMA), logistical regression (LR), support vector regression (SVR), k-nearest neighbor (KNN), stacked autoencoders (SAEs), gated recurrent unit (GRU), and long short-term memory (LSTM), respectively, and achieves 71.25% accuracy improvement on average.