Smart City Transportation Research Articles

Enabling Intelligent Transport in Smart Cities: A 5G Valley in Türkiye

Enabling Intelligent Transport in Smart Cities: A 5G Valley in Türkiye

Exploring the effect of government incentives on electric vehicle purchase intention in smart cities

The use of electric vehicles is gaining momentum throughout the world. Low-carbon mobility is a core area of focus for sustainable transportation in smart cities. Policy makers and governments in many countries are incentivizing the manufacture, sale, and purchase of low carbon-emitting vehicles such as electric vehicles. Not many studies have focused on the usage of electric vehicles in smart cities from the perspectives of customer behavior and government incentive. Therefore, the aim of this study is to understand how the moderator government incentives influence potential customers' intention to purchase electric vehicles for smart mobility in smart cities. With the help of the theory of planned behavior and various literature, a theoretical model has been developed which is later validated using CB-SEM technique considering responses from 415 potential customers of electric vehicles. This study has demonstrated that subjective norms and personal norms supported by users’ attitude and behavioral control to use EVs could impact on the purchase intention of electric vehicles with active support of the government incentives. This study has proposed a pragmatic framework helpful for the policymakers to motivate the potential purchasers of the smart cities to purchase EVs.

Development of an Intelligent Drone Management System for Integration into Smart City Transportation Networks

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations.

Directive transportation in smart cities with line connectivity at distinctive points using mode control algorithm

This article examines the operational functionality of intelligent transport systems to enhance smart cities by reducing traffic congestion. Given the increasing populations of smart cities, there is a growing demand for public transit systems to address the issue of traffic congestion. Therefore, the suggested system is developed using a few parametric design models, which combine point-to-point protocol and mode control optimization. The multi-objective parametric design for a smart transportation system is conducted using min–max functions to minimize the waiting time period for end users. Furthermore, customers are given the option to utilize a line following mechanism that offers suitable connectivity, along with independent identification and revitalize functions. The predicted model effectively eliminates the delay produced by transportation devices when positioning units are involved, ensuring that individual messages are delivered without any interruptions. In order to evaluate the results of the proposed system model, four different scenarios were examined. A comparison analysis revealed that the suggested method achieves a suitable directional flow for 96% of smart transport units. Additionally, it reduces delays and waiting periods by 2% and 6% respectively, while increasing energy consumption by 29%.

Supply chain and logistics in smart cities: A systematic literature review

Rapid urban expansion gives rise to smart cities which pose immense logistical and supply chain challenges. The COVID-19 pandemic transformed the holistic system identified by Zhao et al. in 2021. The system encompasses logistics and supply chain integral to the concept of smart cities, with a focus on sustainability. This transformation requires an in-depth study on challenges of a common framework of policies for smart cities in countries comprising the Organisation for Economic Cooperation and Development (OECD). The study employs an extensive literature analysis for the period 2020–2022. an approach which contextualizes the model. The model identifies the causes, impact, and spillovers of new trends in logistics and supply, including the sustainability of adopted technologies. The study includes the variables involved, and barriers to creating a shared model. The results reveal that the two elements affecting the supply chain and transport in smart cities are Industry 4.0 and 5.0 technologies supporting specific sectors. The resilience of small and medium-sized enterprises positively impacts the sustainability of large urban centres. The study presents both factors that help and hinder the adoption of environmental, social, and economic sustainability technologies.

Analysis of passenger transportation and the public transportation impact on the reduction in a smart city

The state of atmospheric pollution is determined by the growth of the population, the amount of transport and the generated volumes of emissions. The object is the process of analyzing passenger transportation in the city. The subject is passenger transport analysis methods. Purpose: analysis of passenger transportation and approaches to optimization of public transport based on the concept of a smart city. Tasks: analysis of passenger transportation, classification of existing conceptual approaches to optimization of public transport with low carbon emissions, systematization of existing methods, means and types of neural networks in smart cities, analysis of successful implementation projects. Methods of statistical analysis, linear and non-linear interpolation, logical generalization, comparison, grouping, analysis and synthesis. Results: the analysis of passenger transportation in the city revealed that statistical data sets indicate a decrease in the main indicators of passenger traffic and an increase in the volume of emissions of carbon-containing compounds. The classification of existing approaches to the optimization of public transport is carried out according to the priority of public transport, hybridization and electrification of vehicles and the implementation of IT monitoring. During the systematization of methods and means in smart cities, the following are highlighted: smart transport systems; electric vehicles; transport sharing networks; smart applications and information systems; innovative payment systems; unmanned vehicles; information boards and announcement systems; networks of bicycle paths and equipped sidewalks; environmental monitoring systems. Among neural networks, recurrent, convolutional, and deep neural networks have been proposed as those that contribute to route optimization and traffic prediction. Conclusions: the statistical analysis of passenger transportation established that reducing carbon dioxide emissions is an unresolved task for both public transport and the transportation system. It is proposed to include methods and means that optimize public transport, reducing the carbon footprint of the initiatives of implementing the concept of a smart city, which are successful all over the world. It is proposed to use recurrent, convolutional and deep neural networks to optimize passenger transportation in smart cities.

Edge computing and AI-driven intelligent traffic monitoring and optimization

Intelligent transportation system is a comprehensive system engineering, involving real-time data processing, security and privacy protection and other challenges. This paper discusses the key role of edge computing in intelligent transportation, especially its combination with SLAM technology. Edge computing enables faster data processing and response times by placing computing and data storage resources near the data source and end users, improving the efficiency and reliability of intelligent transportation systems. At the same time, edge computing can also enhance information security and privacy protection, and bring more possibilities for the future development of intelligent transportation systems. In the future, with the progress and development of technology, we can look forward to the deeper application of edge computing and SLAM technology in the field of intelligent transportation, bringing more innovation and development to smart city transportation.

Enhanced Ridesharing Using Directions API and Geolocation

Abstract: In the ever-evolving realm of urban transportation, the fusion of technological advancements and ubiquitous mobile connectivity has sparked a paradigm shift in how cities are navi- gated. This shift has led to innovative approaches in ridesharing and crowdsourcing, offering promising solutions to tackle traffic congestion, reduce emissions, and prioritize travel needs in urbanareas. Unlike traditional carpooling methods, where arrange-ments are made in advance, ridesharing in smart cities operates on real-time matching of drivers and riders, with priority given to factors such as destination and purpose.This study delves into the intricate dynamics of ridesharing and crowdsourcing for smart city transportation, investigating the complex interplay between technology and social factors.Additionally, it considers aspects of security, policy, and pricing strategies to ensure a seamlessand equitable experience for all stakeholders.Drawing insights from comprehensive case studies and practical implementations, this exploration highlights the advantages of ridesharing and crowdsourcing, including the reduction of carbon dioxide emis- sions and the prioritization of travel needs based on purpose. It offers guidance for stakeholders navigating the complexities and embracing the transformative potential of these innovative solutions in shaping the future of urban mobility.

Artificial intelligence of things for smart cities: advanced solutions for enhancing transportation safety

In the context of smart cities, ensuring road safety is crucial due to increasing urbanization and the interconnected nature of contemporary urban environments. Leveraging innovative technologies is essential to mitigate risks and create safer communities. Thus, there is a compelling imperative to develop advanced solutions to enhance road safety within smart city frameworks. In this article, we introduce a comprehensive vehicle safety framework tailored specifically for smart cities in the realm of Artificial Intelligence of Things (AIoT). This framework seamlessly integrates a variety of sensors, including eye blink, ultrasonic, and alcohol sensors, to bolster road safety. The utilization of eye blink sensor serves to promptly detect potential hazards, alerting drivers through audible cues and thereby enhancing safety on smart city roads. Moreover, ultrasonic sensors provide real time information about surrounding vehicle speeds, thereby facilitating smoother traffic flow. To address concerns related to alcohol consumption and its potential impact on road safety, our framework incorporates a specialized sensor that effectively monitors the driver’s alcohol levels. In instances of high alcohol content, the system utilizes GPS and GSM technology to automatically adjust the vehicle’s speed while simultaneously notifying pertinent authorities for prompt intervention. Additionally, our proposed system optimizes inter-vehicle communication in smart cities by leveraging Li-Fi technology, enabling faster and more efficient data transmission via visible light communication (VLC). The integration of Li-Fi enhances connectivity among connected vehicles, contributing to a more cohesive and intelligent urban transportation network. Through the structured integration of AIoT technologies, our framework lays a robust foundation for a safer, smarter, and more sustainable future in smart city transportation. It offers significant advancements in road safety and establishes the groundwork for further enhancement in intelligent urban transportation networks.

Interpretable Bike-Sharing Activity Prediction with a Temporal Fusion Transformer to Unveil Influential Factors: A Case Study in Hamburg, Germany

Bike-sharing systems (BSS) have emerged as an increasingly important form of transportation in smart cities, playing a pivotal role in the evolving landscape of urban mobility. As cities worldwide strive to promote sustainable and efficient transportation options, BSS offer a flexible, eco-friendly alternative that complements traditional public transport systems. These systems, however, are complex and influenced by a myriad of endogenous and exogenous factors. This complexity poses challenges in predicting BSS activity and optimizing its usage and effectiveness. This study delves into the dynamics of the BSS in Hamburg, Germany, focusing on system stability and activity prediction. We propose an interpretable attention-based Temporal Fusion Transformer (TFT) model and compare its performance with the state-of-the-art Long Short-Term Memory (LSTM) model. The proposed TFT model outperforms the LSTM model with a 36.8% improvement in RMSE and overcomes current black-box models via interpretability. Via detailed analysis, key factors influencing bike-sharing activity, especially in terms of temporal and spatial contexts, are identified, examined, and evaluated. Based on the results, we propose interventions and a deployed TFT model that can improve the effectiveness of BSS. This research contributes to the evolving field of sustainable urban mobility via data analysis for data-informed decision-making.

Sustainable logistics and passenger transport in smart cities

The rapid growth of urban populations, coupled with the imperatives of decarbonization and the relentless march of urbanization, has thrust modern cities into a crucible of multifaceted challenges. In response, the Smart City concept has emerged as a shared paradigm for addressing these urban complexities. This transformative approach touches upon various facets of urban life, encompassing areas such as the economy, education, and governance. Among these, logistics stands out as a pivotal component of the Smart City framework, necessitating innovative and sustainable solutions. This article delves into the intricate nexus between sustainable logistics systems and the evolution of the Smart City concept. Drawing from both qualitative and quantitative research methodologies, including multivariate analysis, the study synthesizes data from primary sources collected during a series of European projects conducted from 2020 to 2023, in addition to secondary data sources. A central inquiry revolves around the symbiotic relationship between e-commerce dynamics and the sustainability of smart city logistics solutions. The findings of this investigation illuminate a compelling correlation between the profitability of logistics enterprises and the key indicators of logistics development underpinning smart cities. By unveiling these interdependencies, this research contributes to our understanding of how sustainable logistics and passenger transport systems are pivotal to the ongoing development of smart cities, offering valuable insights for urban planners, policymakers, and industry stakeholders alike.

Efficient Usage of Energy Infrastructure in Smart City Using Machine Learning

The concept of smart cities revolves around utilizing modern technologies to manage and optimize city operations, including energy infrastructure. One of the biggest problems that smart cities have to deal with is ensuring the efficient usage of energy infrastructure to reduce energy consumption, cost, and environmental impact. Machine learning is a powerful tool that can be utilized to optimize energy usage in smart cities. This paper proposes a framework for efficient usage of energy machine learning for city infrastructure in smart cities. The proposed framework includes three main components: data collection, machine learning model development, and energy infrastructure optimization. The data collection component involves collecting energy consumption data from various sources, such as smart meters, sensors, and other IoT devices. The collected data is then pre-processed and cleaned to remove any inconsistencies or errors. The machine learning model development component involves developing machine learning models to predict energy consumption and optimize energy usage. The models can be developed using various techniques such as regression, classification, clustering, and deep learning. These models can predict energy consumption patterns based on historical data, weather conditions, time of day, and other factors. The energy infrastructure optimization component involves utilizing the machine learning models to optimize energy usage. The optimization process involves adjusting energy supply and demand to reduce energy consumption and cost. The optimization process can be automated, and SVM based machine learning models can continuously enhance their precision over time by studying the data. The proposed framework has several benefits, including reducing energy consumption, cost, and environmental impact. It can also improve the reliability and stability of energy infrastructure, reduce the risk of blackouts, and improve the overall quality of life in highly developed urban areas. Last but not least, the projected framework for efficient usage of energy machine learning for city infrastructure in smart cities is a promising solution to optimize energy usage and reduce energy consumption and cost. The framework can be implemented in various smart city applications, including buildings, transportation, and industrial processes.

UHD Database Focus on Smart Cities and Smart Transport

“Smart city” refers to a modern solution to organizing a city’s services, using cloud technologies to collect and evaluate large amounts of data, including data from camera systems. Smart city management covers several areas that can be implemented separately, but only their combination can realize the overall desired smart city function. One of the core areas of smart city automation is smart city transport. Transportation is a crucial system in any city, and this is why it needs to be monitored. The primary objective of this publication is to generate top-notch 4K UHD video sequences that are solely dedicated to showcasing smart cities and their transportation systems. The resulting comprehensive database will be made accessible to all professionals in the field, who can utilize it for extensive research purposes. Additionally, all the reference video sequences will be transcoded into various quality settings by altering critical parameters like the resolution, compression standard, and bit rate. The ultimate aim is to determine the best combination of video parameters and their respective settings based on the measured values. This in-depth evaluation will ensure that each video sequence is of the highest quality and provides an unparalleled experience for the service providers offering the service. The video sequences captured will be analyzed for quality assessments in smart cities or smart transport technologies. The database will also include objective and subjective ratings, along with information about the dynamics determined by spatial and temporal information. This will enable a comparison of the subjective evaluation of a selected sample of our respondents with the work of other researchers, who may evaluate it with a different sample of evaluators. The assumption of our future research is to predict the subjective quality based on the type of sequence determined by its dynamicity.

Enhancing urban economic efficiency through smart city development: A focus on sustainable transportation

An urban energy execution for smart transportation, evaluation, and impacting element model has been additionally built-in smart cities by the improved stochastic sustainable investigation technique (ISSIT), and board information from commonplace capitals has been considered for instance to do an exact examination. The urban energy execution appraisal and impacting variable model could diminish the issues brought about by two-organized execution, evaluation, and factor examination, measure the impacts of appraisal pointers and affecting elements of urban energy execution, and mirror the real execution of various urban communities in smart city transportation. Exact results show that urban energy execution of common capital in China has been expanding. The potential for energy-saving in Chinese common capital is also very high. When evaluating urban energy execution, it was critical to include the energy contribution as an appraisal pointer. Although there was a negative relationship between population thickness and urban energy execution, the temperature record, urbanization rate, and family vehicle possession were all extremely associated with urban energy execution. Chinese common capitals gradually lost their ability to execute urban energy from east to west. As a result of these findings, a few approach recommendations for improving urban energy execution are proposed for smart transportation.

A collaborative privacy-preserving approach for passenger demand forecasting of autonomous taxis empowered by federated learning in smart cities

The concept of Autonomous Taxis (ATs) has witnessed a remarkable surge in popularity in recent years, paving the way toward future smart cities. However, accurately forecasting passenger demand for ATs remains a significant challenge. Traditional approaches for passenger demand forecasting often rely on centralized data collection and analysis, which can raise privacy concerns and incur high communication costs. To address these challenges, We propose a collaborative model using Federated Learning (FL) for passenger demand forecasting in smart city transportation systems. Our proposed approach enables ATs in different regions of the smart city to collaboratively learn and improve their demand forecasting models through FL while preserving the privacy of passenger data. We use several backpropagation neural networks as local models for collaborating to train the global model without directly sharing their data. The local model shares only the model updates with a global model that aggregates them, which is then sent back to local models to improve them. Our collaborative approach reduces privacy concerns and communication costs by facilitating learning from each other’s data without direct data sharing. We evaluate our approach using a real-world dataset of over 4500 taxis in Bangkok, Thailand. By utilizing MATLAB2022b, the proposed approach is compared with popular baseline methods and existing research on taxi demand forecasting systems. Results demonstrate that our proposed approach outperforms in passenger demand forecasting, surpassing existing methods in terms of model accuracy, privacy preservation, and performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and R-squared (R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document}). Furthermore, our approach exhibits improved performance over time through the collaborative learning process as more data becomes available.

Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection

Investigating Digitally Inflected Intercity Cycle Commuting

ABSTRACT Multiple smart-city cycle technologies have been developed to support cycle commuting and create lower carbon cities. Such technologies hold significant potential to reshape how cycle commuting is experienced, understood, and governed. To investigate such phenomena, this article provides a rich account of digitally inflected inter-city cycle commuting. It shows how technologies including gamification elements script cycle commuting and consequently reshape cycling practices and infrastructures. As such digitally inflected cycle commuting is revealed as a complex multifaceted process of selfhood, reflexivity, embodiment, and social interaction which challenges imaginaries of mobile subjects underpinning standardized smart city transport policy prescriptions.

Design and Implementation of an Android-Based Intelligent Parking Reservation Management System

With urban traffic congestion and parking issues becoming increasingly severe, the emergence of smart parking systems offers a solution. This research designs and implements an Android-based intelligent parking reservation management system. It aims to provide convenient and efficient parking services by combining mobile technology with intelligent management. The system includes core functions such as user management, parking facility information retrieval, parking space reservation, and driving navigation, catering to diverse needs in urban parking scenarios. Through rigorous system testing, this solution demonstrates excellent performance in user experience, functionality, and efficiency. The Android-based parking reservation management system proposed in this study offers an innovative solution to address urban parking challenges. It paves the way for new possibilities in smart city transportation management.

Fundamental Elements of Drone Management Systems in Air Traffic Planning

Drones or Unmanned Aerial Systems (UAV – Unmanned Aerial Vehicles or UAS – UnmannedAerial Systems) are vehicles that can fly without the need for a pilot or passengers. Drones can be controlled remotely through radio waves or independently (with a previously determined route). The amount of documented accidents involving the hazardous use of drones has risensignificantly due to the increased usage of drones. To perform and increase the use of drones in air traffic management (ATM), especially in smart city planning, a variety of regulations andmanagement procedures will be implemented. This paper aims to propose management rules or regulations for drones in smart city transportation management and some approaches related to drone management and drone control. To present controlling approaches through the parameters in mathematical modelling for drones, we need a control rule, data gathering from the surroundings (usage of GIS), and a dynamic model of drones, and to present controlling and managing it with the help of a drone-following model based on a dynamic model of drones.

GSAA: A Novel Graph Spatiotemporal Attention Algorithm for Smart City Traffic Prediction

With the development of 5G and Internet of Things technologies, the application process of smart transportation in smart cities continues to advance. Sensors are a key source of information for smart transportation, and their data commonly includes complicated traffic scene information. Urban traffic scheduling and efficiency can be significantly increased by deploying data from smart sensors to forecast traffic flows. Despite the fact that some related works have focused on the prediction task of traffic flows, they have not completely mined the traffic spatiotemporal information present in smart sensor data. We offer a novel graph spatio-temporal attention algorithm (GSAA) for traffic prediction in this paper. To completely exploit the geographical and temporal correlations among complicated roadways for traffic forecast, the algorithm combines a spatiotemporal attention mechanism with a graph neural network.To take full advantage of how much effect various hyperparameters provide, deep reinforcement learning is used to obtain the optimal hyperparameters while the predictive model is trained. Experimental results on real-world public datasets show that the algorithm proposed in this paper achieves performance improvements of about 5.47% and 13.10% over the MAE (mean absolute error) than the best baseline strategies for short-term and long-term traffic forecasting, respectively.