Smart Cities

22 pages, 1360 KiB

Open AccessArticle

Comparison of Optimisation Techniques for the Electric Vehicle Scheduling Problem

by Jacques Wüst, Marthinus Johannes Booysen and James Bekker

Smart Cities 2025, 8(3), 85; https://doi.org/10.3390/smartcities8030085 - 21 May 2025

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The Electric Vehicle Scheduling Problem (E-VSP) addresses the challenge of efficiently assigning predetermined trips to an electric vehicle fleet while accounting for charging infrastructure and battery range constraints. Despite numerous optimisation approaches proposed in the literature, comparative analyses of these methods remain scarce, [...] Read more.

The Electric Vehicle Scheduling Problem (E-VSP) addresses the challenge of efficiently assigning predetermined trips to an electric vehicle fleet while accounting for charging infrastructure and battery range constraints. Despite numerous optimisation approaches proposed in the literature, comparative analyses of these methods remain scarce, with researchers typically focusing on developing novel algorithms rather than evaluating existing algorithms. Moreover, studies often employ convenient assumptions tailored to improve the performance of their optimisation technique. This study presents a comprehensive comparison of several optimisation techniques (mixed integer linear programming (MILP) using the branch-and-cut algorithm, metaheuristics, and heuristics) applied to the E-VSP under identical assumptions and constraints. The techniques are evaluated across multiple metrics, including solution quality, computational efficiency, and implementation complexity. Findings reveal that the branch-and-cut algorithm cannot solve instances with more than 10 trips in a reasonable time. Among metaheuristics, only genetic algorithms and simulated annealing demonstrate competitive performance, but both struggle with instances exceeding 100 trips. Our recently developed heuristic algorithm consistently found better solutions in significantly shorter computation times than the metaheuristics due to its ability to efficiently navigate the solution space while respecting the unique constraints of the E-VSP. Full article

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39 pages, 2336 KiB

Open AccessArticle

Transforming Energy Management with IoT: The Norwegian Smart Metering Experience

by Moutaz Haddara, Ingeborg Johnsen, Julie Løes and Karippur Nanda Kumar

Smart Cities 2025, 8(3), 84; https://doi.org/10.3390/smartcities8030084 - 21 May 2025

Viewed by 131

Abstract

The rapid adoption of smart technologies is increasingly evident in both personal and business contexts. The ‘post-pandemic’ economic recovery of 2022 and 2023 coincided with a global energy supply shortage driven by heightened energy demand and supply chain disruptions stemming from the ongoing [...] Read more.

The rapid adoption of smart technologies is increasingly evident in both personal and business contexts. The ‘post-pandemic’ economic recovery of 2022 and 2023 coincided with a global energy supply shortage driven by heightened energy demand and supply chain disruptions stemming from the ongoing Russian-Ukrainian conflict. The implementation of smart metering systems is a central component of European policies aimed at enhancing the competitiveness and environmental sustainability of energy markets. However, limited research exists on the acceptance of Smart Meter Technology (SMT) in general, specifically in Norway, as compared to other nations. SMT devices offer the potential for real-time energy consumption monitoring, enabling users to track and modify their usage patterns for optimized consumption. This study employs a mixed-methods research design to gather insights from both SMT consumers and vendors. Findings underscore the pivotal roles of familiarity, cost, social influence, and perceived usefulness in shaping consumer adoption of SMT. This article provides critical insights and implications for researchers, network operators, electricity companies, and government agencies. Full article

(This article belongs to the Special Issue Energy Strategies of Smart Cities)

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32 pages, 7433 KiB

Open AccessArticle

Evaluating the Quality of High-Frequency Pedestrian Commuting Streets: A Data-Driven Approach in Shenzhen

by Xin Guo, Yuqing Hu, Yixuan Zhang, Shengao Yi and Wei Tu

Smart Cities 2025, 8(3), 83; https://doi.org/10.3390/smartcities8030083 - 13 May 2025

Viewed by 472

Abstract

Streets, as critical public space nexuses, require synergistic quality–utilization alignment—where quality without use signifies institutional inefficiency, and use without quality denotes operational ineffectiveness. Focusing on high-frequency pedestrian commuting streets (HFPCSs) that not only crucially mediate metropolitan mobility patterns but also shape citizens’ daily [...] Read more.

Streets, as critical public space nexuses, require synergistic quality–utilization alignment—where quality without use signifies institutional inefficiency, and use without quality denotes operational ineffectiveness. Focusing on high-frequency pedestrian commuting streets (HFPCSs) that not only crucially mediate metropolitan mobility patterns but also shape citizens’ daily urban experiences and satisfaction, this study proposes a data-driven diagnostic framework for street quality–utilization assessment, integrating multi-source urban big data through a case study of Shenzhen. By integrating multi-source urban big data, we identify HFPCSs using LBS data and develop a multi-dimensional evaluation system that incorporates 1.07 million Points of Interest (POIs) for assessing convenience, utilizes DeepLabv3+ for the semantic segmentation of street view imagery to evaluate comfort, and leverages 15,374 km of road network data for accessibility analysis. The results expose dual mismatches: merely 2.15% of HFPCSs achieve balanced comfort–convenience–accessibility benchmarks, while over 70% of these are clustered in northern districts, exhibiting systematically inferior quality metrics across dimensions. Diagnostic analysis reveals specific planning and spatial configurations contributing to these disparities, informing targeted retrofitting strategies for priority street typologies. This approach establishes a replicable model for megacity street renewal, deploying supply–demand diagnostics to synchronize infrastructure upgrades with pedestrian flow realities. By bridging data insights with human-centric urban improvements, this framework demonstrates how smart city technologies can concretely address the quality–utilization paradox—advancing sustainable urbanism through evidence-based street transformations. Full article

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40 pages, 2483 KiB

Open AccessArticle

Improving Time Series Data Quality: Identifying Outliers and Handling Missing Values in a Multilocation Gas and Weather Dataset

by Ali Suliman AlSalehy and Mike Bailey

Smart Cities 2025, 8(3), 82; https://doi.org/10.3390/smartcities8030082 - 7 May 2025

Viewed by 385

Abstract

High-quality data are foundational to reliable environmental monitoring and urban planning in smart cities, yet challenges like missing values and outliers in air pollution and meteorological time series data are critical barriers. This study developed and validated a dual-phase framework to improve data [...] Read more.

High-quality data are foundational to reliable environmental monitoring and urban planning in smart cities, yet challenges like missing values and outliers in air pollution and meteorological time series data are critical barriers. This study developed and validated a dual-phase framework to improve data quality using a 60-month gas and weather dataset from Jubail Industrial City, Saudi Arabia, an industrial region. First, outliers were identified via statistical methods like Interquartile Range and Z-Score. Machine learning algorithms like Isolation Forest and Local Outlier Factor were also used, chosen for their robustness to non-normal data distributions, significantly improving subsequent imputation accuracy. Second, missing values in both single and sequential gaps were imputed using linear interpolation, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and Akima interpolation. Linear interpolation excelled for short gaps (R² up to 0.97), and PCHIP and Akima minimized errors in sequential gaps (R² up to 0.95, lowest MSE). By aligning methods with gap characteristics, the framework handles real-world data complexities, significantly improving time series consistency and reliability. This work demonstrates a significant improvement in data reliability, offering a replicable model for smart cities worldwide. Full article

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24 pages, 2595 KiB

Open AccessArticle

Synergizing Gas and Electric Systems Using Power-to-Hydrogen: Integrated Solutions for Clean and Sustainable Energy Networks

by Rawan Y. Abdallah, Mostafa F. Shaaban, Ahmed H. Osman, Abdelfatah Ali, Khaled Obaideen and Lutfi Albasha

Smart Cities 2025, 8(3), 81; https://doi.org/10.3390/smartcities8030081 - 6 May 2025

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Abstract

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, [...] Read more.

The rapid growth in natural gas consumption by gas-fired generators and the emergence of power-to-hydrogen (P2H) technology have increased the interdependency of natural gas and power systems, presenting new challenges to energy system operators due to the heterogeneous uncertainties associated with power loads, renewable energy sources (RESs), and gas loads. These uncertainties can easily spread from one infrastructure to another, increasing the risk of cascading outages. Given the erratic nature of RESs, P2H technology provides a valuable solution for large-scale energy storage systems, crucial for the transition to economic, clean, and secure energy systems. This paper proposes a new approach for the co-optimized operation of gas and electric power systems, aiming to reduce combined operating costs by 10–15% without jeopardizing gas and energy supplies to customers. A mixed integer non-linear programming (MINLP) model is developed for the optimal day-ahead operation of these integrated systems, with a case study involving the IEEE 24-bus power system and a 20-node natural gas system. Simulation results demonstrate the model’s effectiveness in minimizing total costs by up to 20% and significantly reducing renewable energy curtailment by over 50%. The proposed approach supports UN Sustainable Development Goals by ensuring sustainable energy (SDG 7), fostering innovation and resilient infrastructure (SDG 9), enhancing energy efficiency for resilient cities (SDG 11), promoting responsible consumption (SDG 12), contributing to climate action (SDG 13), and strengthening partnerships (SDG 17). It promotes clean energy, technological innovation, resilient infrastructure, efficient resource use, and climate action, supporting the transition to sustainable energy systems. Full article

(This article belongs to the Section Smart Grids)

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27 pages, 6086 KiB

Open AccessArticle

A Systematic Roadmap for Energy Transition: Bridging Governance and Community Engagement in Ecuador

by Gabriela Araujo-Vizuete and Andrés Robalino-López

Smart Cities 2025, 8(3), 80; https://doi.org/10.3390/smartcities8030080 - 6 May 2025

Viewed by 337

Abstract

This study develops a comprehensive roadmap for Ecuador’s energy transition using a hybrid governance model that balances top–down and bottom–up approaches. By integrating national directives with local participation, this framework aims to enhance energy consumption and drive sustainable transitions. This research employs a [...] Read more.

This study develops a comprehensive roadmap for Ecuador’s energy transition using a hybrid governance model that balances top–down and bottom–up approaches. By integrating national directives with local participation, this framework aims to enhance energy consumption and drive sustainable transitions. This research employs a mixed methodology, combining bibliometric analysis and governance structure assessment to evaluate Ecuador’s centralized energy system and its challenges. A three-phase strategy is proposed: Phase 1 introduces short-term interventions such as efficiency improvements and public awareness campaigns. Phase 2 focuses on decentralization, fostering local renewable energy production and community involvement. Phase 3 envisions a fully decentralized system where local entities operate autonomously within a supportive regulatory framework. The central research question is, how can a balanced governance framework foster sustainable ECB in Ecuador? By aligning national policies with local needs, this approach enhances policy adaptability, inclusivity, and long-term sustainability. Anticipated outcomes include improved energy efficiency, reduced reliance on fossil fuels, and increased community engagement in decision making. The findings contribute to global discussions on energy governance, demonstrating how hybrid models can facilitate sustainable energy transitions, particularly in developing countries with historically centralized systems. Full article

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44 pages, 1234 KiB

Open AccessSystematic Review

A Comprehensive Literature Review on Modular Approaches to Autonomous Driving: Deep Learning for Road and Racing Scenarios

by Kamal Hussain, Catarina Moreira, João Pereira, Sandra Jardim and Joaquim Jorge

Smart Cities 2025, 8(3), 79; https://doi.org/10.3390/smartcities8030079 - 6 May 2025

Viewed by 707

Abstract

Autonomous driving technology is advancing rapidly, driven by integrating advanced intelligent systems. Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review [...] Read more.

Autonomous driving technology is advancing rapidly, driven by integrating advanced intelligent systems. Autonomous vehicles typically follow a modular structure, organized into perception, planning, and control components. Unlike previous surveys, which often focus on specific modular system components or single driving environments, our review uniquely compares both settings, highlighting how deep learning and reinforcement learning methods address the challenges specific to each. We present an in-depth analysis of local and global planning methods, including the integration of benchmarks, simulations, and real-time platforms. Additionally, we compare various evaluation metrics and performance outcomes for current methodologies. Finally, we offer insights into emerging research directions based on the latest advancements, providing a roadmap for future innovation in autonomous driving. Full article

(This article belongs to the Section Smart Transportation)

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30 pages, 4151 KiB

Open AccessReview

A Systematic Literature Review on Flow Data-Based Techniques for Automated Leak Management in Water Distribution Systems

by Gopika Rajan and Songnian Li

Smart Cities 2025, 8(3), 78; https://doi.org/10.3390/smartcities8030078 - 29 Apr 2025

Viewed by 496

Abstract

Smart cities integrate advanced technologies, data-driven decision-making, and interconnected infrastructure to enhance urban living and resource efficiency. Among these, Smart Water Management (SWM) is crucial for optimizing water distribution and reducing Non-Revenue Water (NRW) losses, a persistent challenge for utilities worldwide. Water leaks [...] Read more.

Smart cities integrate advanced technologies, data-driven decision-making, and interconnected infrastructure to enhance urban living and resource efficiency. Among these, Smart Water Management (SWM) is crucial for optimizing water distribution and reducing Non-Revenue Water (NRW) losses, a persistent challenge for utilities worldwide. Water leaks contribute significantly to NRW, necessitating real-time leak detection and management systems to minimize detection time and human effort. Achieving this requires seamless integration of SWM technologies, including advanced metering infrastructure, the Internet of Things (IoT), and Artificial Intelligence (AI). While previous studies have explored various leak detection techniques, many lack a focused analysis of real-time data integration and automated alerts in SWM systems. This Systematic Literature Review (SLR) addresses this gap by examining advancements in automatic data collection, leak detection models, and real-time alert mechanisms. The findings highlight the growing potential of data-driven approaches to enhance leak detection accuracy and efficiency, particularly those leveraging flow and pressure data. Despite advancements, model accuracy, scalability, and real-world applicability remain. This review provides critical insights for future research, guiding the development of automated, AI-driven leak management systems to improve water distribution, minimize losses, and enhance sustainability in smart cities. Full article

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36 pages, 22746 KiB

Open AccessReview

The Road to Intelligent Cities

by João Carlos N. Bittencourt, Thiago C. Jesus, João Paulo Just Peixoto and Daniel G. Costa

Smart Cities 2025, 8(3), 77; https://doi.org/10.3390/smartcities8030077 - 29 Apr 2025

Viewed by 602

Abstract

The smart-city revolution has been promoted as the next step in urban development, leveraging technology to achieve enhanced development standards amid the increasingly complex challenges of urbanization. However, despite the implementation of more efficient urban services, issues regarding their tangible effects and impact [...] Read more.

The smart-city revolution has been promoted as the next step in urban development, leveraging technology to achieve enhanced development standards amid the increasingly complex challenges of urbanization. However, despite the implementation of more efficient urban services, issues regarding their tangible effects and impact on people’s lives remain unresolved. In this context, the concept of intelligent cities is seen as a necessary evolution of the smart-city paradigm, positioning human factors as the driving forces behind urban technological evolution. This integrative concept embodies advanced technology to enhance essential urban functions, with sustainability, equity, and resilience as macro-development goals. This study reviews the multifaceted dimensions of intelligent cities, from designing and deploying smart infrastructure to implementing citizen-centric decision-making processes. Additionally, it critically examines the digital divide and highlights the importance of equitable development policies as essential for enabling transformative urban change. By linking technological advancement to social issues, this article provides practical insights and case studies from the cities of Helsinki, Barcelona, and Buenos Aires, demonstrating that smart-city initiatives are still failing to bridge the equity service distribution gap. This comprehensive assessment approach ultimately serves as a reference for future evaluations of intelligent urban transformations. Full article

(This article belongs to the Section Applied Science and Humanities for Smart Cities)

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Graphical abstract

23 pages, 75202 KiB

Open AccessArticle

Enhancing Modern Distribution System Resilience: A Comprehensive Two-Stage Approach for Mitigating Climate Change Impact

by Kasra Mehrabanifar, Hossein Shayeghi, Abdollah Younesi and Pierluigi Siano

Smart Cities 2025, 8(3), 76; https://doi.org/10.3390/smartcities8030076 - 27 Apr 2025

Viewed by 295

Abstract

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires [...] Read more.

Climate change has emerged as a significant driver of the increasing frequency and severity of power outages. Rising global temperatures place additional stress on electrical grids that must meet substantial electricity demands, while extreme weather events such as hurricanes, floods, heatwaves, and wildfires frequently damage vulnerable electrical infrastructure. Ensuring the resilient operation of distribution systems under these conditions poses a major challenge. This paper presents a comprehensive two-stage techno-economic strategy to enhance the resilience of modern distribution systems. The approach optimizes the scheduling of distributed energy resources—including distributed generation (DG), wind turbines (WTs), battery energy storage systems (BESSs), and electric vehicle (EV) charging stations—along with the strategic placement of remotely controlled switches. Key objectives include preventing damage propagation through the isolation of affected areas, maintaining power supply via islanding, and implementing prioritized load shedding during emergencies. Since improving resilience incurs additional costs, it is essential to strike a balance between resilience and economic factors. The performance of our two-stage multi-objective mixed-integer linear programming approach, which accounts for uncertainties in vulnerability modeling based on thresholds for line damage, market prices, and renewable energy sources, was evaluated using the IEEE 33-bus test system. The results demonstrated the effectiveness of the proposed methodology, highlighting its ability to improve resilience by enhancing system robustness, enabling faster recovery, and optimizing operational costs in response to high-impact low-probability (HILP) natural events. Full article

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24 pages, 3578 KiB

Open AccessArticle

A Knowledge Graph-Enhanced Hidden Markov Model for Personalized Travel Routing: Integrating Spatial and Semantic Data in Urban Environments

by Zhixuan Zeng, Jianxin Qin and Tao Wu

Smart Cities 2025, 8(3), 75; https://doi.org/10.3390/smartcities8030075 - 24 Apr 2025

Viewed by 389

Abstract

Personalized urban services are becoming increasingly significant in smart city systems. This shift from intelligent transportation to smart cities broadens the scope of personalized services, encompassing not just travel but a wide range of urban activities and needs. This study proposes a knowledge [...] Read more.

Personalized urban services are becoming increasingly significant in smart city systems. This shift from intelligent transportation to smart cities broadens the scope of personalized services, encompassing not just travel but a wide range of urban activities and needs. This study proposes a knowledge graph-based Hidden Markov Model (KHMM) to improve personalized route recommendations by incorporating both spatial and semantic relationships between Points of Interest (POIs) in a unified decision-making framework. The KHMM expands the state space of the traditional Hidden Markov Model using a knowledge graph, enabling the integration of multi-dimensional POI information and higher-order relationships. This approach reflects the spatial complexity of urban environments while addressing user-specific preferences. The model’s empirical evaluation, focused on Changsha, China, examined how temporal variations in public attention to POIs influence route selection. The results show that incorporating dynamic temporal and spatial data significantly enhances the model’s adaptability to changing user behaviors, supporting real-time, personalized route recommendations. By bridging individual preferences and road network structures, this research provides key insights into the factors shaping travel behavior and contributes to the development of adaptive and responsive urban transportation systems. These findings highlight the potential of the KHMM to advance intelligent travel services, offering improved spatial accuracy and personalized route planning. Full article

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20 pages, 863 KiB

Open AccessPerspective

On Smart Cities and Triple-Helix Intermediaries: A Critical-Realist Perspective

by Dimos Chatzinikolaou

Smart Cities 2025, 8(3), 74; https://doi.org/10.3390/smartcities8030074 - 23 Apr 2025

Viewed by 390

Abstract

I conducted an integrative literature review by utilizing theoretical and methodological elements of critical realism (i.e., the distinction between ontology and epistemology) to evaluate the significance of triple-helix intermediaries. This review involved examining all published research on smart cities in “elite” ABS (Chartered [...] Read more.

I conducted an integrative literature review by utilizing theoretical and methodological elements of critical realism (i.e., the distinction between ontology and epistemology) to evaluate the significance of triple-helix intermediaries. This review involved examining all published research on smart cities in “elite” ABS (Chartered Association of Business Schools) journals (4, 4*). My findings indicate that the philosophical foundations of the examined literature are predominantly grounded on “positivism”, “postmodernism”, “interpretivism”, and “pragmatism”, without delving into the ontological reinforcement of capitalist institutions through innovation creation and diffusion—a central concern of critical realism. I argue that this oversight stems from the prevailing “paradigm” within these “elite” journals, which often excludes historical and critical perspectives. In response, I propose a reoriented intermediary, the Triple-Helix Business Clinic, grounded in critical-realist assumptions. This new theoretical framework can guide practical policy development aimed at reinforcing business innovation and driving broader socioeconomic progress. Full article

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29 pages, 6913 KiB

Open AccessArticle

Intersection Sight Distance in Mixed Automated and Conventional Vehicle Environments with Yield Control on Minor Roads

by Sean Sarran and Yasser Hassan

Smart Cities 2025, 8(3), 73; https://doi.org/10.3390/smartcities8030073 - 23 Apr 2025

Viewed by 214

Abstract

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five [...] Read more.

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article

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35 pages, 3860 KiB

Open AccessArticle

A Cross-Sectional Study on the Public Perception of Autonomous Demand-Responsive Transits (ADRTs) in Rural Towns: Insights from South-East Queensland

by Shenura Jayatilleke, Ashish Bhaskar and Jonathan M. Bunker

Smart Cities 2025, 8(3), 72; https://doi.org/10.3390/smartcities8030072 - 23 Apr 2025

Viewed by 440

Abstract

Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential [...] Read more.

Rural public transport networks face significant challenges, often characterised by suboptimal service quality. With advancements in technology, various applications have been explored to address these issues. Autonomous Demand-Responsive Transits (ADRTs) represent a promising solution that has been investigated over recent years. Their potential to enhance the overall quality of transport systems and promote sustainable transportation is well-recognised. In our research study, we evaluated the viability of ADRTs for rural networks. Our methodology focused on two primary areas: the suitability of ADRTs (considering vehicle type, service offerings, trip purposes, demographic groups, and land use) and the broader impacts of ADRTs (including passenger performance, social impacts, and environmental impacts). Perceptions of ADRT suitability peaked for university precincts and 24/7 operations. However, they were less favoured by mobility-disadvantaged groups (disabled, seniors, and school children). We also examined demographic heterogeneity and assessed the influence of demographic factors (age, gender, education, occupation, household income level, and disability status) on the implementation of ADRTs in rural settings. The findings delineate the varied perceptions across these socio-demographic strata, underscoring the necessity for demographic-specific trials. Consequently, we advocate for the implementation of ADRT services tailored to accommodate the diverse needs of these demographic cohorts. Full article

(This article belongs to the Special Issue Cost-Effective Transportation Planning for Smart Cities)

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Smart Cities, Volume 8, Issue 3 (June 2025) – 14 articles

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