Advancing urban planning and autonomous vehicles integration through scaled models

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.

The integration of autonomous vehicles (AVs) into urban transportation systems has significant potential to enhance traffic efficiency and reduce environmental impacts. This study evaluates the impact of different AV penetration scenarios (0%, 10%, 30%, 50%) on traffic performance and carbon emissions along Prince Mohammed bin Salman bin Abdulaziz Road in Riyadh, Saudi Arabia. Using microscopic simulation (SUMO) based on real-world datasets, we assess key performance indicators such as travel time, stop frequency, speed, and CO2 emissions. Results indicate notable improvements with increasing AV deployment, including up to 25.5% reduced travel time and 14.6% lower emissions at 50% AV penetration. Coordinated AV behavior was approximated using adjusted simulation parameters and Python-based APIs, effectively modeling vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-network (V2N) communications. These findings highlight the benefits of harmonized AV–human vehicle interactions, providing a scalable and data-driven framework applicable to smart urban mobility planning.

This article introduces a model designed to assess the lifecycle maintenance costs associated with asphalt pavement, taking into account various distribution patterns influenced by autonomous vehicles. The primary objective is to establish a robust framework for managing mixed traffic flows and to facilitate the adoption of technologies pertinent to autonomous driving. The model employs lifecycle maintenance costs as the key evaluative metric, incorporating considerations such as maintenance, fuel consumption, and environmental impact to analyze the effects of autonomous vehicles on pavement maintenance expenditures. The results indicate that the integration of autonomous vehicles can lead to a substantial decrease in the overall maintenance costs of asphalt pavement. This cost reduction is primarily attributed to the more consistent driving patterns exhibited by autonomous vehicles, which diminish the frequency of necessary repairs and maintenance. By estimating the lifecycle maintenance costs of asphalt pavement under various distribution scenarios resulting from autonomous vehicles, this research demonstrates that an optimal distribution of these vehicles can significantly postpone the formation of ruts, potentially reducing maintenance costs by as much as 69.5%. Conversely, the maintenance costs associated with asphalt pavement under a zero distribution scenario are found to increase by 13.7%.

This dissertation explores the intersection of Last-Mile Delivery, Autonomous Vehicles, and Multimodal Transport, collectively referred to as Autonomous Multimodal Last-Mile Delivery. The research applies Operational Research techniques to assess the potential benefits of autonomous vehicles, such as drones and self-driving robots, in overcoming the challenges faced in last-mile delivery. The main objective is to explore how service providers can leverage multimodal delivery systems, integrating trucks, drones, and self-driving robots, to enhance efficiency.<br/><br/>This dissertation follows two main directions. The first involves designing delivery systems that address urban last-mile logistics challenges. This includes optimizing interactions between different vehicles and modeling these systems using Mixed-Integer Linear Programs (MILPs). The second direction is developing and analyzing optimization techniques tailored to solve the autonomous multimodal last-mile delivery problems introduced earlier. <br/><br/>Through this research, we provide valuable insights into the integration of autonomous vehicles in multimodal delivery systems, offering potential solutions for improving efficiency and addressing the complexities of last-mile delivery in urban environments.

Concerns regarding cybersecurity, safety, privacy, and public acceptance are raised by the integration of autonomous vehicles (AVs) into sophisticated transportation networks. The purpose of this study is to investigate and evaluate the various perspectives that surround AVs. Evaluating public acceptability entails determining degrees of confidence and concerns about AV technology. The ability of AVs to handle challenging traffic situations while maintaining pedestrian and passenger safety is a safety concern. Concerns about privacy mostly concern how personal information is gathered, used, and safeguarded in AV systems. Implications for cybersecurity also highlight how susceptible antivirus software is to possible hackers or system breaches. This research contributes to a thorough knowledge of the potential and problems associated with the broad adoption of autonomous cars within futuristic transportation ecosystems by looking at these factors.

The integration of autonomous vehicles (AVs) onto public roads presents both technical and social challenges. Public understanding and acceptance of AVs requires engagement with people who live in, work at or visit cities where they are deployed on public road networks. We investigate the impact of one of the first placements of AV passenger transport on public roadways: the Sion "SmartShuttle". This late-breaking research presents preliminary results from interviews with local shopkeepers, residents, pedestrians and drivers to understand their attitudes and opinions of the shuttle. We also discuss video-based fieldwork that demonstrates how drivers negotiate next moves with one another through their windscreens using embodied signals such as gestures, lip-reading, and head nods to coordinate and manage a traffic situation. Finally, we consider the implications for how fully autonomous vehicles might be designed to take into account the subtle negotiations that road users engage in to coordinate with one another.

The introduction of autonomous vehicles (AVs) has the potential to drastically change society, planning, design, and development strategies. This study uses the PRISMA protocol to carry out a systematic literature review, focusing on the backcasting method as an analytic tool. By examining. 21 studies published between 2003 and 2024, this paper highlights the phases of backcasting: visioning, policy packaging, and appraisal, and identifies critical factors necessary for the successful integration of AVs. Visioning for future driverless cities includes high-quality urban areas, active mobility, and innovative developments. Policies and Packaging suggested a focus on restricting vehicular access, transit-oriented development, and encouraging public transportation. Appraisal reveals skepticism about the positive impacts of AVs, urging policies that limit access to urban areas and promote sustainable modes of transportation. The main contribution of this study lies in its comprehensive application of backcasting to AV implementation, offering a structured approach to envisioning future urban scenarios, formulating supportive policies, and evaluating their impact. This analysis provides a solid foundation for future research, urging us to explore the intersection between AVs, citizen participation, and environmental sustainability to achieve more efficient and sustainable cities.

Rapid advancements in vehicle automation technology have enabled autonomous vehicles (AVs) to move beyond simple, structured highway settings and operate increasingly within more complex urban environments. As AVs become more prevalent, they will inevitably share the road with diverse traffic participants, particularly pedestrians. The high uncertainty and dynamic nature inherent in human behavior significantly complicate the AV’s decision-making process. To ensure both safety and efficiency while exhibiting socially acceptable behavior, AVs must make real-time decisions and continuously adapt their strategies in response to surrounding pedestrian behaviors. This poses a major challenge to existing AV decision-making systems. Therefore, this study focuses on developing the decision-making strategies for AVs in urban pedestrian-involved environments, spanning scenarios from simple interactions with a single pedestrian to complex cases involving multiple pedestrians. The core objective is to uncover the underlying interaction dynamics and to improve the AV’s capability to make decisions that are safe, efficient and socially acceptable in dynamic, human-centric contexts. Initially, this study investigates the critical factors influencing the decision making processes of human drivers and pedestrians during vehicle–pedestrian interactions. A series of controlled experiments were conducted using a virtual reality platform designed to collect realistic behavioral data. The data analysis mainly focused on kinematic variables that are easily measurable in real-world deployments. AdaBoost classification and Partial Dependence Plots were employed to identify and visualize the most influential factors affecting pedestrian crossing intentions and driver approaching behaviors. The results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Based on these insights, a simplified mathematical model was developed to relate observable kinematic parameters to pedestrian crossing intentions, providing a practical tool for dynamically inferring crossing intentions during interactions. Additionally, the study explored driver yielding patterns under varying degrees of pedestrian intention clarity. These findings support the development of interpretable and implementable models for real-time decision-making in vehicle–pedestrian interaction scenarios. In addition, this study examines the decision-making strategies of the AVs when interacting with a single pedestrian in unsignalized intersections. A novel framework was proposed that integrates the Partially Observable Markov Decision Process with behavioral game theory to dynamically model interactive behaviors between the AVs and the pedestrian. Both agents were modeled as dynamic-belief-induced quantal cognitive hierarchy models, considering human reasoning limitations and bounded rationality in the decision-making process. Moreover, a dynamic belief updating mechanism allowed the AV to update its understanding of the opponent’s rationality degree in real-time based on observed behaviors and adapt its strategies accordingly. The analysis results indicate that proposed models effectively simulate vehicle-pedestrian interactions and the AV decision-making approach performs well in safety, efficiency, and smoothness. It captures key patterns of the driving behavior operated by real human drivers in virtual reality experiments and even achieves more comfortable navigation compared to our previous virtual reality experimental data. Finally, this study investigates the decision-making strategies of the AVs operating in pedestrian-rich shared spaces. A novel framework was proposed for modeling interactions between the AV and multiple pedestrians. In this framework, a cognitive process modeling approach inspired by the Free Energy Principle was integrated into both the AV and pedestrian models to simulate more realistic interaction dynamics. Specifically, the proposed pedestrian Cognitive-Risk Social Force Model adjusts goal-directed and repulsive forces using a fused measure of cognitive uncertainty and physical risk to produce human-like trajectories. Meanwhile, the AV leverages this fused risk to construct a dynamic, riskaware adjacency matrix for a Graph Convolutional Network within a Soft Actor-Critic architecture, allowing it to make more reasonable and informed decisions. The qualitative and quantitative results indicate that the proposed strategy effectively improves safety, efficiency, and smoothness of AV navigation compared to the state-of-the-art method. In conclusion, this study addresses key challenges in AV decision-making within human-centric urban environments, providing valuable insights as well as practical solutions to support safe, efficient, and socially aware autonomous navigation.

This chapter explores the transformative potential of Artificial Intelligence (AI) in revolutionizing urban mobility through the integration of multimodal transport systems. As cities face escalating challenges of congestion, inefficiency, and limited accessibility, AI-driven solutions, such as predictive analytics, demand-responsive transport (DRT), and intelligent traffic management, offer innovative pathways for enhancing the efficiency and sustainability of urban transportation networks. The chapter delves into the role of AI in optimizing route planning, improving traffic flow, and enabling seamless mobility across various transport modes. Furthermore, the integration of autonomous vehicles, human-driven cars, and alternative transport services is examined within the framework of AI-enhanced traffic signal control and real-time decision-making. Key topics such as AI-powered Mobility as a Service (MaaS) platforms, real-time data analysis, and the role of predictive models in demand forecasting are discussed in depth. By addressing challenges like data privacy, system interoperability, and equitable access, this chapter outlines a roadmap for creating smart, adaptive transportation systems that are efficient, inclusive, and resilient to future urban mobility demands.

Understanding foot traffic in real-time at a city scale is significant from different perspectives such as safety, redesigning public spaces, traffic management, hazard management, and integration of autonomous vehicles. Nevertheless, human factors are the most challenging to grasp when addressing urban dynamics, mobility, and transport problems. Moreover, accomplishing this requires extensive data on pedestrians' movement and walkable urban networks. This paper proposes an optimization-based cost-effective methodology for estimating hourly, city-wide pedestrian flow between districts, using limited pedestrian count data. Additionally, employing a network-based approach, the simulation of generated trips obtained from the flow estimation is highly accurate. We applied our method to the city of Tartu, Estonia. The results obtained in the laboratory and real field testing confirmed that our proposed model with a low error rate could be potentially applied in large urban areas with scarce data sources. Moreover, the system can benefit from real-time monitoring applications, as demonstrated in Tartu city's real-time modal split application <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> A Real-time system for daily modal split analysis in Tartu city: https://its.cs.ut.ee/modsplit.

The rapid advancement of large language models (LLMs) has sparked growing interest in their potential applications within urban planning. These models offer novel capabilities in natural language processing tasks, potentially providing advanced support across various aspects of urban planning. Despite the promises, little is known about how and to what extent LLMs have been applied in the planning literature. This study addresses this gap by conducting a comprehensive literature review using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. The review depicts five key domains where LLMs are being applied in urban planning: (a) planning and management; (b) public services and participation; (c) transportation and urban mobility; (d) environmental monitoring and sustainability; and (e) urban design and architecture. Additionally, the review identifies four essential characteristics that LLMs should possess to be effective in urban planning: (a) professionalism; (b) inclusivity; (c) trustworthiness; and (e) convenience. Considering the findings, a conceptual framework is developed that illustrates how these characteristics enhance the flexibility and effectiveness of LLMs in supporting urban planning tasks. This framework offers a theoretical foundation for urban planners to effectively integrate LLMs into practice and provides a roadmap for future research and technological innovations in the application of LLMs within urban environments.

The rapid urbanization of cities worldwide has propelled the concept of Smart City development to the forefront of urban planning discussions. This paper provides a comprehensive review of technological integration in urban planning within the context of Smart City development. The paper examines the evolution of Smart Cities, emphasizing the crucial role of technology in enhancing the quality of urban life. The review begins by outlining the key components of Smart Cities, such as smart infrastructure, data analytics, Internet of Things (IoT), and artificial intelligence. It explores how these components are integrated into urban planning processes to create more efficient, sustainable, and livable cities. The paper delves into case studies from various global cities, showcasing successful implementations of technology-driven solutions in areas like transportation, energy management, and public services. Furthermore, the review highlights the challenges associated with technological integration in Smart City development, including issues related to data privacy, security, and digital equity. It discusses the importance of establishing robust governance frameworks and public engagement strategies to address these challenges and ensure the responsible deployment of technology in urban environments. The role of collaboration between the public and private sectors is emphasized throughout the review, emphasizing the need for partnerships to leverage resources and expertise effectively. The paper also explores emerging technologies that are likely to shape the future of Smart Cities, such as 5G connectivity, blockchain, and edge computing. In conclusion, this paper underscores the transformative impact of technological integration on urban planning and Smart City development. By providing insights into successful implementations, challenges, and future trends, it contributes to the ongoing discourse on creating intelligent and sustainable urban environments for the benefit of present and future generations.

Rapid urbanization has intensified air pollution and climate-related risks, challenging sustainable city planning. Green infrastructure (GI) has emerged as a vital strategy for mitigating these environmental stressors. However, selecting optimal GI interventions requires the integration of multiple, often conflicting, criteria such as pollution reduction efficiency, cost-effectiveness, and resilience to climate variability. Conventional decision-making approaches in urban planning often struggle to handle the uncertainty and complexity inherent in multi-criteria environmental assessments. This limitation hampers the identification of effective GI solutions tailored to specific urban contexts, leading to suboptimal pollution and climate risk mitigation. This study proposes a Deep Neural Fuzzy Multi-Criteria Decision Support System (DNF-MCDSS) for prioritizing GI strategies in urban environments. The framework combines fuzzy logic with deep neural networks to model uncertainty and non-linear relationships among environmental, social, and economic criteria. Input data covering air quality indices, climatic variables, land use patterns, and socioeconomic factors are processed through the hybrid network to generate a ranked list of GI interventions. The model’s performance is evaluated using case studies from metropolitan regions, with validation against expert assessments and conventional multi-criteria decision methods. Experimental results demonstrate that the proposed DNF-MCDSS consistently identifies GI strategies that maximize pollution reduction while enhancing climate resilience. For example, green roofs, urban tree corridors, and constructed wetlands were prioritized in scenarios with high pollution loads and extreme heat events. Compared with traditional weighted-sum and AHP methods, the framework achieved a 15–20% improvement in alignment with expert recommendations, showing its ability to capture complex interdependencies and uncertainty in urban environmental planning.

This study employs the PTV VISSIM simulation software to investigate the impact of increasing traffic volumes on conventional vehicles and autonomous vehicles (AVs) with distinct behavioural traits: cautious, normal, and aggressive. The simulations cover a range of traffic volumes, from 100 to 600 vehicles, and measure the effects on travel time, emissions (CO, NOX, VOC), and fuel consumption. The results show that with increasing penetration rates of AVs, travel times generally decrease, with aggressive AVs achieving the shortest times, followed by normal, then cautious AVs. Emissions and fuel consumption also tend to decrease as the penetration rate of AVs increases. Notably, the results demonstrate that aggressive AVs excel in reducing travel time, while normal AVs consistently balance between efficiency and reduced emissions, and cautious AVs emphasize safety and lower emissions. Despite the differing behavioural traits, all AV types exhibit a marked improvement over conventional vehicles in terms of travel time, emissions, and fuel consumption. At every penetration rate, AVs lead to shorter travel times and lower emissions, with aggressive AVs being the most efficient, followed by normal and then cautious AVs. These findings emphasize the potential benefits of integrating autonomous vehicles into transportation networks. They suggest that optimizing AV behaviour, depending on the context and objectives, can lead to more efficient, environmentally friendly traffic systems. The study offers valuable insights for policymakers, urban planners, and researchers aiming to leverage the distinct strengths of each AV behaviour to create a more sustainable and efficient future for autonomous transportation.

Code of the Streets: Videogames and the City