Harmonized Autonomous–Human Vehicles via Simulation for Emissions Reduction in Riyadh City

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.

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.

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%.

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.

In the evolving landscape of urban planning and transportation, the integration of autonomous vehicles (AVs) into the urban environment presents a transformative opportunity. This paper explores the potential of scaled models in advancing urban planning and AV integration, highlighting the intricate interdependence of transportation systems, urban planning, and socio-economic factors. The emergence of AVs promises unparalleled efficiency, safety, and environmental sustainability in urban mobility. However, their successful integration necessitates meticulous planning and a comprehensive understanding of the urban landscape. Scaled models offer a dynamic platform for urban planners and policymakers to simulate, assess, and strategize the incorporation of AVs into cities, enabling the visualization of potential changes and the formulation of sustainable and equitable development strategies. Despite the promising prospects of scaled models, challenges such as scaling accuracy and the simplification of complex urban dynamics persist. Addressing these challenges is crucial for bridging the gap between model experiments and real-world urban complexities. By harnessing the power of scaled models, this paper aims to deepen our understanding of the interaction between AVs and urban environments and to strategize their integration, marking a significant step towards smarter, safer, and more sustainable cities.

Context: Autonomous Vehicles (AVs) will transform the way we live and work. Several benefits are envisaged, including: reduction in traffic deaths, drop in harmful emissions, improvement in fuel economy, reduction in travel time, and consumer savings. Indeed, the trend in the automobile industry is the development of AVs in preparation for the introduction and mass implementation of driverless vehicles. However, given the complexity and increasing connectivity of the AV, the challenges for effective and efficient development are immense. Many problems are related to misconceptions in the requirements engineering phase for AVs. Hence, a Requirements Engineering (RE) process is crucial in the development of AVs. Objective: The purpose of this work is to identify and analyse the current RE approaches used for AVs development. The analysis is based on answers related to the type of RE problems addressed by the study, the RE phases covered by the approach, requirements modelling styles used, the type of requirements described in the study, the specific AVs considered in the study, and the open problems reported. Method: We conducted a Systematic Literature Review (SLR) as the basis for our work. Results: Our SLR draws on 31 studies in which we identified the RE problems addressed by the studies and the phases of the RE processes considered. We also uncovered the languages and description styles used to describe the requirementes of the AVs. Special attention was paid to the non-functional requirements of interest. Different types of AVs were identified. Last but not least, several challenges were revealed. Conclusions: This paper reports the current state of the art of RE for AVs and identifies some open issues that deserve further investigation.

Traffic Modeling and Safety Analysis on Motorway Ramps

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.

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.

Exploring parking choices under the coexistence of autonomous and conventional vehicles

Transitioning to a driverless city: Evaluating a hybrid system for autonomous and non-autonomous vehicles

This study explores the impact of driving behaviors, specifically cautious and aggressive, on the performance of human-driven vehicles (HDVs) and autonomous vehicles (AVs) in traffic flow dynamics. It focuses on various metrics, including level of service (LOS), average speed, traffic volume, queue delays, carbon emissions, and fuel consumption, to assess their effects on overall performance. The findings reveal significant differences between cautious and aggressive AVs, particularly at varying market penetration rates (MPRs). Aggressive autonomous vehicles demonstrate greater traffic efficiency compared to their cautious counterparts. They achieve higher levels of service, improving from poor performance at low MPRs to significantly better performance at higher MPRs and in fully autonomous scenarios. In contrast, cautious AVs often experience poor service ratings at low MPRs, with an improvement in performance only at higher MPRs. Regarding environmental performance, aggressive AVs outperform cautious ones in terms of reduced emissions and fuel consumption. The emissions produced by aggressive AVs are significantly lower than those from cautious AVs, and they further decrease as the MPRs increases. Additionally, aggressive AVs show a considerable reduction in fuel usage compared to cautious AVs. While cautious AVs improve slightly at higher MPRs, they continue to generate higher emissions and consume more fuel than their aggressive counterparts. In conclusion, aggressive AVs offer better traffic efficiency and environmental performance than both cautious AVs. Their ability to improve road efficiency and reduce congestion positions them as a valuable asset for sustainable transportation. Strategically incorporating aggressive AVs into transportation systems could lead to significant advancements in traffic management and environmental sustainability.

Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. The calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.

Total shoulder arthroplasty (TSA) is a common treatment to decrease pain and improve shoulder function in patients with severe osteoarthritis (OA). In Canada, patients requiring this procedure often wait a year or more. Our objective was to determine patient preferences related to accessing TSA, specifically comparing out-of-pocket payments for treatment, travel time to hospital, the surgeon's level of experience and wait times. We administered a discrete choice experiment among patients with endstage shoulder OA currently waiting for TSA. Respondents were presented with 14 different choice sets, each with 3 options, and they were asked to choose their preferred scenario. A conditional logit regression model was used to estimate the relative preference and willingness to pay for each attribute. Sixty-two respondents completed the questionnaire. Three of the 4 attributes significantly influenced treatment preferences. Respondents had a strong preference for an experienced surgeon (mean 0.89 ± standard error [SE] 0.11), while reductions in travel time (-0.07 ± 0.04) or wait time (-0.04 ± 0.01) were of less importance. Respondents were found to be strongly averse (-1.44 ± 0.18) to surgical treatment by a less experienced surgeon and to paying out-of-pocket for their surgical treatment (-0.56 ± 0.05). Our results suggest that patients waiting for TSA to treat severe shoulder OA have minimal willingness to pay for a reduction in wait time or travel time for surgery, yet will pay higher amounts for treatment by an experienced surgeon.

How to improve the success rate of autonomous underwater vehicle (AUV) path planning and reduce travel time as much as possible is a very challenging and crucial problem in the practical applications of AUV in the complex ocean current environment. Traditional reinforcement learning algorithms lack exploration of the environment, and the strategies learned by the agent may not generalize well to other different environments. To address these challenges, we propose a novel AUV path planning algorithm named the Noisy Dueling Double Deep Q-Network (ND3QN) algorithm by modifying the reward function and introducing a noisy network, which generalizes the traditional D3QN algorithm. Compared with the classical algorithm [e.g., Rapidly-exploring Random Trees Star (RRT*), DQN, and D3QN], with simulation experiments conducted in realistic terrain and ocean currents, the proposed ND3QN algorithm demonstrates the outstanding characteristics of a higher success rate of AUV path planning, shorter travel time, and smoother paths.