Autonomous-Vehicle Public Transportation System: Scheduling and Admission Control

Connected vehicle (CV) and autonomous vehicle (AV) technologies are rapidly advancing. Traffic control for future hurricane evacuation can be radically different compared to the current approaches in use. Extensive modeling research and limited field deployments have demonstrated the benefits of CV, AV technologies in achieving higher mobility, safety, and environmentally friendly solutions. One area that has not been explored in prior research is the benefits these technologies may offer to the emergency evacuation performance. Specifically, evacuation traffic control is one area where CV and AV technologies can have a transformative effect. At-grade intersections and grade-separated interchanges on evacuation routes are susceptible to congestion due to the increased demand competing for road usage. Traffic control methods that efficiently route traffic at intersections and interchanges also enhance the overall evacuation performance. This paper proposes an autonomous reservation-based intersection control (AReBIC) system for evacuation traffic control. The AReBIC system receives and processes reservation requests from approaching vehicles in real-time and routes them free of conflict. The results of a case study of Hampton Roads, Virginia, showed that the AReBIC system outperformed the existing traffic control practice in terms of mobility and safety. The average speeds increased by 29% and total delays reduced by 88% when intersection control was changed to the AReBIC system. The number of crossing conflicts decreased by 88% and lane change conflicts decreased by 47%.

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.

As autonomous vehicle (AV) technology progresses, a substantial amount of AV simulation work has studied potential future outcomes that could occur given different assumptions, scopes and technologies. As a revolutionary technology, AVs could have significant impacts on urban economic, environmental and social systems. In many cases, the research is directed towards decisionmakers: transportation authorities, policymakers and fleet operators. However, due to the complexity and uncertainty of AV adoption in interconnected urban systems such as transportation and land use, it is difficult for policymakers to understand the overlapping policy suggestions and results across the many simulation studies. In this paper, we summarise the suggested policies in AV simulation research from 170 studies published during 2016–2024 and provide context for the policies being suggested. Specifically, the key question we try to answer is: what are the recommended AV policies based on simulation studies? Many simulation results have shown increased vehicle miles/kilometers travelled (VMT/VKT) due to induced demand, empty vehicle repositioning, or cruising instead of parking. In response, we found common policy recommendations such as congestion pricing, subsidising car-sharing and ride-pooling, increasing parking costs, investing in public transportation and restricting AV demand as ways to address the increase in VMT/VKT. We also identified common policy bundles, or joint recommendations, such as congestion pricing with increased parking costs. Finally, we compare and contrast simulation policy suggestions with a few qualitative AV policy studies, pointing out key research gaps for the future and possible mixed-methods opportunities.

Nowadays, scientific developments in the vehicle and infrastructure automation sectors are developing steadily to benefit end users in ways like reducing traffic congestion, enhancing safety, facilitating stress-free travel, reducing fuel costs, and enabling smart parking. Autonomous vehicles sometimes known as self-driving cars, can entirely replace conventional automobiles to accomplish safe and reliable intelligent transportation systems (ITS). Autonomous vehicles have improved sensing capabilities and can drive on roads that would be difficult for humans. Software, technology, and people, together with their interactions, are the building blocks of Autonomous vehicles. Even though Autonomous vehicles have been studied extensively, many issues have yet to be fixed. One of the biggest obstacles to developing Autonomous vehicles is improving their ability to communicate with other vehicles and pedestrians. Second, it's unclear whether society will benefit or suffer from people's tendency to place an excessive amount of faith in Autonomous vehicles and assume that they will never malfunction. Capabilities analysis is essential to making sure autonomous vehicles are safe and widely accepted. The proposed review outlines the key benefits, drawbacks, and difficulties of autonomous vehicle technology and provides a detailed analysis of the different autonomous vehicle mechanisms. Further, the developed review intends to formulate future prospects by examining the advantages and disadvantages of autonomous driving technology to encourage the development of innovative autonomous vehicle technologies to prevent human error and increase road safety and security.

Preparing drivers for the future: Evaluating the effects of training on drivers’ performance in an autonomous vehicle landscape

Autonomous ground vehicles have evolved a lot in the past decade. Several prototypes have been presented and tested in different conditions. A major challenge for such vehicles is the fact that they depend on advanced sensing technologies to perceive the environment and make real-time decisions. The complexity of this task typically requires multiple sensing devices, which is both a technical challenge as well as a limiting factor for autonomous vehicles evolution to mass-production. In this paper we review the current main sensing technologies for autonomous ground vehicles, their suitability and maturity status, as well as the way they have been used in prototype cars. This analysis gives a perspective of their evolution so far, as well as an indication of possible future trends.

Human acceptance of autonomous vehicles: Research status and prospects

Demand-capacity coordination method in autonomous public transportation

Different perspectives discuss autonomous vehicle (AV) as predicted future transportation that includes design, engineering, economics, computer science, social, economic, legislation, and ethical aspects. On the other hand, AV technology offers new problems such as new engineering aspects, social and ethical problems, and presents in the form of an idealized decision-making problem. There are many benefits to AV vehicles, which are at the forefront of technology development. However, the level of cybersecurity provided by this technology is poorly understood. Cyber-attacks against AV can have severe implications for safety, privacy, manufacturers' reputation, and overall perception. In this research, we examine public opinion regarding the implications and challenges that might face AV technology in the United Arab Emirates (UAE). We claim that the successful adoption of AV in future transport systems will be achieved by reasonable risk management approaches that deal with different potential threats of cybersecurity. This survey produced usable responses from 204 participants. The significant result was that the respondents had not previously heard of vehicle-related technology; however, most had a positive first opinion on technology. Around 85% of participants felt that the expected benefits were likely to occur. They expressed a high level of concern regarding security and performance issues. Respondents stated that safety is the most crucial aspect of driverless vehicles. Mostly, it stated that personal communication devices should integrate with the AV's internet connection. The primary outcomes are that the population feels positive about AV and have optimistic expectations regarding its benefits. While maintaining some concerns, most of the people are willing to drive the AV.

How do socio-demographic and political factors affect public acceptance of autonomous passenger vehicle technology for large trucks?

Autonomous vehicles are the group of vehicles that do not or partially need a human driver to drive them. So, they are also known as driverless or robotic vehicles. Despite the name or group or size of vehicles, the purpose behind the technology is same. Besides the rapid development in technology of autonomous vehicles, the experiments have been started in early 1920 and due to lack of highly powerful computing devices, they were just controlled by radio wave technologies popularly known as remotes. Then, later on, the research and development took its speed in 1950 with the development of computers. But, nowadays with the development of highly powerful and cost-efficient computing hardware GPU the enhancement in technology becomes so rapid that it is updating day by day and becoming capable of concurring every aspect of human drivers’ skills. AI and Machine Learning algorithms are being developed to train the model for autonomous driving over huge amount of recorded driving data of human drivers. As the time goes on these models started mimicking drivers’ behavior and improves it day by day. So, as these Autonomous driving getting normalize and passing various parameters for driving the automobile industries saw a large potential for business and profit in autonomous vehicles leading the giant companies (Waymo Google, Tesla, Volvo, Renault, Uber, Toyota, Audi, Mercedes-Benz, Nissan, General Motors, Bosch and Continental's motors) started developing Level-3 Autonomous vehicles and released in 2020. By the rigorous development and solving challenges, autonomous vehicles are becoming our irreplaceable need for future.KeywordsAutonomous vehiclesArtificial intelligenceMachine learning

Problem definition: The ongoing large-scale deployment of autonomous vehicle (AV) technology has the potential to fundamentally change the transportation landscape. Due to the high cost of AV hardware, the most likely path to widespread AV use is via platforms that can sustain high utilization, such as ride-hailing and delivery services. Our work studies the incentives of stakeholders in such deployments, possible misalignments, and contracting solutions to overcome them. Methodology/results: We consider four potential operational models to commercialize AVs, which we model as a supply chain game between a platform, an AV supplier, and human individual contractors (ICs), including (1) an open platform in which the AV supplier and ICs bring their own vehicles to the system, (2) an AV-supplier operated platform without ICs, (3) a platform that sources AVs through leasing contracts, and (4) an integrated supply chain. We find that except for the AV-only platform, all deployment models are subject to a risk of AV underutilization due to the need to maintain the ICs’ utilization sufficiently high to ensure ICs remain engaged. As AV underutilization propagates in a nonintegrated supply chain, an open platform can become arbitrarily worse than supply chain integration, and careful usage commitments are needed to overcome the efficiency loss. Managerial implications: This paper identifies a new kind of supply chain misalignment that is likely to emerge as AVs become deployed technology, which both platform operators and AV suppliers should be mindful of. Moreover, we demonstrate the value of usage commitment in the deployment of AVs through an open platform, which makes it more efficient than AV-only/AV leasing platforms. History: This paper was selected as part of the 1RR initiative between the M&SOM Journal and the MSOM Society. This particular paper was part of the 2024 MSOM Service Operations SIG Conference. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2024.1175 .

Autonomous vehicle (AV) technologies are expected to play a crucial role in reducing traffic crashes occurred by human factors; however, foreseeable coexistence of AVs with human-driven vehicles (HDVs) for an extended transition period raises safety concerns. Understanding factors influencing AV-involved crashes is crucial, especially as human drivers may struggle to comprehend the behavior of AVs during interactions. This study addresses this gap by employing a random parameter probit model with heterogeneity in means and variances. Dataset comprises AV crash records obtained from the California Department of Motor Vehicles from 2018 to the first quarter of 2024. Crashes on roadway segments and intersection are modeled separately. Modeling results reveal that factors such as poor lighting conditions, braking maneuver of AVs, proceeding straight movement of HDVs, involvement of bikes/scooters, residential land-use significantly contribute to AV-involved crash occurrence on segments and at intersections. On segments, first quarter of the year, the retail/entertainment land use, sideswipe collision, dangerous maneuver of HDVs and proceeding straight moment of AVs affect the likelihood of AV-involved crashes. Meanwhile, at intersection, rear-end collision, raining/snowing, unusual road conditions, four-leg intersection, lack of pedestrian island/intersection control significantly increases the probability of AV-involved crashes while angle collision and large skew angle decreases it. The findings highlight the need for more targeted goals to improve AV’s safety, such as enhancing AV sensor perception capabilities, incorporating scenario-based tests by categorization of crash location, and developing mass education initiatives to facilitate the broader acceptance and understanding of AV technologies.

Autonomous vehicles (AVs) are receiving attention in many countries, including Thailand. However, implementing an intelligent transport system has many challenges, such as safety and reliability and the lack of policy supporting such technology use, leading to hazards for passengers and pedestrians. Hence, factors affecting the adoption of autonomous vehicles require better understanding. This research proposes and employs an extended Technology Acceptance Model (TAM) by integrating ethical standards, legal concerns, and trust to predict the intended use of autonomous vehicles by Thai citizens. A total of 318 questionnaires were collected from online panel respondents. Research hypotheses were tested using a structural equation modelling approach. The study results suggest that ethical standards have a significant positive effect on the intention to use the technology. Meanwhile, the intention was negatively affected by perceived usefulness, perceived ease of use and legal concerns. On the other hand, the results indicate that perceived ease of use directly affected trust, leading to AV adoption. However, other factors influenced trust insignificantly. This study demonstrates the vital role of trust in AV adoption. The study also suggests ideas for further study and discusses the implications for the government and autonomous vehicle companies. The article aims to forecast a success factor that the Thai government should use to consider the policy for autonomous vehicle adoption in Thailand. This paper relies on the technology acceptance model to assess and forecast autonomous vehicle adoption. The theoretical model also includes ethical issues, legal concerns and trust in technology. The model was analysed using the structure equation modelling technique to confirm the factor affecting Thailand’s successful autonomous vehicle adoption. This research confirmed that ethical standards, legal concerns, and trust in technology are the factors significantly affecting the intention to use an autonomous vehicle in Thailand. On the other hand, the perceived ease of use significantly affects the trust in autonomous vehicle technology. This research found that such social factors as ethical standards, legal concerns, and trust in technology affect technology adoption significantly, especially technology related to AI operation. Therefore, the technology acceptance model could be modified to confirm technology adoption in terms of social factors. The government could use the research results to develop a public policy for the regulation and standard supporting autonomous vehicle adoption in Thailand.

The rapid growth in Autonomous Vehicle (AV) technology endeavors increased attention towards road safety in recent days. Particularly, a higher number of road accidents occurs when the AV tries to overtake or change the lane. To cut down the number of accidents and improve traffic reliability, the AV should be capable of making intelligent decisions and communicating those to other AVs. Therefore, in this paper, a Cooperative Collision avoidance scheme for AVs at Overtaking and Lane Changing maneuver (CCAV-OLC) is proposed. The Inverse Reinforcement Learning (IRL) in the CCAV-OLC scheme, processes on the given number of expert demonstrations for automatically acquiring the reward function, and thereby imitating actual human driving strategy and decisions. However, the adaptability of IRL to a high-dimensional AV environment restricts the performance of the CCAV-OLC scheme. To overcome this, the IRL in CCAV-OLC leverages the Gaussian Process (GP) regression model (IRL-GP), which enables data-efficient Bayesian prediction even when the number of demonstrations is very low. After taking intelligent decisions in overtaking and lane changing maneuvers, the AVs cooperatively communicate and exchange the decisions with each other by 6th Generation Vehicle-to-Everything (6G-V2X) communications, which further improves the accuracy and lessens the time taken for making optimal decisions. The experimental results show that the AVs clone the expert's optimal driving strategy and avoid the collisions to a greater extent.