Optimal scheduling of connected and autonomous vehicles at a reservation-based intersection.

Reservation-based intersection control has been evaluated with better performance over traditional signal controls in terms of intersection safety, efficiency, and emission. Controlling connected and autonomous vehicles (CAVs) at a reservation-based intersection in terms of improving intersection efficiency is performed via two factors: trajectory (speed profile) and arrival time of CAVs at the intersection. In an early stage of the reservation-based intersection control, an intersection controller at the intersection may fail to find a feasible solution for both the trajectory and arrival time for a CAV at a certain planning horizon. Leveraging a deeper understanding of the control problem, reservation-based intersection control methods are able to optimize both trajectory and arrival time simultaneously while overcoming the infeasible condition. Furthermore, in order to achieve real-time control at the reservation-based intersection, a scheduling problem of CAV crossing the intersection has been widely modeled to optimize the intersection efficiency. Efficient solution algorithms have been proposed to overcome the curse of dimensionality. However, a control methodology consisting of trajectory planning and arrival time scheduling that can overcome the infeasible condition has not been explicitly explained and defined. Furthermore, an optimal control framework for joint control of the trajectory planning and arrival time scheduling in terms of global intersection efficiency has not been theoretically established and numerically validated; and mechanisms of how to reduce the time complexity meanwhile solving the scheduling problem to an optimal solution are not fully understood and rigorously defined. In this dissertation, a control method that eliminates the infeasible problem at any planning horizon is first explicitly explained and defined based on a time-speed-independent trajectory planning and scheduling model. Secondly, this dissertation theoretically defines the optimal control framework via analyzing various control methods in terms of intersection capacity, throughput and delay. Furthermore, this dissertation theoretically analyzes the mechanism of the scheduling problem and designs an exact algorithm to further reduce the time complexity. Through theoretical analyses of the properties of the scheduling problem, the reasons that the time complexity can be reduced are fundamentally explained. The results first validate that the defined control framework can adapt to extremely high traffic demand scenarios with feasible solutions at any planning horizon for all CAVs. Under extensive sensitivity analyses, the theoretical definition of the optimal control framework is validated in terms of maximizing the intersection efficiency. Moreover, numerical examples validate that a proposed scheduling algorithm finds an optimal solution with lower computation time and time complexity

A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy

Modeling and optimizing network infrastructure for autonomous vehicles

Autonomous vehicle (AV) technology has matured sufficiently to be in testing on public roads. However, traffic models of AVs are still in development. Most previous work has studied AV technologies in micro-simulation. The purpose of this dissertation is to model and optimize AV technologies for large city networks to predict how AVs might affect city traffic patterns and travel behaviors. To accomplish these goals, we construct a dynamic network loading model for AVs, consisting of link and node models of AV technologies, which is used to calculate time-dependent travel times in dynamic traffic assignment. We then study several applications of the dynamic network loading to predict how AVs might affect travel demand and traffic congestion. AVs admit reduced perception-reaction times through technologies such as (cooperative) adaptive cruise control, which can reduce following headways and increase capacity. Previous work has studied these in micro-simulation, but we construct a mesoscopic simulation model for analyses on large networks. To study scenarios with both autonomous and conventional vehicles, we modify the kinematic wave theory to include multiple classes of flow. The flow-density relationship also changes in space and time with the class proportions. We present multiclass cell transmission model and prove that it is a Godunov approximation to the multiclass kinematic wave theory. We also develop a car-following model to predict the fundamental diagram at arbitrary proportions of AVs. Complete market penetration scenarios admit dynamic lane reversal -- changing lane direction at high frequencies to more optimally allocate road capacity. We develop a kinematic wave theory in which the number of lanes changes in space and time, and approximately solve it with a cell transmission model. We study two methods of determining lane direction. First, we present a mixed integer linear program for system optimal dynamic traffic assignment. Since this program is computationally difficult to solve, we also study dynamic lane reversal on a single link with deterministic and stochastic demands. The resulting policy is shown to significantly reduce travel times on a city network. AVs also admit reservation-based intersection control, which can make greater use of intersection capacity than traffic signals. AVs communicate with the intersection manager to reserve space-time paths through the intersection. We create a mesoscopic node model by starting with the conflict point variant of reservations and aggregating conflict points into capacity-constrained conflict regions. This model yields an integer program that can be adapted to arbitrary objective functions. To motivate optimization, we present several examples on theoretical and realistic networks demonstrating that naïve reservation policies can perform worse than traffic signals. These occur due to asymmetric intersections affecting optimal capacity allocation and/or user equilibrium route choice behavior. To improve reservations, we adapt the decentralized backpressure wireless packet routing and P0 traffic signal policies for reservations. Results show significant reductions in travel times on a city network. Having developed link and node models, we explore how AVs might affect travel demand and congestion. First, we study how capacity increases and reservations might affect freeway, arterial, and city networks. Capacity increases consistently reduced congestion on all networks, but reservations were not always beneficial. Then, we use dynamic traffic assignment within a four-step planning model, adding the mode choice of empty repositioning trips to avoid parking costs. Results show that allowing empty repositioning to encourage adoption of AVs could reduce congestion. Also, once all vehicles are AVs, congestion will still be significantly reduced. Finally, we present a framework to use the dynamic network loading model to study shared AVs. Results show that shared AVs could reduce congestion if used in certain ways, such as with dynamic ride-sharing. However, shared AVs also cause significant congestion. To summarize, this dissertation presents a complete mesoscopic simulation model of AVs that could be used for a variety of studies of AVs by planners and practitioners. This mesoscopic model includes new node and link technologies that significantly improve travel times over existing infrastructure. In addition, we motivate and present more optimal policies for these AV technologies. Finally, we study several travel behavior scenarios to provide insights about how AV technologies might affect future traffic congestion. The models in this dissertation will provide a basis for future network analyses of AV technologies.Civil, Architectural, and Environmental Engineerin

Learn to Bet: Using Reinforcement Learning to Improve Vehicle Bids in Auction-Based Smart Intersections

With the advent of IoT, cities will soon be populated by autonomous vehicles and managed by intelligent systems capable of actively interacting with city infrastructures and vehicles. In this work, we propose a model based on reinforcement learning that teaches to autonomous connected vehicles how to save resources while navigating in such an environment. In particular, we focus on budget savings in the context of auction-based intersection management systems. We trained several models with Deep Q-learning by varying traffic conditions to find the most performance-effective variant in terms of the trade-off between saved currency and trip times. Afterward, we compared the performance of our model with previously proposed and random strategies, even under adverse traffic conditions. Our model appears to be robust and manages to save a considerable amount of currency without significantly increasing the waiting time in traffic. For example, the learner bidder saves at least 20% of its budget with heavy traffic conditions and up to 74% in lighter traffic with respect to a standard bidder, and around three times the saving of a random bidder. The results and discussion suggest practical adoption of the proposal in a foreseen future real-life scenario

Scalable FastMDP for Pre-departure Airspace Reservation and Strategic De-conflict

Pre-departure flight plan scheduling for Urban Air Mobility (UAM) and cargo delivery drones will require on-demand scheduling of large numbers of aircraft. We examine the scalability of an algorithm known as FastMDP which was shown to perform well in deconflicting many dozens of aircraft in a dense airspace environment with terrain. We show that the algorithm can adapted to perform first-come-first-served pre-departure flight plan scheduling where conflict free flight plans are generated on demand. We demonstrate a parallelized implementation of the algorithm on a Graphics Processor Unit (GPU) which we term FastMDP-GPU and show the level of performance and scaling that can be achieved. Our results show that on commodity GPU hardware we can perform flight plan scheduling against 2000-3000 known flight plans and with server-class hardware the performance can be higher. We believe the results show promise for implementing a large scale UAM scheduler capable of performing on-demand flight scheduling that would be suitable for both a centralized or distributed flight planning syste

Sustainability effects of next-generation intersection control for autonomous vehicles

Transportation sustainability is adversely affected by recurring traffic congestions, especially at urban intersections. Frequent vehicle deceleration and acceleration caused by stop-and-go behaviours at intersections due to congestion adversely impacts energy consumption and ambient air quality. Availability of the maturing vehicle technologies such as autonomous vehicles and Vehicle-To-Vehicle (V2V) / Vehicle-To-Infrastructure (V2I) communications provides technical feasibility to develop solutions that can reduce vehicle stops at intersections, hence enhance the sustainability of intersections. This paper presents a next-generation intersection control system for autonomous vehicles, which is named ACUTA. ACUTA employs an enhanced reservation-based control algorithm that controls autonomous vehicles’ passing sequence at an intersection. Particularly, the intersection is divided into n-by-n tiles. An intersection controller reserves certain time-space for each vehicle, and assures no conflict exists between reservations. The algorithm was modelled in microscopic traffic simulation platform VISSIM. ACUTA algorithm modelling as well as enhancement strategies to minimize vehicle intersection stops and eventually emission and energy consumption were discussed in the paper. Sustainability benefits offered by this next-generation intersection were evaluated and compared with traditional intersection control strategies. The evaluation reveals that multi-tile ACUTA reduces carbon monoxide (CO) and Particulate Matter (PM) 2.5 emissions by about 5% under low to moderate volume conditions and by about 3% under high volume condition. Meanwhile, energy consumption is reduced by about 4% under low to moderate volume conditions and by about 12% under high volume condition. Compared with four-way stop control, single-tile ACUTA reduces CO and PM 2.5 emissions as well as energy consumption by about 15% under any prevailing volume conditions. These findings validated the sustainability benefits of employing next-generation vehicle technologies in intersection traffic control. In addition, extending the ACUTA to corridor level was explored in the paper

How can we create the university of the future?

Higher education is facing change across the world, but nowhere more so than in the UK with the impact of changing funding structures, a changing student body and diminishing resources. It is not just the Browne Review though that heralds these changes, UK HE has already been subject to new circumstances over the past ten or more years but it has not always responded as a sector with agility and speed. What is clear, though, now is that we need to radically rethink our current models and consider what the University of the future should look like. This paper will outline the key note lecture I presented which considers the changes that HE has faced over the past fifteen years, describes some of the challenges we are facing now and looks to some models of the future in response to those current challenges. It includes feedback provided by hose present at the lecture in terms of their views for the past, present and future

A New Harvest: Using Solar Power to Refine Biodiesel on Native American Reservations

Sustainability is a matter of survival. The rising cost of energy and the dwindling fossil fuel resources are disproportionally affecting Indigenous communities, making them the proverbial canary in the coalmine. Native American reservations can leapfrog past fossil fuel based energy production and assert their energy sovereignty by developing renewable energy projects. Native American tribes have already been investing in renewable energy projects with the goal of becoming energy independent. The thesis proposes that tribes invest in solar powered biodiesel refining to remove their dependence on off-reservation produced energy. I use spatial analysis and GIS to demonstrate which reservations will receive the most benefit from this technology. The goal of this project is to provide tribes with a practical, real-world solution that can elevate their energy concerns and ready for implementation

Future cities and autonomous vehicles: analysis of the barriers to full adoption

The inevitable upcoming technology of autonomous vehicles (AVs) will affect our cities and several aspects of our lives. The widespread adoption of AVs repose at crossing distinct barriers that prevent their full adoption. This paper presents a critical review of recent debates about AVs and analyse the key barriers to their full adoption. This study has employed a mixed research methodology on a selected database of recently published research works. Thus, the outcomes of this review integrate the barriers into two main categories; (1) User/Government perspectives that include (i) Users' acceptance and behaviour, (ii) Safety, and (iii) Legislation. (2) Information and Communication Technologies (ICT) which include (i) Computer software and hardware, (ii) Communication systems V2X, and (iii) accurate positioning and mapping. Furthermore, a framework of barriers and their relations to AVs system architecture has been suggested to support future research and technology development