Automation-driven transformation of road infrastructure: a multi-perspective case study

Automated driving is widely assumed to play a major role in future mobility. In this paper, we focus on “high driving automation” (SAE level 4) and analyze potentials in terms of more efficient traffic flows, travel times, and user benefits as well as potential impacts on urban neighborhoods and potentials for sustainable urban development. Along selected use cases of automated vehicles in the region of Karlsruhe, Germany, we show that at least moderate user benefits can be expected from travel time savings, with the extent depending on the defined operational design domain of the vehicles and the routes taken. With regard to residential development of urban neighborhoods, there are opportunities for repurposing public space. However, these are limited and require parallel regulatory measures to become effective

Strategic Infrastructure Planning for Autonomous Vehicles

Compared with conventional human-driven vehicles (HVs), AVs have various potential benefits, such as increasing road capacity and lowering vehicular fuel consumption and emissions. Road infrastructure management, adaptation, and upgrade plays a key role in promoting the adoption and benefit realization of AVs.This dissertation investigated several strategic infrastructure planning problems for AVs. First, it studied the potential impact of AVs on the congestion patterns of transportation networks. Second, it investigated the strategic planning problem for a new form of managed lanes for autonomous vehicles, designated as autonomous-vehicle/toll lanes, which are freely accessible to autonomous vehicles while allowing human-driven vehicles to utilize the lanes by paying a toll.This new type of managed lanes has the potential of increasing traffic capacity and fully utilizing the traffic capacity by selling redundant road capacity to HVs. Last, this dissertation studied the strategic infrastructure planning problem for an infrastructure-enabled autonomous driving system. The system combines vehicles and infrastructure in the realization of autonomous driving. Equipped with roadside sensor and control systems, a regular road can be upgraded into an automated road providing autonomous driving service to vehicles. Vehicles only need to carry minimum required on-board devices to enable their autonomous driving on an automated road. The costs of vehicles can thus be significantly reduced

Towards Prototyping Driverless Vehicle Behaviors, City Design, and Policies Simultaneously

Autonomous Vehicles (AVs) can potentially improve urban living by reducing accidents, increasing transportation accessibility and equity, and decreasing emissions. Realizing these promises requires the innovations of AV driving behaviors, city plans and infrastructure, and traffic and transportation policies to join forces. However, the complex interdependencies among AV, city, and policy design issues can hinder their innovation. We argue the path towards better AV cities is not a process of matching city designs and policies with AVs' technological innovations, but a process of iterative prototyping of all three simultaneously: Innovations can happen step-wise as the knot of AV, city, and policy design loosens and tightens, unwinds and reties. In this paper, we ask: How can innovators innovate AVs, city environments, and policies simultaneously and productively toward better AV cities? The paper has two parts. First, we map out the interconnections among the many AV, city, and policy design decisions, based on a literature review spanning HCI/HRI, transportation science, urban studies, law and policy, operations research, economy, and philosophy. This map can help innovators identify design constraints and opportunities across the traditional AV/city/policy design disciplinary bounds. Second, we review the respective methods for AV, city, and policy design, and identify key barriers in combining them: (1) Organizational barriers to AV-city-policy design collaboration, (2) computational barriers to multi-granularity AV-city-policy simulation, and (3) different assumptions and goals in joint AV-city-policy optimization. We discuss two broad approaches that can potentially address these challenges, namely, "low-fidelity integrative City-AV-Policy Simulation (iCAPS)" and "participatory design optimization".Comment: Published to the CHI '23 Workshop: Designing Technology and Policy Simultaneousl

The Impact of Autonomous Vehicles on Urban Land Use Patterns

Autonomous vehicles are coming. The only questions are how quickly they will arrive, how we will manage the years when they share the road with conventional vehicles, and how the legal system will address the issues they raise. This Article examines the impact the autonomous vehicle revolution will have on urban land use patterns. Autonomous vehicles will transform the use of land and the law governing that valuable land. Automobiles will drop passengers off and then drive themselves to remote parking areas, reducing the need for downtown parking. These vehicles will create the need for substantial changes in roadway design. Driverless cars are more likely to be shared, and fleets may supplant individual ownership. At the same time, people may be willing to endure longer commutes, working while their car transports them. These dramatic changes will require corresponding adaptations in real estate and land use law. Zoning laws, building codes, and homeowners\u27association rules will have to be updated to reflect shifting needs for parking. Longer commutes may create a need for stricter environmental controls. Moreover, jurisdictions will have to address these changes while operating under considerable uncertainty, as we all wait to see which technologies catch on, which fall by the wayside, and how quickly this revolution arrives. This Article examines the legal changes that are likely to be needed in the near future. It concludes by recommending that government bodies engage in scenario planning so they can act under conditions of ambiguity while reducing the risk of poor decisions.

Modeling and understanding the implications of future truck technology scenarios for performance-based freight corridor planning

Autonomous highway vehicles are coming. The question regarding this technology has shifted from “if” to “when”. Many advocates predict that autonomous trucks, in particular, will be commercially available within the next decade, and perhaps even before autonomous passenger vehicles. This includes the emergence of autonomous and connected multi-vehicle truck platoons. Unfortunately, this technology is developing more rapidly than the public sector is preparing for it; the situation is exacerbated by the fact that the timeframe for which the technology is expected to make up a substantial portion of the motor vehicle fleet is within the current planning horizon of most transportation planning agencies. Thus, there is an immediate need to explore the implications of this technology for public agency planning purposes; exploring these implications will in turn require the development of tools to quantify the potential costs and benefits involved. With these needs in mind, the objectives of this dissertation were to (1) develop a simulation modeling and performance measurement tool that incorporates autonomous and connected truck platooning technology into the long-range planning process, (2) demonstrate how this tool can be applied to a selected interstate corridor in Georgia (I-85 and I-285), and (3) develop a scenario planning framework that uses the results from the tool to guide policy development. The model consists of an iteratively linked, supply-demand equilibrium based multi-commodity and multi-vehicle class truck trip distribution and a highway traffic assignment model, requiring changes be made to the typical travel demand modeling process to capture the characteristics of platooning technology. The results from an empirical application of this model were then used to assess the safety-, economic-, congestion-, and emissions-related impacts of platooning technology. The model developed is flexible enough to allow for a number of variations in platooning details, and was supported by a multi-variable sensitivity analysis of key input variables. This sensitivity analysis showed a range of costs and benefits of the technology, with the greatest benefits seen when labor costs were cut by allowing some of the trucks to be driverless (which would also help to alleviate a currently significant shortage of experienced truck drivers). Allowing the autonomous trucks to operate on a dedicated lane was found to tremendously reduce travel time and congestion for those trucks. However, the magnitude of cost savings depends on a variety of factors, including the deployment of platoons of different sizes, the potential for platoon-supported fuel savings, and the level of corridor traffic congestion. In some scenarios, these congestion benefits came at the expense of the convenience of other vehicles, while in other scenarios, these vehicles experienced modest congestion-reduction benefits. The emissions impacts varied; the benefits for fuel consumption and emissions for platoons were as much as 9.6% at optimal speeds. While these findings are insightful, it is important to note that they are based on a specific set of assumptions and do not consider infrastructure costs related to the implementation of the technology. Changing the assumptions in some cases could significantly change the results. This research is one of the first efforts to modify a traditional travel demand model to simulate autonomous truck platoons. One of the key components of this contribution is the use of an origin-user equilibrium (OUE) traffic assignment, a relatively new path-based assignment which allows the user to specify vehicle class and origin specific traffic flows, and assign them to the network simultaneously. The OUE assignment has yet to be explored in depth with respect to multiple truck class-based, notably platoon-inclusive freight movements. Additionally, the research presents a new application of the Freight Analysis Framework, which is a widely used freight database within the United States. Given the uncertainty associated with platooning technology, there are a number of limitations associated with this research, and the final chapter of this dissertation discusses such limitations and presents opportunities for future work. As the details of platooning technology become clearer, tools such as the one developed here can assist transportation planners with incorporating such technological advances into their planning processes.Ph.D

Embracing Disruption: Urban Streets and Infrastructure of the 21st Century

Mass production of affordable automobiles in the early 20th century have completely reshaped American landscape. Since then, every part of American cities and neighborhoods have been designed to move cars rather than people. On the brink of a broad adoption of autonomous vehicle (AV) technology, cities have an historic opportunity to reorient urban spaces and redesign its streets. Concurrent with the evolving public health challenges and shifting in urban demographics and consumer habits, how are these changes of the 21st century impact the design of urban streets and public realm of the future? This thesis aims to explore and propose an urban incubator dedicated to the advancements of all mobility and reimagine how urban living of the future look like as people reclaim the urban streets