Smart intersections could reduce autonomous car congestion
In the not-so-distant future, city streets could be flooded with autonomous vehicles. Self-driving cars can move faster and travel closer together, allowing more of them to fit on the road – potentially leading to congestion and gridlock on city streets.
A new study by Cornell researchers developed a first-of-its-kind model to control traffic and intersections in order to increase car capacity on urban streets, reduce congestion and minimize accidents.
“For the future of mobility, so much attention has been paid to autonomous cars,” said Oliver Gao, professor of civil and environmental engineering and senior author of “Optimal Traffic Control at Smart Intersections: Automated Network Fundamental Diagram,” which published Dec. 15 in Transportation Research Part B.
“If you have all these autonomous cars on the road, you’ll see that our roads and our intersections could become the limiting factor,” Gao said. “In this paper we look at the interaction between autonomous cars and our infrastructure on the ground so we can unlock the real capacity of autonomous transportation.”
The researchers’ model allows groups of autonomous cars, known as platoons, to pass through one-way intersections without waiting, and the results of a mircosimulation showed it increased the capacity of vehicles on city streets up to 138% over conventional traffic signal systems, according to the study. The model assumes only autonomous cars are on the road; Gao’s team is addressing situations with a combination of autonomous and human-driven cars in future research.
Car manufacturers and researchers around the world are developing prototypes of self-driving cars, which are expected to be introduced by 2025. But until now, little research has focused on the infrastructure that will support these driverless cars.
Autonomous vehicles will be able to communicate with each other, offering opportunities for coordination and efficiency. The researchers’ model takes advantage of this capability, as well as smart infrastructure, in order to optimize traffic so cars can pass quickly and safely through intersections.
“Instead of having a fixed green or red light at the intersection, these cycles can be adjusted dynamically,” Gao said. “And this control can be adjusted to allow for platoons of cars to pass.”
Models exist to optimize today’s intersections in order to ease the flow of traffic, but these aren’t directly applicable to autonomous vehicles. The number of cars that can operate on urban streets depends on the precision and speed of sensors, vehicle-to-vehicle and vehicle-to-infrastructure communication, and the system that actually controls the machines.
Most models assume that, for greater efficiency, autonomous vehicles will travel in platoons, heading in the same direction for a period before peeling off and joining different platoons. The researchers’ framework determines the optimal traffic configuration – the number of cars traveling in each platoon approaching intersections – as one of its primary variables.
However, mathematical errors associated with this coordination can cause operational failures or accidents. To counter this, the researchers developed a formula that considers the probability of failures and, accordingly, adds a time gap of an optimal length between crossing platoons.
“By coordinating the platoon size and the gap length between cars and platoons, we can maximize the flow and capacity,” Gao said. This allows platoons of self-driving vehicles to pass through intersections that don’t have traffic signals without interruption, limiting congestion.
The paper’s first author is postdoctoral associate Mahyar Amirgholy; Mehdi Nourninejad of the University of Toronto also contributed. The research was supported by the U.S. Department of Transportation; the Center for Transportation, Environment and Community Health; the National Science Foundation; and the Lloyd’s Register Foundation.
Find the Cornell Chronicle article link here.
Emerging Issues Workshop – Leveraging High-Resolution Transportation Data for Healthier Cities
CTECH, Environmental Defense Fund (EDF) and CARTEEH co-organized the workshop “Emerging Issues – Leveraging High-Resolution Transportation Data for Healthier Cities”. The workshop was held at the Cornell ILR NYC facility with participants from government, MPOs, and academics. Please see attached agenda and participants.
On November 21-22, 2019, Environmental Defense Fund (EDF) hosted the workshop “Leveraging High-Resolution Transportation Data for Healthier Cities,” in partnership with the Texas Transportation Institute (TTI) and the Center for Transportation, Environment and Community Health (CTECH) led by Cornell University. The workshop brought together major stakeholders in urban transportation planning and evaluated how high-resolution modeling of transportation impacts on air pollution and health can facilitate city and regional policy-making. The goal was to identify new opportunities for transportation planning, with an aim to embed air quality and health benefits in shaping real-world policy decisions. Oliver Gao participated in a panel discussion entitled State of the Science Part 2: Using full-chain assessment to connect the dots.
The collaboration continues via a working group and research papers summarizing the proceedings, including: (1) an evaluation of the enablers and barriers for full-chain health impact analysis implementation in policy and decision making; (2) recommended best practices for how policymakers, academics and other stakeholders can optimize efforts to collaborate in efforts to leverage high-resolution transportation data to design policies that maximize health benefits from reductions in traffic-related emissions and associated air pollution exposures; and (3) key opportunities where new collaborations across sectors (public and private) and academic disciplines can facilitate scientific innovation that supports implementation and adoption of these best practices.
In cities, people are constantly exposed to pollution from cars, buses and trains. Pollutants from traffic, like nitrogen dioxide, are estimated to be responsible for nearly 1 in 5 new cases of childhood asthma in major urban areas. And the health burden of pollution is not distributed evenly: Transportation emissions and pollutant concentrations are known to vary dramatically across neighborhoods in cities, meaning there are also important environmental justice and economic equity implications. Yet, air quality and health have often been left out of decisions that impact traffic emissions, such as infrastructure and public transit investments, congestion pricing and policies that promote electric vehicle adoption or reduce vehicle miles traveled. Among other things, policymakers are often limited by: (1) a lack of available data, and (2) a lack of collaboration across disciplines that makes it difficult to “connect the dots” among available information.
Fortunately, an enormous amount of data relevant to the transportation sector is increasingly available to city, regional and state officials, including telematics, traffic light and streetlight-mounted technologies, air quality monitoring data and satellite data, alongside opportunities for new data collection. Experts in the academic sector are using diverse data sets and sophisticated modeling to provide insight into how transportation emissions are distributed in urban areas, and what impact those emissions have on air quality and health outcomes.
Looking to the future, transportation planners are seeking to meet critical priorities like optimizing traffic flow, improving safety and reducing climate emissions, while local governments are developing plans to accommodate multimodal transportation and disruptive technologies like electric and autonomous vehicles. As the transportation sector undergoes these seismic shifts, there is a critical need for good data and robust modeling. By taking into account the real-world health impacts of transport choices, planners can help create equitable cities that are not only more connected and efficient, but are also cleaner and healthier.
For more information, please email Maia Draper: mdraper@edf.org or see the Agenda and Summary of Participant Surveys
New Free Software Helps Create Walkable Cities Of The Future
Nov 13, 2019, Forbes
Ithaca, NY – Researchers at Cornell University recently launched Urbano, a free software that employs data, metrics, and a user-friendly interface to help planners, developers, and architects assess and improve walkability features in their designs.
Architects and planners have traditionally relied on trial-and-error methods, historical data, or specialized simulations when developing walkable neighborhoods. These site analysis techniques can be difficult to accurately incorporate into project planning due to inconsistencies with real-time data.
Timur Dogan, assistant professor of architecture and lead developer of Urbano, created the Urbano software as a way to “allow architects and urban designers to simulate their designs and get feedback early in the process.” Dogan explained that site-specific, real-time data allows those involved in the conceptual phase of a project to “make decisions based on facts and data, so they can create the sustainable and livable urban environments of the future.”
The Urbano model is centered around the goal of developing walkable cities as a viable solution to the negative socio-economic and environmental impacts of traffic congestion, which according to the study’s findings, causes around 3.3 million deaths and $121 billion in economic losses every year.
The Urbano team proposes that assessing walkability in the early phases of a project will allow architects and planners to pivot and viably incorporate pedestrian-friendly features into their designs, since attempting to make such shifts would prove costly and cumbersome once a project is underway.
Since its launch on October 26, 2019, Urbano has been downloaded over 400 times by universities and architecture firms around the world.
The research was partly funded by Cornell’s Center for Transportation, Environment and Community Health, and the Cornell Atkinson Center for Sustainability.
For more information, visit Urbano.
Forbes article link.
Software helps planners design walkable cities
Walkable cities reduce traffic congestion, which causes around 3.3 million deaths and $121 billion in economic losses every year. But when architects are developing pedestrian-friendly neighborhoods, they often rely on trial and error, intuition or specialized simulations that are hard to use and to incorporate into their designs.
Urbano, a free software launched Oct. 26 by Cornell researchers, employs data, metrics and an easy-to-use interface to help planners and architects add and assess walkability features in their designs as effectively as possible.
“We wanted to create something that would allow architects and urban designers to simulate their designs and get some feedback early in the process,” said Timur Dogan, assistant professor of architecture and lead developer of Urbano. “This lets them make decisions based on facts and data, so they can create the sustainable and livable urban environments of the future.”
Since its launch, Urbano has been downloaded more than 400 times by universities and architecture firms around the world. The tool is the product of a collaboration between the College of Architecture, Art and Planning’s Environmental Systems Lab, which Dogan directs, and the Department of Civil and Environmental Engineering in the College of Engineering.
The team most recently presented a paper on Urbano in June 2018, at the Symposium on the Simulation for Architecture and Urban Design, and new research is forthcoming in TAD, the journal of Technology, Architecture and Design.
The researchers sought to create a tool that works well with the design process, which can be fast, messy and circuitous. Simulations that are difficult to perform or take too long to produce aren’t practical, Dogan said.
“We worked on new algorithms that are fast,” he said. “We worked on user interfaces that are intuitive. And we made sure the software can be integrated smoothly into the design process, so from the very first ideas and sketches you can get some feedback and nudge the design in the right direction.”
Urbano relies on three metrics to assess walkability: Streetscore, which calculates how streets are used for certain routes; Walkscore, a customizable measurement that rates whether popular amenities are within walking distance of homes and workplaces; and AmenityScore, which considers demographics to estimate the usefulness of various services.
“This is really helpful information for designers doing site analysis,” Dogan said, “because then they can see if there are certain services or amenities missing in neighborhoods, or others that are underutilized or overutilized.”
Assessing walkability early makes it more likely that pedestrian-friendly features will be incorporated, since shifting gears once the process is underway can be costly and complex. And while experienced architects will automatically consider walkability in their designs, Urbano provides simulations backed up by facts and data.
“Oftentimes, if you cannot quantify the benefit of something, then it’s difficult to convince someone to do it,” Dogan said. “This tool lets professionals quantify everything so the stakeholders can have confidence in what they propose.”
To develop Urbano, the researchers created automated ways to collect data. Information from geographic information system portals such as New York City’s Open Data Initiative – plus information from other cities and websites and social networks, including Google and Yelp – comes in inconsistent formats. Because it can be difficult to access, many designers don’t use this data, despite its wealth of information.
After determining which metrics would be most helpful to designers, the researchers developed algorithms to compute factors such as the shortest path to certain amenities and their utilization rates.
Currently, the research team is working on software that can assess energy use in models of cities, as well as a simulation tool, called Eddy3d, that considers data about urban microclimates. He hopes to eventually create a comprehensive toolkit for sustainable urban design.
“They’re all really important questions that an urban designer needs to consider,” Dogan said. “Our next step is to link them so you can compute the outdoor comfort of the street and at the same time compute the walkability of the street, and somehow use that information together to predict the likelihood of people walking.”
Other contributors to Urbano include Samitha Samaranayake, assistant professor of civil and environmental engineering; Nikhil Saraf ’20; and master’s student Yang Yang. The research was partly funded by Cornell’s Center for Transportation, Environment and Community Health, and the Cornell Atkinson Center for Sustainability.
Cornell Chronicle link to article