How to predict city traffic
COMPLEXITY SCIENCE HUB VIENNA
IMAGE: DIFFERENT MOBILITY PATTERNS FOR DIFFERENT ZONES OF THE CITY OF MILAN DURING WORKING DAYS view more
CREDIT: © COMPLEXITY SCIENCE HUB AND SONY CSL
A new machine learning model can predict traffic activity in different zones of cities. To do so, a Complexity Science Hub researcher used data from a main car-sharing company in Italy as a proxy for overall city traffic. Understanding how different urban zones interact can help avoid traffic jams, for example, and enable targeted responses of policy makers - such as local expansion of public transportation.
Understanding people's mobility patterns will be central to improving urban traffic flow. “As populations grow in urban areas, this knowledge can help policymakers design and implement effective transportation policies and inclusive urban planning”, says Simone Daniotti of the Complexity Science Hub.
For example, if the model shows that there is a nontrivial connection between two zones, i.e., that people commute from one zone to another for certain reasons, services could be provided that compensate for this interaction. If, on the flip side, the model shows that there is little activity in a particular location, policymakers could use that knowledge to invest in structures to change that.
MODEL ALSO FOR OTHER CITIES LIKE VIENNA
For this study a major car-sharing company provided the data: the location of all cars in their fleet in four Italian cities (Rome, Turin, Milan, and Florence) in 2017. The data was obtained by constantly querying the service provider's web APIs, recording the parking location of each car, as well as the start and end timestamps. "This information allows us to identify the origin and destination of each trip," Daniotti explains.
Daniotti used that as a proxy for all city traffic and created a model that not only allows accurate spatio-temporal forecasting in different urban areas, but also accurate anomaly detection. Anomalies such as strikes and bad weather conditions, both of which are related to traffic.
The model could also make predictions about traffic patterns for other cities such as Vienna. "However, this would require appropriate data," Daniotti points out.
OUTPERFORMING OTHER MODELS
While there are already many models designed to predict traffic behavior in cities, "the vast majority of prediction models on aggregated data are not fully interpretable. Even though some structure of the model connects two zones, they cannot be interpreted as an interaction" explains Daniotti. This limits understanding of the underlying mechanisms that govern citizens' daily routines.
Since only a minimal number of constraints are considered and all parameters represent actual interactions, the new model is fully interpretable.
BUT WHAT IS PREDICTION WITHOUT INTERPRETATION?
"Of course it is important to make predictions," Daniotti explains, "but you can make very accurate predictions, and if you don't interpret the results correctly, you sometimes run the risk of drawing very wrong conclusions."
Without knowing the reason why the model is showing a particular result, it is difficult to control for events where the model was not showing what you expected. “Inspecting the model and understanding it, helps us, and also policy makers, to not draw wrong conclusions,” Daniotti points out.
FIND OUT MORE:
The study “A maximum entropy approach for the modelling of car-sharing parking dynamics” has been published in Scientific Reports.
ABOUT THE COMPLEXITY SCIENCE HUB
The mission of the Complexity Science Hub (CSH Vienna) is to host, educate, and inspire complex systems scientists dedicated to making sense of Big Data to boost science and society. Scientists at the Complexity Science Hub develop methods for the scientific, quantitative, and predictive understanding of complex systems.
The CSH Vienna is a joint initiative of AIT Austrian Institute of Technology, Central European University CEU, Danube University Krems, Graz University of Technology, Medical University of Vienna, TU Wien, VetMedUni Vienna, Vienna University of Economics and Business, and Austrian Economic Chambers (WKO). https://www.csh.ac.at
JOURNAL
Scientific Reports
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
People
ARTICLE TITLE
A maximum entropy approach for the modelling of car-sharing parking dynamics
Andrawes to lead transportation infrastructure research center
Grant and Award AnnouncementIMAGE: PHOTO OF BASSEM O. ANDRAWES view more
CREDIT: THE GRAINGER COLLEGE OF ENGINEERING AT THE UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN
Bassem O. Andrawes, professor in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign, will lead a research initiative to improve the durability and extend the life of transportation infrastructure by advancing the technologies used in precast concrete systems, thanks to a $2 million grant from the U.S. Department of Transportation to establish a University Transportation Center. The Transportation Infrastructure Precast Innovation Center (TRANS-IPIC) will be a consortium of five universities, including Purdue University, Louisiana State University, SUNY University at Buffalo and the University of Texas San Antonio.
“Deterioration of transportation infrastructure is a pressing national problem,” Andrawes said. “We cannot address this critical problem without adopting transformative technologies that are specifically tailored for transportation infrastructure systems. Many of the emerging technologies such as nano and high-performance materials, robotics and automated manufacturing sound exciting theoretically, but are faced with major practical challenges that hinder or prevent their application.”
A major reason for this issue, he said, is the difficulty and high cost of incorporating these very delicate technologies into the construction site using conventional construction techniques. Providing a well-controlled manufacturing environment would significantly increase the feasibility of incorporating these new technologies in the delivery, maintenance and management of transportation infrastructure.
Precast concrete (PC) is, by definition, manufactured in a controlled environment, so the path for introducing and implementing new technologies that can drastically and swiftly impact the durability and service life of infrastructure is much more feasible and straightforward using PC.
“A quite large sector of our transportation infrastructure is built or repaired using PC – for example, bridges, tunnels, railroads, pavement and ports – so deploying new PC technologies will impact the durability of multiple modes of transportation,” Andrawes said.
The new center will focus on the following three key research topic areas: A) Application of New Materials and Technologies, B) Construction Methodologies and Management, and C) Condition Monitoring and Remote Sensing. The research that TRANS-IPIC will offer will play a significant role in
supporting the U.S. DOT Strategic Plan goals including Transformation, Climate & Sustainability, and Safety.
TRANS-IPIC’s mission is to leverage research innovation and strong industry support to foster research and education that focus on utilizing PC and its related technologies as an economic approach to provide a quick boost for the durability, safety and climate-adaptability of various modes of transportation networks in the U.S. through infrastructure repair or reconstruction. The consortium will address various aspects of PC technologies including materials, design, modeling, manufacturing, quality control, installation, operations and condition assessment.
The research that will be carried out through the center will focus on a broad range of innovative topics pertinent to the advancement of the durability, resilience and economics of PC transportation infrastructure. TRANS-IPIC researchers will study the use of PC-related solutions that are based on innovative and smart materials – for example, smart composites and metals – and novel emerging manufacturing methods that involve robotics and automated manufacturing – for example, 3D printing, Unmanned Aerial Vehicles and Building Information Modeling – guided by big data analytics and Artificial Intelligence.
The center will also provide long-term solutions by replacing our existing infrastructure with more durable components that are built in a controlled environment with advanced durable materials – for example, ultra-high performance concrete, fiber reinforced concrete and fiber reinforced polymers – and built more efficiently to reduce cost and carbon emissions and increase quality and productivity using advanced design optimization techniques like topology optimization, innovative manufacturing methods, quality control technologies and industrial operating processes.
The center will also work on developing a new generation of intelligent transportation infrastructure that has an innovative built-in capability of self-condition assessment using smart materials and remote sensing.
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