Upcoming Events

Project Webinar: Anil Yazici, Link Criticality Index (LCI) for Analysis of Large Transportation Networks @ Webinar
Apr 10 @ 12:00 pm – 1:00 pm

Designing and maintaining resilient and robust transportation systems require identification of links that are critical for the functionality of the network. Network scientists have suggested various topological measures to identify critical network components in multiple domains such as energy, water supply, telecommunication, social, and transportation. However, topological criticality measures usually do not conform to traffic flow dynamics (e.g., non-linear link performance functions) and they do not necessarily produce realistic results for road transportation networks. In order to overcome this issue, researchers incorporate traffic characteristics (travel time, link flow, etc.) into criticality metrics, mostly through traffic assignment. The studies that employ traffic assignment generally utilize a network performance measure (e.g., total system travel time) for the complete network, remove each link one by one, re-run traffic assignment and calculate the selected network performance measure for each link removal, and identify each link’s criticality based on the change in the network performance measure. Such full network scan approach creates computational burden, especially for large networks. Link removals can also cause network disconnectivity which makes it problematic to run traffic assignment. This talk will present the recently developed Link Criticality Index (LCI) which does not require link removals and can provide transportation link criticality rankings for large networks with a single User Equilibrium (UE) traffic assignment. The LCI scores are calculated based on the link flow changes and link performance functions during UE iterations. In order to take the alternative OD paths into account, the links scores are weighted based on the travel time differences between identified OD paths. Also, to reflect the higher importance of links which serve multiple ODs (or higher demand ODs), demand weights are used. The LCI was tested on well-known large transportation test networks and compared with other criticality measures. The findings showed that LCI successfully provides balanced link criticality rankings with respect to network connectivity and redundancy as well as the network flow conditions. Due to elimination of link removals, the LCI runtime is considerably shorter than comparable traffic assignment based approaches, i.e., in the order of the number of links in the network. The inclusion of network topology and demand characteristics, and shorter runtime make the LCI an appealing approach for transportation link criticality analysis.



Dr. Anil Yazici is an assistant professor at Stony Brook University Civil Engineering Department. He holds B.S. and M.S. degrees in Civil Engineering from Bogazici University, Turkey; M.S. and Ph.D. degrees respectively in Operation Research and Civil & Environmental Engineering from Rutgers University, NJ. Dr. Yazici’s research interests include vulnerability and resilience in transportation networks, emergency evacuation, smart cities and big data analytics, and transportation safety. His research has been funded by various federal and state institutions such as National Science Foundation (NSF), New York State and City Departments of Transportation (NYSDOT, NYCDOT), New York State Energy and Research Development Authority (NYSERDA) and Federal Highway Administration (FHWA)-USDOT.


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CANCELLED Seminar: Hui Xiong, Mobile Analytics: Prospects and Opportunities @ C2SMART Center Lab 6W-23
Apr 24 @ 12:30 pm – 1:30 pm
This event has been cancelled. It will be rescheduled for a later date.



Advances in sensor, wireless communication, and information infrastructure such as GPS, WiFi, and mobile phone technology have enabled us to collect and process massive amounts of mobile data from multiple sources but under operational time. These big data have become a major driving force of new waves of productivity growth, application innovation, and consumer surplus. The big data are usually immense, fine-grained, diversified, dynamic, and sufficiently information-rich in nature, and thus demand a radical change in the philosophy of data analytics. In this talk, we discuss the technical and domain challenges of big data analytics in mobile environments. In particularly, it is especially important to investigate how the underlying computational models can be adapted for managing the uncertainties in relation to big data process in a huge nebulous environment. The theme to be covered will include AI enabled map services (e.g. multi-modal travel recommendation), context-aware POI recommendations, POI knowledge graph, and urban cognitive computing.



Hui Xiong is a Professor at Rutgers University, and served as the Deputy Dean of Baidu Research Institute at Baidu Inc. His research interests include data mining and mobile computing, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has authored over 200 research articles, and co-edited or coauthored 4 books including the widely used Encyclopedia of GIS. Dr. Xiong has served as chair/co-chair for many international conferences in data mining, including a Program Co-Chair (2013) and a General Co-Chair (2015) for the IEEE International Conference on Data Mining (ICDM), and a Program Co-Chair of the Research Track (2018) and the Industry Track (2012) for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). Dr. Xiong’s research has generated substantive impact beyond academia. He has been honored by the 2018 Ram Charan Management Practice Award as the Grand Prix winner from the Harvard Business Review, the 2017 IEEE ICDM Outstanding Service Award, and the ICDM-2011 Best Research Paper Award. For his outstanding contributions to data mining and mobile computing, he was named an ACM Distinguished Scientist in 2014 and an IEEE Fellow in 2020.


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Joseph Chow, Yueshuai Brian He, Open Source Multi-Agent Virtual Simulation Test Bed in NYC @ Webinar
Apr 29 @ 2:00 pm – 3:00 pm
Joseph Chow headshotAbstract:

C2SMART researchers at NYU have developed a virtual testbed for NYC using large-scale transportation simulation models built with the MATSim and SUMO open-source simulation platforms. In addition to the development of the integrated and open simulation platform, several algorithms and novel approaches for on-line calibration, real-time computation, etc. were developed using this new simulation tool. The research efforts produced a large scale simulation testbed that can be used to evaluate new technologies and policies on transportation systems produced in the research phase for NYC Open Data and other government databases. A series of test cases can be conducted using either tool and by combining them together, such as planning and forecasting the effects of network changes are policies such as for-hire vehicle caps and congestion/cordon pricing. Applications include on-demand robotic taxi, traffic flow modifications to allow for connected vehicles, and dockless bike-share. The research provides resources and a virtual framework for supporting and helping the public sector’s decision making to fill in the gap between basic research and field deployment. Subsequent implementations of the testbed in other cities will form the basis for a “Network of Living Labs” to accelerate shared knowledge transfer.



Joseph Chow (pictured)

Dr. Joseph Chow is an Assistant Professor in the Department of Civil & Urban Engineering and Deputy Director at the C2SMART University Transportation Center at NYU and heads BUILT@NYU: the Behavioral Urban Informatics, Logistics, and Transport Laboratory. His research expertise lies in transportation systems, with emphasis on multimodal networks, behavioral urban logistics, smart cities, and transport economics. He is an NSF CAREER award recipient; he serves as the elected Vice-Chair of the Urban Transportation SIG at INFORMS Transportation Science & Logistics Society and is an appointed member of the Editorial Boards for Transportation Research Part B and the Committee on Transportation Network Modeling (ADB30) at the Transportation Research Board of the National Academies. Prior to NYU, Dr. Chow was the Canada Research Chair in Transportation Systems Engineering at Ryerson University. From 2010 to 2012, he was a Lecturer at the University of Southern California and a Postdoctoral Scholar at UC Irvine, where he led the development of a statewide freight forecast model for Caltrans. He has a Ph.D. in Civil Engineering from UC Irvine (‘10), and an M.Eng. (‘01) and B.S. (‘00) in Civil Engineering from Cornell University with a minor in Applied Math. Dr. Chow is a former Eisenhower and Eno Fellow and a licensed PE in NY.


Yueshuai Brian He

Yueshuai Brian He is a PhD candidate of Transportation Planning and Engineering in New York University. He received both his bachelor’s degree in Electronic Engineering and master’s degree in Transportation Information Engineering from Beihang University (BUAA). His research interests include privacy control for transportation network data sharing, agent-based simulation, and travel demand models.

Seminar: Yan Leng, Collective Behavior over Social Networks @ C2SMART Center Lab 6W-23
May 15 @ 12:30 pm – 1:30 pm
Social networks are ubiquitous and shape individual behaviors; yet, behavioral data over networks are complex and stretch the limit of conventional analysis and models. In this talk, I present two complementary projects that extend existing prediction and inference methods over social networks. First, I investigate how social network influences behavior. Using two large-scale mobile phone data, I find that social influence on non-routine behaviors spreads up to more than three degrees of separation with a decay pattern. To explain such a phenomenon, I build a Bayesian model in which individuals locally aggregate information and dynamically make adoption decisions. Second, I introduce a machine learning framework to infer the unobserved network structures from decisions. Specifically, I model individual decisions using the linear-quadratic game and invert the sparse network structures from the equilibrium actions. Together, this line of work contributes to the understanding of human behavior over networks via computational methods, which are important for managing large-scale behavioral change.



Yan Leng is a Ph.D. candidate at the MIT Media Lab. She will join the McCombs School of Business, the University of Texas at Austin as an Assistant Professor in Information Management in Fall 2020. She holds master degrees in Computer Science and Transportation Engineering, both from MIT. Yan is a network scientist working on social science problems. Her research lies in the intersection of machine learning, network theory, and causal inference. She uses large-scale behavioral data to understand collective human behavior over social networks and builds computational techniques for solving societal and organizational issues.

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