Sean Qian
· H.J. Heinz III ProfessorCarnegie Mellon University · Civil and Environmental Engineering
Active 2005–2024
About
Sean Qian is a H.J. Heinz III Professor jointly appointed at the Department of Civil and Environmental Engineering, Heinz College of Information Systems and Public Policy, and the Department of Electrical and Computer Engineering at Carnegie Mellon University (CMU). He directs the Mobility Data Analytics Center (MAC) at CMU. His research interests include large-scale dynamic network modeling and data analytics for multi-modal transportation systems, development of intelligent transportation systems (ITS), and understanding infrastructure system interdependency. Qian's work focuses on advancing infrastructure systems through innovative applications of AI/ML technologies, digital twin technologies, and big data analytics to improve transportation efficiency, safety, and resilience. His research has been supported by numerous public agencies and private firms, and he has contributed to the development of AI-enabled traffic incident management systems, smart loading zones, and digital twin collaborations with Fujitsu. Qian is actively involved in the academic community as an associate editor for several transportation journals and as a member of key committees related to network modeling and AI. He has received notable awards including the NSF CAREER award in 2018 and the Greenshields Prize from the Transportation Research Board in 2017.
Research topics
- Computer Science
- Data Mining
- Artificial Intelligence
- Transport engineering
- Engineering
- Theoretical computer science
- Mathematics
- Statistics
- Econometrics
- Environmental health
- Machine Learning
- Business
- Medicine
- Geography
- Environmental science
- Environmental planning
- Computer network
Selected publications
Inferring heterogeneous treatment effects of work zones on crashes
Accident Analysis & Prevention · 2022 · 21 citations
Senior authorCorresponding- Computer Science
- Environmental science
- Transport engineering
The increasing number of work zone crashes has been a significant concern for road users, transportation agencies, and researchers. Crashes can be caused by work zones, and this effect changes across different work zone configurations, traffic volumes, roadway functional classifications, and weather conditions. This is typically represented by Crash Modification Functions (CMFunctions). However, current methods for developing work zone CMFunctions have two major limitations: (1) They focus on analyzing statistical associations and fail to mitigate the confounding bias due to possible unobservable roadway characteristics; and (2) They cannot address CMFunctions of multiple variables simultaneously, such as weather and traffic conditions, since they are represented using mixed data types (continuous and categorical) that could potentially affect the causal effect of work zones on crashes. In this study, we develop a method that utilizes causal forest with fixed-effect modeling to mitigate the confounding bias while identifying CMFunctions conditioning on various environmental characteristics, including work zone configurations, traffic volume, roadway functional classification, and weather conditions. The developed method was applied to 3378 work zones that occurred in Pennsylvania between 2015 and 2017. The results were validated via a series of robustness tests. The validations demonstrate that this method can mitigate the confounding bias and identify CMFunctions of multiple variables. The results also show that the causal effect of a work zone on crash occurrence is significantly positive (p<0.05) on roadways with high traffic volumes (e.g., > 20,000 vehicles per day) and on medium length (e.g., 2000 to 5000 m) work zones. It appears that having medium-long (e.g., between 6000 and 8000 m) work zones or long duration (e.g., longer than 4 h) work zones do not necessarily lead to extra crashes.
Hierarchical Graph Convolution Network for Traffic Forecasting
Proceedings of the AAAI Conference on Artificial Intelligence · 2021 · 194 citations
- Computer Science
- Computer Science
- Data Mining
Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how to establish a spatial-temporal model to express the non-stationary traffic patterns. Recently, the latest Graph Convolution Network (GCN) has been introduced to learn spatial features while the time neural networks are used to learn temporal features. These GCN based methods obtain state-of-the-art performance. However, the current GCN based methods ignore the natural hierarchical structure of traffic systems which is composed of the micro layers of road networks and the macro layers of region networks, in which the nodes are obtained through pooling method and could include some hot traffic regions such as downtown and CBD etc., while the current GCN is only applied on the micro graph of road networks. In this paper, we propose a novel Hierarchical Graph Convolution Networks (HGCN) for traffic forecasting by operating on both the micro and macro traffic graphs. The proposed method is evaluated on two complex city traffic speed datasets. Compared to the latest GCN based methods like Graph WaveNet, the proposed HGCN gets higher traffic forecasting precision with lower computational cost.The website of the code is https://github.com/guokan987/HGCN.git.
Inferring the causal effect of work zones on crashes: Methodology and a case study
Analytic Methods in Accident Research · 2021 · 25 citations
Senior authorCorresponding- Computer Science
- Computer Science
- Transport engineering
The increasing number of crashes occurring in work zones has received considerable attention in recent years. Previous studies have mainly focused on associations between work zone configurations and crash occurrence. Although identification of associational relations helps us understand how work zones co-exist with crashes, it does not provide interventional guidelines necessary to improve safety of work zone operations. In this paper, a causal inference model based on the potential outcome framework is proposed to rigorously infer the causal effects of work zone presence on crash risks under various work zone configurations, along with robustness tests. In developing such a causal model, three research gaps are identified and addressed: (1) potential confounding bias due to unobservable roadway characteristics; (2) potential bias caused by unobserved variables in multisource data; and (3) lack of actually observed traffic data and weather information at the exact time when a crash occurred and lack of large-scale high-granular data. The proposed methodology is applied to 5,006 work zones in Pennsylvania from 2015 to 2017, and the results are validated via a series of robustness tests. The results show that the causal effect of a work zone on crash occurrence is significantly positive, especially on roadways with high traffic volumes, on long-distance work zones, and work zones conducted during daytime. It appears that conducting work zones during nighttime with the current deployment strategies on Pennsylvania state roads does not necessarily increase crash risks, but a work zone significantly increases crash risks during day time.
IEEE Transactions on Intelligent Transportation Systems · 2020 · 190 citations
- Computer Science
- Data Mining
- Computer Science
Traffic forecasting is a challenging problem in the transportation research field as the complexity and non-stationary changing of the traffic data, thus the key to the issue is how to explore proper spatial and temporal characteristics. Based on this thought, many creative methods have been proposed, in which Graph Convolution Network (GCN) based methods have shown promising performance. However, these methods depend on the graph construction, which mainly uses the prior knowledge of the road network. Recently, some works realized the fact of the road network graph changing and tried to construct dynamic graphs for GCN, but they do not fully exploit the spatial and temporal properties of the traffic data in the graph construction. In this paper, we propose a novel dynamic graph convolution network for traffic forecasting, in which a latent network is introduced to extract spatial-temporal features for constructing the dynamic road network graph matrices adaptively. The proposed method is evaluated on several traffic datasets and the experimental results show that it outperforms the state of the art traffic forecasting methods. The website of the code is <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/guokan987/DGCN.git</uri> .
Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction
IEEE Transactions on Intelligent Transportation Systems · 2020 · 355 citations
- Computer Science
- Computer Science
- Data Mining
Traffic prediction is a core problem in the intelligent transportation system and has broad applications in the transportation management and planning, and the main challenge of this field is how to efficiently explore the spatial and temporal information of traffic data. Recently, various deep learning methods, such as convolution neural network (CNN), have shown promising performance in traffic prediction. However, it samples traffic data in regular grids as the input of CNN, thus it destroys the spatial structure of the road network. In this paper, we introduce a graph network and propose an optimized graph convolution recurrent neural network for traffic prediction, in which the spatial information of the road network is represented as a graph. Additionally, distinguishing with most current methods using a simple and empirical spatial graph, the proposed method learns an optimized graph through a data-driven way in the training phase, which reveals the latent relationship among the road segments from the traffic data. Lastly, the proposed method is evaluated on three real-world case studies, and the experimental results show that the proposed method outperforms state-of-the-art traffic prediction methods.
Travel Impacts of a Complete Street Project in a Mixed Urban Corridor
2020 · 20 citations
Senior authorCorresponding- Transport engineering
- Geography
- Environmental planning
Recent grants
NSF · $532k · 2018–2025
NSF · $355k · 2019–2023
NSF · $280k · 2015–2019
NSF · $158k · 2016–2019
Frequent coauthors
- 18 shared
Wei Ma
Hong Kong Polytechnic University
- 17 shared
Baocai Yin
Beijing University of Technology
- 15 shared
Yongli Hu
Beijing University of Technology
- 14 shared
Xidong Pi
Carnegie Mellon University
- 12 shared
Rick Grahn
National Renewable Energy Laboratory
- 11 shared
Kan Guo
Beihang University
- 10 shared
Junbin Gao
University of Sydney
- 10 shared
Yanfeng Sun
Labs
Education
- 2004
B.S., Civil Engineering
Tsinghua University
- 2006
M.S., Civil Engineering
Tsinghua University
- 2011
Ph.D., Civil Engineering
UC Davis
- 2012
M.S., Statistics
Stanford University
Awards & honors
- NSF CAREER Award (2018)
- Greenshields Prize from the Transportation Research Board (2…
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