Towards Quality Aware Crowdsourced Road Sensing for Smart Cities

NSF CNS-1737590

Principle Investigators

  • Lu Su, Assistant Professor, Department of Computer Science and Engineering
  • Chunming Qiao, Professor and Chair, Department of Computer Science and Engineering
  • Jing Gao, Assosiate Professor, Department of Computer Science and Engineering
  • Adel W. Sadek, Professor, Department of Civil, Structural and Environmental Engineering
  • Alex Anas, Professor and Chair, Department of Economics


  • Chuishi Meng, PhD student
  • Weida Zhong, PhD student
  • Wenjun Jiang, PhD student
  • Abhishek Gupta, PhD student
  • Sayan De Sarkar, PhD student

Award Information

This website is based upon work supported by the National Science Foundation under grant CNS-1737590. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Project Background and Goals

With nearly a billion automobiles on the road today, the current transportation systems have begun to show signs of serious strain, such as congestions, traffic accidents, road surface defects, and malfunctioning traffic regulation infrastructures. Therefore, it is of great importance to collect and disseminate road/traffic condition information accurately, efficiently, and timely. Traditionally, road and traffic monitoring are conducted through either stationary sensors or instrumented probe vehicles. Unfortunately, the prohibitively high deployment cost of such devices makes it impossible to achieve large-scale deployment, leading to limited road coverage and delayed information update. To mitigate these problems, this project develops QuicRoad, a Quality of Information (QoI) aware crowdsourced road sensing system that can collect road/traffic information from a variety of sources, including smartphones, social media and transportation authorities (as well as future connected vehicles), and then distribute the collected information in real time.

Project Impact

This project integrates across both social and technological research dimensions. In the technological dimension, it leads to a novel Quality of Information (QoI) aware information integration framework that can jointly optimize the estimation of the QoI of various sources, and the information-integration as well as decision-making process. In the social dimension, it answers fundamental questions such as whether and to what degree the road/traffic condition information provided by the proposed QuicRoad system would change the social behavior of the travelers. By seamlessly integrating the technological and social dimensions, the proposed research can not only improve the coverage and quality of assisted driving and road navigation services for travelers, but also support policy-making in traffic planning and operations by transportation authorities. The research will potentially benefit a wide spectrum of real-world road sensing applications aimed at improving road safety, mitigating traffic congestions, and reducing fuel consumption and emissions, and eventually contribute to building a sustainable society.


  • Chuishi Meng, Yu Cui, Qing He, Lu Su, and Jing Gao, "Towards the Inference of Travel Purpose with Heterogeneous Urban Data," IEEE Transactions on Big Data (TBD), accepted 2019.
  • Haiming Jin, Lu Su, Danyang Chen, Hongpeng Guo, Klara Nahrstedt, Jinhui Xu, "Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing," IEEE Transactions on Mobile Computing (TMC), Vol.18, No.8, pp.1951-1964, August 2019.[pdf]
  • Yu Cui, Chuishi Meng, Qing He, and Jing Gao, "Forecasting Current and Next Trip Purpose with Social Media Data and Google Places," Transportation Research Part C Vol.97, pp.159-174, December 2018.[pdf]
  • Samuel Shaw, Yunfei Hou, Weida Zhong, Qingquan Sun, Tong Guan, and Lu Su, "Instantaneous Fuel Consumption Estimation Using Smartphones," the 90th Vehicular Technology Conference (VTC 2019-Fall), Honolulu, HI, September 2019.
  • Hengtong Zhang, Yaliang Li, Fenglong Ma, Jing Gao, Lu Su, "TextTruth: An Unsupervised Approach to Discover Trustworthy Information from Multi-Sourced Text Data," the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, United Kingdom, August 2018.[pdf]
  • Liuyi Yao, Lu Su, Qi Li, Yaliang Li, Fenglong Ma, Jing Gao, Aidong Zhang, "Online Truth Discovery on Time Series Data," the 18th SIAM International Conference on Data Mining (SDM 2018), San Diego, CA, May 2018.[pdf]
  • Zhenhua Zhang, Qing He, Jing Gao, and Ming Ni, "A Deep Learning Approach for Detecting Traffic Accidents from Social Media Data," Transportation Research Part C Vol.86, pp.580-596, January 2018.[pdf]
  • Wenjun Jiang, Chenglin Miao, Lu Su, Qi Li, Shaohan Hu, Shiguang Wang, Jing Gao, Hengchang Liu, Tarek F. Abdelzaher, Jiawei Han, Xue Liu, Yan Gao, Lance Kaplan, "Towards Quality Aware Information Integration in Distributed Sensing Systems," IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol.29, No.1, pp.198-211, January 2018.[pdf]
  • Chuishi Meng, Yu Cui, Qing He, Lu Su, Jing Gao, "Travel Purpose Inference with GPS Trajectories, POIs, and Geo-tagged Social Media Data," the 5th IEEE International Conference on Big Data (Big Data 2017), Boston, MA, December 2017.[pdf]
  • Chuishi Meng, Xiuwen Yi, Lu Su, Jing Gao, Yu Zheng, "City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories," the 25th ACM International Conference on Advances in Geographical Information Systems (SIGSPATIAL 2017), Redondo Beach, CA, November 2017.[pdf]
  • Yunfei Hou, Abhishek Gupta, Tong Guan, Shaohan Hu, Lu Su, Chunming Qiao, "VehSense: Slippery Road Detection Using Smartphones," the 85th Vehicular Technology Conference (VTC 2017-Spring), Sydney, Australia, June 2017.[pdf]


  • CSE 489/589: Modern Network Concepts
  • CSE 728: Selected Topics on Internet of Things
  • CSE 726: Selected Topics in Crowd Sensing Systems
  • CSE 721: Selected Topics in Mobile Sensing
  • STL 505 ĘC Transportation Systems Modeling Fundamentals
  • CIE440/539 Travel Demand Forecasting
  • ECO 763SEM Pub Finance & Fiscl Pol 1
  • CSE 601 Data Mining and Bioinformatics