Towards Quality Aware Crowdsourced Road Sensing for Smart Cities

NSF CNS-1737590

Principle Investigators

  • Lu Su, Associate Professor, School of Electrical and Computer Engineering, Purdue University
  • Chunming Qiao, Professor, Department of Computer Science and Engineering, University at Buffalo
  • Jing Gao, Associate Professor, School of Electrical and Computer Engineering, Purdue University
  • Adel W. Sadek, Professor, Department of Civil, Structural and Environmental Engineering, University at Buffalo
  • Alex Anas, Professor, Department of Economics, University at Buffalo

Students

  • Chuishi Meng, PhD student
  • Weida Zhong, PhD student
  • Wenjun Jiang, PhD student
  • Abhishek Gupta, PhD student
  • Enshu Wang, PhD student
  • Yi Zhu, PhD student
  • Hongfei Xue, PhD student
  • Hengtong Zhang, PhD student
  • Sayan De Sarkar, PhD student
  • Hoang Xuan Dao, 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.

Publications

  • Enshu Wang, Rong Ding, Zhaoxing Yang, Haiming Jin, Chenglin Miao, Lu Su, Fan Zhang, Chunming Qiao, Xinbing Wang, "Joint Charging and Relocation Recommendation for E-Taxi Drivers via Multi-Agent Mean Field Hierarchical Reinforcement Learning," IEEE Transactions on Mobile Computing (TMC), Vol.21, No.4, April 2022.[pdf]
  • 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), Vol.8, No.1, January/February 2022.[pdf]
  • Yi Zhu, Abhishek Gupta, Shaohan Hu, Weida Zhong, Lu Su, Chunming Qiao, "Driver Behavior-aware Parking Availability Crowdsensing System Using Truth Discovery," ACM Transactions on Sensor Networks (TOSN), Vol.17, Issue 4, No.41, July 2021.[pdf]
  • Weida Zhong, Qiuling Suo, Abhishek Gupta, Xiaowei Jia, Chunming Qiao, Lu Su, "MetaTP: Traffic Prediction with Unevenly-Distributed Road Sensing Data via Fast Adaptation," the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2021), Virtual Conference, September 2021.[pdf]
  • Weida Zhong, Qiuling Suo, Xiaowei Jia, Aidong Zhang, Lu Su, "Heterogeneous Spatio-Temporal Graph Convolution Network for Traffic Forecasting with Missing Values," the 41st International Conference on Distributed Computing Systems (ICDCS 2021), Virtual Conference, July 2021.[pdf]
  • Abhishek Gupta, Abhinav Khare, Haiming Jin, Adel Sadek, Lu Su, and Chunming Qiao, "Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones," the 28th ACM International Conference on Advances in Geographical Information Systems (SIGSPATIAL 2020), Seattle, WA, November 2020.[pdf]
  • Weida Zhong, Qiuling Suo, Fenglong Ma, Yunfei Hou, Abhishek Gupta, Chunming Qiao, Lu Su, "A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling," the ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp 2020), Cancun, Mexico, September 2020.[pdf]
  • Abhishek Gupta, Shaohan Hu, Weida Zhong, Adel Sadek, Lu Su, Chunming Qiao, "Road Grade Estimation Using Crowd-sourced Smartphone Data," the 19th ACM/IEEE Conference on Information Processing in Sensor Networks (IPSN 2020), Sydney, Australia, April 2020.[pdf]
  • 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]

Courses

  • 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: Transportation Systems Modeling Fundamentals
  • CIE 440/539: Travel Demand Forecasting
  • ECO 763: Public Finance & Fiscal Policy
  • CSE 601: Data Mining and Bioinformatics