Award Abstract # 1737590
SCC-IRG Track 2: Towards Quality Aware Crowdsourced Road Sensing for Smart Cities

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK
Initial Amendment Date: August 22, 2017
Latest Amendment Date: April 1, 2021
Award Number: 1737590
Award Instrument: Standard Grant
Program Manager: David Corman
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2017
End Date: August 31, 2022 (Estimated)
Total Intended Award Amount: $1,000,000.00
Total Awarded Amount to Date: $1,032,000.00
Funds Obligated to Date: FY 2017 = $1,000,000.00
FY 2019 = $16,000.00

FY 2020 = $16,000.00
History of Investigator:
  • Chunming Qiao (Principal Investigator)
    qiao@computer.org
  • Alex Anas (Co-Principal Investigator)
  • Adel Sadek (Co-Principal Investigator)
  • Jing Gao (Co-Principal Investigator)
  • Lu Su (Co-Principal Investigator)
  • Lu Su (Former Principal Investigator)
  • Chunming Qiao (Former Co-Principal Investigator)
Recipient Sponsored Research Office: SUNY at Buffalo
520 LEE ENTRANCE STE 211
AMHERST
NY  US  14228-2577
(716)645-2634
Sponsor Congressional District: 26
Primary Place of Performance: SUNY at Buffalo
338 Davis Hall
Buffalo
NY  US  14260-2000
Primary Place of Performance
Congressional District:
26
Unique Entity Identifier (UEI): LMCJKRFW5R81
Parent UEI: GMZUKXFDJMA9
NSF Program(s): S&CC: Smart & Connected Commun,
Special Projects - CNS
Primary Program Source: 01001718DB NSF RESEARCH & RELATED ACTIVIT
01001920DB NSF RESEARCH & RELATED ACTIVIT

01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 042Z, 9251
Program Element Code(s): 033Y00, 171400
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

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. The PIs team up with local transportation agencies in the Buffalo-Niagara region on applications related to road surface and traffic condition monitoring, border crossing delay estimation, and incident management.

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 PRODUCED AS A RESULT OF THIS RESEARCH

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

(Showing: 1 - 10 of 17)
Cui, Yu and Meng, Chuishi and He, Qing and Gao, Jing "Forecasting current and next trip purpose with social media data and Google Places" Transportation research. Part C, Emerging technologies , 2018 Citation Details
Gupta, Abhishek and Hu, Shaohan and Zhong, Weida and Sadek, Adel and Su, Lu and Qiao, Chunming "Road Grade Estimation Using Crowd-Sourced Smartphone Data" The 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN 2020) , 2020 10.1109/IPSN48710.2020.00-25 Citation Details
Gupta, Abhishek and Khare, Abhinav and Jin, Haiming and Sadek, Adel and Su, Lu and Qiao, Chunming "Estimation of Road Transverse Slope Using Crowd-Sourced Data from Smartphones" SIGSPATIAL '20: Proceedings of the 28th International Conference on Advances in Geographic Information Systems , 2020 https://doi.org/10.1145/3397536.3422239 Citation Details
Hou, Yunfei and Gupta, Abhishek and Guan, Tong and Hu, Shaohan and Su, Lu and Qiao, Chunming "VehSense: Slippery Road Detection Using Smartphones" Proceedings of the IEEE 85th Vehicular Technology Conference , 2017 10.1109/VTCSpring.2017.8108301 Citation Details
Jin, Haiming and Su, Lu and Chen, Danyang and Guo, Hongpeng and Nahrstedt, Klara and Xu, Jinhui "Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing" IEEE Transactions on Mobile Computing , v.18 , 2019 10.1109/TMC.2018.2868106 Citation Details
Meng, Chuishi and Cui, Yu and He, Qing and Su, Lu and Gao, Jing "Towards the Inference of Travel Purpose with Heterogeneous Urban Data" IEEE Transactions on Big Data , v.8 , 2022 https://doi.org/10.1109/TBDATA.2019.2921823 Citation Details
Meng, Chuishi and Cui, Yu and He, Qing and Su, Lu and Gao, Jing "Travel purpose inference with GPS trajectories, POIs, and geo-tagged social media data" 2017 IEEE International Conference on Big Data , 2017 10.1109/bigdata.2017.8258062 Citation Details
Meng, Chuishi and Yi, Xiuwen and Su, Lu and Gao, Jing and Zheng, Yu "City-wide Traffic Volume Inference with Loop Detector Data and Taxi Trajectories" Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems , 2017 10.1145/3139958.3139984 Citation Details
Shaw, Samuel and Hou, Yunfei and Zhong, Weida and Sun, Qingquan and Guan, Tong and Su, Lu "Instantaneous Fuel Consumption Estimation Using Smartphones" 2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall) , 2019 10.1109/VTCFall.2019.8891261 Citation Details
Wang, Enshu and Ding, Rong and Yang, Zhaoxing and Jin, Haiming and Miao, Chenglin and Su, Lu and Zhang, Fan and Qiao, Chunming and Wang, Xinbing "Joint Charging and Relocation Recommendation for E-Taxi Drivers via Multi-Agent Mean Field Hierarchical Reinforcement Learning" IEEE Transactions on Mobile Computing , v.21 , 2022 https://doi.org/10.1109/TMC.2020.3022173 Citation Details
Yao, Liuyi and Su, Lu and Li, Qi and Li, Yaliang and Ma, Fenglong and Gao, Jing and Zhang, Aidong "Online Truth Discovery on Time Series Data" The 18th SIAM International Conference on Data Mining (SDM 2018) , 2018 10.1137/1.9781611975321.19 Citation Details
(Showing: 1 - 10 of 17)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project has the following outcomes:

(1) This project leads to the development of QuicRoad, a Quality of Information (QoI) aware crowdsourced road sensing system that can collect and integrate road and traffic information from a variety of sources, including smartphones (GPS, accelerometer, gyroscope, etc), social media, probe vehicles, as well as transportation authorities. QuicRoad is built upon a smartphone app which collects data via smartphone sensors including accelerometer, gyroscope and GPS, as well as an OBD (On-board diagnostics) II scanner that is connected to the smartphone via Bluetooth. The app can also display road and traffic information to the end users.

(2) The PIs team up with local transportation agencies in the Buffalo-Niagara region, including the Niagara International Transportation Technology Coalition (NITTEC), Niagara Frontier Transportation Authority (NFTA), and Erie County Department of Public Works, on applications related to road surface and traffic condition monitoring.

(3) 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.

(4) This project develops a crowd sensing system that can provide spot-level availability in a parking lot by analyzing the behavior of the drivers using their smartphone data.

(5) This project develops a deep learning framework that can predict traffic conditions with limited road sensing data that are temporally sparse and unevenly distributed across regions. The project team implements a program to pull the GPS data from 150+ Niagara Frontier Transportation Authority (NFTA) buses and use them to estimate and predict traffic condition in Buffalo. The developed crowdsourced road sensing system is integrated into the NITTEC Mobile app, which provides users with customized real-time traveler information in the Buffalo-Niagara region, and visualize the estimated/predicted traffic condition on NITTEC app.

(6) This project develops a crowdsourced road geometry estimation system that can leverage vehicle-carried smartphone sensory data to estimate various road geometric features, such as road grade, cross slope, and super-elevation. One of the applications of the proposed research on crowd-sourced road geometry estimation is enhancing existing smartphone based localization and navigation services. To this end, the project team developed novel methods to estimate 6DoF vehicle states such as acceleration, velocity and orientation using a smartphone on-board a vehicle.

(7) To study the feasibility and usability of the developed crowdsourced road geometry estimation system, the project team has conducted 150+ interviews with (a) Mapping companies such as Google, Lyft, HERE, TomTom, Uber, DoorDash, Mapbox, and Spin, (b) Autonomous vehicles companies such as Waymo, Cruise, and Adastec, (c) Trucking software companies such as Trimble maps, KeepTruckin, and RoadAware, and (d) Transportation agencies such as NCDOT, NYSDOT, NREL, etc. They have filed a new technology disclosure through UB's Technology Transfer Office, and been working with them on filing a patent for the proposed work.

(8) The research results of this project are published in various top conferences and journals, such as VTC, SIGSPATIAL, IEEE Big Data, KDD, IPSN, UbiComp, ICDCS, TOSN, TPDS, TMC, TPDS, TBD, and Transportation Research Part C.

(9) During the five-year lifetime of this project, 10 PhD students and 6 undergraduate students have been involved in this project. Through this project, the students have been trained systematically. The project team has weekly project meeting, regular one-to-one discussions, shared paper reading list and idea exchange folder, and training on writing and presentation. The students' research skills have been greatly improved, as shown in their publications in top conferences and journals.

(10) The PIs have integrated their research into courses that cover a wide spectrum of areas, ranging from mobile systems, wireless networks, data mining, to transportation modeling, control, and economics. The research results developed in this project have been incorporated into courseware to expose students to the emerging challenges and results, as well as provide a hands-on environment for experimentation with the new concepts learned in class. The testbeds developed on this grant serve as an ideal platform for course projects where students are able to explore their own ideas.

Hopefully, the outputs of this project can inspire new research ideas in not only computer science but also many intelligent transportation applications aimed at improving road safety, mitigating traffic congestions, and reducing fuel consumption and emissions, and eventually contribute to building a sustainable society.


Last Modified: 11/21/2022
Modified by: Lu Su

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page