
NSF Org: |
CNS Division Of Computer and Network Systems |
Recipient: |
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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 2019 = $16,000.00 FY 2020 = $16,000.00 |
History of Investigator: |
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Recipient Sponsored Research Office: |
520 LEE ENTRANCE STE 211 AMHERST NY US 14228-2577 (716)645-2634 |
Sponsor Congressional District: |
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Primary Place of Performance: |
338 Davis Hall Buffalo NY US 14260-2000 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): |
S&CC: Smart & Connected Commun, Special Projects - CNS |
Primary Program Source: |
01001920DB NSF RESEARCH & RELATED ACTIVIT 01002021DB NSF RESEARCH & RELATED ACTIVIT |
Program Reference Code(s): |
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Program Element Code(s): |
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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
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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
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