| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | February 1, 2017 |
| Latest Amendment Date: | March 1, 2018 |
| Award Number: | 1657350 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Erik Brunvand
CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2017 |
| End Date: | July 31, 2020 (Estimated) |
| Total Intended Award Amount: | $174,956.00 |
| Total Awarded Amount to Date: | $190,956.00 |
| Funds Obligated to Date: |
FY 2018 = $16,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
100 INSTITUTE RD WORCESTER MA US 01609-2280 (508)831-5000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
100 Institute Rd Worcester MA US 01609-2247 |
| 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): |
CRII CISE Research Initiation, Special Projects - CNS |
| Primary Program Source: |
01001819DB 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
In today's cities, both public and private transits have clear limitations to fulfill passengers' needs for quality of experience (QoE): (a) private transits have high transit fare; (b) with fixed time table/routes, public transits fail to timely provide adequate supply to match the time-varying trip demands. This project develops CityLines, a transformative urban transit system, employing hybrid hub-and-spoke transit service with shared shuttles. The proposed CityLines system routes urban trips among spokes through a few hubs or direct paths, with travel time as short as private transits and fare as low as public transits.
To develop CityLines system, this project investigates three innovative ideas. First, A cyber-analytics module is developed to conduct a series of trip-centered analysis, including trip demand prediction, trip QoE modeling, and incentive mechanism analysis. Second, a unifying hybrid hub-and-spoke framework is developed to quantify and address various design choices and trade-offs. Third, novel cyber-control methods are designed to guarantee the system scalability (enabling real-time transit service planning for urban scale trip demands), adaptability (allowing hub relocation and route re-planning to cope with trip demand dynamics), reliability (to traffic disruptions), and compatibility (providing inter-transit coordination with other transit services).
CityLines system has the potential to reduce the overall travel time, for commute, grocery, entertainment, and more. It also has the potential to reduce pollution and emissions to green the urban environments. It also has the potential to improve the urban trip convenience, where passengers do not need to worry about the trip paths or missing a stop. CityLines system will be a nice step towards a greener, more convenient, and vibrant smart city. Educational activities include curriculum development and training of students in the areas of cyber-physical systems and urban computing. Result dissemination is planned via publication in relevant peer-reviewed conferences and journals.
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.
In today's cities and metro areas, there are two primary modes of urban transit services: public transits, e.g., bus and subway, and private transits, such as taking a taxi, renting a car, a car-sharing service (like Zipcar or car2go), or a ride-hailing service (like Uber or Lyft). However, these urban transit services have clear limitations to fulfill passengers needs for quality of experience (QoE): i) Without efficient ride-sharing, private transits have high transit fare; ii) with fixed time table/routes, public transits fail to timely provide adequate supply to match the time-varying trip demands; iii) public transits rely on stops and transfer stations to allow more passengers to share the ride, which lead to longer travel time. All these issues are due to the lack of pre-knowledge about the urban demands and closed-loop cyber-control to manage the transit services.
In this project, the PI proposes CityLines, a scalable, dynamic, and reliable hybrid hub-and-spoke transit service with shared shuttles. The CityLines framework aims to provide transit services with travel time as short as private transits and fare as low as public transit. Moreover, through inter-transit coordination with other transit modes, CityLines enables an intelligent multi-modal urban transportation system.
The project has led to the development of novel techniques for tackling practical challenges of the proposed CityLines framework. Specifically, in the project, the project has developed (i) a scalable algorithm to plan CityLines transit network (including hub station placement, and route planning), such that both passengers' average travel time and (monetary) travel cost are minimized; (ii) an inverse preference learning algorithm to infer the passengers' preferences and predict the future human behavior changes, e.g., ridership of CityLines transit system before its deployment; (iii) a novel "off-deployment traffic estimation problem", namely, to foresee the traffic status changes in a region prior to the deployment of a urban deployment plan (for example, before deploying a CityLines plan).
The outcomes of the project add new techniques to the literature of spatial-temporal data mining, transportation cyber-physical systems (CPS), and artificial intelligence, and the interdisciplinary research on smart cities. The developed techniques have been applied and validated on datasets collected from urban transportation systems, such as taxi trajectories, passenger records of taking subway trains and buses. The research in this project provided teaching materials for courses at different levels taught at Worcester Polytechnic Institute. Twenty-four (24) research articles related to the work of this project have been published at major journals or international conferences. The results of the project have been disseminated through presentations, guest lectures, and seminars at academic conferences and forums.
All the project results are maintained on the project website: http://users.wpi.edu/~yli15/CityLines/
Last Modified: 10/01/2020
Modified by: Yanhua Li
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