Principal Investigator

Sean Qian

Status

Completed

Start Date

March 7, 2014

End Date

Dec. 31, 2014

Project Type

Research Advanced

Grant Program

MAP-21 TSET - Tier 1 (2012 - 2016)

Grant Cycle

2014 TSET UTC

Visibility

Public

Abstract

Transportation agencies are turning to smarter ways with a variety of new technologies to improve travel experience and to manage the transportation system more efficiently. Over the last decade, new technologies and innovative transportation systems produce big data, which has tremendously enriched ways to monitor and manage the transportation systems. A challenging question is, how do we make the best use of the big data to design and operate our transportation system? In addition, those data sources are usually established by disparate public agencies and private sector. They rarely communicate with each other and as a result, each part of the transportation systems is individually operated and clearly, the entire transportation system is far from being socially optimal. Integrating and learning the big data are the keys to success of smarter transportation systems, which consist of the following three components. (1) Integration of various data sources: (2) Understanding of integrated data: and (3) Optimal decision making for systems management and for individual travelers. A mobility data analytics center is necessary to accommodate needs of data fusion and analytics. The ultimate objective of mobility data analytics center is to: (1) Provide archived and real-time traffic data of every element of multi-modal transportation systems; (2) Reveal the behavior information for both passenger transportation and freight transportation: (3) Serve as a key managerial instrument for legislators, transportation planners, researchers, and engineers: (4) Serve as a key information platform for individual travelers and transportation industries. This research serves as a first stage of developing the mobility data center. The project will focus on integrating and learning large-scale transportation data sets that are immediately available to collect and the most promising from public agencies and travelers. The City of Pittsburgh was chosen as a demonstration city. Pittsburgh has been successful in moving towards smart cities and smart transportation systems, with ample resources or us to collect and work with large-scale citywide data.    

Description

-

Timeline

-

Strategic Description / RD&T

Deployment Plan

-

Expected Outcomes/Impacts

-

Expected Outputs

TRID

Individuals Involved

Email	Name	Affiliation	Role	Position
seanqian@cmu.edu	Qian, Sean	Carnegie Mellon University	PI	Faculty - Research/Systems

Budget

Amount of UTC Funds Awarded

$75000.00

Total Project Budget (from all funding sources)

$75000.00

Documents

Type	Name	Uploaded
Final Report	208_-_Report_RTIS_Benedum.pdf	June 29, 2018, 5:09 a.m.

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