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Project

#133 Real-Time Incident Detection using Social Media Data


Principal Investigator
Sean Qian
Status
Completed
Start Date
May 1, 2015
End Date
May 1, 2016
Project Type
Research Advanced
Grant Program
PennDOT
Grant Cycle
PennDOT
Visibility
Public

Abstract

The objective of this project is to analyze social media streaming data (Twitter and traffic radio streams as the test case) for real-time incident detection on the major road infrastructure of Pennsylvania. Determine the feasibility of creating a real-time capability to alert Traffic Management Centers (TMCs) about ongoing statewide incidents, broadly defined as significant disruption to the traffic flow on major road infrastructure.    
Description
-
Timeline
May 2015 - May 2016
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
$92716.00
Total Project Budget (from all funding sources)
$92716.00

Documents

Type Name Uploaded
Final Report 133_-_Real_time_Incident_Detection_Using_Social_Media_Data.pdf June 22, 2018, 12:53 p.m.
Publication Estimating multi-year 24/7 origin-destination demand using high-granular multi-source traffic data. Dec. 2, 2020, 9:08 a.m.
Publication An Empirical Assessment and Investigation of the Driver Injury Severities in Rain-Related Rural Single-Vehicle Crashes Using Mixed and Latent-Class Logit Models Dec. 2, 2020, 9:14 a.m.
Publication Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models. Dec. 2, 2020, 9:17 a.m.
Publication Cluster Analysis of Probabilistic Origin-destination Demand Using Day-to-day Traffic Data Dec. 2, 2020, 9:26 a.m.

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