#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
| Name | Affiliation | Role | Position | |
|---|---|---|---|---|
| seanqian@cmu.edu | Qian, Sean | Carnegie Mellon University | PI | Faculty - Research/Systems |
Budget
Amount of UTC Funds Awarded
$92716.00Total Project Budget (from all funding sources)
$92716.00Documents
| 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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