Login

Project

#121 Mobility Data Analytics Center

Principal Investigator
Sean Qian
Status
Completed
Start Date
July 1, 2015
End Date
June 30, 2016
Project Type
Research Advanced
Grant Program
MAP-21 TSET National (2013 - 2018)
Grant Cycle
2015 TSET UTC - National
Visibility
Public

Abstract

Mobility Data Analytics Center aims at building a data engine to efficiently manipulate multi-modal multi-jurisdictional data for smart decision making. Integrating and learning the massive data are the key to the data engine. This research is a continuation of UTC funded research ‘Mobility Data Analytics Center’ in the year of 2014. In the past year, we have started building the data engine and a prototype web application to demonstrate the feasibility of Mobility Data Analytics Center. In this continuation, we set our focus on the following three aspects:

Data integration and system enhancement. We continue to collect up-to-date data from various data providers in the Pittsburgh region and improve the web application. The web application will be substantially enhanced so that it will allow travelers and agencies access historical, real-time, and forecasted traffic metrics (such as travel time, delay, crash rate, etc.) in multi-modal transportation systems (roadway, parking and transit).

Data analytics. We will conduct in-depth data analytics on two selected data sets, traffic data on the major arterials in the city and transit APC-AVL data in Allegheny County. These two data sets are among the most promising to demonstrate the effectiveness of Mobility Data Analytics for decision making. We analyze real-time traffic data collected by the signal control system in the East Liberty Area, and to develop and test incident detection/management tools on urban streets. We analyze large-scale APC-AVL transit data to provide both travelers and transit agencies fine-grained customizable information regarding transit service performance (efficiency, reliability and quality). Additionally, scheduled departure time will be optimized.

Network models. We will build a sophisticated multi-modal transportation network model for the City of Pittsburgh. Operational strategies and policies can be fully examined in the network model in terms of system delay, reliability, vehicle-miles traveled (VMT), fuel consumption and emissions.
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
 $99998.00
    
Total Project Budget (from all funding sources)
 $99998.00

    
Documents

    
        

            
                

                    Type
                    Name
                    Uploaded
                

            
            
                
                    
                
                    
                
                    
                    

                        Final Report
                        
                            121_-Qian2_TSETFinalReport.pdf
                        
                        June 22, 2018, 12:54 p.m.
                    

                    
                
                    
                
            
        

    

    
Match Sources

    
        
No match sources!

    


    
Partners

    
        
No partners!