Mobility Data Analytics Center
|University:||Carnegie Mellon University|
|Principal Investigator:||Sean Qian|
|PI Contact Information:||email@example.com|
|Funding Source(s) and Amounts Provided (by each agency or organization):||$99,998.00|
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|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.
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