Predictive model of campus foot traffic

We collect foot traffic data from phones in an area with our sensors. we make a predictive foot traffic model for the area using past data and elements that impact such as weather, day of the week, local events. Sensors are installed in Geisel and Biomed Library, RIMAC and Main Gym, and other locations.
Type of Data: 
Numeric values
Approximate Data Size: 
Over 120 million data points of signals counts in different areas
Domain Expert: 
Methods Expert: 
Methods Student Openings: 
Methods Student Funding: 
Methods Student Prerequisites: 
Predictive analysis knowledge (time series analysis experience in Python) Data collection techniques: Web scrapping (python or javascript), database exposure (relational or noSQL)