Real time travel time estimation from big data

Millions of cars on road generate tons of data every day. Vehicle communication technology (including drivers' smart phones connected with 3G/4G/WiFi) provides us a new way of accessing this rich dataset.

University of Michigan Transportation Research Institute (UMTRI) hosts a connected vehicle research project called SafetyPilot originally intending to improve traffic safety by utilizing vehicle communication technology. Each participating connected vehicle has a message broadcasting equipment, as well as a GPS sensor. Some more advanced vehicles may equiped with message receiving device and/or even onboard warning system. Basic Safety Message is the essential information transmitted by these connected vehicles and some Road Side Equipment (RSE). BSM contains two parts of information. The first part is mandatory, including vehicle's GPS location, speed, acc/dec rates, paddel status, ligth status, heading direction and much more. The second part is optional environmental information, including for example weather condition, bus schedule etc. These BSMs are transmitted via Dedicated Short Range Communication (DSRC) at 10Hz frequence (That's really alot of data).

Evey 3-6 month, these vehicles will come back to UMTRI and upload their data. Since the commencement of the project in 2012, UMTRI is now hosting more than 70 billion BSM records on more than 4 million trips, all come from about 3000 connected vehicles (3~4% of the total car ownership).

Diving into this database, we are able to locate the data records we need in order to produce a road travel time estimation for the City of Ann Arobr. Due to the data availability, we present the visualized map for this city at 07:00 to 10:00 on Dec 2, 2013. Without doubt, same data processing method could be replicated to get road travel time estimation for any other time window, and even real time.

The tool we used to produce the map is ArcGIS 10.2. We produced a map for each 10 minutes. Each map, as a frame in the video, will be shown for 3 seconds. For your convenience, static maps (click for larger view) are also included at the end of the post.

 

 

 

 

 

 

 

 

 

Use Python to plot the vehicle count between two consecutive intersections

# this is part of a research project in which we are estimating the real time vehicle count on a road segment

# this preliminary result is produced by processing vehicle count data from loop detectors embedded under two consecutive intersections

# we studied three consecutive links (i.e., in total four sets of detectors included)

# the time frame for the data is 0700~0800, Dec 01~31, 2008

# the vehicle count(T) = vehicle count(T-1) + upstream detector count - downstream detector count [1]

Figure 1: vehicle counts on each link during weekdays

Figure 2: vehicle counts on each link during weekends

The vehicle counts, as one may noticed, are sometimes negative.

This is because, as in [1], we don't have data on the vehicle count(T-1). It is assumed to be 0.

The consequent of this assumption is that, the ploted figures (could be also taken as the distribution), are shifted figures from the original real data. One can add a base on X to reconstruct the real situiation, as long as the base is well estimated/measured.