Traffic Data Hits on Real-Time Needs
For decades, state and local departments of transportation have collected traffic data by means of a variety of methods — including sub-surface magnetic induction loops, pneumatic hoses laid across lanes, piezoelectric sensors placed alongside roadways, and vehicle counts by human observers. These traditional traffic data collection methods, however, are limited in coverage and expensive to implement and maintain.
In recent years, alternative sources for traffic data have evolved rapidly, for two reasons: first, the increasing demand for high quality, real-time data to enable intelligent transportation systems (ITS); second, the widespread availability of data on the location, heading, and speed of vehicles from GPS receivers embedded in them and in the smart phones of their drivers — known as crowd-sourced data. This data has greatly increased the ability of government agencies to manage traffic and of private companies to help drivers avoid it. While these developments are making access to real-time traffic information routine, systematic implementation of traffic management systems in metropolitan areas still faces obstacles — most notably, the limited market penetration of smart phones and the fragmentation of authority over roads among many public agencies.