Emergency response vehicle travel time analysis
Fire departments, ambulance services and police departments often worry if they are providing prompt response times in case of an emergency. To be effective, emergency response vehicle (ERV) have to be on the scene within a certain time of the initial emergency call. Emergency response vehicles are exempt from many traffic regulations like speed limit, crossing red signal and moreover other vehicles are expected to yield for ERV. Hence the response time analysis of ERV is very different from the regular traffic study. Advancements in the field of traffic signal control technology brought into picture new traffic signal control device (TCD). These TCDs automatically detect arrival of an ERV to turn traffic signal green for the fire engine to go through. Unfortunately, high installation costs limit the number of TCDs that can be deployed. The key goal of this article is to identify potential intersections in a traffic system for the installation of a TCDs. In this article we propose a method of using Global Positioning System (GPS) data from ERVs to identify slow spots in the traffic system. we start with a brief overview of different map matching techniques. But, most of the map matching algorithms only try to relate the GPS points to the nearest road segment with the objective of only recreating the original path. These methods doesn't help analyze travel time. So we present Pre, Post map match process along with a customized map matching process including a nodal network of entire routes in Austin. Dynamic sizing for identify candidate points. Segment wise wait time analysis. Determine Busy intersection by frequency weighed time delay. We finally present results of the algorithm on 2 years of ERV GPS data from Austin Fire Department. Determine important intersection by frequency weighed time delay. The result can be used by different ERV to significantly improve response times, while meeting the budget restrictions.