Master of Science (MS)
Civil and Environmental Engineering
The identification of congestion hot spots plays an important role in congestion monitoring of highway and arterial systems. Traditionally, vehicle detection systems or probe vehicles are used to measure travel time and identify congestion hot spots. This approach is time-consuming and resource intensive. With the advent of new technologies like crowdsourcing, travel time can be collected more cost-effectively using fewer resources. The research presented in this paper develops a new methodology to identify congestion hot spots in an urban area using freely available historical speed data gathered by Google and published as a traffic layer in Google maps. The methodology uses image analysis techniques like image segmentation and pixel analysis to formulate a sequence of steps that identify areas consistently experiencing congestion. As a case study, the developed methodology was applied in the Baton Rouge metropolitan area for the months of August and September 2013 and then compared with speeds and travel time obtained from Bluetooth signal detection devices deployed in the same region. The results showed that the methodology developed in this thesis could identify congestion hotspots that are consistent with data acquired through Bluetooth-driven measurement of travel time. The methodology can be used for areas where Google traffic maps (or other maps with color coded traffic data) are freely available as a quick and inexpensive method of identifying congestion hotspots. Since the method also quantifies the degree of congestion, the method can be used to prioritize corridors for applying congestion mitigation measures. The research presented in this paper is of potential benefit to traffic engineers, State DOT’s and other policy makers who are interested in identifying congestion hotspots in urban areas.
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Mokkapati, Aditya, "Determining the Location of Congestion Hot Spots in Urban Areas Using Image Analysis" (2014). LSU Master's Theses. 890.