Highway Network Analysis and Detection Assessment Framework

Dynamic traffic management (DTM) monitors the traffic situation and tries to optimize it by applying different control measures like speed limits, route guidance, lane closure, ramp metering, intersection control or others to the traffic network. Traffic models are used to support traffic engineers with the optimization task by predicting the effect of different measures before applying them to the real network.Vital for the DTM cycle is traffic data collection to enable the feedback loop to traffic operations. Roadside detection is nowadays the most common way of traffic data collection, but probe vehicle data, GSM data and vehicle to infrastructure communication are getting more and more momentum. With the new variety in data collection methods, research on data fusion, to combine different data sources to better traffic state information increases, but the detector placement problem is a less explored research topic in transportation. In this paper we will describe a framework for detection installation assessment on highway networks, taking into account the infrastructure, demand and authority policies. In a first step we will present algorithms to automatically detect points of interest in the network that require more dense detection as a starting point for practitioners to define the policy based level of information they require for the network. The level of information can be defined by adjusting data demand functions resulting from the analysis. Placed detectors in the network are represented with data supply functions based on the sensor technology and measurement entity. A comparison of both functions for each link in the entire network will result in a level of detection that can be used to assess detection installation objective.