In this contribution we address the problem of using cellular network signaling for inferring real-time road traffic information. We survey and categorize the approaches that have been proposed in the literature for a cellular-based road monitoring system and identify advantages and limitations. We outline a unified framework that encompasses UMTS and GPRS data collection in addition to GSM, and prospectively combines passive and active monitoring techniques. We identify the main research challenges that must be faced in designing and implementing such an intelligent road traffic estimation system via third-generation cellular networks.

In this paper we present a road traffic estimation system built on top of the cellular network infrastructure. Based on the concept that many road users are also customers of a cellular operator, we show that specific road conditions map to certain signaling patterns in the cellular core network. In order to estimate the road traffic, signaling is collected from the core network of an operational mobile network. We explore the feasibility of using mobility-related signaling for pantomiming local-loop sensors, i.e. counting the number of vehicles crossing a specific road section. In this work we present the main system component and discuss a number of practical issues to be considered in the deployment of such system. Based on the explorative analysis of real signaling data, we show how normal and abnormal road conditions (e.g. accidents) map into mobility signaling in a real cellular network. I. INTRODUCTIONRoad congestions contribute to air pollution and greenhouse gas emissions, and often cause non negligible economic losses to many developing and developed countries. The US has one of the world's highest level of road congestions, leading to yearly costs up to 200 billion USD [1]. In Europe, road traffic congestion is estimated to affect 10% of the road network leading to yearly costs of 0.9-1.5% European Union GDP [2]. In Japan, the Ministry of land, infrastructure, and transport estimates an economy loss of about 12 trillion yen caused by road congestions [3]. In order to optimize the use of the road networks and improve road-safety, several public institutions launched intelligent transport system (ITS) projects.Improving road traffic management for reducing road congestions is one of the key challenges for ITS projects. Systems for real-time traffic and travel information (RTTI) need to be deployed in order to assist drivers in choosing appropriate routes and avoiding congestions. This includes means to collect information about road traffic status, algorithms for processing the data, and means to inform the final users.With the current state-of-the-art technologies, traffic data collection is based on short-range sensors placed alongside the road, cctvs, and emergency calls from drivers. Such data are then processed by the traffic control center of the road operator domain and forwarded to third parties for the final dissemination to the drivers via FM radio or other communication means. This approach does not scale well: a system that covers the entire road network is not economically feasible. As a result, sensors are usually placed only in important traffic junctions, thus traffic estimation is limited to certain areas.In this work we envision the framework of an intelligent road traffic management system that exploits the cellular network infrastructure for sensing the road status. The system is based on the concept that almost all road users are also subscribers of the cellular network operator and carry one or more active mobile phones in the vehicle. Hence, a flow of mobile

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