Danilo Valerio

On Wireless Links for Vehicle-to-Infrastructure Communications

Future intelligent transportation systems (ITS) will necessitate wireless vehicle-to-infrastructure (V2I) communications. This wireless link can be implemented by several technologies, such as digital broadcasting, cellular communication, or dedicated short-range communication (DSRC) systems. Analyses of the coverage and capacity requirements are presented when each of the three systems is used to implement the V2I link. We show that digital broadcasting systems are inherently capacity limited and do not appropriately scale. Furthermore, we show that the Universal Mobile Telecommunications System (UMTS) can implement the V2I link using either a dedicated channel (DCH) or a multimedia broadcast/multicast service (MBMS), as well as a hybrid approach. In every case, such V2I systems scale well and are capacity limited. We also show that wireless access in vehicular environment (WAVE) systems scale well, provide ample capacity, and are coverage limited. Finally, a direct quantitative comparison of the presented systems is given to show their scaling behavior with the number of users and the geographical coverage.

Exploiting Cellular Networks for Road Traffic Estimation: A Survey and a Research Roadmap

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.

UMTS on the Road: Broadcasting Intelligent Road Safety Information via MBMS

In this work we explore the feasibility of implementing infrastructure-to-vehicle (I2V) communication through the UMTS infrastructure. We identify the problems that arise when providing I2V services on top of legacy UMTS networks as conceived in 3GPP Release 5 and consider an I2V architecture that exploits a new feature introduced by 3GPP Release 6, namely the multimedia broadcast/multicast services (MBMS). By means of an analytical model, we quantify the performance achievable with both releases in some realistic scenarios. We show that MBMS is able to provide I2V services efficiently on top of the UMTS network.

Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation

Road traffic can be monitored by means of static sensors and derived from floating car data, i.e., reports from a sub-set of vehicles. These approaches suffer from a number of technical and economical limitations. Alternatively, we propose to leverage the mobile cellular network as a ubiquitous mobility sensor. We show how vehicle travel times and road congestion can be inferred from anonymized signaling data collected from a cellular mobile network. While other previous studies have considered data only from active devices, e.g., engaged in voice calls, our approach exploits also data from idle users resulting in an enormous gain in coverage and estimation accuracy. By validating our approach against four different traffic monitoring datasets collected on a sample highway over one month, we show that our method can detect congestions very accurately and in a timely manner.

The Cellular Network as a Sensor: From Mobile Phone Data to Real-Time Road Traffic Monitoring

Mobile cellular networks can serve as ubiquitous sensors for physical mobility. We propose a method to infer vehicle travel times on highways and to detect road congestion in real-time, based solely on anonymized signaling data collected from a mobile cellular network. Most previous studies have considered data generated from mobile devices active in calls, namely Call Detail Records (CDR), an approach that limits the number of observable devices to a small fraction of the whole population. Our approach overcomes this drawback by exploiting the whole set of signaling events generated by both idle and active devices. While idle devices contribute with a large volume of spatially coarse-grained mobility data, active devices provide finer-grained spatial accuracy for a limited subset of devices. The combined use of data from idle and active devices improves congestion detection performance in terms of coverage, accuracy, and timeliness. We apply our method to real mobile signaling data obtained from an operational network during a one-month period on a sample highway segment in the proximity of a European city, and present an extensive validation study based on ground-truth obtained from a rich set of reference datasources - road sensor data, toll data, taxi floating car data, and radio broadcast messages.

Road traffic estimation from cellular network monitoring: A hands-on investigation

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.