Cellular Network Events for Traffic State Estimation

Several measurement technologies exist. Nevertheless, it is a general aim to create an efficient measurement system in high traffic areas of the cities. Ideally, each road link of the network could provide traffic information, e.g. by loop detectors, camera, on-board units. A novel approach to this problem is the traffic measurement and estimation through the events generated in the cellular phone network. Thus, one of our main research subjects is the applicability of mobile signaling events for traffic measurement. Related to this research our laboratory has been in cooperation with Nokia Siemens Networks to develop travel time estimator since 2011.

The use of signaling events of the cellular network opens a way for road traffic estimation and modeling in a macro level. This can be realized as real-time algorithm providing a base for adaptive intelligent traffic management. The basic elements of cellular phone network (Fig. 1) are the cell (covered by the base station) and the Location Area (LA) combining several cells. The shape of them depends on several factors and is never circles exactly. Nevertheless, just for the better explanation: the radius of the cells is about a few hundred meters and the same of the LA is about a few kilometers in urban area.


Figure 1: Schematic representation of the GSM network (Küpper, 2005)

Two characteristic events of the network are Handover (HO) and Location Area Update (LAU) which are generated by the transitions between the cells and LAs (Fig. 1). HO occurs when the mobile phone is in call and changes the cell. LAU is generated by idle phones (not in calls) changing the LA. HO/LAU events are automatically stored by the operator of the telecommunication network through the base stations. Our research aim is to develop efficient algorithms which exploit the knowledge of HO/LAU events for traffic estimation.

The mobile users create traces in the traffic network through their HO/LAU events depicted in Fig. 1. These data can be efficiently utilized in an aggregate way to estimate traffic even in real-time mode. The proposed method has two steps. First, a trip matrix of the traffic network must be created. Trip or OD (origin-destination) matrix determines the traffic flows between each OD pairs of the network (Fig. 2).


Figure 2: Structure of the OD-matrix (Luspay et al., 2011)

OD matrix can be created through traditional methods (surveys) or by the appropriate use of the LAU events (Calabrese et al., 2011). The second step of the method consists of the traffic assignment which is generally applied based on OD matrix. Typically, traffic assignment is an optimization procedure which determines the route choices with traffic volumes between the OD pairs. The determination of the potential paths through traffic assignment is an easy task. However, the traffic volumes assigned to the paths may easily become inaccurate since assignment considers generally “only” the data of the OD matrix and the network topology. A potential solution to the reliability problem is given by the use of HO events. Although HOs are generated only by phones in call, by collecting all of them the typical traffic behaviors can be identified. Practically, the trajectories revealed through HOs can be fitted to the paths defined by the traffic assignment. Therefore, more accurate estimates are provided concerning the traffic volumes.

The results of the research has already been published (Tettamanti et al., 2012 and Tettamanti et al., 2014). The paper presents the preparation step for the path-fitting method described above. For the modeling of the radio cells the Voronoi tessellation was applied (Candia et al., 2008) which requires only the coordinates of the base stations (Fig. 3).


Figure 3: Voronoi tessellation for the modeling of radio cells

The method is represented through the results of a test measurement in Budapest. The cells provided by the HOs of the test device were fitted to the routes resulted from the traffic assignment. One path was found as the most likely one among the potential routes. Path 4 (Fig. 4) had the smallest squared deviation compared to the others.


Figure 4: Path with the smallest squared deviation between the origin-destination pair

We plan to investigate further possibilities in the field of mobile phone event based traffic applications, as well the efficient fusion of these data with other measurements (e.g. loop detector).


Related publications

Luspay, T., Tettamanti, T., Varga I.: Forgalomirányítás, Közúti járműforgalom modellezése és irányítása. Typotex Elektronikus Kiadó Kft. ISBN 978-963-279-665-9, 2011

Tettamanti, T., Demeter, H., Varga, I.: Route choice estimation based on cellular signaling data, Acta Polytechnica Hungarica, 9(4):207–220, 2012

Tettamanti, T., Ludvig, Á., Varga, I.: Travel time estimation in urban road traffic networks based on radio signaling data, MITIP, Budapest, 2012, pp. 514-527. ISBN 978-963-311-373-8

Tettamanti, T., Varga, I.: Urban road traffic estimation based on cellular signaling data, MITIP, Budapest, 2012, pp. 220-230. ISBN 978-963-311-373-8

Tettamanti T and Varga I (2014), "Mobile Phone Location Area Based Traffic Flow Estimation in Urban Road Traffic", Columbia International Publishing Advances in Civil and Environmental Engineering. Vol. 1(1), pp. 1-15.



Calabrese, F., Di Lorenzo, G., Liang, L., Ratti, C.: Estimating Origin-Destination flows using mobile phone location data. 2011, Pervasive Computing IEEE, 10(4):36-44.

Candia, J., González, M. C., Wang, P., Schoenharl, T., Madey, G., Barabási, A.-L.: Uncovering individual and collective human dynamics from mobile phone records. Journal of Physics A: Mathematical and Theoretical, 41(22):224015, 2008.

Küpper, A.: Location-based Services. John Wiley & Sons, 2005.

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