Exploiting cellular networks for road traffic estimation: a survey and a research roadmap Danilo Valerio∗§ , Alessandro D’Alconzo∗ , Fabio Ricciato∗† , Werner Wiedermann‡ ∗ Telecommunications
Research Center Vienna (ftw.), A-1220 Vienna, Austria of Applied Science Technikum Wien, A-1200 Vienna, Austria † Universit`a del Salento, Lecce, Italy ‡ Mobilkom Austria AG Email: {valerio,dalconzo,ricciato}@ftw.at;
[email protected]
§ University
Abstract—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.
I. I NTRODUCTION Providing real-time road traffic information to road users has become the natural next step in traffic telematic after the proliferation of navigation system equipments. The knowledge of the user location only does not allow navigation equipments to estimate journey durations and calculate the best routes by considering the current road traffic conditions. In order to provide such a service, GPS technology needs to be combined with other systems for the collection, the processing, and the distribution of road status data to the end users. Currently, data collection is done by road operators by using road sensors, cctvs, and emergency calls from road users. Data is then processed in traffic control centers and forwarded to third party entities for the final dissemination to the road users via FM radio or other communication means. This approach presents a cost hurdle: a complete coverage of the road network would not be possible without the employment of new infrastructure. Driven by the fact that each road user on a car is also a potential user of a cellular network, it is natural to consider mobile operators as an alternative source of road traffic information. The use of cellular networks for estimating patterns of human mobility within a country has been subject of several studies in the past decade (e.g. [1]). Only recently, the increased network coverage, the refinement of positioning methods, and a full market penetration of cellular technologies led to the idea of using mobile networks to monitor the road traffic. A feasible scheme would let cellular operators grab a potential share of market by either selling this service to navigation system equipment vendors and/or road operators, or providing this added value service to their customers. In this work we make the following contribution: First, we present a survey of existing approaches for inferring
road traffic condition by using cellular network signaling. We summarize and categorize research works presented in literature and cite the main activities that have been conducted by industries and public institutions. Second, we review the existing approaches and identify the main problems and limitations in the light of the ongoing evolution toward third and fourth generation cellular networks. Finally, we propose a novel framework that extends the existing monitoring systems to UMTS and GPRS data and combines passive and active monitoring techniques, highlighting the main research challenges. The remainder of this paper is organized as follows: in section II we categorize the existing approaches and describe their main advantages and limitations. Section III describes our framework and the technical aspects in the development of a road monitoring system via cellular network. In section IV a research agenda is presented and the main challenges are identified. Finally, in section V the conclusions are given. II. S URVEY OF R ELATED W ORKS AND P ROJECTS The concept that a flow of mobile phones can be mapped to a stream of road users led in the last few years to relevant industrial activity. Several methods can be used for collecting mobility/location data from the cellular network. A first high level differentiation can be done between active and passive monitoring systems. A. Active techniques In active monitoring, the procedure used by the network to gather information about users’ location and/or position1 generates additional signaling traffic. A typical procedure used for actively refine a user location information is the paging procedure, which is normally used by the network for localizing a User Equipment (UE) in case of incoming calls/connections. Paging might be used by the network to localize generic UE, even when no incoming calls are present. On the other hand, the positioning mechanisms defined in 1 Though the terms “location” and “position” are often used as synonyms, throughout this work we refer to location as the location of the user terminal in the logical structure of the network (cell, routing area, location area, etc.) and to position as the geographical position of the user terminal in terms of geographical coordinates.
the context of location services (LCS) are more complex procedures used for retrieving information from a UE and calculating its geographical position, e.g. CellID-based positioning, Observed Time Difference Of Arrival (OTDOA), and Assisted-GPS (A-GPS). Positions derived by active monitoring can be highly accurate, ranging from the cell dimension (CellID-based positioning) down to a few meters (A-GPS). Solutions based on active monitoring are quite common on the market. A good survey on various field-tests in Europe and USA can be found in [2] and [3]. Simulation-based analysis of active monitoring systems have also been presented in literature. In [4] the impact of several system parameters is studied, such as sampling frequency, accuracy of the locations, number of locations available in a given area, etc. In [5] a segment-based method for active monitoring is analyzed by considering several variables, such as data collection interval, location update interval, and mobile penetration rate. Application-based active monitoring is a special subcategory of active monitoring systems. It is based on a client-server model. The mobiles run a dedicated software that reports location or movements to a server outside the cellular network. Such reports are transparent from the network perspective. A typical example consists of a car-mounted GPS receiver equipped with a GPRS transceiver, which reports the position to a server via the cellular network. Application-based active monitoring has gained popularity in the last years. In 20002002 the Optimized Traffic in Sweden (OPTIS) project [6] focused on the estimation of road traffic conditions by GPS report via the GSM network. Results showed that travel time information of good quality can be produced with the OPTIS concept. However computer simulations showed that high quality travel times with updates each minute can be produced only with a penetration of probes above 3-5% in a mid sized city. In the Netherlands, GPS user positioning data are used together with other GSM information to generate road traffic data [7]. At the time of writing the product is available on the market. In [8] a software installed on volunteers’ mobile phones is used for collecting Cellular Dwell Time (CDT). The latter is then used to characterize walking users and sky trains.
The first project aimed at using mobile phone as traffic probes was the Cellular Applied to ITS Tracking and Location (CAPITAL) project [9]. The goal was to generate traffic condition estimates, such as speed and travel time, by using monitoring equipments in eight cellular towers to geolocalize all the mobile phones being actively used. The system was not able to accurately estimate the car speeds and detect incidents. In Europe, the STRIP project [10] focused on the calculation of travel times from GSM signaling captured on A/Abis interfaces. A field test in the Rhone Corridor of Lyons (France) [11] showed good correlation between the cellular phone data and the loop detector data in the motorway segments. However a speed underestimation of ca. 30% was observed in correspondence of urban ring segments with many commercial stops. In [12] cellular phones are tracked using measurements report records generated by the terminal to the network. Results showed that the number of measurements was sufficient to generate average speeds with an accuracy of 8-16 Km/h. In the same period, several other projects have focused on GSM passive monitoring, e.g. [2], [13], [14]. In mid 2003, a product of Applied Generics was tested in the Netherlands in partnership with Logica plc (RoDIN24). The software evolved and it is now used by Vodafone Netherlands in cooperation with Tomtom Mobility Solutions [7]. It provides Road status information by using GSM users positioning combined with GPS positions reports. However the technical approach and the used business model are not clear, because of lack of publicly available information. In Germany, the final project report for the research initiative Trafficonline is expected for the end of 2008 [15] and a following commercial exploitation by the Vodafone traffic.online service is expected. First results of a field test are given in [16]. The objective of the project is to use the GSM network data to generate traffic data information and to determine whether call volume is related to traffic volume. Other commercial projects are recently active in the area of Floating Phone Data (FPD), or equivalently Cellular Floating Vehicle Data (CFVD) (e.g. [17] and [18]), but none of them present a detailed description of methods, algorithms, and results to the research community. C. Problems and Limitations
B. Passive techniques In contrast to active monitoring, with passive techniques signaling is silently collected from one or more points in the network with no impact in the offered network load. Realtime information, such as users behavior, terminal movement history, and terminal positions are then retrieved by processing the collected signaling. The amount/type of the retrieved information depends on the placement of the monitoring points and on the state of the user terminal. Details on such a dependency will be given in section III for both GSM/GPRS and UMTS technologies. In general, the closer the monitoring point is to the base station (node B in UMTS), the larger the amount of relevant data that can be collected. Similarly, the higher the activity of the user on its terminal, the larger the information that can be collected.
A thorough review of the various mechanisms presented in literature reveals a number of limitations. Active techniques might present scalability problems when used in the context of road monitoring, where a massive number of UE should be localized. Using paging or LCS positioning mechanisms for a large number of UEs requires the transfer of a considerable amount of data. The risk is to exhaust precious network resources, particularly on control channels of the radio interface, and impact the service availability for conventional cellular users. Another drawback relates to the UE battery: each time a terminal reports information to the network it consumes power, reducing the stand-by time. In case of application-based active techniques battery life is not an issue (the typical scenario is a car-mounted GPS receiver equipped with a GPRS transceiver). However, such a
technique is ineffective until the devices running the dedicated software reach a considerable market penetration. Also the billing issue needs to be taken into account. Passive techniques suffer from different types of problems, primarily the quality and granularity of the available information. All previous studies on passive techniques focus on the Circuit Switched (CS) GSM network and ignore the ongoing evolution towards Packet Switched (PS) networks, which is the main feature of 3G and 4G systems [19]. Though most of the 3G devices can be attached to both CS and PS domains, the trend is to migrate some common mobility functions to the PS domain (e.g. combined Routing Area and Location Area updates, as discussed in Section III). Therefore, monitoring exclusively the CS domain might lead to a serious shortage of data in future cellular network. To the best of our knowledge, at the moment of writing only one publication provides a theoretical description of the new possibilities offered by 3G networks in the context of road traffic monitoring [20]. The latter however focuses on the Radio Access Network (RAN), leaving the new Core Network (CN) mechanisms unexplored in this field. III. E XTENDED M ONITORING F RAMEWORK In this section we propose a novel framework, which aims at overcoming the above limitations by combining different techniques in an integrated framework. The idea is to deploy a system that goes beyond GSM monitoring by also covering UMTS and GPRS, i.e. the packet switched domain, whose market penetration is increasing quickly. Moreover, while current works concentrate either on active or passive network monitoring, we propose a hybrid bi-modal scheme that uses passive monitoring by default and switches to active monitoring in selected areas, whenever a passive-only monitoring is not sufficient to achieve the required accuracy. This is typically the case when the number of active/connected users is low. Implementing an intelligent traffic management infrastructure on top of the cellular network consists of several steps (see Fig. 1). First, location and mobility data needs to be collected from the network. The collected information is then sent to Cellular data collection
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a processing unit that, with the help of geographical maps, signal strength maps, and other data/information available to the cellular operator, filters out data related to non-road users. Finally, in a third phase users of different roads are differentiated and the road traffic condition is inferred out of the collected data and then distributed to the system users.
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At the moment of writing we are deploying the passive monitoring component for the PS domain. Fig. 2 depicts the architecture of our monitoring system. Probes are installed across the CN infrastructure of an Austrian cellular operator. In particular, we collect signaling messages from Gb, IuPS, and IuCS interfaces. We plan to extend our system with Iub interface by 2009/Q2. In this study we focus on the CN data. Signaling messages are first anonymized to preserve the privacy of the users. Tracing units collect and aggregate signaling data, extract events, and deliver them in the form of event-based tickets to a processing unit. The accuracy of mobility events collected from the network probes is bound to the 3GPP Mobility Management (MM) protocol. MM lays its foundation into three logical entities: Location Area (LA), Routing Area (RA), and cell. Without the use of active techniques, these are the only location information that we can gather from our probes. A cell is the area covered by a base station (called BTS in GSM and node B in UMTS). Cells are grouped in RA (for the PS domain), and LA (for the CS domain). Note that different RA can be part of the same LA, i.e. RA are smaller than LA. The type of events collected by our system varies depending on the MM state of the user terminals. Fig. 3 depicts the MM state machines used by the UE and the CN for different type of services. When a terminal is attached to the CS domain (IMSI attach) it follows the MM state machine depicted in Fig. 3(a). If not used, the UE stays in MM idle state. In this state, it sends Location Area Updates (LAU) whenever a new Location Area Identifier (LAI) is detected. When the user starts a call the UE switches from MM idle state to MM connected state. In this state the UE signals to the CN all cell changes with Cell Update messages. If the terminal is attached to the PS domain in GPRS Iu mode (Packet IMSI attach), it follows the state diagram depicted in Fig. 3(b). In PMM idle state the CN knows the location of a UE down to the accuracy of a RA. This makes PS information in idle state more accurate than the CS counterpart — recall that RA are smaller than LA. On the other side, the location of a UE in PMM connected state is known by the CN at the granularity of the Serving RNC (SRNC). If the terminal is attached to the old GPRS A/Gb mode (GPRS attach), it follows the state machine depicted
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in Fig. 3(c) — note that the terminology is different from the diagrams seen above but the semantic of the states is the same. When the terminal is switched on but not engaged in any connection, it stays in Standby state and informs the CN only upon RA changes. When a GPRS session starts, the UE switches to the Ready state and the CN can track its location at the cell level. In summary, by monitoring both CS and PS domain interfaces, the monitoring system is able to capture the location at cell granularity for terminals in ready state and MM connected state, at RA granularity for terminals in Standby state and PMM idle state, and at LA granularity for terminal in MM idle state. 3G UE can be attached to both CS and PS domains simultaneously. In this case, in order to avoid duplicated signaling, networks can be set to operation mode I: the UEs perform only RA update terminating at the SGSN, and the latter communicate the location change to the MSC/VLR through the newly introduced Gs interface. Besides mobility events, the processing unit collects additional signaling information that might relate to particular road condition. More specifically, we collect the following events: • Gb interface: Attach, Detach, and CS Paging; • IuCS interface: Detach, CS Paging, SMS mobileoriginated, SMS mobile-terminated, Call Setup, Connect Ack, and Disconnect; • IuPS interface: Attach, Detach, and PS paging. At the time of writing we have completed the deployment of the tracing and processing units and we are collecting signaling events from the operational network. We are currently exploring the data at hand for the design of road status inference algorithms. In the next section we highlight the primary research challenges that we are set to address in the continuation of our work. IV. R ESEARCH C HALLENGES The final goal of the proposed system is to map events on the cellular network to events on the road network. To this end, two software agents run in sequence on the processing unit. The first agent takes as input network events (i.e. mobility and activity patterns of anonymized users) and singles out data concerning road users on the road of interest. The output of
the first module is then processed by the second agent in order to infer the road traffic condition. The complexity of each of these steps depends on the considered scenario. For the sake of simplicity, we first focus on highways in rural areas. We will then extend our framework to highways near urban areas, where several entry points or parallel roads/railways increase the complexity of the system. Finally, we will consider urban area roads where data collected from probes on the radio access network are needed. A. Single out road users The first step is to obtain a data subset of road users only. Static cellular users (e.g. in-building users) present specific characteristics as they remain camped into a single LA/RA/CELL for considerably longer periods in comparison to moving outdoor users. Hence, active users can be easily filtered out by looking at the cell update rate, whereas idle users can be filtered out by looking at LA/RA update rate. However, the remaining subset is not yet restricted to road users only. Walking users and users on transportation systems different than cars (e.g. trains, trams, etc.) need to be identified and excluded from the processing. In [8] a model for differentiating walking users from sky train passengers is proposed by statistically analyzing the user permanence in a cell. Experimental results showed promising performance with accuracy up to 93%. In [16] a method for filtering train passengers is evaluated in a scenario where the railway is parallel to the road. Finally, [21] proposes methods to classify subscribers in public transport vehicles in spite of low position accuracy. By exploring the collected traces we found that close to the railways the trend of LA/RA/CELL updates presents regular spikes. The latters are generated by large number of users updating their position almost at the same time, i.e. train passengers. These users can be easily identified because their trajectories are highly correlated. Once the subset of the road users has been obtained, users on different roads should be distinguished. Note that this task can be difficult when considering users on parallel roads, since they might present extremely similar LA/RA/CELL update time sequences. In this case we propose to track LA/RA/CELL updates history, for each user, for a suitable amount of time. Looking at the recent trajectory records for individual users
C. Smart Integration of Active and Passive Techniques Altough monitoring CS and PS domain can suffice in detecting road condition in simple scenarios, a pure passive monitoring might fail in more complex situations. For this reason it can be necessary to complement a passive system with active techniques. In two cases active techniques can be used to improve the accuracy of passive systems: Low number of active users: Passive monitoring can deliver road status information only if a sufficiently high number of users is active (calling or connected). When the number of active users decreases in a certain area the system looses resolution. On the other hand, a low number of active users means that the network capacity is underutilized, therefore active monitoring can be activated without fear of impacting the network performance. Event uncertainty: When passive monitoring detects some abnormal condition in the road that cannot be clearly mapped to a road event, active monitoring can be temporarily enabled as a magnifying lens to gather more detailed information. V. C ONCLUSIONS In this contribution we have reviewed the existing approaches for road traffic estimation via cellular networks. In order to follow the ongoing evolution of the cellular infrastructure, passive monitoring of PS and CS signaling monitoring must be combined in a single framework. Furthermore, we have outlined the vision of a hybrid system that complements passive monitoring with active techniques. We have recently deployed a data collection component for PS and are currently exploring the data at hand. As for our future work, we are set to progress the development of an integrated road estimation system, with particular focus on the research challenges highlighted in this contribution.
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B. Road condition estimation The road estimation module performs the mapping of the signaling information to the road conditions and rises warnings in case of relevant deviations from the “typical behavior” observed in the past, i.e. anomalies. The main challenge in developing anomaly detection algorithms is that the “ground truth” about the real traffic conditions is generally unknown. The idea is that abrupt changes in some network signaling events might be the symptom of a road anomaly (accident/congestion), for example: (a) drop in the handover rate; (b) abrupt change in the LR update; (c) increase in the number of calls/SMS; (d) drastic change in the number of road users. Though, properly detecting these events is challenging due to the non-stationarity of the processes. Fig. 4 depicts the number of LA updates per minute along one week. The strong daily variation and the marked differences between working and weekend days are evident. This implies that simple detection schemes based on thresholding aggregate data would not work, making the case for more sophisticated algorithms based on individual trajectory tracking.
1 LAU/min (rescaled)
would help to disentangle walking users and slow moving car user on a congested segment.
Time chart of (rescaled) Location Area Updates per minute.
R EFERENCES [1] M. C. Gonzalez, et al., “Understanding individual human mobility patterns,” Nature, vol. 453, no. 7196, pp. 779–782, June 2008. [2] Y. Youngbin, “The State of Cellular Probes,” California PATH Research Project, University of California, CA, USA, Tech. Rep., July 2003. [3] NHCRP Project 70-01, “Probe-based Traffic Monitoring - State-of-thePractice Report,” University of Virginia - Center for Transportation Studies, Tech. Rep., Nov. 2005. [4] R. Cayford and T. Johnson, “Operational Parameters Affecting the Use of Anonymous Cell Phone Tracking for Generating Traffic Information,” Transportation Research Board Annual Meeting, TRB 2003, Washington, D.C., USA, Tech. Rep., Jan. 2003. [5] W. C. Mark Hsiao and S. K. Jason Chang, “Segment based Traffic Information Estimation Method Using Cellular Network Data,” in IEEE Intelligent Transportation Systems Conference, Austria, Sept. 2005. [6] Karlsson and Niclas, “Floating Car Data Deployment and Traffic Advisory Services, The OPTIS project 2000-2002.” Volvo Technology Corporation, Presentation, 2003. [7] B. Rutten and R. Sch¨afer, “The Connected Car,” TOMTOM Mobility Solutions, Whitepaper, June 2007. [8] K. Puntumapon and W. Pattara-atikom, “Classification of Cellular Phone Mobility using Naive Bayes Model,” in IEEE Vehicular Technology Conference, VTC 2008 Spring, Singapore, May 2008. [9] University of Maryland Transportation Studies Center, “Final Evaluation Report for the CAPITAL-ITS Operational Test and Demonstration Program,” University of Maryland, College Park, Tech. Rep., 1997. [10] J. Remy, “Computing travel time-estimates from GSM signalling messages: the STRIP project,” in IEEE Intelligent Transportation Systems Conference, Oackland, CA, USA, Aug. 2001. [11] J. Ygnace, “Travel timespeed estimates on the French Rhone Comdor Network using cellular phones as probes,” INRETS, Lyon, France, Tech. Rep., Dec. 2001. [12] R. Cayford and Y. Youngbin, “Field Operational Test using Anonymous Cell Phone Tracking for Generating Traffic Information,” Transportation Research Board Annual Meeting, TRB 2006, Washington, D.C., USA, Tech. Rep., Jan. 2006. [13] J. Virtanen, “Mobile phones as probes in travel time monitoring,” Finnish Road Administration, Helsinki, Finland, Tech. Rep., 2002. [14] A. Hillson and M. De Santis, “Traffic Monitoring Application of Cellular Positioning Technology: Proof of Concept,” Cell-Loc Inc., Calgary, Canada, Tech. Rep., June 2002. [15] C. Birle, “Via GSM network data to traffic information in real time,” Intelligent Transport Systems Munich, Presentation, Dec. 2007. [16] M. H¨opfner, et. al, “Cellular Data for Traffic Management,” in 6th European Congress and Exhibition on Intelligent Transport System and Services, June 2007. [17] Cellint TrafficSense, “Performance and Limitations of Cellular Based Traffic Monitoring Systems,” 6th European Congress and Exhibition on Intelligent Transport System and Services, Presentation, June 2007. [18] “ITIS Holdings plc,” http://www.itisholdings.com/cfvd.asp. [19] P. Beming, et al., “Lte-sae architecture and performance,” Ericsson Review, vol. 20, no. 3, 2007. [20] D. Gundlegard and J. Karlsson, “Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS,” in 6th International Conference on ITS Telecommunications, June 2006. [21] R. Schollmeyer and T. Wiltschko, “Classification of public transport vehicles using cellular mobile radio data,” in 6th European Congress and Exhibition on Intelligent Transport System and Services, June 2007.
