This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the definitive publisher-authenticated version, please refer directly to publishing house’s archive system. keywords Intelligent transportation system (ITS), location-based services (LBS), wireless locationing, cellphone network, GSM, GPS, real time control systems Real-Time Urban Monitoring Using Cellular Phones: a Case-Study in Rome Francesco Calabrese Massimo Colonna Piero Lovisolo Dario Parata Carlo Ratti
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This paper might be a pre-copy-editing or a post-print author-produced .pdf of an article accepted for publication. For the definitive publisher-authenticated version, please refer directly to publishing house’s archive system.
keywords Intelligent transportation system (ITS), location-based services (LBS), wireless locationing, cellphone network, GSM, GPS, real time control systems
Real-Time Urban Monitoring Using Cellular Phones: a Case-Study in Rome
Francesco CalabreseMassimo ColonnaPiero LovisoloDario ParataCarlo Ratti
Real-Time Urban Monitoring Using Cellular Phones: a Case-Study in Rome
Francesco Calabrese (1), Massimo Colonna (2), Piero Lovisolo (2), Dario Parata (2), Carlo Ratti (1) (1) SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
(2) TILAB, Telecom Italia, Turin, Italy
Address for correspondence:
Francesco Calabrese, SENSEable City Laboratory, Massachusetts Institute of Technology, 77,
Massachusetts Avenue, Cambridge, MA, 02139, USA, e-mail: [email protected]
Abstract — This paper reports on the Real Time Rome project, exhibited at the 10th International Architecture Exhibition of the Venice Biennale during Fall 2006. The project used the LocHNESs platform developed by Telecom Italia for the real time evaluation of urban dynamics based on the anonymous monitoring of cellphone networks. In addition, data were supplemented based on the instantaneous locationing of buses and taxis using GPS. All data were then processed and updated to provide information about urban mobility in real time, from the condition of vehicular traffic to the movements of pedestrians and foreigners in the city. For the first time a large urban area, which covered most of the city of Rome, was monitored in real time using a variety of sensing systems - hopefully opening the way to a new paradigm to understand and optimize urban dynamics.
Index Terms — Intelligent transportation system (ITS), location-based services (LBS), wireless locationing, cellphone network, GSM, GPS, real time control systems.
1. Introduction Intelligent Transportation Systems (ITS) are transportation systems which use Information and
Communication Technologies (ICT) to address and alleviate transportation and congestion problems. In
general, an ITS relies on location-based information: it monitors the location of a certain number of
vehicles (used as probes) and processes it in order to obtain traffic information such as travel time
estimation, congestion or incident situations, etc. Using a relatively large amount of probes, the early
stages of bottlenecks can be detected early and traffic can be directed to other routes to mitigate
congestion and to provide faster and more efficient itineraries for travellers.
Various kinds of sensors can be used to obtain traffic information. Traditionally, two main
categories have been identified: fixed sensors on the road and GPS receivers located in vehicles.
Fixed sensors on the road provide information about vehicles’ speed and road capacity.
Moreover, they can provide additional information such as vehicles’ category, distance between
The key data detected by LocHNESs through the Abis interface are MEASurement RESult [13]
messages, which are used to report the results of radio channel measurements made by the BTS (uplink
measurements) and the measurement reports received by the BTS from the ME (downlink
measurements)1 to the BSC. The MEASurement RESult message contains GSM parameters such as the
average signal quality (RXQUAL) as measured by both ME and BTS, the received signal strength (RXLEV)
as measured by the BTS (uplink measurement), the received signal strength on the serving BTS and on
the neighbouring BTSs as measured by the ME (downlink measurement) and the actual Timing Advance
(TA). The MEASurement RESult message related to each active connection (ME in the state “connected”)
is sent to the BSC every 480 ms, allowing LocHNESs to determine the complete trajectory of the call
with the same time resolution. To reduce the computational load of the platform, however, the number
of events notified to LocHNESs for each call is reduced by the probes according to a fixed sampling
ratio (for example 1:10, i.e. with a time resolution of 4.8 s).
Using the above data, the LocHNESs platform produces aggregated traffic maps in raster form:
the area under analysis is split into a number of contiguous square pixels of varying size (typically
250x250 m in urban areas and 500x500 m in extra-urban areas2). For each pixel the platform estimates
a number of parameters, such as the average speed in the four quadrants (North West, North East,
South East and South West) of a Cartesian reference system centred in the centre of the pixel, the total
average speed, the number of moving users, etc. In order to have real time applications for vehicular
traffic these traffic maps are constantly updated with a given periodicity (for instance, every 5
minutes).
It is important to note that the LocHNESs platform complies with the 2002 Directive by the
European Parliament and Council on privacy.3 At no time could individual users be identified based on
the collected and analyzed data. In this sense, we hope that this project might stimulate a dialogue on
the responsible access to locational data and on how it could provide value-added services, such as
traffic monitoring, to local and regional communities.
2.2. Localization engine The Localization engine estimates the instantaneous position of each ME involved in a call
using the data extracted from the MEASurement RESult messages, received from the probes. Location is
calculated using an Enhanced Cell Id with Timing Advance algorithm (E-CI+TA) [14], named DFL (Data
Fit Location); its principal components are the following (Fig. 2a):
1 These measurements form the basic raw data for the handover algorithms in BSC/MSC. 2 This length depends on the accuracy of the localization algorithm and can be modified accordingly. 3 Directive 2002/58/EC of the European Parliament and of the Council of 12 July 2002 concerning the processing of personal data
and the protection of privacy in the electronic communication sector (Directive on privacy and electronic communications).
Six computers at the Venice Biennale exhibit continuously accessed the SENSEable City Lab FTP
server and ran Java software (developed using Processing, and OpenGL) to visualize the different
dynamic maps of the city in real-time, using Google maps as background.
Furthermore, three computers connected to three audio streaming sources (coming from the
three audio sensors installed in Rome) played locational traffic noise in time-synchronization with the
visual software.
5. Processing and Visual Software In the following subsections, detailed description of the six processing and visual software is
given, starting form the questions they address.
5.1. Pulse: Where in Rome are people converging over the course of a day? (see Fig. 4a). This software visualized the intensity of mobile phone calls in Rome at the present moment and
compared it to the previous day’s data.
It is based on a geographic interpolation of the served traffic intensity provided by each
monitored antenna. To this end, the Rome urban area was divided in 40 x 40 m pixels and the traffic
intensity was assigned at each pixel considering the distance ,i jd between the pixel
, 1, , _jz j n pixels= K and the surrounding antennas , 1, , _ia i n cells= K , the cell type, the
location of the pixel in the city and an exponential distribution function. The software showed the real time data (updated every 15 minutes) on the left side, and a loop
of the previous day’s data, constituted by 96 different images, on the right side.
5.2. Gatherings: How do people occupy and move through certain areas of the city during special events? (see Fig. 4b).
This software showed the pre-recorded intensity of mobile phone usages during important
events in Rome:
• viewing the World Cup final match between Italy and France on July 9, 2006 and celebrating the arrival
in Rome of the winning Italy national team on July 10;
• Madonna’s concert in Rome on August 6, 2006.
The software was realized by concatenating 3D visualizations of the served traffic in specific
areas of Rome and at different times of the selected days.
5.3. Icons: Which landmarks in Rome attract more people? (see Fig. 5a). This software showed the density of people using mobile phones at different historic attractions
in Rome. To this end, the served traffic intensity data was processed using the following function 1
of the pixels jz located in correspondence of the attraction site k . Clearly the cardinality of the set
kI (
kcard I ) depended on the planar extension of the site.
A bar on the top of each attraction showed the relative traffic intensity, while at the bottom of
the screen a week-long data comparison between the most popular site and the least popular site was
shown. Every 15 minutes, when the new data was available, both the top and the bottom of the screen
were updated based on the new ranking.
5.4. Visitors: Where are the concentrations of foreigners in Rome? (see Fig. 5b). This 3-D software highlighted a 24 hours loop of the locations around the Stazione Termini
neighbourhood of Rome where foreigners were speaking on mobile phones. An algorithm created a 48-
length data queue where, with a sampling rate of 1/30minutes, the newest foreigners locational data
was added (deleting the oldest one).
For the 3D visualization purpose, an algorithm was also used to spatially-interpolate the
foreigners’ locational data in order to create a 10 x 10 m pixels matrix (based on a 2D smoothing
technique) and a loop function was used to recursively read the queue and graph the related 3D image.
5.5. Connectivity: Is public transportation where the people are? How do the movement patterns of buses and taxis and pedestrians overlap in the Stazione Termini neighbourhood of Rome? (see Fig. 6a).
This software showed the changing positions of Atac buses and Samarcanda taxis indicated by
yellow points, and the relative densities of mobile phone users, represented by the red areas.
An algorithm was used to acquire and update the buses and taxis location in real time (based
on a hash table). It also estimated buses and taxis paths based on the previous three locations,
drawing a yellow tail on the map. If the tail was long, this meant that a bus or a taxi was moving fast.
The algorithm acquired the pedestrian locational data every 5 minutes, showing a red layer on
the top of the map (areas coloured by a deeper red had a higher density of pedestrians).
5.6. Flow: Where is traffic moving? (see Fig. 6b). This software visualized the locational data of mobile phone callers travelling in vehicles. It
focused on the area around the Stazione Termini and the Grande Raccordo Anulare (Rome’s ring road).
The software crated a layer on the top of the map, showing 250 x 250 m pixels whose colours were
related to vehicle speeds. Red indicated areas where traffic was moving slowly, green showed areas
where vehicles were moving quickly.
If the average speed associated to the pixel was higher that 40km/h, the software also showed
an arrow in the centre of the pixel whose direction was the dominant direction of travel and magnitude