Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

Calabrese, Francesco; Colonna, Massimo; Lovisolo, Piero; Parata, D; Ratti, Carlo

doi:10.1109/tits.2010.2074196

Cited by 559 publications

(339 citation statements)

References 8 publications

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“…More recent papers has been published by Calabrese et al [17,18]. In [17], they describe the use of the Enhanced Cell-ID with Timing Advance (TA) algorithm to localize mobile phones and to compute behavioural mobility patterns of the monitored users in Rome.…”

Section: Related Workmentioning

confidence: 99%

“…In [17], they describe the use of the Enhanced Cell-ID with Timing Advance (TA) algorithm to localize mobile phones and to compute behavioural mobility patterns of the monitored users in Rome. In our approach, we use different cellular network data (probed by the A 2G and Iu-CS 3G interfaces) to predict traffic situations in order to overcome the limits of solutions based on TA algorithms (i.e.…”

Section: Related Workmentioning

confidence: 99%

“…Mobility patterns describe the behaviour of individual citizens in living the city and in affecting the behaviour of other people [1][2][3][4]10,15,17]. In this paper, we discuss new algorithms to collect and elaborate in real time the big data coming from the VI cellular network to compute mobility patterns.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Cell phone big data to compute mobility scenarios for future smart cities

Tosi

2017

Int J Data Sci Anal

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Efficient mobility is a key aspect for the future smart cities. The real added value for smart cities is the real-time optimization of vehicular and public transportation flows to reduce traffic congestions, costs, and emissions. Observing constantly the behaviour of people moving around the city can help policy makers to act promptly and to fix congested flows dynamically. In this paper, we describe from a technical point-of-view an original use of big data (coming from the cellular network of the Vodafone Italy Telco operator) to compute mobility patterns for smart cities. The paper also discusses five innovative mobility patterns that describe different mobility scenarios of the city, starting from how people move around point-of-interests of the city in real time. The mobility patterns have been experimentally validated in a real industrial setting and for the Milan metropolitan city. The study conducted confirmed the quality of the patterns and their importance in smart cities, by showing how cell phone big data can complete other sources of people information. These mobility patterns can be exploited by policy makers to improve the mobility in a city, or by Navigation Systems and Journey Planners to provide final users with accurate travel plans.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Cell phone big data to compute mobility scenarios for future smart cities

Tosi

2017

Int J Data Sci Anal

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“…These indices have proved to be of great value in the analysis and understanding of the roles played by networks like social networks [12,23,24], citation networks [17], computer networks [6,16], urban networks [8,9,25], and others.…”

Section: Introductionmentioning

confidence: 99%

New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks

Agryzkov

Tortosa

Vicent

2016

Applied Mathematics and Computation

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The Adapted PageRank Algorithm (APA) proposed by Agryzkov et al. provides us a method to establish a ranking of nodes in an urban network. We can say that it constitutes a centrality measure in urban networks, with the main characteristic that is able to consider the importance of data obtained from the urban networks in the process of computing the centrality of every node. Starting from the basic idea of this model, we modify the construction of the matrix used for the classification of the nodes in order of importance. In the APA model, the data matrix is constructed from the original idea of PageRank vector, given an equal chance to jump from one node to another, regardless of the topological distance between nodes. In the new model this idea is questioned. A new matrix with the data network is constructed so that now the data from neighbouring nodes are considered more likely than data from the nodes that are farther away. In addition, this new algorithm has the characteristic that depends on a parameter α, which allows us to decide the importance attached, in the computation of the centrality, to the topology of the network and the amount of data associated with the node. Various numerical experiments with a network of very small size are performed to test the influence of the data associated with the nodes, depending always on the choice of the parameter α. Also we check the differences between the values produced by the original APA model and the new one. Finally, these measures are applied to a real urban network, in which we perform a visual comparison of the results produced by the various measures calculated from the algorithms studied.

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“…Similar approaches have been applied for cellular technologies in [1], [2], [3]. The way how a mobile device searches for nearby APs, with WiFi probes, or how beacons related to Bluetooth device discovery procedure are transmitted (explored in [4]), can be exploited to estimate the number of active devices in a certain area.…”

mentioning

confidence: 99%

Public transport occupancy estimation using WLAN probing

Mikkelsen

Buchakchiev

Madsen

et al. 2016

2016 8th International Workshop on Resilient Networks Design and Modeling (RNDM)

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Abstract-Prediction of availability of physical services can be a valuable addition to transportation systems operation. In this paper we are focusing on estimation of public transport occupancy (PTO), or more specifically, on estimating bus passenger load, i.e., the number of people on the bus. This information can be used by bus operators as input to the analysis of bus routes' efficiency, or to provide an app indicating passenger load. PTO estimation based on collecting WiFi probes emitted by WiFi enabled devices is cheap and easy to install. This paper presents a prototype implementation of this method, analysis of the collected data and of the estimation algorithm accuracy. Analysis of passenger load in a bus has indicated that there are two main challenges of the estimation using WiFi probes. The algorithm provides overestimation due to inclusion of WiFi devices that are outside the bus and underestimation due to exclusion of people without an active WiFi enabled device or by missing out probes in the detection algorithm from devices carried on board. We have shown how by fine-tuning parameters of the algorithm the probes received from people outside the bus can be filtered out thereby reducing the severity of the underestimation problem. The typical approach to combat the overestimation problem is to make the adjustments based on a statistical ratio of people possessing a WiFi enabled smart device over the whole population.

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Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

Cited by 559 publications

References 8 publications

Cell phone big data to compute mobility scenarios for future smart cities

Cell phone big data to compute mobility scenarios for future smart cities

New highlights and a new centrality measure based on the Adapted PageRank Algorithm for urban networks

Public transport occupancy estimation using WLAN probing

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