Computing travel time estimates from GSM signalling messages: the STRIP project

Remy, Jean-Gabriel

doi:10.1109/itsc.2001.948620

Cited by 12 publications

(8 citation statements)

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“…The European STRIP project [21] aims at computing travel-time estimates from GSM signalling messages. The feasibility of the approach has been recently experimented on the French Rhone corridor network, showing good estimations if compared to the data collected by the detectors.…”

Section: Related Workmentioning

confidence: 99%

“…InfoBlu is a service that provides real-time traffic data for the most important roads of the Italian network. We applied linear, loglog, and polynomial regression functions to the six case studies, using the Ordinary Least Squares (OLS) regression [21]. We used the stringent 0.01 as the statistical significance threshold, as is customarily done in empirical studies.…”

Section: Technical Aspectsmentioning

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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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: Technical Aspectsmentioning

confidence: 99%

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

Tosi

2017

Int J Data Sci Anal

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“…We applied linear, loglog, and polynomial regression functions to the six case studies, using the Ordinary Least Squares (OLS) regression [15]. We used the stringent 0.01 as the statistical significance threshold, as is customarily done in empirical studies.…”

Section: B Technical Aspectsmentioning

confidence: 99%

Big Data from Cellular Networks: Real Mobility Scenarios for Future Smart Cities

Tosi

Marzorati

2016

2016 IEEE Second International Conference on Big Data Computing Service and Applications (BigDataService)

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Efficient mobility is a key aspect for the future smart cities. The real-time optimization of vehicular and public transportation flows to reduce traffic congestions, costs and emissions is the real added value for smart cities. In this paper, we describe a novel use of big data coming from the cellular network of the Vodafone Italy Telco operator to compute mobility patterns for smart cities. These mobility patterns are able to describe different mobility scenarios of the city, starting from how people move around Point Of Interests of the city in real-time. 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. The paper discusses five main new mobility patterns and their experimental validation in a real industrial setting and for the Milan metropolitan city

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“…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.…”

Section: B Passive Techniquesmentioning

confidence: 99%

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

Valerio

D’Alconzo

Ricciato

et al. 2009

VTC Spring 2009 - IEEE 69th Vehicular Technology Conference

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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.

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Computing travel time estimates from GSM signalling messages: the STRIP project

Cited by 12 publications

References 0 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

Big Data from Cellular Networks: Real Mobility Scenarios for Future Smart Cities

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

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