Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment

Herrera, Juan Carlos; Work, Daniel B.; Herring, Ryan; Ban, Xuegang; Jacobson, Quinn; Bayen, Alexandre M.

doi:10.1016/j.trc.2009.10.006

Cited by 776 publications

(392 citation statements)

References 18 publications

(18 reference statements)

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“…Advantages of mobile sensing, in comparison with fixed-location sensing (e.g. using loop detectors and cameras), include a potentially complete spatial and temporal coverage of traffic network and high positioning accuracy [29]. Moreover, the mobile sensing system is advantageous as it does not need to be installed separately and it barely incurs any maintenance cost.…”

Section: Discussionmentioning

confidence: 99%

Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Dumbliauskas

Barauskas

2017

PROMET

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Section: Discussionmentioning

confidence: 99%

Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Dumbliauskas

Barauskas

2017

PROMET

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“…Alternatively, in Floating GPS Data, GPS user positioning information is used to derive road traffic data [23][24][25].…”

Section: Related Workmentioning

confidence: 99%

“…Alternatively, more modern approaches try to exploit passively alternative sources of data, such as social network discussions, GPS data, and cellular network data (i.e. data exchanged between antennas and mobile devices) to compute traffic estimations [22,23]. While approaches based on social networks and GPS data are quite limited in practice (for example, due to the reliability of social discussions or the limited availability of data points collected from GPS sources), cellular network data are pervasive and always available [12][13][14]19].…”

Section: Introductionmentioning

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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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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“…Use of probe data is commonplace. Google's "Maps for Mobile" combines location data taken from participants' GPS-equipped mobile phones ("crowdsourced" data) with a traditional sensor infrastructure to overlay road maps with congestion information on arterial roads [9] and the Mobile Millennium Project's Mobile Century field experiment demonstrated the feasibility of traffic monitoring systems based on mobile phone probes [10]. Researchers have explored the feasibility of using commercial transport fleets of vehicles, such as taxis, in estimating traffic conditions on arterial roads.…”

Section: Mobile Sensorsmentioning

confidence: 99%

Using Real-Time Road Traffic Data to Evaluate Congestion

Bacon

Bejan

Beresford

et al. 2011

Dependable and Historic Computing

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Abstract. Providing citizens with accurate information on traffic conditions can encourage journeys at times of low congestion, and uptake of public transport. The TIME project (Transport Information Monitoring Environment) has focussed on urban traffic, using the city of Cambridge as an example. We have investigated sensor and network technology for gathering traffic data, and have designed and built reusable software components to distribute, process and store sensor data in real time. Instrumenting a city to provide this information is expensive and potentially invades privacy. Increasingly, public transport vehicles are equipped with sensors to provide arrival time estimates at bus stop displays in real-time. We have shown that these data can be used for a number of purposes. Firstly, archived data can be analysed statistically to understand the behaviour of traffic under a range of "normal" conditions at different times, for example in and out of school term. Secondly, periods of extreme congestion resulting from known incidents can be analysed to show the behaviour of traffic over time. Thirdly, with such analyses providing background information, real-time data can be interpreted in context to provide more reliable and accurate information to citizens. In this paper we present some of the findings of the TIME project.

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Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment

Cited by 776 publications

References 18 publications

Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

Application of Google-based Data for Travel Time Analysis: Kaunas City Case Study

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

Using Real-Time Road Traffic Data to Evaluate Congestion

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