Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring

Freschi, Valerio; Delpriori, Saverio; Klopfenstein, Lorenz Cuno; Lattanzi, Emanuele; Luchetti, Gioele; Bogliolo, Alessandro

doi:10.1109/iccve.2014.7297643

Cited by 28 publications

(19 citation statements)

References 10 publications

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“…Such methods include (1) data weighting (done in fisheries stock assessments; Francis ); (2) data subsetting and thinning (a geospatial technique, Freschi et al. ); (3) data supplementing (done in the social sciences; Foster et al. ); or (4) expert opinion elicitation (employed in psychology; Taylor et al.…”

Section: Discussionmentioning

confidence: 99%

“…In situations where synthesis of conflicting data sources results in model failure (e.g., unrealistic and/or unidentifiable parameter estimates, lack of model convergence, estimates biased towards the most datarich source), ecologists may consider borrowing methods from other fields to resolve discrepancies. Such methods include (1) data weighting (done in fisheries stock assessments; Francis 2011); (2) data subsetting and thinning (a geospatial technique, Freschi et al 2014); (3) data supplementing (done in the social sciences; Foster et al 2016); or (4) expert opinion elicitation (employed in psychology; Taylor et al 2017), which can be used to formulate priors in Bayesian analyses (Murray et al 2009).…”

Section: Guidance For Resolving Data Discrepancies With Ipmsmentioning

confidence: 99%

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Disentangling data discrepancies with integrated population models

Saunders

Farr

Wright

et al. 2019

Ecology

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A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long‐term data sets (1963–2015) within an IPM to estimate continental‐scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

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Section: Discussionmentioning

confidence: 99%

Section: Guidance For Resolving Data Discrepancies With Ipmsmentioning

confidence: 99%

Disentangling data discrepancies with integrated population models

Saunders

Farr

Wright

et al. 2019

Ecology

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“…Freschi et al proposed a data aggregation method [41] to monitor the roughness of road surfaces. In addition, a series of data aggregation schemes [17,18,19,20] have been proposed.…”

Section: Related Workmentioning

confidence: 99%

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

Wang

et al. 2017

Sensors

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Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles’ location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency.

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“…Recent studies have shown that it is possible to exploit the triaxial accelerometers with which smartphones are equipped to obtain a sound indicator of road surface roughness [1,2]. A crowdsensing system, called SmartRoadSense [3], has been developed to allow any driver to contribute with his/her smartphone in monitoring the status of the roads he/she travels by car.…”

mentioning

confidence: 99%

Demo

Alessandroni

Bogliolo

Carini

et al. 2015

Proceedings of the 13th Annual International Conference on Mobile Systems, Applications, and Services

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Figure 1: System overview.The roughness of the road surface affects driving safety and comfort. Having complete and up to date information on the state of the road network is essential for maintenance planning, but it entails time consuming and costly monitoring activities. As a matter of fact, maintenance interventions are mainly based on the results of case by case inspections prompted by drivers' reports. Moreover, qualitative observations are seldom supported by objective measures, making it difficult for administrators to collect data providing a clear perception of the actual priorities.Recent studies have shown that it is possible to exploit the triaxial accelerometers with which smartphones are equipped to obtain a sound indicator of road surface roughness [1, 2]. A crowdsensing system, called SmartRoadSense [3], has been developed to allow any driver to contribute with his/her smartphone in monitoring the status of the roads he/she travels by car.As shown in Fig. 1, Smartroadsense is composed of: a mobile application, a cloud-based backend, and a web portal. The application runs in background on any car-mounted Android smartphone, reads the accelerometer data at a freFigure 2: Road roughness map.quency of 100Hz, and computes roughness values once per second. All the values are associated with GPS coordinates and opportunistically sent to the server as soon as a suitable data connection is available. Traces are processed in cloud to compensate GPS inaccuracy and to aggregate data provided by different users. OpenStreetMap is used for map matching and aggregation, performed every 6 hours. Aggregated roughness estimates are then made publicly available online and displayed in color-scale on interactive maps (Fig. 2).The demo shows the entire system at work, allowing the audience to directly install and test the app on their smartphones and to follow the processing steps by looking inside databases and software components. The main purpose of the demo is to highlight the distributed and collaborative nature of SmartRoadSense, which handles a large quantity of data in real time exploiting mobile terminals and cloudbased resources.Demo trailer at http://smartroadsense.it/demo/.

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Geospatial data aggregation and reduction in vehicular sensing applications: The case of road surface monitoring

Cited by 28 publications

References 10 publications

Disentangling data discrepancies with integrated population models

Disentangling data discrepancies with integrated population models

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

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