Long-term urban traffic monitoring based on wireless multi-sensor network

Loga-Księska, Wiktoria; Sordyl, Justyna; Ryguła, Artur

doi:10.1515/eng-2020-0018

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…The results showed that the smallest average relative error between the estimated number of cars and the actual traffic was less than 10%. In [18], the authors performed a traffic variability and dynamics analysis using a multi-sensor Bluetooth network for traffic volume monitoring, concluding that the long-term research indicated only minor variation in the number of vehicle detections and relatively stable traffic volume.…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

Gheorghiu

Iordache

Cormoș

2021

Sensors

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As road traffic networks become more congested and information systems are implemented to manage traffic flows, real-time data gathering becomes increasingly important. Classic detectors are placed in one point of the network and are able to provide information only from that area. As useful as this is, it lacks the big picture of the routes the vehicles usually travel. There are applications developed to help individuals make their way into the road network, but these are no solutions that deal with the cause of traffic; rather, they counteract the effects. It becomes obvious that a proper management system, with knowledge of all the relevant aspects will better serve all travelers. The detection solution proposed in this paper is based on Bluetooth detectors. This system is able to match detected devices in the road network, filter the results, and generate a vehicle count that is proved to follow RADAR detection results.

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Section: Related Workmentioning

confidence: 99%

Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

Gheorghiu

Iordache

Cormoș

2021

Sensors

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“…Thanks to its advanced, inbuilt algorithms, the OnDynamic system is an advanced, automated measuring environment. The system has proved a useful tool for monitoring traffic conditions and analysing traffic long-term variability [33].…”

Section: Measurement Systemmentioning

confidence: 99%

An Integrated System for Simultaneous Monitoring of Traffic and Pollution Concentration—Lessons Learned for Bielsko-Biała, Poland

2021

Self Cite

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The challenge of maintaining the required level of mobility and air quality in cities can be met by deploying an appropriate management system in which the immediate vicinity of roads is monitored to identify potential pollution hotspots. This paper presents an integrated low-cost system which can be used to study the impact of traffic related emission on air quality at intersections. The system was used for three months in 2017 at five locations covering intersections in the centre of a mid-sized city. Depending on the location, pollution hotspots with high PM2.5 and PM10 concentrations occurred 5–10% of the time. It was shown that despite the close mutual proximity of the locations, traffic and the immediate surroundings lead to significant variation in air quality. At locations with adverse ventilation conditions a tendency towards more frequent occurrences of moderate and sufficient air quality was observed than at other locations (even those with more traffic). Based on the results, a practical extension of the system was also proposed by formulating a model for the prediction of PM2.5 concentration using a neural network. Information on transit times, meteorological data and the background level of PM10 concentration were used as model input parameters.

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“…For this reason, it is necessary to permanently monitor over a long period of time vehicle traffic and analyze the recorded data [12][13][14]. This will allow for a more precise analysis and a more objective assessment of the phenomena taking place as well as the values of their parameters.…”

Section: Introductionmentioning

confidence: 99%

Measurements of urban traffic parameters before and after road reconstruction

Bąkowski

Radziszewski

2021

Open Engineering

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The study analyzed the parameters of vehicle traffic and noise on the national road in the section in the city from 2011 to 2016. In 2013–2014 this road was reconstructed. It was found that in most cases, the distribution of the tested variable was not normal. The median and selected percentiles of vehicle traffic parameters and noise were examined. The variability and type A uncertainty of the results were described and evaluated. The results obtained for the data recorded on working and non-working days were compared. The vehicle cumulative speed distributions, for two-way four-lane road segments in both directions were analyzed. A mathematical model of normalized traffic flow has been proposed. Fit factor R2 of the proposed equations to the experimental data for passenger vehicles ranges from 0.93 to 0.99. It has been shown that two years after the road reconstruction, the median noise level did not increase even though traffic volumes and vehicle speeds increased. The Cnossos noise model was validated for data recorded over a period of 6 years. A very good agreement of the medians determined according to the Cnossos-EU model and the measured ones was obtained. It should be noted, however, that for the other analyzed percentiles, e.g. 95%, the discrepancies are larger.

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Long-term urban traffic monitoring based on wireless multi-sensor network

Cited by 8 publications

References 8 publications

Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

Analysis of the Possibility to Detect Road Vehicles via Bluetooth Technology

An Integrated System for Simultaneous Monitoring of Traffic and Pollution Concentration—Lessons Learned for Bielsko-Biała, Poland

Measurements of urban traffic parameters before and after road reconstruction

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