Road-RFSense: A Practical RF Sensing--Based Road Traffic Estimation System for Developing Regions

Sen, Rijurekha; Maurya, Abhinav; Raman, Bhaskaran; Mehta, Rupesh; Kalyanaraman, Ramkrishnan; Singh, Amarjeet

doi:10.1145/2560189

Cited by 4 publications

(5 citation statements)

References 30 publications

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“…This work presents a traffic detection system based on a LoRa network that works with a communication frequency of 868 MHz. We can find in the literature other works of a similar nature that use WiFi communication technologies, IEEE 802.11b [ 14 , 15 ], IEEE 802.11n [ 13 ] or Low Power Wireless Sensor Networks (LPWN) as IEEE 802.15.4 [ 16 ] with communication frequencies in the 2.4 GHz band. Both communication frequencies are within the Industrial, Scientific and Medical (ISM) bands.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…Regarding the algorithms in the treatment of the data, they use Support Vector Machines (SVM) [ 14 , 15 , 16 ] and machine learning algorithms such as k-Nearest Neighbors (k-NN) [ 13 ]. These algorithms for data analysis allow them to classify the type of vehicle is going.…”

Section: Data Analysis and Resultsmentioning

confidence: 99%

“…The capacity of these systems grants an ability to monitor human motion by referring to the movement of their limbs [ 12 ]. Other works oriented to monitoring the traffic flow propose the use of different WiFi and ZigBee signals to detect traffic flow, classify the different vehicles, and even monitor their speed [ 10 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

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LoRa-Based Traffic Flow Detection for Smart-Road

Asiain¹,

Antolín²

2021

Sensors

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This paper presents a wireless traffic flow detection system, mainly focused on conditions in which the traffic flow is slow or stopped, which increases the risk of highway accidents. To achieve this goal, a Low Power Wide Area Network (LPWAN) based on LoRa called Short LoRa has been developed. This LoRa sub-network complies with the European Telecommunications Standards Institute (ETSI) harmonized standard for its compatibility in Europe countries. In addition, the development of the devices has allowed them to also work on a LoRaWAN network. The introduced development has been compared to a reference system mounted with laser barriers that provided a high accurate comparison. Field tests of the system have been carried out and the data obtained in the measurement has been analyzed with two different methods, and both of them were valid for the application. The results can determine vehicle speed with adequate precision at low speeds. The attenuating behavior of the communication signal is also analyzed through the Radio Signal Strength Indicator (RSSI). The relationship between vehicle speed, gate distances and RSSI attenuation has been studied. The system is proven to have efficient results in detecting traffic flow under the conditions for which it has been developed.

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Section: Data Analysis and Resultsmentioning

confidence: 99%

Section: Data Analysis and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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LoRa-Based Traffic Flow Detection for Smart-Road

Asiain¹,

Antolín²

2021

Sensors

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“…Impact Prediction. Except for studies regarding to the detection of abnormal circumstances of transportation incidents, e.g., railway failures, traic congestion [10,14,28,32], there have also been eforts made to predict their impact. Some works predict the impact by reasoning human reactions or the damage to network structure, most of which lack measurement study of real incidents.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Impact of Disruptions to Urban Rail Transit Systems

Mo¹,

Cao²,

Li³

et al. 2022

ACM Trans. Sen. Netw.

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Service disruptions of rail transit systems have become more frequent in the past decade in urban cities, due to various reasons such as power failures, signal errors, etc. Smart transit cards provide detailed tapping records of commuters, which enable us to infer their trajectories under both normal and disruptive circumstances. In this article, we study and predict the impact of disruptions on commuters, and further evaluate the vulnerability of the rail system. Specifically, we define two metrics, stay ratio and travel delay, to quantify the impact, and derive the predictor of each metric based on the inferred alternative route choices of commuters under disruptive circumstances. We demonstrate the alternative route choices contribute to more similar feature distribution among different disruptions, which is crucial to tackling the main challenge of abnormal data scarcity and is beneficial for obtaining more reliable predictors for future disruptions. We evaluate our approach with a real-world transit card dataset. The result demonstrates the effectiveness of our method. Based on the predictors, we further analyse the vulnerability of the rail system. An evaluation with cross validation from taxi GPS trajectory data indicates its efficacy in discovering vulnerable rail stations as well as Origin-Destination (OD) pairs.

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“…Quantiles are better suited to extract the different aspects of data [11,12]. A feature selection technique can further be applied to select the most appropriate quantile features such as done in [29,9,35].…”

mentioning

confidence: 99%

A new quantile tracking algorithm using a generalized exponentially weighted average of observations

2018

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The Exponentially Weighted Average (EWA) of observations is known to be state-of-art estimator for tracking expectations of dynamically varying data stream distributions. However, how to devise an EWA estimator to rather track quantiles of data stream distributions is not obvious. In this paper, we present a lightweight quantile estimator using a generalized form of the EWA. To the best of our knowledge, this work represents the first reported quantile estimator of this form in the literature. An appealing property of the estimator is that the update step size is adjusted online proportionally to the difference between current observation and the current quantile estimate. Thus, if the estimator is off-track compared to the data stream, large steps will be taken to promptly get the estimator back on-track. The convergence of the estimator to the true quantile is proven using the theory of stochastic learning. Extensive experimental results using both synthetic and real-life data show that our estimator clearly outperforms legacy state-of-the-art quantile tracking estimators and achieves faster adaptivity in dynamic environments. The quantile estimator was further tested on real-life data where the objective is efficient online control of indoor climate. We show that the estimator can be incorporated into a concept drift detector for efficiently decide when a machine learning model used to predict future indoor temperature should be retrained/updated.

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Road-RFSense: A Practical RF Sensing--Based Road Traffic Estimation System for Developing Regions

Cited by 4 publications

References 30 publications

LoRa-Based Traffic Flow Detection for Smart-Road

LoRa-Based Traffic Flow Detection for Smart-Road

Predicting the Impact of Disruptions to Urban Rail Transit Systems

A new quantile tracking algorithm using a generalized exponentially weighted average of observations

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