Detection and Estimation of Road Congestion Using Cellular Phones

Hansapalangkul, T.; Keeratiwintakorn, Phongsak; Pattara-Atikom, Wasan

doi:10.1109/itst.2007.4295851

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…These solutions are not efficient due to coverage limitations and effort required in terms of installation and maintenance. To cope with these problems [2,8,10,12,18,22,23] propose the use of mobile subscriber data in traffic flow estimation. In [8,10,18] the cell dwelling time and global positioning system (GPS) coordinates of a mobile subscriber are used to estimate the traffic congestion.…”

Section: Related Workmentioning

confidence: 99%

“…To cope with these problems [2,8,10,12,18,22,23] propose the use of mobile subscriber data in traffic flow estimation. In [8,10,18] the cell dwelling time and global positioning system (GPS) coordinates of a mobile subscriber are used to estimate the traffic congestion. These methods however have the disadvantage of high power consumption on a mobile device due to constant use of GPS, and are inherently intrusive.…”

Section: Related Workmentioning

confidence: 99%

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Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning

Yaghoubi¹,

Catovic²,

Gusmao³

et al. 2021

Preprint

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As the demand for vehicles continues to outpace construction of new roads, it becomes imperative we implement strategies that improve utilization of existing transport infrastructure. Traffic sensors form a crucial part of many such strategies, giving us valuable insights into road utilization. However, due to cost and lead time associated with installation and maintenance of traffic sensors, municipalities and traffic authorities look toward cheaper and more scalable alternatives. Due to their ubiquitous nature and wide global deployment, cellular networks offer one such alternative. In this paper we present a novel method for traffic flow estimation using standardized LTE/4G radio frequency performance measurement counters. The problem is cast as a supervised regression task using both classical and deep learning methods. We further apply transfer learning to compensate that many locations lack traffic sensor data that could be used for training. We show that our approach benefits from applying transfer learning to generalize the solution not only in time but also in space (i.e., various parts of the city). The results are very promising and, unlike competing solutions, our approach utilizes aggregate LTE radio frequency counter data that is inherently privacy-preserving, readily available, and scales globally without any additional network impact.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning

Yaghoubi¹,

Catovic²,

Gusmao³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Cellular phone position-based traffic information generation uses the GPS from among the various sensors of a cellular phone to predict speed, congestion, and other conditions on the road [1,2,3,4,5,6,7]. There is also a method of predicting the volume of traffic based on the hours of cellular phone use [8,9,10,11]. However, these methods have the weakness of increasing battery consumption due to the frequent sampling of the GPS in the driver’s cellular phone.…”

Section: Related Workmentioning

confidence: 99%

Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

Ji¹,

Hong

2019

Sensors

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In this paper, we propose a method for deep-learning-based real-time road traffic predictions using long-term evolution (LTE) access data. The proposed system generates a road traffic speed learning model based on road speed data and historical LTE data collected from a plurality of base stations located within a predetermined radius from the road. Real-time LTE data were the input for the generated learning model in order to predict the real-time speed of traffic. Since the system was developed using a time-series-based road traffic speed learning model based on LTE data from the past, it is possible for it to be used for a road where the environment has changed. Moreover, even on roads where the collection of traffic data is invalid, such as a radio shadow area, it is possible to directly enter real-time wireless communications data into the traffic speed learning model to predict the traffic speed on the road in real time, and in turn, raise the accuracy of real-time road traffic predictions.

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“…However, existing methods estimate traffic speed based on signaling data with limitation of several certain conditions. First, some methods (Hansapalangkul et al, 2007) require special software of collecting GPS location points running on smart phone to improve accuracy of positioning. Unfortunately, in most cases we can't collect these private data.…”

Section: Related Workmentioning

confidence: 99%

A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data

Zhu

Song

et al. 2016

International Journal of Web Services Research

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With the growing popularity of cell phones, using massive cellular signaling data as probe to track the vehicles movement trajectory and obtain the real-time traffic condition has become one of the most attractive candidate techniques. However, traditional approaches may offer a poor performance in removing noisy data and minimizing deviation of traffic speed in adjacent time intervals. In this paper, a novel approach is proposed to solve these two issues. The authors move noisy data by comparing the cellular signaling data with the trained data set, and adopt a modified Kalman filter algorithm to minimize the deviations. The experiment results show that the accuracy of the approach performs better in comparison to other two traffic speed estimation approaches.

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Detection and Estimation of Road Congestion Using Cellular Phones

Cited by 9 publications

References 3 publications

Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning

Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning

Deep-Learning-Based Real-Time Road Traffic Prediction Using Long-Term Evolution Access Data

A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data

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