Estimating Road Traffic Congestion from Cell Dwell Time using Neural Network

Pattara-Atikom, Wasan; Peachavanish, Ratchata

doi:10.1109/itst.2007.4295824

Cited by 31 publications

(8 citation statements)

References 5 publications

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“…In our previous work [5], we described Cell Dwell Time (CDT) as the time period that a mobile phone remains registered to a base station and that there is a statistically significant correlation between CDT and the degree of traffic congestion. In our recent work [6], we used CDT to estimate traffic congestion and the results further supported our suggestion that CDT data have the potential to be a traffic congestion estimation measure. In that study, smoothen-out average CDT values computed from multiple adjacent cells were used to estimate traffic congestion on signaled road.…”

Section: Related Worksupporting

confidence: 71%

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Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

Hongsakham

Pattara-Atikom

Peachavanish

2008

2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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This research proposes alternative methods for estimating degrees of road traffic congestion by using Cell Dwell Time (CDT) information available from cellular networks. CDT is the duration that a cellular phone remains associated to a base station between handoff events. As a phone in a vehicle travels along a road having different degrees of congestion, the value of CDT varies accordingly. Measurements of CDT were taken and classified into one of the three degrees of congestion using 1) Kmeans clustering algorithm and 2) backpropagation neural network. These machine-assigned classifications were then compared against human opinion to assess the accuracy. The results demonstrate the feasibility of using K-means and neural networks in classifying degrees of traffic congestion and that the neural network approach performs well for this task.

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Section: Related Worksupporting

confidence: 71%

“…In our previous work [6], we also used a neural network to predict the degrees of congestion from CDT data. To effectively communicate degrees of congestion to users, a color scheme was used to describe different traffic conditions.…”

Section: Related Workmentioning

confidence: 99%

Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

Hongsakham

Pattara-Atikom

Peachavanish

2008

2008 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technolog

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“…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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“…Several attempts can be found in the past few years to adapt the mobile phone technology for transporttation planning and operations purposes. Projects regarding feasibility and field-tests of mobile phone location-based ITSs have been developed in Rome [4], Israel [5], Spain [6], Bangkok [7] and China [8].…”

Section: Literature Reviewmentioning

confidence: 99%

“…This method has great potential and the results inferred are much more cost-effective than those generated with traditional techniques. Cell Dwell Time (CDT) has been used by Pattara-atikom and Peachavanish [7] to estimate the degree of traffic congestion. CDT is the duration that a mobile phone is registered to a base station before handling off to another base station.…”

Section: Literature Reviewmentioning

confidence: 99%

Travel Time Estimation and Prediction using Mobile Phones: A Cost Effective Method for Developing Countries

Satyakumar¹,

Anil²,

Sivakumar³

2014

ced

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Conventional data collection methods lack real time information and involve excessive cost of installation and maintenance. A real-time, low cost travel time data collection system can be developed using mobile phones. This project examines the use of mobile phones for travel time prediction of public transit vehicles and develops a dynamic travel time prediction model. Personnel were employed in public transit vehicles with mobile phones and these mobile phones were tracked continuously. Space information of the mobile phones represents the position of the buses and movement pattern of these mobile phones in turn represents the movement pattern of the public buses. The starting and arrival time at sections obtained from the cellular database were used to get the travel time and speed. Results obtained were statistically significant and it shows that use of mobile phone for travel time data collection is a low cost data collection technique for Indian cities.

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Estimating Road Traffic Congestion from Cell Dwell Time using Neural Network

Cited by 31 publications

References 5 publications

Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

Estimating road traffic congestion from cellular handoff information using cell-based neural networks and K-means clustering

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

Travel Time Estimation and Prediction using Mobile Phones: A Cost Effective Method for Developing Countries

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