A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Liu, Boyi; Cheng, Jieren; Liu, Qiang; Tang, Xiangyan

doi:10.20944/preprints201808.0163.v1

Cited by 2 publications

(4 citation statements)

References 15 publications

(15 reference statements)

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“…Table 6 shows the performance of the system when a, which is the importance placed on QoS, is varied and b, which is the importance placed on energy consumption, is kept at a minimum of 0.1; the energy consumption is low. This attests to the result obtained in (31), which shows that the optimum setting of the load ρ * = λ * E[s] will depend on υ(t)P on (t) + P tx (t) and on the ratio b/a. Table 7 shows the performance of the system when the emphasis is placed on energy consumption b and the QoS priority is at its minimum.…”

Section: Effect Of Traffic Load On Server Response Timessupporting

confidence: 85%

“…In another contribution, an improved long short-term memory (LSTM) was used in [31] to obtain accurate and fast traffic flow forecasting in intelligent transportation systems. Moreover, a time series prediction for extracting useful information from historical records to determine their future values was studied in [32], where a random connectivity LSTM (RCLSTM) model was used to reduce the computational complexity associated with LSTM and was tested and verified for traffic prediction and user mobility in wireless networks.…”

Section: The One-step-ahead Predictive Modelmentioning

confidence: 99%

“…This allows us to compute the value of the arrival rate that minimizes C(υ, ρ). The result in (31) indicates the optimum setting of the load ρ * = λ * E[s] and its dependence on υ(t)P on (t) + P tx (t) and on the ratio b/a given as follows:…”

Section: Prioritization and Energy Consumption Per Servicementioning

confidence: 99%

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QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Hlophe

Maharaj

2020

J. Commun. Netw.

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One of the major challenges facing the realization of cognitive radios (CRs) in future mobile and wireless communications is the issue of high energy consumption. Since future network infrastructure will host real-time services requiring immediate satisfaction, the issue of high energy consumption will hinder the full realization of CRs. This means that to offer the required quality of service (QoS) in an energy-efficient manner, resource management strategies need to allow for effective trade-offs between QoS provisioning and energy saving. To address this issue, this paper focuses on single base station (BS) management, where resource consumption efficiency is obtained by solving a dynamic resource allocation (RA) problem using bipartite matching. A deep learning (DL) predictive control scheme is used to predict the traffic load for better energy saving using a stacked auto-encoder (SAE). Considered here was a base station (BS) processor with both processor sharing (PS) and first-come-first-served (FCFS) sharing disciplines under quite general assumptions about the arrival and service processes. The workload arrivals are defined by a Markovian arrival process while the service is general. The possible impatience of customers is taken into account in terms of the required delays. In this way, the BS processor is treated as a hybrid switching system that chooses a better packet scheduling scheme between mean slowdown (MS) FCFS and MS PS. The simulation results presented in this paper indicate that the proposed predictive control scheme achieves better energy saving as the traffic load increases, and that the processing of workload using MS PS achieves substantially superior energy saving compared to MS FCFS.

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Section: Effect Of Traffic Load On Server Response Timessupporting

confidence: 85%

Section: The One-step-ahead Predictive Modelmentioning

confidence: 99%

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QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Hlophe

Maharaj

2020

J. Commun. Netw.

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“…In [23], authors present a hybrid prediction approach, SDLSTM-ARIMA, which combines an improved LSTM-RNN with the time series model. This method, derived from the Recurrent Neural Networks (RNN) model, assesses the singularity of traffic data over time in conjunction with probability values from the dropout module, integrating them at irregular time intervals to achieve precise traffic flow predictions [24].…”

Section: Introductionmentioning

confidence: 99%

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

Katambire,

Musabe,

Uwitonze

et al. 2023

Forecasting

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Traffic operation efficiency is greatly impacted by the increase in travel demand and the increase in vehicle ownership. The continued increase in traffic demand has rendered the importance of controlling traffic, especially at intersections. In general, the inefficiency of traffic scheduling leads to traffic congestion, resulting in a rise in fuel consumption, exhaust emissions, and poor quality of service. Various methods for time series forecasting have been proposed for adaptive and remote traffic control. The prediction of traffic has attracted profound attention for improving the reliability and efficiency of traffic flow scheduling while reducing congestion. Therefore, in this work, we studied the problem of the current traffic situation at Muhima Junction one of the busiest junctions in Kigali city. Future traffic rates were forecasted by employing long short-term memory (LSTM) and autoregressive integrated moving average (ARIMA) models, respectively. Both the models’ performance criteria for adequacy were the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The results revealed that LSTM is the best-fitting model for monthly traffic flow prediction. Within this analysis, we proposed an adaptive traffic flow prediction that builds on the features of vehicle-to-infrastructure communication and the Internet of Things (IoT) to control traffic while enhancing the quality of service at the junctions. The real-time actuation of traffic-responsive signal control can be assured when real-time traffic-based signal actuation is reliable.

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A Long Short-term Traffic Flow Prediction Method Optimized by Cluster Computing

Cited by 2 publications

References 15 publications

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

QoS provisioning and energy saving scheme for distributed cognitive radio networks using deep learning

Forecasting the Traffic Flow by Using ARIMA and LSTM Models: Case of Muhima Junction

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