Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method

2017 ◽  
Vol 8 (2) ◽  
pp. 38-50 ◽  
Author(s):  
Aditya R. Raikwar ◽  
Rahul R. Sadawarte ◽  
Rishikesh G. More ◽  
Rutuja S. Gunjal ◽  
Parikshit N. Mahalle ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Data Science ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Forecasting ◽  
Important Solution ◽  
System Data ◽  
Long Term Forecasting ◽  
Access To Data

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.

Long-Term and Short-Term Traffic Forecasting Using Holt-Winters Method

2018 ◽  
pp. 1758-1772
Author(s):  
Aditya R. Raikwar ◽  
Rahul R. Sadawarte ◽  
Rishikesh G. More ◽  
Rutuja S. Gunjal ◽  
Parikshit N. Mahalle ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Data Science ◽  
Forecasting Model ◽  
Short Term ◽  
Traffic Forecasting ◽  
Important Solution ◽  
System Data ◽  
Long Term Forecasting ◽  
Access To Data

The need of faster life has caused the exponential growth in No. of vehicles on streets. The adverse effects include frequent traffic congestion, less time efficiency, unnecessary fuel consumption, pollution, accidents, etc. One of most important solution for resolving these problems is efficient transportation management system. Data science introduces different techniques and tools for overcoming these problems and to improve the data quality and forecasting inferences. The proposed long-term forecasting model can predict numerical values of effective attributes for a particular day on half-hourly basis, at least 24 hours prior to the time of prediction. The proposed forecasting model for short-term analysis will be having access to data as close as 30-minute difference from the time of prediction. Our proposed solution has integrated use of Holt-Winters (HW) method along with comparability schemes for seasonal approach.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Forecasting Australian port throughput: Lessons and Pitfalls in the era of Big Data

2019 ◽  
Author(s):  
soumya banerjee
Keyword(s):  
Big Data ◽  
Forecasting Model ◽  
Short Term ◽  
Port Development ◽  
Forecasting Accuracy ◽  
Port Throughput ◽  
Infrastructure Investments ◽  
The Cost ◽  
Modelling And Forecasting

Modelling and forecasting port throughput enables stakeholders to make efficient decisions ranging from management of port development, to infrastructure investments, operational restructuring and tariffs policy. Accurate forecasting of port throughput is also critical for long-term resource allocation and short-term strategic planning. In turn, efficient decision-making enhances the competitiveness of a port. However, in the era of big data we are faced with the enviable dilemma of having too much information. We pose the question: is more information always better for forecasting? We suggest that more information comes at the cost of more parameters of the forecasting model that need to be estimated. We comparemultiple forecasting models of varying degrees of complexity and quantify the effect of the amount of data on model forecasting accuracy. Our methodology serves as a guideline for practitioners in this field. We also enjoin caution that even in the era of big data more information may not always be better. It would be advisable for analysts to weigh the costs of adding more data: the ultimate decision would depend on the problem, amount of data and the kind of models being used.

Time Series Forecasting in Retail Sales Using LSTM and Prophet

2021 ◽  
pp. 241-262
Author(s):  
Clony Junior ◽  
Pedro Gusmão ◽  
José Moreira ◽  
Ana Maria M. Tome
Keyword(s):  
Time Series ◽  
Data Science ◽  
Short Term Memory ◽  
Special Kind ◽  
Time Series Forecasting ◽  
Short Term ◽  
Term Memory ◽  
Use Of Time

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

scholarly journals Detection of Road Traffic Anomalies Based on Computational Data Science

2022 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Data Science ◽  
Road Traffic ◽  
Smart Devices ◽  
Traffic Data ◽  
Forecasting Accuracy ◽  
Traffic Efficiency ◽  
Computational Data

Abstract The development of 5G has enabled the autonomous vehicles (AVs) to have full control over all functions. The AV acts autonomously and collects travel data based on various smart devices and sensors, with the goal of enabling it to operate under its own power. However, the collected data is affected by several sources that degrade the forecasting accuracy. To manage large amounts of traffic data in different formats, a computational data science approach (CDS) is proposed. The computational data science scheme introduced to detect anomalies in traffic data that negatively affect traffic efficiency. The combination of data science and advanced artificial intelligence techniques, such as deep leaning provides higher degree of data anomalies detection which leads to reduce traffic congestion and vehicular queuing. The main contribution of the CDS approach is summarized in detection of the factors that caused data anomalies early to avoid long- term traffic congestions. Moreover, CDS indicated a promoting results in various road traffic scenarios.

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