scholarly journals Modelling and Forecasting Vehicle Registration System: An Arma Approach

2013 ◽  
Vol 5 ◽  
pp. 1-9
Author(s):  
Dawud Adebayo Agunbiade ◽  
E.N. Peter
Keyword(s):  
Road Network ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
National Unity ◽  
Registration System ◽  
Vehicle Registration ◽  
Modeling And Forecasting ◽  
Modelling And Forecasting

The role of transportation in the promotion of national unity and socio-economic integration in Nigeria cannot be overemphasized. Transport stimulates asense of oneness multi understanding in the cultural diversification of the mostpopulous nation in the sub Sahara of African Continent. it is therefore of interest tostudy using the Autoregressive Moving Average, the transportation system in Nigeriausing Lagos State (being the commercial centre) and also maintains a robust data basethrough the AUTOREG System as a case study by modeling and forecasting theVehicle registration system in terms of types and ownership. The result of theAutoregressive Moving Average (ARMA) approach indicated that there is tendencyfor an increase in the registration of Vehicles in the future. It is therefore suggested that to accommodate an increase in the number of Vehiclesregistration, a robust Vehicle database should be designed across the country forsecurity, research and adequate planning; and Nigerian government at all levels shouldstrive to provide adequate and reliable road network system to meet this emergingdevelopmental activities among others.

Modeling and Forecasting Sales Data by Time Series Analysis

1981 ◽  
Vol 18 (1) ◽  
pp. 94-100 ◽  
Author(s):  
S. G. Kapoor ◽  
P. Madhok ◽  
S. M. Wu
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time Series Modeling ◽  
Average Model ◽  
Sales Data ◽  
Autoregressive Moving Average Model ◽  
Deterministic Function ◽  
Modeling And Forecasting ◽  
Moving Average Model

Time series modeling technique is used to model a series of sales data in which seasonality causes distinct spike peaks. The analysis of actual sales data shows that the seasonality in the data can be approximated by a deterministic function and the stochastic component is a sixth-order autoregressive moving average model. Use of the combined deterministic and stochastic models to derive the minimum mean squared forecast yields reliable results.

The limitations and usefulness of streamflow generation methods: a case study

1980 ◽  
Vol 7 (1) ◽  
pp. 185-191
Author(s):  
W. J. Stolte
Keyword(s):  
Probabilistic Models ◽  
Moving Average ◽  
Arma Model ◽  
Physical World ◽  
Model Complexity ◽  
Autoregressive Moving Average ◽  
Streamflow Generation ◽  
Recorded Data ◽  
Hydrologic Design

Probabilistic models have become important hydrologic tools. However, increasing model complexity makes the connections between the model and the physical world more and more vague. This can lead to a de-emphasis of engineering judgment, since model validity is easily assumed when even partial verification must await future occurrences. A simple autoregressive model was used to generate stochastic flow sequences for the dam and reservoir being constructed on the Red Deer River in Alberta. The results from this model were compared with those obtained from a more complex autoregressive moving average (ARMA) model. Both models have similar deficiencies. It is concluded that since stochastic generation can never represent future conditions with certainty, the common practice of basing the hydrologic design of reservoirs on actually recorded data is usually the most valid procedure. However, stochastic streamflow generation can be used to give valuable probabilities of reservoir storage failure.

Role of Exposure in Analysis of Road Accidents

2005 ◽  
Vol 1908 (1) ◽  
pp. 96-103 ◽  
Author(s):  
Filip Van den Bossche ◽  
Geert Wets ◽  
Tom Brijs
Keyword(s):  
Traffic Safety ◽  
Moving Average ◽  
Added Value ◽  
Autoregressive Moving Average ◽  
Road Accidents ◽  
Safety Research ◽  
Primary Determinant ◽  
Missing Variables ◽  
The Impact

Exposure is a key variable in traffic safety research. In the literature, it is noted as the first and primary determinant of traffic safety. In many cases, however, no valid exposure measure is available. In Belgium, monthly traffic counts for 12 years are available. This offers the opportunity to investigate the added value of exposure in models, next to legal, economic, and climatologic variables. Multiple regression with autoregressive moving average (ARMA) errors is used to quantify the impact of these factors on aggregated traffic safety. For each dependent variable, a model with and without exposure is constructed. The models show that exposure is significantly related to the number of accidents with persons killed and seriously injured and to the corresponding victims, but not to the lightly injured outcomes. Moreover, the addition or deletion of exposure does not influence the effects of the remaining variables in the model. The effects of exposure clearly depend on the type of measure used and on the time horizon considered. The framework of a regression model with ARMA errors allows for missing variables being accounted for by the error term. Even without a variable such as exposure, valid models can be constructed.

Linear Predictability: A Sea Surface Height Case Study

2018 ◽  
Vol 31 (7) ◽  
pp. 2599-2611 ◽  
Author(s):  
Maike Sonnewald ◽  
Carl Wunsch ◽  
Patrick Heimbach
Keyword(s):  
Prediction Error ◽  
Moving Average ◽  
Sea Surface Height ◽  
Information Criteria ◽  
Sea Surface ◽  
Autoregressive Moving Average ◽  
Error Growth ◽  
State Estimate ◽  
Seasonal Signal

A benchmark of linear predictability of sea surface height (SSH) globally is presented, complementing more complicated studies of SSH predictability. Twenty years of the Estimating the Circulation and Climate of the Ocean (ECCOv4) state estimate (1992–2011) are used, fitting autoregressive moving average [ARMA([Formula: see text])] models where the order of the coefficients is chosen by the Akaike information criteria (AIC). Up to 50% of the ocean SSH variability is dominated by the seasonal signal. The variance accounted for by the nonseasonal SSH is particularly distinct in the Southern and Pacific Oceans, containing >95% of the total SSH variance, and the expected prediction error growth takes a few months to reach a threshold of 1 cm. Isolated regions take 12 months or more to cross an accuracy threshold of 1 cm. Including the trend significantly increases the time taken to reach the threshold, particularly in the South Pacific. Annual averaging has expected prediction error growth of a few years to reach a threshold of 1 cm. Including the trend mainly increases the time taken to reach the threshold, but the time series is short and noisy.

scholarly journals Considerations on the role of modernizing the road infrastructure in the prevention of road accidents

2019 ◽  
Vol 290 ◽  
pp. 06004
Author(s):  
Cristian Deac ◽  
Lucian Tarnu
Keyword(s):  
Road Safety ◽  
Road Network ◽  
Road Traffic ◽  
Road Accidents ◽  
Road Infrastructure ◽  
The Road ◽  
Traffic Conditions ◽  
Pedestrian Crossing

The realizing and improvement of road infrastructure, of modern road networks provides normal, safe and pleasant road traffic conditions and also help prevent road accidents. The road network, with its constructive characteristics, has to offer optimal conditions for the movement of vehicles, pedestrians and other categories of participants in the road traffic. Starting from the case study of a road sector with heavy road traffic, the current paper analyzes the increase in road safety in Romanian localities along European and national roads through the implementation of specific measures such as setting up sidewalks, installing New Jersey median barriers, expanding the road sectors with 2+1 lanes, replacing normal pedestrian crossings with elevated crossings or with pedestrian crossing with mid-road waiting areas etc.

scholarly journals The Responsibility of Government and Society Towards Social Cohesion: A Family Perspective

2021 ◽  
Author(s):  
Fred Bidandi
Keyword(s):  
Social Cohesion ◽  
Common Ground ◽  
Government Policies ◽  
National Unity ◽  
Related Factors ◽  
Family Perspective ◽  
The Family ◽  
Responsibility Of Government

Social cohesion, the foundation that keeps society together, is influenced by various inter-related factors such as education social, cultural, religious, and business, among others. Current debates indicates that unless social cohesion in its various dimensions is addressed, be it through reconciliation, tackling inequality, crafting a national identity, or bridging rural-urban divides, the implementation of any Southern African Development Plan will be challenging. In this paper, social cohesion is viewed as an intervention for coexistence; as an invitation to find common ground and allowing the sharing of social spaces; and to forge a common identity whilst recognising societal diversity. This paper postulates that although social cohesion is intended to contribute towards nation-building and national unity, government policies are fundamental to the advancement thereof. The paper defines, unpacks, and identifies the challenges of social cohesion using South Africa as a case study. The paper argues that the family is instrumental in building social cohesion. Government through its policies processes has an important role to play in strengthen the family. The lessons learnt could contribute to the role of family towards social cohesion on the African continent.

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