Space-time multivariate Negative Binomial regression for urban short-term traffic volume prediction

Daadoo

2016

MATEC Web Conf.

Self Cite

Abstract. Although speed bumps are used to force drivers reduce car speed for avoiding accidents, these bumps may cause car crash or accident when drivers do not notice them. Studies have proposed different methods to detect bumps and alert drivers. However, these methods have limitations and require modifications to enable accurate detection. Also these methods did not propose speed reduction approaches. Therefore, in this research, we propose a method that utilizes smartphone microelectronic mechanical technology for speed bump detection. The system uses the gravity sensor to detect the vertical vibration of cars passes over bumps and the GPS to determine the position of the bump. To give accurate detection results, data are collected from crowd, stored and processed on the cloud. The system also contains a speed reduction unit which is attached to the brake pedal and reduces the speed if a bump is detected. A small scale experiment showed that the system detected the position and the height of bumps with a very small error. The system also reduced the speed of cars at the moment they hit the bumps to a point that does not cause any harm to cars or passengers.

Section: Resultsmentioning

confidence: 99%

Intelligent Smartphone based system for detecting speed bumps and reducing car speed

Daadoo

2016

MATEC Web Conf.

Self Cite

“…Also, the NBAM is more efficient because the computational time needed for training and forecasting is lower than the time of the other models. The negative binomial based models were successfully applied to forecasting traffic data that is autocorrelated, overdispersed and have seasonal patterns [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Forecasting for smart energy: an accurate and effificient negative binomial additive model

Daadoo

et al. 2020

IJEECS

Self Cite

<div>Smart energy requires accurate and effificient short-term electric load forecasting to enable effificient</div><div>energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char</div><div>acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns.</div><div>Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting</div><div>methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel</div><div>model for short-term electric load forecasting. The model adopts the negative binomial additive models</div><div>(NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season</div><div>ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of</div><div>load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM</div><div>captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has</div><div>low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its</div><div>accuracy and effificiency outperform those of the other models used in this context.</div>

“…The negative binomial-based models were successfully tested in our previous work on traffic data, which are autocorrelated, are overdispersed, and have seasonal patterns. [20][21][22] The negative binomial was also used to mine big data in social science, and it was found an accurate and efficient method. 23 The proposed model is evaluated using a real-world data set of mixed load collected from Jericho city.…”

Section: Introductionmentioning

confidence: 99%

Accurate and time‐efficient negative binomial linear model for electric load forecasting in IoE

Trans Emerging Tel Tech

Alsaadi

et al. 2019

Accurate and efficient model predictive control (MPC) is essential for Internet of energy (IoE) to enable active real-time control, decentralized demand-supply balance, and dynamic energy management. The MPC consists of short-term electric load forecasting, whose accuracy is affected by the load characteristics, such as overdispersion, autocorrelation, and seasonal patterns. The forecasting efficiency depends on the computational time that is required to produce accurate results and is affected by the IoE data volume. Although several fundamental short-term forecasting models have been proposed, more accurate and efficient models are needed for IoE. Therefore, we propose a novel forecasting temporal negative binomial linear model (NBLM) that handles overdispersion and captures nonlinearity of electric load. We also classify the load into low, moderate, and high intraday seasons to increase the forecast accuracy by modeling the autocorrelation in each season, separately. The temporal NBLM was evaluated using real-world data from Jericho city, and its results were compared to other forecasting models. The temporal NBLM is found more accurate than the other models as the mean absolute percentage error (MAPE) is reduced by 29% compared to the ARMA model. In addition, the proposed model is more efficient as its running time is reduced by 63% in the training phase and by 87% in the forecast phase compared to the Holt-Winter model. This increase in accuracy and efficiency makes the proposed model applicable for load forecasting in IoE contexts where data volume is large and load is highly fluctuated, is overdispersed, is autocorrelated, and follows seasonal patterns.