A Repeated Commuting Driving Cycle Dataset with Application to Short-term Vehicle Velocity Forecasting

2021 ◽  
pp. 1-44
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Energy Management ◽  
Dynamic Properties ◽  
Critical Role ◽  
Driving Cycle ◽  
Support Vector ◽  
Chain Model ◽  
Short Term ◽  
Model Framework ◽  
Vehicle Velocity

Abstract Vehicle velocity forecasting plays a critical role in operation scheduling of varying systems and devices for a passenger vehicle. The forecasted information serves as an indispensable prerequisite for vehicle energy management via predictive control algorithms or vehicle ecosystem control Co-design. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to simulate a daily commuting route and serves as a base for further energy management study. To explore the dynamic properties, these driving cycles are piecewise divided into cycle segments via intersection/stop identification. A vehicle velocity forecasting model pool is then developed for each segment, including the hidden Markov chain model, long short-term memory network, artificial neural network, support vector regression, and similarity methods. To further improve the forecasting performance, higher-level algorithms like localized model selection, ensemble approaches, and a combination of them are investigated and compared. Results show that (i) the segment-based forecast improves the forecasting accuracy by up to 20.1%, compared to the whole cycle-based forecast; and (ii) the hybrid localized model framework that combines dynamic model selection and an ensemble approach could further improve the accuracy by 9.7%. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

Short-Term Vehicle Velocity Forecasting via Cycle Segmentation Model Selection

2021 ◽  
Author(s):  
Yuanzhi Liu ◽  
Jie Zhang
Keyword(s):  
Model Selection ◽  
Waiting Time ◽  
Short Term Memory ◽  
Critical Role ◽  
Time Estimation ◽  
Forecasting Model ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Vehicle Velocity ◽  
Selection Framework

Abstract Vehicle velocity forecasting plays a critical role in scheduling the operations of varying systems and devices in a passenger vehicle. This paper first generates a repeated urban driving cycle dataset at a fixed route in the Dallas area, aiming to contribute to the improvement of vehicle energy efficiency for commuting routes. The generated driving cycles are divided into cycle segments based on intersection/stop identification, deceleration and reacceleration identification, and waiting time estimation, which could be used for better evaluating the effectiveness of model localization. Then, a segment-based vehicle velocity forecasting model is developed, where a machine learning model is trained/developed at each segment, using the hidden Markov chain (HMM) model and long short-term memory network (LSTM). To further improve the forecasting accuracy, a localized model selection framework is developed, which can dynamically choose a forecasting model (i.e., HMM or LSTM) for each segment. Results show that (i) the segment-based forecast could improve the forecasting accuracy by up to 24%, compared the whole cycle-based forecast; and (ii) the localized model selection framework could further improve the forecasting accuracy by 6.8%, compared to the segment-based LSTM model. Moreover, the potential of leveraging the stopping location at an intersection to estimate the waiting time is also evaluated in this study.

scholarly journals Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaoqing Dai ◽  
Lijun Sun ◽  
Yanyan Xu
Keyword(s):  
Model Selection ◽  
Smart Card ◽  
Probabilistic Model ◽  
Travel Demand ◽  
Combination Method ◽  
Support Vector ◽  
Short Term ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Smart Card Data

Reliable prediction of short-term passenger flow could greatly support metro authorities’ decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.

Robust Predictive Energy Management of Connected Power-split Hybrid Electric Vehicles Using Dynamic Traffic Data

2021 ◽  
Author(s):  
Seyedeh Mahsa Sotoudeh ◽  
Baisravan HomChaudhuri
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Driving Cycle ◽  
Traffic Data ◽  
Energy Management Strategy ◽  
Short Term ◽  
Low Level ◽  
Hybrid Electric

Abstract This research focuses on the predictive energy management of connected human-driven hybrid electric vehicles (HEV) to improve their fuel efficiency while robustly satisfying system constraints. We propose a hierarchical control framework that effectively exploits long-term and short-term decision-making benefits by integrating real-time traffic data into the energy management strategy. A pseudospectral optimal controller with discounted cost is utilized at the high-level to find an approximate optimal solution for the entire driving cycle. At the low-level, a Long Short-Term Memory neural network is developed for higher quality driving cycle (velocity) predictions over the low-level's short horizons. Tube-based model predictive controller is then used at the low-level to ensure constraint satisfaction in the presence of driving cycle prediction errors. Simulation results over real-world driving cycles show an improvement in fuel economy for the proposed controller that is real-time applicable and robust to the driving cycle's uncertainty.

Supervised electricity tariff prediction using random forest validated through user comfort and constraint for a home energy management scheme

2021 ◽  
Vol 40 (1) ◽  
pp. 745-757
Author(s):  
Ganesh Kumar Chellamani ◽  
M. Firdouse Ali Khan ◽  
Premanand Venkatesh Chandramani
Keyword(s):  
Random Forest ◽  
Energy Management ◽  
Performance Metrics ◽  
Support Vector ◽  
End User ◽  
Short Term ◽  
Management Scheme ◽  
Home Energy Management ◽  
Electricity Tariff ◽  
User Comfort

Day-ahead electricity tariff prediction is advantageous for both consumers and utilities. This article discusses the home energy management (HEM) scheme consisting of an electricity tariff predictor and appliance scheduler. The random forest (RF) technique predicts a short-term electricity tariff for the next 24 hours using the past three months of electricity tariff information. This predictor provides the tariff information to schedule the appliances at the most preferred time slot of a consumer with minimum electricity tariff, aiming high consumer comfort and low electricity bill for consumers. The proposed approach allows a user to be aware of their demand and their comfort. The proposed approach makes use of present-day (D) tariff and immediate previous 30 days (D-1, D-2, ...  , D-30) of tariff information for training achieves minimum error values for next day electricity tariff prediction. The simulation results demonstrate the benefits of the RF approach for tariff prediction by comparing it with the support vector machine (SVM) and decision tree (DT) predicted tariffs against the actual tariff, provided by the utility day-ahead. The outcomes indicate that the RF produces the best results compared to SVM and DT predictions for performance metrics and end-user comfort.

Short-term Forecasting Ability of Hybrid Models for BRIC Currencies

2020 ◽  
pp. 097215092095461
Author(s):  
Latha Sreeram ◽  
Samie Ahmed Sayed
Keyword(s):  
Hybrid Model ◽  
Seasonal Pattern ◽  
Hybrid Models ◽  
Least Square ◽  
Support Vector ◽  
Short Term ◽  
Model Framework ◽  
Linear Pattern ◽  
Non Linear ◽  
Short Term Forecasting

This article proposes a new framework to improve short-term forecasting accuracy of exchange rates of BRIC nations, that is, Brazil (USD/BRL), Russia (USD/RUB), India (USD/INR) and China (USD/CNY). The study employs three methodologies for a 42-day forecast: hybrid models based on least square support vector machine, residual hybrid model and automatic hybrid model forecasting using R software. The results show that the proposed residual hybrid model framework, including autoregressive integrated moving average-artificial neural network (ARIMA–ANN)-TBATS, outperformed other models with Brazil and China return series reflecting the best accuracy in ANN model and India and Russia demonstrating the best accuracy in trigonometric seasonal, box-cox transformation, ARIMA residuals, trend and seasonality (TBATS) model. Further, the results indicate that Brazil and China return series follow a non-linear pattern, while India and Russia follow a non-linear complex seasonal pattern. The highest level of forecast accuracy has been observed in China followed by Brazil, India and Russia.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

The capacity of short term memory from an evolutionary perspective

2019 ◽  
Author(s):  
Majid Manoochehri
Keyword(s):  
Short Term Memory ◽  
Memory Span ◽  
Critical Role ◽  
Experimental Studies ◽  
Cognitive System ◽  
Evolutionary Approach ◽  
Evolutionary Perspective ◽  
Short Term ◽  
Term Memory

Memory span in humans has been intensely studied for more than a century. In spite of the critical role of memory span in our cognitive system, which intensifies the importance of fundamental determinants of its evolution, few studies have investigated it by taking an evolutionary approach. Overall, we know hardly anything about the evolution of memory components. In the present study, I briefly review the experimental studies of memory span in humans and non-human animals and shortly discuss some of the relevant evolutionary hypotheses.

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