A Neural Network-based Approach for Public Transportation Prediction with Traffic Density Matrix

2018 ◽  
Author(s):  
Dancho Panovski ◽  
Veronica Scurtu ◽  
Titus Zaharia
Keyword(s):  
Neural Network ◽  
Density Matrix ◽  
Public Transportation ◽  
Traffic Density

Optimization of Novel Corner Module for Urban Electric Vehicle

2017 ◽  
Author(s):  
Allison J. Waters ◽  
Amir Khajepour
Keyword(s):  
Automotive Industry ◽  
Public Transportation ◽  
Mass Production ◽  
Ride Comfort ◽  
Lateral Displacement ◽  
Traffic Density ◽  
Ride Height ◽  
Short Supply ◽  
Module Design ◽  
Camber Angle

With urban populations on the rise, sustainable design of cities will be necessary to maintain reasonable quality of life for its inhabitants. Space to accommodate citizens in these densely populated cities will be in short supply and high demand. Strategic shifts in the transportation industry can alleviate the lack of space for residential and commercial facilities in densely populated areas. One opportunity to mitigate this growing problem is to reduce the size of personally owned, commuter vehicles. Smaller vehicles will reduce the storage space and increase the density of vehicles on roads. Another solution gaining traction in the automotive industry today are autonomous vehicles. Autonomous technology can allow cars to travels closer to one another without increasing the likelihood of a crash. Lastly, changing the market from personally owned vehicles to fleets owned by the company to be used as public transportation would reduce the traffic density. These changes to the automotive industry will facilitate a change in the layout and packaging of commercial vehicles to meet new objectives. This paper proposes a novel corner module design that meets the market’s needs for mass production of X-by-wire systems integrated into a compact space while maintaining current levels of vehicle stability, handling and ride comfort. The proposed corner module features an in-wheel motor with electronic steering and braking. To increase the handling of the vehicle, the corner module has active camber control and can be modified for active ride height adjustment. Furthermore, the simplicity and minimal quantity of the components makes the corner module design ready for mass production. The geometry of the purposed corner module was optimized using a genetic algorithm. The objectives were to target a wheel lateral displacement of 10 cm at the −15° of camber angle and to minimize the longitudinal displacement of the wheel in a steer range of −20° to 20° at 0° of camber angle. The optimization had three types of constraints: packaging space limits, component interference and cylinder size. The optimization successfully found a solution that met both objectives while remaining within the constraints. The workspace of the wheel was limited by the rear cylinder size and the fixed length of the linkage.

scholarly journals PERAMALAN DATA PENUMPANG KERETA API DENGAN MENGGUNAKAN MAXIMAL OVERLAP DISCRETE WAVELET TRANSFORM- RECURRENT NEURAL NETWORK (MODWT-RNN)

2019 ◽  
Vol 12 (2) ◽  
pp. 164
Author(s):  
Mira Andriyani ◽  
Subanar Subanar
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Accurate Result ◽  
Nonlinear Models ◽  
Forecasting Model ◽  
Discrete Wavelet ◽  
The Public

The train is one of the public transportation that is very popular because it is affordable and free of congestion. There is often a buildup of train passengers at the station so that it sometimes causes an accumulation of passengers at the station and makes the situation at the station to be not conducive. In order to avoid a buildup of passengers, forecasting the number of passengers can be done. Forecasting is determined based on data in previous times. Data of train passengers in Java (excluding Jabodetabek) forms a non-stationary and contains nonlinear relationships between the lags. One of the nonlinear models that can be used is Recurrent Neural Network (RNN). Before RNN modeling, Maximal Overlap Wavelet Transform (MODWT) was used to make data more stationary. Forecasting model of train passengers in Java excluding Jabodetabek, Indonesia using MODWT-RNN results forecasting with RMSE is 252.85, while RMSE of SARIMA and RNN are 434.97 and 320.48. These results indicate that the MODWT-RNN model gives a more accurate result than SARIMA and RNN.

scholarly journals Traffic Density Classification Using Twitter Data and GPS Based On Android Application

2020 ◽  
Vol 14 (2) ◽  
pp. 113
Author(s):  
Mohammad Afrizal ◽  
Idham Ananta Timur
Keyword(s):  
Public Transportation ◽  
Traffic Density ◽  
The Public ◽  
The Road ◽  
Twitter Data ◽  
Average Accuracy ◽  
Android Applications ◽  
On The Road ◽  
The City ◽  
Special Region

Increasing the number of vehicles in Special Region of Yogyakarta caused by congestion occurred at various traffic points in Special Region of Yogyakarta. The solution to reducing congestion is by increasing the use of public transportation within the city, but it still not in demand by the public. Optimizing daily activities, community always tries to avoid the traffic density on the road to be bypassed.Some research on social media has been used to detect traffic density anomalies. However, the system still cannot provide traffic density information on roads that will be passed by the user because it is just a mapping. Based on this problem, this study aims to classify the traffic density on the road that will be passed by users in the Special Region of Yogyakarta into the category of high traffic and low traffic by utilizing Twitter and GPS data.The results show that Android Applications are able to classify traffic density on the road to be traversed using Geonames.org API. Using the naïve bayes classification algorithm, the system can classify traffic density on 14 streets with an average accuracy of 77.5%, 90% precision, 79.1% recall, and 82.8% f-score.

scholarly journals Short-Term IoT Data Forecast of Urban Public Bicycle Based on the DBSCAN-TCN Model for Social Governance

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Dazhou Li ◽  
Chuan Lin ◽  
Wei Gao ◽  
Guangbao Yu ◽  
Jian Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Public Transportation ◽  
Clustering Algorithm ◽  
Smart Cities ◽  
Dropout Rate ◽  
Urban Transport ◽  
Transport Systems ◽  
Social Governance ◽  
Shared Bicycle

Internet of Things will play a vital role in the public transport systems to achieve the concepts of smart cities, urban brains, etc., by mining continuously generated data from sensors deployed in public transportation. In this sense, smart cities applied artificial intelligence techniques to offload data for social governance. Bicycle sharing is the last mile of urban transport. The number of the bike in the sharing stations, to be rented in future periods, is predicted to get the vehicles ready for deployment. It is an important tool for the implementation of smart cities using artificial intelligence technologies. We propose a DBSCAN-TCN model for predicting the number of rentals at shared bicycle stations. The proposed model first clusters all shared bicycle stations using the DBSCAN clustering algorithm. Based on the results of the clustering, the data on the number of shared bicycle rentals are fed into a TCN neural network. The TCN neural network structure is optimized. The effects of convolution kernel size and Dropout rate on the model performance are discussed. Finally, the proposed DBSCAN-TCN model is compared with the LSTM model, Kalman filtering model, and autoregressive moving average model. Through experimental validation, the proposed DBSCAN-TCN model outperforms the traditional three models in terms of two metrics, root mean squared logarithmic error, and error rate, in terms of prediction performance.

scholarly journals Learning Dynamic Factors to Improve the Accuracy of Bus Arrival Time Prediction via a Recurrent Neural Network

2019 ◽  
Vol 11 (12) ◽  
pp. 247
Author(s):  
Xin Zhou ◽  
Peixin Dong ◽  
Jianping Xing ◽  
Peijia Sun
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Public Transportation ◽  
Prediction Accuracy ◽  
Arrival Time ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Support Vector ◽  
Data Set ◽  
Dynamic Factors

Accurate prediction of bus arrival times is a challenging problem in the public transportation field. Previous studies have shown that to improve prediction accuracy, more heterogeneous measurements provide better results. So what other factors should be added into the prediction model? Traditional prediction methods mainly use the arrival time and the distance between stations, but do not make full use of dynamic factors such as passenger number, dwell time, bus driving efficiency, etc. We propose a novel approach that takes full advantage of dynamic factors. Our approach is based on a Recurrent Neural Network (RNN). The experimental results indicate that a variety of prediction algorithms (such as Support Vector Machine, Kalman filter, Multilayer Perceptron, and RNN) have significantly improved performance after using dynamic factors. Further, we introduce RNN with an attention mechanism to adaptively select the most relevant input factors. Experiments demonstrate that the prediction accuracy of RNN with an attention mechanism is better than RNN with no attention mechanism when there are heterogeneous input factors. The experimental results show the superior performances of our approach on the data set provided by Jinan Public Transportation Corporation.

scholarly journals Acoustic Classification of Surface and Underwater Vessels in the Ocean Using Supervised Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3492 ◽  
Author(s):  
Jongkwon Choi ◽  
Youngmin Choo ◽  
Keunhwa Lee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Density Matrix ◽  
Spectral Density ◽  
Input Data ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Matrix Elements ◽  
Spectral Density Matrix ◽  
Cross Spectral Density

Four data-driven methods—random forest (RF), support vector machine (SVM), feed-forward neural network (FNN), and convolutional neural network (CNN)—are applied to discriminate surface and underwater vessels in the ocean using low-frequency acoustic pressure data. Acoustic data are modeled considering a vertical line array by a Monte Carlo simulation using the underwater acoustic propagation model, KRAKEN, in the ocean environment of East Sea in Korea. The raw data are preprocessed and reorganized into the phone-space cross-spectral density matrix (pCSDM) and mode-space cross-spectral density matrix (mCSDM). Two additional matrices are generated using the absolute values of matrix elements in each CSDM. Each of these four matrices is used as input data for supervised machine learning. Binary classification is performed by using RF, SVM, FNN, and CNN, and the obtained results are compared. All machine-learning algorithms show an accuracy of >95% for three types of input data—the pCSDM, mCSDM, and mCSDM with the absolute matrix elements. The CNN is the best in terms of low percent error. In particular, the result using the complex pCSDM is encouraging because these data-driven methods inherently do not require environmental information. This work demonstrates the potential of machine learning to discriminate between surface and underwater vessels in the ocean.

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