scholarly journals Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Dongbo Liu ◽  
Jian Lu ◽  
Wanjing Ma
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Supply And Demand ◽  
Optimization Strategy ◽  
Trajectory Data ◽  
Demand Prediction ◽  
Data User ◽  
User Characteristic ◽  
Destination Station ◽  
The One

One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the balance of demand and supply can be largely improved. The multisource data include trajectory data, user application log data, order data, station data, and user characteristic data. Based on these data, a return demand prediction model was used to predict whether the user will return the vehicle in 15 min in real time, and a destination station prediction model was applied to forecast which station the user will park at. Finally, a case study using ten stations’ one-week field data was conducted to test the benefit of the dynamic return demand prediction. The results showed that the return demand prediction improves the efficiency of the relocations by mitigating the condition that the station parking space is full or empty. The potential application of this study would effectively reduce unnecessary relocation and further formulate an active operation optimization strategy to reduce the system’s operational cost and improve the service quality of the system.

scholarly journals Study on Fractal Multistep Forecast for the Prediction of Driving Behavior

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Longhai Yang ◽  
Hong Xu ◽  
Xiqiao Zhang ◽  
Shuai Li ◽  
Wenchao Ji
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Time Series Data ◽  
Back Propagation ◽  
Series Data ◽  
Vehicle Speed ◽  
Trajectory Data ◽  
Time Data ◽  
Characteristic Analysis ◽  
Real Time Data

The application and development of new technology make it possible to acquire real-time data of vehicles. Based on these real-time data, the behavior of vehicles can be analyzed. The prediction of vehicle behavior provides data support for the fine management of traffic. This paper proposes speed and acceleration have fractal features by R/S analysis of the time series data of speed and acceleration. Based on the characteristic analysis of microscopic parameters, the characteristic indexes of parameters are quantified, the fractal multistep prediction model of microparameters is established, and the BP (back propagation neural networks) model is established to estimate predictable step of fractal prediction model. The fractal multistep prediction model is used to predict speed acceleration in the predictable step. NGSIM trajectory data are used to test the multistep prediction model. The results show that the proposed fractal multistep prediction model can effectively realize the multistep prediction of vehicle speed.

scholarly journals A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

2020 ◽  
Vol 15 (5) ◽  
Author(s):  
Yonghong Tian ◽  
Qi Wu ◽  
Yue Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Short Term Memory ◽  
Supply And Demand ◽  
Original Data ◽  
Market Demand ◽  
Short Term ◽  
Term Memory ◽  
Demand Prediction ◽  
Long Short Term Memory

In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments.

scholarly journals Tourist Demand Prediction Model Based on Improved Fruit Fly Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chuangle Guo ◽  
Wei Shang
Keyword(s):  
Prediction Model ◽  
Fruit Fly ◽  
Fruit Fly Optimization Algorithm ◽  
Optimization Strategy ◽  
Fruit Fly Optimization ◽  
Demand Prediction ◽  
Model Based ◽  
Change Trend ◽  
Tourist Demand ◽  
Robust Prediction

To accurately predict the development and change trend of the future, tourism market can effectively improve the planning and purpose of tourism development. In order to improve the accuracy of tourist demand prediction, this paper studies the tourist demand prediction model based on improved fruit fly algorithm. Aiming at the optimization defects of the traditional fruit fly optimization algorithm (FOA), the model introduces two concepts of sensitivity and pheromone, improves the optimization strategy and position replacement of fruit fly, improves the diversity of fruit fly population, modifies the global optimization characteristics of the algorithm, and improves the local search ability and search efficiency of the algorithm. By combining the improved AFOA with echo state network (ESN), a two-stage combined prediction model (AAFOA-ESN) is constructed. The experimental results show that the minimum prediction error accuracy of the model is only 0.55%, which has more robust prediction effect, faster convergence speed, and higher prediction accuracy.

scholarly journals Optimization strategy of automobile dispatching based on neural network model

2021 ◽  
Vol 271 ◽  
pp. 01020
Author(s):  
Chuyuan Wang
Keyword(s):  
Neural Network ◽  
Public Transportation ◽  
Missing Values ◽  
Supply And Demand ◽  
Sharing Economy ◽  
Optimization Strategy ◽  
Data Standardization ◽  
Demand Prediction ◽  
The City

As a representative product of the sharing economy era and a powerful supplement to public transportation shared cars have the characteristics of convenience, efficiency, environmental protection, and green travel, and to a certain extent alleviate the contradiction between supply and demand, and solve the problem of long-term idle vehicles and overloaded operation of roads problems. But the uneven distribution of shared cars, the coexistence of no cars, and empty seats will happen. To solve the above problems, this article first analyzes data outliers, data missing values, and data standardization processing on the attached data, and then builds a BP neural network demand prediction model to obtain the distribution of shared car usage in the city.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Comparative study on e-bikes’ decision-making behaviors under flashing green and green countdown

2021 ◽  
pp. 1-8
Author(s):  
Haoyang Meng ◽  
Sheng Dong ◽  
Jibiao Zhou ◽  
Shuichao Zhang ◽  
Zhenjiang Li
Keyword(s):  
Decision Making ◽  
Critical Issue ◽  
Trajectory Data ◽  
Decision Points ◽  
Binary Logit ◽  
The One ◽  
Common Signal ◽  
And Function ◽  
Decision Behaviors ◽  
Stop Line

Green flash light (FG) and green countdown (GC) are the two most common signal formats applied in green-red transition that provides drivers additional alert before termination of green phase. Due to their importance and function in stop-pass decision-making process, proper use of them has become a critical issue to greatly improve the safety and efficiency of signalized intersections. Gradually e-bike riders have become more important commuters in China, however, the influence of FG or GC on them is not clear yet and need pay more attention to it. This study chooses two almost identical intersections to obtain highly accurate trajectory data of e-bike riders to study their decision-making behaviors under FG or GC. The e-bike riders’ behavior is classified into four categories and is to identify their stop-pass decision points using the acceleration trend. Two binary-logit models were built to predict the stop–pass decision behaviors for the different e-bike rider groups, explaining that the potential time to the stop-line is the dominant independent factor of the different behaviors of GC and FG. Furthermore empirical analysis of decision points indicated that GC provides the earlier stop-pass decision point and longer decision making duration on the one side while results in more complexity of decision making and greater risk of stop-line crossing than FG on the other side.

scholarly journals An Adaptive RBF-NMPC Architecture for Trajectory Tracking Control of Underwater Vehicles

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 105
Author(s):  
Zhenzhong Chu ◽  
Da Wang ◽  
Fei Meng
Keyword(s):  
Model Predictive Control ◽  
Prediction Model ◽  
Real Time ◽  
Predictive Control ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Working Environment ◽  
Gray Wolf ◽  
Trajectory Tracking Control ◽  
System Output

An adaptive control algorithm based on the RBF neural network (RBFNN) and nonlinear model predictive control (NMPC) is discussed for underwater vehicle trajectory tracking control. Firstly, in the off-line phase, the improved adaptive Levenberg–Marquardt-error surface compensation (IALM-ESC) algorithm is used to establish the RBFNN prediction model. In the real-time control phase, using the characteristic that the system output will change with the external environment interference, the network parameters are adjusted by using the error between the system output and the network prediction output to adapt to the complex and uncertain working environment. This provides an accurate and real-time prediction model for model predictive control (MPC). For optimization, an improved adaptive gray wolf optimization (AGWO) algorithm is proposed to obtain the trajectory tracking control law. Finally, the tracking control performance of the proposed algorithm is verified by simulation. The simulation results show that the proposed RBF-NMPC can not only achieve the same level of real-time performance as the linear model predictive control (LMPC) but also has a superior anti-interference ability. Compared with LMPC, the tracking performance of RBF-NMPC is improved by at least 43% and 25% in the case of no interference and interference, respectively.

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