A novel passenger flow prediction model using deep learning methods

Currently, deep learning has been successfully applied in many fields and achieved amazing results. Meanwhile, big data has revolutionized the transportation industry over the past several years. These two hot topics have inspired us to reconsider the traditional issue of passenger flow prediction. As a special structure of deep neural network (DNN), an autoencoder can deeply and abstractly extract the nonlinear features embedded in the input without any labels. By exploiting its remarkable capabilities, a novel hourly passenger flow prediction model using deep learning methods is proposed in this paper. Temporal features including the day of a week, the hour of a day, and holidays, the scenario features including inbound and outbound, and tickets and cards, and the passenger flow features including the previous average passenger flow and real-time passenger flow, are defined as the input features. These features are combined and trained as different stacked autoencoders (SAE) in the first stage. Then, the pre-trained SAE are further used to initialize the supervised DNN with the real-time passenger flow as the label data in the second stage. The hybrid model (SAE-DNN) is applied and evaluated with a case study of passenger flow prediction for four bus rapid transit (BRT) stations of Xiamen in the third stage. The experimental results show that the proposed method has the capability to provide a more accurate and universal passenger flow prediction model for different BRT stations with different passenger flow profiles.

A Review on Kalman Filter Models

Passenger flow prediction is important for the operation of urban rail transit. The prediction of abnormal passenger flow is difficult due to rare similar history data. A model based on the fusion of support vector regression (SVR) and long short-term memory (LSTM) neural network is proposed. The inputs of the model are the abnormal features, which consist of the recent real volume series and the predicted volume series based on the periodic features. A two-stage training method is designed to train the LSTM model, which can reflect the large fluctuations of abnormal flow more timely and approximately. A combination method based on the real-time prediction errors is proposed, on which the outputs of SVR and LSTM are combined into the final outputs of the prediction model. The results of the experiments show that the SVR-LSTM model more accurately reflects the abnormal fluctuations of passenger flow, which performs well and yields greater forecast accuracy than the individual models.

/pdf/short-term-abnormal-passenger-flow-prediction-based-on-the-32hndm82dv.pdf

Short-Term Abnormal Passenger Flow Prediction Based on the Fusion of SVR and LSTM

DeepMTT: A deep learning maneuvering target-tracking algorithm based on bidirectional LSTM network

This paper proposes an Interactive Multiple Model-based Pattern Hybrid (IMMPH) approach to predict short-term passenger demand. The approach maximizes the effective information content by assembling the knowledge from pattern models using historical data and optimizing the interaction between them using real-time observations. It can dynamically estimate the priori pattern models combination in advance for the next time interval. The source demand data were collected by Smart Card system along one bus service route over one year. After correlation analysis, three temporal relevant pattern time series are generated, namely, the weekly, daily and hourly pattern time series. Then statistical pattern models are developed to capture different time series patterns. Finally, an amended IMM algorithm is applied to dynamically combine the pattern models estimations to output the final demand prediction. The proposed IMMPH model is validated by comparing with statistical methods and an artificial neural network based hybrid model. The results suggest that the IMMPH model provides a better forecast performance than its alternatives, including prediction accuracy, robustness, explanatory power and model complexity. The proposed approach can be potentially extended to other short-term time series forecast applications as well, such as traffic flow forecast.

Predicting short-term bus passenger demand using a pattern hybrid approach

Improved IMM Algorithm for Nonlinear Maneuvering Target Tracking

The invention relates to a multi-port regional gridding VRS (Virtual Reference Station) differential positioning information broadcasting device and a work method thereof, belonging to the field of measuring methods. The multi-port regional gridding VRS differential positioning information broadcasting device comprises a network RTK (Real Time Kinematic) system reference station, a data processing center and a data FM (Frequency Modulation) broadcasting unit, wherein the network RTK system reference station is connected with a data processing center server through optical fiber, and the data processing center server is connected with the data FM broadcasting unit through a serial port; and the data FM broadcasting unit is used for carrying out FM broadcasting on differential correction data packaged by a UDP (User Datagram Protocol). The work method comprises the steps of: receiving network RTK system reference station information by the data processing center, and dividing grids for the covered region of the network RTK system reference station; then solving all differential correction information of all grid virtual reference stations by utilizing a VRS algorithm, and then appointing an unique UDP port number for each grid virtual reference station and packaging the differential correction information by using the UDP protocol; finally, carrying out FM broadcasting on the UDP data packets, three-dimensional coordinates and UDP port numbers of all the grid virtual reference stations through the data FM broadcasting unit.

Multi-port regional gridding VRS (Virtual Reference Station) differential positioning information broadcasting device and work method thereof

Localization is an essential supporting technology of wireless sensor networks (WSNs). Most of traditional localization algorithms are proposed for static WSNs. There are only a few algorithms proposed for mobile wireless sensor networks (MWSNs). In this paper, we propose a range-free localization algorithm for MWSNs, named distributed grid-based localization algorithm (DGL). In this algorithm, anchor nodes can increase their transmitting power, that is to say, they can change their communication range. Each sensor node can establish rectangular coordinate system itself, and then divide coordinate system into square grids. Simulation results demonstrate this algorithm outperforms other well-known localization algorithms.

Distributed Grid-Based Localization Algorithm for Mobile Wireless Sensor Networks

In this paper, an Improved FastIMM algorithm is presented by optimizing the acceleration gain factor γ and tracking indicesλof the α-β and α-β-γ filters. In this algorithm, a better acceleration gain factor γ is selected to improve the FastIMM filter’s performance in target tracking. Besides, to get the appropriate ranges for tracking indices of the α-β and α-β-γ filters, numerical simulations are carried out and the Root Mean Square Error (RMSE) of the position and velocity are analyzed. Simulation results show that the Improved FastIMM has much higher tracking accuracy while keeping the same computational burden.

An Improved FastIMM Algorithm Based on α-β and α-β-γ Filters

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