Abstract Taking advantage of big data technology has become a new concept and practice to improve the capability of traffic management and control in data-driven intelligent transportation systems, and especially timely and accurate traffic flow forecasting (TFF) is significant for mitigating traffic congestion. To solve the problems of calculation and storage in dealing with traffic big data using the traditional centralized models on a single machine, this invention presents a Spark-based Weighted Bidirectional Long Short-Term Memory (SW-BiLSTM) model to improve the robustness, accuracy, and timeliness of TFF in real time. Specifically, we utilize the resilient distributed dataset (RDD) to preprocess mobile trajectory big data (e.g., large-scale GPS trajectories of taxicabs) based on the Spark parallel distributed com puting platform and then employ the Kalman filter (KF) approach to eliminate abnormal GPS points and achieve discrete smoothing of traffic flow data. Moreover, a distributed SW-BiLSTM model on Spark is proposed to enhance the accuracy and efficiency of real time TFF, combined with the normal distribution to weigh the interaction between adjacent road segments and the time window for achieving the optimization of BiLSTM. Finally, the SW-BiLSTM model is implemented on a Spark parallel computing framework to improve the efficiency and scalability of TFF. The present invention has broad applications in big data analytics.
A Spark-Based Deep Learning Method for Data-Driven Traffic Flow Forecasting
2020-10-29
Patent
Electronic Resource
English
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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