Separated flow prediction and assessment using LES and machine learning

The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

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Gap, techniques and evaluation: traffic flow prediction using machine learning and deep learning

Journal Of Big Data ◽

10.1186/s40537-021-00542-7 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Noor Afiza Mat Razali ◽

Nuraini Shamsaimon ◽

Khairul Khalil Ishak ◽

Suzaimah Ramli ◽

Mohd Fahmi Mohamad Amran ◽

...

Keyword(s):

Machine Learning ◽

Traffic Flow ◽

Traffic Congestion ◽

Intelligent Transportation System ◽

Short Term Memory ◽

Smart Cities ◽

Experimental Studies ◽

Machine Learning Techniques ◽

Traffic Flow Prediction ◽

Flow Prediction

AbstractThe development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques.

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