Title

TRAFFIC CONGESTION PREDICTION USING LSTM IMPROVED WITH RULES OBTAINED FROM ANOMALIES

Abstract

Abstract

Traffic flow forecasting is crucial in order to create a successful smart transportation system. The precision and timeliness of the flow data are critical to the forecasts effectiveness. The lack of data has led to the usage of shallow architectures in traffic forecasting models or the creation of models based on fabricated measurement data. These models did not have a high level of success in predicting outcomes. In the world of big data, the variety and scale of the acquired traffic data has increased in lockstep with the increase in traffic density. It has a great importance for any field to be able to determine the situations that may occur in the future from the events of the past. History repeats itself constantly and in many time series problems it might be possible to make predictions for the future from what happened in the past. The LSTM structure in the Machine Learning has implemented in order to be able to do this prediction. LSTM is a special type of recurrent neural network and its main difference from standard recurrent neural network is its success in modeling long-term information. For LSTM usage, there must be ongoing data for a certain period of time, and from this historical data the future state is predicted. In this research, we use LSTM to estimate the number of vehicles in traffic and by estimating it, we aim to see traffic congestions before they occur. In order to overcome the difficulty of LSTM when predicting car count at an anomaly state, we added a new layer that puts these anomalies in a certain rule set and makes corrections according to these rules in our predictions. In this study, an improved hybrid model was created by adding anomaly based rules on top of the LSTM model, thus a model that achieves successful results even in anomaly situations.

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https://zoom.us/j/95863299367?pwd=b3Z3UDVmV0RDMXdUY3UyNUJHcmhQZz09

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Supervisor(s)

Supervisor(s)

YASIN BERK GULTEKIN

Date and Location

Date and Location

2021-09-09 10:00:00

Category

Category

MSc_Thesis