Abstract
Due to the massive congestion in ground transportation in Beijing, underground rail transit has gradually become the main mode of travel for residents of large urban areas. Because the average daily traffic of the Beijing subway is over 12 million passengers, ensuring the safety of underground rail transit is particularly important. Big data shows that more than 4000 passengers participate in Long-term Stay in the Subway every day. However, the behaviors of these passengers have not been characterized. This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyze its travel mode. In combination with the past research of scholars, we try to quantify the suspected behavior with a database of assumed suspected behavior records. Finally, we extract the spatial-temporal travel characteristics of passengers and we propose a SAE-DNN algorithm to identify suspected anomalies; the accuracy of the training set can reach 95.7%, and the accuracy of the test set can also reach 93.5%, which provides a reference for the subway operators and the public security system.
Highlights
Statistics show that there are approximately 1.2 million people riding the subway every day in Beijing [1]
We found an overfitting about decision trees and Bayesian, which cause the test set accuracy to only be about 50%, while other models performed well on both the training and test sets
This paper proposes a method for identifying the Long-term Staying in Subway System (LSSS) in the subway based on the shortest path and analyzes its travel mode
Summary
Statistics show that there are approximately 1.2 million people riding the subway every day in Beijing [1]. The rules for the travel patterns of passengers vary by station, time period and route [3,4,5,6]. We discovered that many passengers travel for a substantially longer period than the expected time of the ride; we named this occurrence Long-term Staying in Subway System (LSSS). The trough in the picture occurred from 14 February to 23 February 2018 This trough occurs during the Spring Festival holiday. In China, there is a Spring Festival travel season, known as ChunYun in Chinese. This is a special period when people who work far away from home return to their families in celebration of the Chinese Lunar New Year (the Spring Festival) [9].
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