TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

Abstract

In this article, we target at recovering the exact routes taken by commuters inside a metro system that are not captured by an Automated Fare Collection (AFC) system and hence remain unknown. We strategically propose two inference tasks to handle the recovering, one to infer the travel time of each travel link that contributes to the total duration of any trip inside a metro network and the other to infer the route preferences based on historical trip records and the travel time of each travel link inferred in the previous inference task. As these two inference tasks have interrelationship, most of existing works perform these two tasks simultaneously. However, our solution TripDecoder adopts a totally different approach. TripDecoder fully utilizes the fact that there are some trips inside a metro system with only one practical route available. It strategically decouples these two inference tasks by only taking those trip records with only one practical route as the input for the first inference task of travel time and feeding the inferred travel time to the second inference task as an additional input, which not only improves the accuracy but also effectively reduces the complexity of both inference tasks. Two case studies have been performed based on the city-scale real trip records captured by the AFC systems in Singapore and Taipei to compare the accuracy and efficiency of TripDecoder and its competitors. As expected, TripDecoder has achieved the best accuracy in both datasets, and it also demonstrates its superior efficiency and scalability.

City Transport Analyzer: A Powerful Qgis Plugin For Public Transport Accessibility And Intermodality Analysis

“…Brahmanage, Kandappu, & Zheng, (2023) introduce MetroWatch, a predictive system utilizing AFC data for estimating passengers' travel attributes within metro systems. Building on TripDecoder's methodology (Tian et al, 2021) MetroWatch provides precise route information and passenger density estimates, influencing our plug-in's predictive capabilities and practical insights for policymakers and urban planners.…”

Passenger flow prediction from AFC data using station memorizing LSTM for metro rail systems

“…2. The different The smart card data has lots of importance in holding the passenger flow information [15,23,21]. All those details about the passenger will be kept in a Central Automated Fare Collection (AFC) system.…”