Public Transportation Prediction with Convolutional Neural Networks

Panovski, Dancho; Zaharia, Titus

doi:10.1007/978-3-030-38822-5_10

Cited by 1 publication

(2 citation statements)

References 23 publications

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“…Despite accurate prediction performance, this study has the following limitations. First, unlike previous studies [8,[35][36][37][38], we observed the negative effect of using contextual features. However, we expect the contextual features to take effect when more training data are collected.…”

Section: Discussioncontrasting

confidence: 99%

“…In [35], holiday status, day of the week, time of the day, temperature, and precipitation were considered, and the prediction accuracy was reported to be 89.67%. Panovski et al [36] used visualized traffic patterns to predict bus arrival time and predicted an MAE of 0.99 min. In [37], the authors generated a vectorized value of the day, time, the distance between stations, the number of bus stops and their orders on the route, the number of intersections, and the number of traffic lights for predicting arrival time with an MAE of 4.55 min and a MAPE of 5.99%.…”

Section: Related Workmentioning

confidence: 99%

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A Novel Bus Arrival Time Prediction Method Based on Spatio-Temporal Flow Centrality Analysis and Deep Learning

Lee¹,

Yoon

2022

Electronics

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This paper presents a method for predicting bus stop arrival times based on a unique approach that extracts the spatio-temporal dynamics of bus flows. Using a new technique called Bus Flow Centrality Analysis (BFC), we obtain the low-dimensional embedding of short-term bus flow patterns in the form of IID (Individual In Degree) and IOD (Individual Out Degree) and TOD (Total Out Degree) at every station in the bus network. The embedding using BFC analysis well captures the characteristics of every individual flow and aggregate pattern. The latent vector returned by the BFC analysis is combined with other essential information such as bus speed, travel time, wait time, dispatch intervals, the distance between stations, seasonality, holiday status, and climate information. We employed a family of recurrent neural networks such as LSTM, GRU, and ALSTM to model how these features change over time and to predict the time the bus takes to reach the next stop in subsequent time windows. We experimented with our solution using logs of bus operations in the Seoul Metropolitan area offered by the Bus Management System (BMS) and the Bus Information System (BIS) of Korea. We predicted arrival times for more than 100 bus routes with a MAPE of 1.19%. This margin of error is 74% lower than the latest work based on ALSTM. We also learned that LSTM performs better than GRU with a 40.5% lower MAPE. This result is even remarkable considering the irregularity in the bus flow patterns and the fact that we did not rely on real-time GPS information. Moreover, our approach scales at a city-wide level by analyzing more than 100 bus routes, while previous studies showed limited experiments on much fewer bus routes.

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