Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro

Abstract

Abstract:The problem of traffic prediction is paramount in a plethora of applications, ranging from individual trip planning to urban planning. Existing work mainly focuses on traffic prediction on road networks. Yet, public transportation contributes a significant portion to overall human mobility and passenger volume. For example, the Washington, DC metro has on average 600,000 passengers on a weekday. In this work, we address the problem of modeling, classifying and predicting such passenger volume in public transportation systems. We study the case of the Washington, DC metro exploring fare card data, and specifically passenger in-and outflow at stations. To reduce dimensionality of the data, we apply principal component analysis to extract latent features for different stations and for different calendar days. Our unsupervised clustering results demonstrate that these latent features are highly discriminative. They allow us to derive different station types (residential, commercial, and mixed) and to effectively classify and identify the passenger flow of "unknown" stations. Finally, we also show that this classification can be applied to predict the passenger volume at stations. By learning latent features of stations for some time, we are able to predict the flow for the following hours. Extensive experimentation using a baseline neural network and two naïve periodicity approaches shows the considerable accuracy improvement when using the latent feature based approach.

Predicting Station-Level Short-Term Passenger Flow in a Citywide Metro Network Using Spatiotemporal Graph Convolutional Neural Networks

“…A mixture of principal component analysis (PCA) and k-nearest neighbor (kNN) regression [6]. PCA is used to select the principal components which are input into kNN for prediction.…”

“…Thus, constructing an effective model to predict the passenger flow volume in a citywide metro network is essential. 2 of 24 During recent decades, many passenger-flow prediction models have been proposed based on statistical and machine learning (ML) algorithms, such as support vector machine (SVM) [2][3][4], Bayesian regression [5], principal component analysis (PCA) [6,7], non-negative matrix factorization (NMF) [8], and artificial neural networks (ANNs) [1,[9][10][11]. However, these conventional methods cannot process raw sample data and require a manual feature engineering procedure.…”

Uncovering Spatio-temporal Travel Patterns Using a Tensor-based Model from Metro Smart Card Data in Shenzhen, China

“…Using visualization techniques, Sun et al [16] demonstrated the spatial and temporal distributions of passenger flows in a holistic manner, as well as the flow directional imbalances. Zhao et al[17] used statistical-based and unsupervised clustering-based methods to understand the hidden regularities and anomalies of travel patterns and classify passengers in terms of the similarity of their travel patterns.Furthermore, the prediction of passengers' future trajectories [20][21][22][23][24] is also an important issue in transit systems. Yang et al [20] argued that future movements of different types of groups can be predicted with high confidence based on previous records.…”

“…Yang et al [20] argued that future movements of different types of groups can be predicted with high confidence based on previous records. Truong et al [21] effectively predicted the inand outflow of passengers at a station over time based on the patterns of passenger flow with respect to time and stations. In the study [22], Shanghai was taken as a case study to discuss the location choice of after-work activities, passengers are more likely to choose a station even closer to home in terms of network distance.…”

“…Furthermore, the prediction of passengers' future trajectories [20][21][22][23][24] is also an important issue in transit systems. Yang et al [20] argued that future movements of different types of groups can be predicted with high confidence based on previous records.…”