Non-Stationary Time Series Model for Station-Based Subway Ridership During COVID-19 Pandemic: Case Study of New York City

Abstract

The COVID-19 pandemic in 2020 has caused sudden shocks in transportation systems, specifically the subway ridership patterns in New York City (NYC), U.S. Understanding the temporal pattern of subway ridership through statistical models is crucial during such shocks. However, many existing statistical frameworks may not be a good fit to analyze the ridership data sets during the pandemic, since some of the modeling assumptions might be violated during this time. In this paper, utilizing change point detection procedures, a piecewise stationary time series model is proposed to capture the nonstationary structure of subway ridership. Specifically, the model consists of several independent station based autoregressive integrated moving average (ARIMA) models concatenated together at certain time points. Further, data-driven algorithms are utilized to detect the changes of ridership patterns as well as to estimate the model parameters before and during the COVID-19 pandemic. The data sets of focus are daily ridership of subway stations in NYC for randomly selected stations. Fitting the proposed model to these data sets enhances understanding of ridership changes during external shocks, both in relation to mean (average) changes and the temporal correlations.

Keywords

data analysis data and data science planning and development public transportation rail transit systems ridership statistical methods subway transit urban transportation data and information systems

Get full access to this article

View all access options for this article.

References

Bonaccorsi

Pierri

Cinelli

Flori

Galeazzi

Porcelli

Schmidt

A. L.

Valensise

C. M.

Scala

Quattrociocchi

Pammolli

Economic and Social Consequences of Human Mobility Restrictions Under COVID-19. Proceedings of the National Academy of Sciences, Vol. 117, No. 27, 2020, pp. 15530–15535.

https://www.nytimes.com/2020/03/22/nyregion/Coronavirus-new-York-epicenter.html.

https://www.nytimes.com/interactive/2020/us/coronavirus-stay-at-home-order.html.

Kamga

Vicuna

Mudigonda

Tchamna

Mobility Trends in New York City During COVID-19 Pandemic: Analyses of Transportation Modes Throughout June 2020. University Transportation Research Center, New York, NY, 2020.

McNally

M. G.

The Four-Step Model. In Handbook of Transport Modelling ( Hensher

D. A.

Button

K. J.

, eds.), Pergamon Press, Oxford, Vol. 1, 2000, pp. 35–41.

Singhal

Kamga

Yazici

Impact of Weather on Urban Transit Ridership. Transportation Research Part A: Policy and Practice, Vol. 69, 2014, pp. 379–391.

Liu

Chen

R.-C.

A Novel Passenger Flow Prediction Model Using Deep Learning Methods. Transportation Research Part C: Emerging Technologies, Vol. 84, 2017, pp. 74–91.

Liu

Jia

DeepPF: A Deep Learning Based Architecture for Metro Passenger Flow Prediction. Transportation Research Part C: Emerging Technologies, Vol. 101, 2019, pp. 18–34.

Ding

Wang

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees. Sustainability, Vol. 8, No. 11, 2016, p. 1100.

10.

Liu

Yao

Holiday Passenger Flow Forecasting Based on the Modified Least-Square Support Vector Machine for the Metro System. Journal of Transportation Engineering, Part A: Systems, Vol. 143, No. 2, 2017, p. 04016005.

11.

Zhang

Wang

X. C.

Transit Ridership Estimation With Network Kriging: A Case Study of Second Avenue Subway, NYC. Journal of Transport Geography, Vol. 41, 2014, pp. 107–115.

12.

Barua

Das

Roy

K. C.

Estimation of Traffic Arrival Pattern at Signalized Intersection Using ARIMA Model. International Journal of Computer Applications, Vol. 128, No. 1, 2015, pp. 1–6.

13.

Willams

B. M.

Hoel

L. A.

Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results. Journal of Transportation Engineering, Vol. 129, No. 6, 2003, pp. 664–672.

14.

Moghimi

Safikhani

Kamga

Hao

Cycle-Length Prediction in Actuated Traffic-Signal Control Using ARIMA Model. Journal of Computing in Civil Engineering, Vol. 32, No. 2, 2018, p. 04017083.

15.

Duan

Mao

Zhang

Wang

STARIMA-based Traffic Prediction With Time-varying Lags. Proc., 19th International Conference on Intelligent Transportation System (ITSC), Rio de Janeiro, Brazil, IEEE, Piscataway, NJ, 2016.

16.

Ding

Duan

Zhang

Using an ARIMA-GARCH Modeling Approach to Improve Subway Short-Term Ridership Forecasting Accounting for Dynamic Volatility. IEEE Transactions on Intelligent Transportation Systems, Vol. 19, No. 4, 2017, pp. 1054–1064.

17.

Paul

Smart

M. J.

The Hangover: Assessing Impact of Major Service Interruptions on Urban Rail Transit Ridership. Transportation Research Record: Journal of the Transportation Research Board, 2017. 2648: pp. 79–85.

18.

Zhu

Levinson

D. M.

Disruptions to Transportation Networks: A Review. In Network Reliability in Practice ( Levinson

Liu

Bell

, eds.), Springer, New York, NY, 2012, pp. 5–20.

19.

van Exel

Job

Rietveld

Public Transport Strikes and Traveller Behaviour. Transport Policy, Vol. 8, No. 4, 2001, pp. 237–246.

20.

Tang

Gao

Traffic-Incident Detection-Algorithm Based on Nonparametric Regression. IEEE Transactions on Intelligent Transportation Systems, Vol. 6, No. 1, 2005, pp. 38–42.

21.

Tsiligkaridis

Paschalidis

I. C.

Anomaly Detection in Transportation Networks Using Machine Learning Techniques. Proc., MIT Undergraduate Research Technology Conference (URTC), Cambridge, MA, IEEE, Piscataway, NJ, 2017, pp. 1–4.

22.

Margreiter

Automatic Incident Detection Based on Bluetooth Detection in Northern Bavaria. Transportation Research Procedia, Vol. 15, 2016, pp. 525–536.

23.

Riveiro

Lebram

Elmer

Anomaly Detection for Road Traffic: A Visual Analytics Framework. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 8, 2017, pp. 2260–2270.

24.

Zhang

Shi

Zhou

Yin

Visual Analysis Method for Abnormal Passenger Flow on Urban Metro Network. Journal of Visualization, Vol. 23, No. 6, 2020, pp. 1–18.

25.

Moghimi

Safikhani

Kamga

Hao

Short-term Prediction of Signal Cycle on an Arterial with Actuated-Uncoordinated Control Using Sparse Time Series Models. IEEE Transactions on Intelligent Transportation Systems, Vol. 20, No. 8, 2018, pp. 2976–2985.

26.

Safikhani

Kamga

Mudigonda

Faghih

S. S.

Moghimi

Spatio-temporal Modeling of Yellow Taxi Demands in New York City Using Generalized STAR Models. International Journal of Forecasting, Vol. 36, No. 3, 2020, pp. 1138–1148.

27.

Brockwell

P. J.

Davis

R. A.

Introduction to Time Series and Forecasting. Springer, New York, NY, 2006.

28.

Safikhani

Shojaie

Joint Structural Break Detection and Parameter Estimation in High-Dimensional Non-Stationary Var Models. Journal of the American Statistical Association, Vol. 117, 2020, pp. 1–26.

29.

Rinaldo

Properties and Refinements of the Fused Lasso. The Annals of Statistics, Vol. 37, No. 5B, 2009, pp. 2922–2952.

30.

Aue

Horváth

Structural Breaks in Time Series. Journal of Time Series Analysis, Vol. 34, No. 1, 2013, pp. 1–16.

31.

Tibshirani

Saunders

Rosset

Zhu

Knight

Sparsity and Smoothness via the Fused Lasso. Journal of the Royal Statistical Society: Series B (Statistical Methodology), Vol. 67, No. 1, 2005, pp. 91–108.

32.

Friedman

Hastie

Höfling

Tibshirani

Pathwise Coordinate Optimization. The Annals of Applied Statistics, Vol. 1, No. 2, 2007, pp. 302–332.

33.

Csörgö

Horváth

Limit Theorems in Change-Point Analysis, Vol. 18. John Wiley & Sons Inc., New York, NY, 1997.