## Abstract

This article proposes several advances to sparse nonnegative matrix factorization (SNMF) as a way to identify large-scale patterns in urban traffic data. The input to our model is traffic counts organized by time and location. Nonnegative matrix factorization additively decomposes this information, organized as a matrix, into a linear sum of temporal signatures. Penalty terms encourage this factorization to concentrate on only a few temporal signatures, with weights which are not too large. Our interest here is to quantify and compare the regularity of traffic behavior, particularly across different broad temporal windows. In addition to the rank and error, we adapt a measure introduced by Hoyer to quantify sparsity in the representation. Combining these, we construct several curves which quantify error as a function of rank (the number of possible signatures) and sparsity; as rank goes up and sparsity goes down, the approximation can be better and the error should decreases. Plots of several such curves corresponding to different time windows leads to a way to compare disorder/order at different time scalewindows. In this paper, we apply our algorithms and procedures to study a taxi traffic dataset from New York City. In this dataset, we find weekly periodicity in the signatures, which allows us an extra framework for identifying outliers as significant deviations from weekly medians. We then apply our seasonal disorder analysis to the New York City traffic data and seasonal (spring, summer, winter, fall) time windows. We do find seasonal differences in traffic order.

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The authors acknowledge the Program for Interdisciplinary and Industrial Internships at Illinois (PI4) and the Illinois Geometry Laboratory (IGL). The many IGL students who have made invaluable contributions to this work are: Raghav Bakshi, James Kerns, Xinyi Li, Xinyu Liu, Yicheng Pu, Gabriel Shindnes, Haozhe Wang, Jing Wang, Ziying Wang, Yu Wu, Zeyu Wu, Bin Xu, and Dajun Xu. The authors would also like to thank the Siebel Energy Institute for its support of this work. This material is based upon work supported by the National Science Foundation under Grant Numbers CMMI 1727785 and DMS 1345032. This work was also supported by a grant from the Siebel Energy Institute. The code for this work is at https://gitlab.engr.illinois.edu/TrafficPatterns/CSNMF.git

Sandia National Laboratories is a multimission laboratory operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration. Sandia Labs has major research and development responsibilities in nuclear deterrence, global security, defense, energy technologies and economic competitiveness, with main facilities in Albuquerque, New Mexico, and Livermore, California.

This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.

## Appendix

### Appendix

For completeness, let’s write down the calculations leading to the algorithm of Sect. 2.5.

Writing out \({\mathcal{E}}_{\beta ,\eta }\) of (5), we get

We seek to minimize this by alternating between minimization problems in *W* and *H*. Namely, if we start with a fixed \((W,H)\in \mathbb {R}^{T\times N}_+\times \mathbb {R}^{N\times L}_+\), we can construct a descent step for the function \({\mathcal{E}}_{\beta ,\eta }(W,\cdot )\) and then, letting \(H'\) be the result, we can construct a descent step for \({\mathcal{E}}_{\beta ,\eta }(\cdot ,H')\). This should decrease the value of \({\mathcal{E}}_{\beta ,\eta }\), and we can then proceed iteratively.

The gradients of \({\mathcal{E}}_{\beta ,\eta }\) in the directions of *W* and *H* are given by

and

As in Kim and Park (2008), we want to iteratively find the critical points of \({\mathcal{E}}_{\beta ,\eta }\), i.e. the solutions of

The above formulae suggest a multiplicative descent rule (which need not be *gradient descent*; see Lee and Seung (2001)). Fix \((W,H)\in \mathbb {R}_+^{T\times N}\times \mathbb {R}_+^{N\times L}\). Assume that

we can then decrease the value of \({\mathcal{E}}_{\beta ,\eta }\) by decreasing \(H_{n,\ell }\). Rewriting (14) as

or rather

since *W*, *H*, and *D* all have nonnegative entries, both sides of this equation are nonnegative. This in turn can be written as \({\chi _{n,\ell }^h(W,H)<1}\) where

Thus, another way to decrease \(H_{n,\ell }\) while still retaining nonnegativity is to multiply it by \(\chi _{n,\ell }^h(W,H)\). Reviewing these steps, we also see that if \(\frac{\partial {\mathcal{E}}_{\beta ,\eta }}{\partial H_{n,\ell }}<0\), we want to increase \(H_{n,\ell }\), and can again multiply by \(\chi _{n,\ell }^h(W,H)\). Finally, if \(\frac{\partial {\mathcal{E}}^\beta }{\partial H_{n,\ell }}=0\) (i.e., we have found a critical point) \(\chi _{n,\ell }^h(W,H)=1\), so multiplying \(H_{n,\ell }\) by \(\chi _{n,\ell }^h(W,H)\) leaves \(H_{n,\ell }\) unchanged.

The update rule for \(W_{t,n}\) is similar. To start, assume that

then we can decrease \({\mathcal{E}}_{\beta ,\eta }\) by decreasing \(W_{t,n}\). We can rewrite (15) as

We can again rewrite this as the comparison of two nonnegative quantities;

This in turn is equivalent to \(\chi _{t,n}^w(W,H)<1\) where

In other words, we can decrease \(W_{t,n}\) by multiplying by \(\chi _{t,n}^w(W,H)\). One can similarly see that if \(\frac{\partial {\mathcal{E}}^\beta }{\partial W_{t,n}}<0\), gradient descent again increases or decreases *W* with the same sign as multiplying by \(\chi _{t,n}^w(W,H)\).

Our proposed update rule for *W* and *H* is now

which is equivalent to (6).

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Karve, V., Yager, D., Abolhelm, M. *et al.* Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis.
*J. Big Data Anal. Transp.* **3, **43–60 (2021). https://doi.org/10.1007/s42421-021-00033-4

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### Keywords

- Traffic
- Normalization
- Sparse nonnegative matrix Factorization