Tracking the evolution of temporal patterns of usage in bicycle-Sharing systems using nonnegative matrix factorization on multiple sliding windows

Cazabet, Rémy; Jensen, Pablo; Borgnat, Pierre

doi:10.1080/12265934.2017.1336468

Cited by 12 publications

(4 citation statements)

References 14 publications

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“…Non-negative Matrix (and tensor) Factorization (NMF) has already been used for urban and network traffic analysis Ahmadi et al (2015), Chen et al (2019), Yufei and Fabien (2011), Han and Moutarde (2013), Han and Moutarde (2016), Hofleitner et al (2012), Liu et al (2017), Ma et al (2018), Sun and Axhausen (2016), Xu et al (2015); see also Lv et al (2015). Furthermore, matrix factorization has also been used in studying train Gong et al (2018), Ito et al (2017, bicycle Cazabet et al (2018), and risk Lee et al (2016) data. Anomaly detection using related methods can be found in Djenouri et al (2018), Zhang et al (2016), Guo et al (2015), Li et al (2015), Wang et al (2019).…”

Section: Related Workmentioning

confidence: 99%

Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

Karve

Yager

Abolhelm

et al. 2021

J. Big Data Anal. Transp.

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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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Section: Related Workmentioning

confidence: 99%

Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

Karve

Yager

Abolhelm

et al. 2021

J. Big Data Anal. Transp.

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show abstract

“…compared to component analysis methods which do not have the non-negativity constraint, and its conceptual simplicity over more involved methods such as topic modeling and functional data analysis methods. The use of NMF for temporal data has also proven to be successful in past studies 36 , 37 . Also, the frequency of screen-on events is always non-negative, which further makes NMF a natural choice.…”

Section: Methodsmentioning

confidence: 95%

Quantifying daily rhythms with non-negative matrix factorization applied to mobile phone data

Aledavood

Kivimäki

Lehmann

et al. 2022

Sci Rep

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Human activities follow daily, weekly, and seasonal rhythms. The emergence of these rhythms is related to physiology and natural cycles as well as social constructs. The human body and its biological functions undergo near 24-h rhythms (circadian rhythms). While their frequencies are similar across people, their phases differ. In the chronobiology literature, people are categorized into morning-type, evening-type, and intermediate-type groups called chronotypes based on their tendency to sleep at different times of day. Typically, this typology builds on carefully designed questionnaires or manually crafted features of time series data on people’s activity. Here, we introduce a method where time-stamped data from smartphones are decomposed into components using non-negative matrix factorization. The method does not require any predetermined assumptions about the typical times of sleep or activity: the results are fully context-dependent and determined by the most prominent features of the activity data. We demonstrate our method by applying it to a dataset of mobile phone screen usage logs of 400 university students, collected over a year. We find four emergent temporal components: morning activity, night activity, evening activity and activity at noon. Individual behavior can be reduced to weights on these four components. We do not observe any clear categories of people based on the weights, but individuals are rather placed on a continuous spectrum according to the timings of their phone activities. High weights for the morning and night components strongly correlate with sleep and wake-up times. Our work points towards a data-driven way of characterizing people based on their full daily and weekly rhythms of activity and behavior, instead of only focusing on the timing of their sleeping periods.

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“…Matrix factorization has been applied to time-varying data sets including mobile phone networks and shared mobility data such as taxi and shared bicycle trips (12)(13)(14)(15). There is also a close relationship between the concepts involved in network community detection and matrix factorization (16).…”

Section: Related Workmentioning

confidence: 99%

Matrix Factorization for Globally Consistent Periodic Flow Prediction in Taxi Systems

Forouzandeh Jonaghani,

Wachowicz,

Hanson

2023

Transportation Research Record: Journal of the Transportation R

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Predicting flow in taxi systems can improve taxi operations and reduce passengers’ waiting time. With the increasing availability of taxi mobility data sets, more studies have tried to analyze and model taxi demand. However, most existing works merely attempt to predict the number of pick-ups and drop-offs by using local observations. Nevertheless, predicting the passengers’ pair-wise flow specifically through simultaneous consideration of local variations and network communities in the taxi systems has been neglected. In this paper, we introduce a globally consistent periodic flow prediction for taxi systems that integrates communities in the matrix factorization model. We apply non-negative tensor factorization to capture the periodic variations in the passenger flow in different areas and predict the flow (and pick-ups and drop-offs) in the next periods by integrating the most recent observation with detected patterns from historical data. The results show an improvement in the prediction accuracy from some baselines and a state-of-the-art method. We also propose a method for visualizing flow that potentially improves taxi operations by assisting drivers with selecting passengers.

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Tracking the evolution of temporal patterns of usage in bicycle-Sharing systems using nonnegative matrix factorization on multiple sliding windows

Cited by 12 publications

References 14 publications

Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

Seasonal Disorder in Urban Traffic Patterns: A Low Rank Analysis

Quantifying daily rhythms with non-negative matrix factorization applied to mobile phone data

Matrix Factorization for Globally Consistent Periodic Flow Prediction in Taxi Systems

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