Exploring Patterns of Demand in Bike Sharing Systems Via Replicated Point Process Models

Gervini, Daniel; Khanal, Manoj

doi:10.1111/rssc.12322

Cited by 22 publications

(32 citation statements)

References 38 publications

(60 reference statements)

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“…, T m iid ∼ f , where f (t) = λ(t)/τ is a density function and τ = T 0 λ(s) ds is a scalar [10], also referred to as intensity factor. Point process techniques have recently also been successfully applied for bike rentals as events, aiming at the analysis of repeated observations of the bike rental point process [15], and the spatial distribution of street robberies [14].…”

Section: Local Fréchet Regression For Point Processes As Responsesmentioning

confidence: 99%

Point process models for COVID-19 cases and deaths

Gajardo

Müller

2021

Journal of Applied Statistics

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The study of events distributed over time which can be quantified as point processes has attracted much interest over the years due to its wide range of applications. It has recently gained new relevance due to the COVID-19 case and death processes associated with SARS-CoV-2 that characterize the COVID-19 pandemic and are observed across different countries. It is of interest to study the behavior of these point processes and how they may be related to covariates such as mobility restrictions, gross domestic product per capita, and fraction of population of older age. As infections and deaths in a region are intrinsically events that arrive at random times, a point process approach is natural for this setting. We adopt techniques for conditional functional point processes that target point processes as responses with vector covariates as predictors, to study the interaction and optimal transport between case and death processes and doubling times conditional on covariates.

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Section: Local Fréchet Regression For Point Processes As Responsesmentioning

confidence: 99%

Point process models for COVID-19 cases and deaths

Gajardo

Müller

2021

Journal of Applied Statistics

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“…Most of these studies focus on how socioeconomic, spatial, and behavioral factors such as bike accessibility (the distance between use and station) and availability (possibility to find a bike) (Kabra et al, 2018), customer characteristics (Guo et al, 2017;Ji et al, 2017), behaviors (Li et al, 2018) and travel patterns (Du and Cheng, 2018), and built environment (Zhang et al, 2017)) could affect the adoption and use of sharing bikes (Efthymiou et al, 2013;Yang and Long, 2016). Since the spatial and temporal imbalance between demand (Gervini and Khanal, 2019;Zhou et al, 2018) and (re)distribution (Ho and Szeto, 2017;Li et al, 2016) of sharing bikes is identified as the key to successful SBP development, some researchers have used different repositioning technologies and models to optimize the station position and address congestion or starvation issues of IT-based SBP (Forma et al, 2015;Ghosh et al, 2017;Szeto and Shui, 2018). This is of particular importance for DSBs due to their flexibility without docking stations, so demand forecasting (Xu et al, 2018), static (Liu et al, 2018) and dynamic repositioning problems (Shui and Szeto, 2018), optimizing location (Sun et al, 2019) and optimizing transportation planning (Sayyadi and Awasthi, 2018) are the key focuses of DSBs research as well in the transportation literature.…”

Section: Figmentioning

confidence: 99%

Characterizing the stocks, flows, and carbon impact of dockless sharing bikes in China

Liu

Zhang

et al. 2020

Resources, Conservation and Recycling

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The booming dockless sharing bikes (DSBs) in China, as a new sharing economy business model, have attracted increasing public and academic attention after 2015. The impact of DSBs development on the stocks and flows of bikes and the resource and climate consequences of shortlived DSBs, however, remain poorly understood. In this study, we characterized the stocks and flows of both DSBs and regular private bikes in China from 1950 to 2020 and evaluated the carbon cost and benefit of booming DSBs. We found China's bike consumption and stock decreased slightly after a fast development from the late 1970s and then a peak in the mid-1990s, resulting in a *Manuscript Click here to download Manuscript: Manuscript -Dockless Bikeshare in China_final_2020_6_27_clean_version.docx Click here to view linked References relatively low ownership of approximately 0.3 unit per person and 70% of production being exported in recent years. Despite a temporal boost, the unsustainable development of DSBs may affect the bike industry in the long term, because of its skyrocketing market share (from less than 1% to 80%) and short lifetime. Nevertheless, DSBs development still leads to an overall climate gain in China, due to its higher stock efficiency and potentials to substitute more carbon intensive trips. We suggest an urgent need for more empirical studies on the use (e.g., substitution ratio for other transportation models) of DSBs in China and a necessity for better management of DSB development with efforts of all relevant stakeholders.

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“…In this way, we are able to explicitly model the functional nature of the data (Bouveyron et al 2015) that would be lost with a simpler approach. Previous works on mobility data have already used FDA to model, for instance, the number of vehicles passing through a specific location (Chiou 2012; Guardiola et al 2014;Crawford et al 2017), or the bike sharing demand at different bike stations (Bouveyron et al 2015;Gervini and Khanal 2019); Torti et al 2021). Differently from other works, however, we mix motifs from FDA and network theory and we represent the road network of Lombardy as a graph evolving over time.…”

Section: Introductionmentioning

confidence: 99%

Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts

et al. 2022

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This paper introduces a methodology to evaluate the socio-economic impacts of closure for maintenance of one or more infrastructures of a large and complex road network. Motivated by a collaboration with Regione Lombardia, we focus on a subset of bridges in the region, although we aim at developing a method scalable to all road infrastructures of the regional network, consisting of more than 10,000 tunnels, bridges and overpasses. The final aim of the endeavor is to help decision-makers in prioritizing their interventions for maintaining and repairing infrastructure segments. We develop two different levels of impact assessment, both providing a unique global score for each bridge closure and investigating its spatio-temporal effects on mobility. We take advantage of a functional data analysis approach enhanced by a complex network theory perspective, thus modelling the roads of Lombardy as a network in which weights attributed to the edges are functional data. Results reveal the most critical bridges of Lombardy; moreover, for each bridge closure, the most impactful hours of the day and the most impacted municipalities of the region are identified. The proposed approach develops a flexible and scalable method for monitoring infrastructures of large and complex road networks.

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Exploring Patterns of Demand in Bike Sharing Systems Via Replicated Point Process Models

Cited by 22 publications

References 38 publications

Point process models for COVID-19 cases and deaths

Point process models for COVID-19 cases and deaths

Characterizing the stocks, flows, and carbon impact of dockless sharing bikes in China

Bridge closure in the road network of Lombardy: a spatio-temporal analysis of the socio-economic impacts

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