Review of Transit Data Sources: Potentials, Challenges and Complementarity

Ge, Liping; Sarhani, Malek; Voß, Stefan; Xie, Lin

doi:10.3390/su132011450

Cited by 26 publications

(8 citation statements)

References 197 publications

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“…We also used data from GitHub, an open-source database (McGovern 2016 ; NYTimes COVID-19 data bot and Sun 2020 ). Using data drawn from widely varying sources in order to analyze transit crowding standards is an increasingly common transportation research method (Ge et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Three’s a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic

Dai

Taylor²

2022

Public Transp

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The COVID-19 pandemic dramatically affected public transit systems around the globe. Because transit systems typically move many people closely together on buses and trains, public health guidance demanded that riders should keep a distance of about two meters to others changed the definition of “crowding” on transit in 2020. Accordingly, this research examines how U.S. public transit agencies responded to public health guidance that directly conflicted with their business model. To do this, we examined published crowding standards before the COVID-19 pandemic for a representative sample of 200 transit systems, including whether they started or changed their published standards during the pandemic, as well as the reasons whether agencies publicize such standards at all. We present both descriptive statistics and regression model results to shed light on the factors associated with agency crowding standards. We find that 56% of the agencies surveyed published crowding standards before the pandemic, while only 46% published COVID-19-specific crowding standards. Regression analyses suggest that larger agencies were more likely to publish crowding standards before and during the COVID-19 pandemic, likely because they are more apt to experience crowding. Pandemic-specific crowding standards, by contrast, were associated with a more complex set of factors. We conclude that the relative lack of pandemic standards reflects the uncertainty and fluidity of the public health crisis, inconsistent and at times conflicting with the guidance from public health officials, and, in the U.S., a lack national or transit industry consensus on appropriate crowding standards during the first year of the pandemic.

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Section: Methodsmentioning

confidence: 99%

Three’s a crowd? Examining evolving public transit crowding standards amidst the COVID-19 pandemic

Dai

Taylor²

2022

Public Transp

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“…For an exception see Ang Pik Yoke et al (2021). In all these cases data availability seems a major issue; for a recent survey regarding public transport see Ge et al (2021).…”

Section: Integrated Vehicle and Crew Schedulingmentioning

confidence: 99%

Revisiting the richness of integrated vehicle and crew scheduling

Kliewer²,

Nourmohammadzadeh

et al. 2022

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The last decades have seen a considerable move forward regarding integrated vehicle and crew scheduling in various realms (airline industry, public transport). With the continuous improvement of information and communication technology as well as general solvers it has become possible to formulate more and more rich versions of these problems. In public transport, issues like rostering, delay propagation or days-off patterns have become part of these integrated problems. In this paper we aim to revisit an earlier formulation incorporating days-off patterns and investigate whether solvability with standard solvers has now become possible and to which extent the incorporation of other aspects can make the problem setting more rich and still keep the possible solvability in mind. This includes especially issues like delay propagation where in public transport delay propagation usually refers to secondary delays following a (primary) disturbance. Moreover, we investigate a robust version to support the claim that added richness is possible. Numerical results are provided to underline the envisaged advances.

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“…Therefore, we have performed an automated web data extraction method: web scraping. Individual scripts were developed to scrape the warehouse information from the Html file and assemble one dataset for each metropolitan area (Ge et al, 2021;Gharahighehi et al, 2021;Jiao et al, 2021;Luo and He, 2021;Pineda-Jaramillo and Pineda-Jaramillo, 2021;Ploessl et al, 2021). This method was mainly composed of seven steps for this paper: (i) find the URL where the data is published; (ii) inspect the webpage to find the data from its source code; (iii) build the prototype code, which was written in R language and using the rvest package and intended to extract and prepare the data; (iv) generalize the code considering functions, loops and debugging and run the code alternating among US metropolitan areas; (v) store the data as an organized data frame; (vi) check and clean the gathered data; (vii) geocode the information on each warehouse.…”

Section: Unstructured Web Data Extraction -Warehouse Informationmentioning

confidence: 99%