Analysis of Washington, DC taxi demand using GPS and land-use data

Yang, Zhuo; Franz, Mark; Zhu, Shanjiang; Mahmoudi, Jina; Nasri, Arefeh; Zhang, Lei

doi:10.1016/j.jtrangeo.2017.10.021

Cited by 91 publications

(71 citation statements)

References 20 publications

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“…For instance, Schaller [18], found that the number of employees using subway, households with no car ownership and the number of airport taxi trips accounted for the increasing number of taxi cabs in the US. Another study [19], linked taxi demand with land use attributes, socio-demographics, and employment factors from 1972's Traffic Analysis Zones (TAZs) in Washington DC [20]. Results confirm that land use measures such as residential density, employment density, average block size, and number of transit stations have a significant and positive impact on ridership demand for taxis.…”

Section: The Comparison Of Ride-hailing and Traditional Taxismentioning

confidence: 90%

“…Studies on ride-hailing travel behavior have employed various approaches for data collection. Some studies have used random neighborhoods as their unit of analysis [21], while others have employed intercept survey conducted in locations with significant ride-hailing ridership [4], or locations that generate a significant number of trips, such as parks [26] The majority of these studies investigated the determinants of ride-hailing demand within a given TAZ [8,20,22] and extracted point-to-point GPS data [28].…”

Section: Determinants Of On-demand Ride-hailing Ridershipmentioning

confidence: 99%

“…Most of the previous studies took advantage of large GPS datasets derived from service platforms such as Uber, Lyft, and DiDi in analyzing ride-hailing trips [20,22]. In this study, access to the Snapp platform was not possible due to the security concerns of Snapp as a private company.…”

Section: Study Area and Surveymentioning

confidence: 99%

See 2 more Smart Citations

Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran

Etminani-Ghasrodashti

Hamidi

2019

Sustainability

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Despite the growing body of research on ride-hailing travel behaviors in Western countries, empirical evidence for changes in travel patterns resulting from the use of app-based services in developing countries remains rare. This study explores factors affecting an Iranian on-demand ride service called Snapp Taxi by using a comprehensive dataset collected from 22 municipality zones in metropolitan Tehran (N = 582). Our conceptual framework emphasizes the transportation mode choice effects of technology adoption, travel mode, ride-sourcing attributes, individual attitudes, land use measures, residential attributes, and socio-economic characteristics of the respondents. Results from Structural Equation Models (SEM) show that factors such as cost effectiveness, trip security, anti-shared mobility, and technology-oriented riders have a significant impact on travel mode choice and the frequency of ride-hailing trips. This study suggests that individuals who prefer driving and semi-public transit also have higher numbers of Snapp trips than other demographics. According to our findings, on-demand ride services could complement or compete with other modes of transport, especially in areas with limited access to public transit. However, the presence of ride-hailing services does not necessarily result in fewer car trips if the service operates as a private (single-party occupancy) vehicle and not as a shared mobility option.

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Section: The Comparison Of Ride-hailing and Traditional Taxismentioning

confidence: 90%

Section: Determinants Of On-demand Ride-hailing Ridershipmentioning

confidence: 99%

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Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran

Etminani-Ghasrodashti

Hamidi

2019

Sustainability

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“…Over the past few decades, the pervasive technologies like cellular networks, GPS devices, and Wi-Fi hotspots have experienced an explosion of development. These datasets are a way to capture human mobility in a higher spatial and temporal granularity (Bagrow & Lin, 2012;Kim, 2018;Yang et al, 2018). Indeed, these kinds of wealth information reflect various aspects of urban life from the perspective of mobility, consumption and environmental impact.…”

Section: Introductionmentioning

confidence: 99%

Understanding Spatial and Temporal Change Patterns of Population in Urban Areas Using Mobile Phone Data

2020

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The wide application of information computing technology has allowed for the emergence of big data on tracing human activities. Therefore, it provides an opportunity to explore temporal profile of population changes in geographical area subdivisions. In this paper, we present a multi-step method to characterize and approximate temporal changes of population in a geographical area subdivision using eigen decomposition. Datasets in weekday and weekend are decomposed to obtain the principal temporal change profiles in Xiamen, China. The Principal Components are common patterns of temporal population changes shared by most geographical area subdivisions. Its corresponding elements in eigenvectors could be regard as a coefficient to principal components. Then, a measure, which is the similarity of each eigenvector to a basis vector, that could characterize the temporal population change is established. Based on this, the coupling interaction between population changes and land use characteristics is explored using this measure. It shows that it is restricted by land use characteristics and also is a reflection of population changes over time. These results provided an insight on understanding temporal population change patterns and it would help to improve urban planning and establish a job-housing balance., 0 Web of Conferences https://doi.org/10.

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“…Shen and Chen (2017) investigated people's spatiotemporal behavior patterns in Nanjing and modeled the relationship between the density of pick-up and drop-off locations and population density, transportation density and per capita disposable income. Another paper scrutinized the correlation between taxi demand, land use pattern and accessibility to other public transit modes in Washington, DC (Yang et al, 2018). However, having in mind that:…”

Section: Introductionmentioning

confidence: 99%

Exploring Driving Factors of Higher Paid Taxi Trips Using Origin–destination GPS Data (Case Study: Green Taxis of New York City)

Mojtabaee

Molavi

Taleai

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Analysis of Washington, DC taxi demand using GPS and land-use data

Cited by 91 publications

References 20 publications

Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran

Individuals’ Demand for Ride-hailing Services: Investigating the Combined Effects of Attitudinal Factors, Land Use, and Travel Attributes on Demand for App-based Taxis in Tehran, Iran

Understanding Spatial and Temporal Change Patterns of Population in Urban Areas Using Mobile Phone Data

Exploring Driving Factors of Higher Paid Taxi Trips Using Origin–destination GPS Data (Case Study: Green Taxis of New York City)

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