How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China

Nie, Yu

doi:10.1016/j.trc.2017.03.017

Cited by 252 publications

(95 citation statements)

References 15 publications

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“…Shanghai (Luo et al, 2017), Beijing (Li et al, 2016), and Shenzhen (Nie, 2017), China revealed long daily 37 driving distances that are likely to exceed BEV range. In terms of dwell patterns, it is found that 38 approximately 80% of the studied taxis in Beijing had average parking time of at least 5 hours per day (Cai 39 et al, 2014), and taxis in Berlin, Germany were in favor of waiting for customers at airports for several 40 hours (Bischoff et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA

Dong

Lin

et al. 2018

Transportation Research Part C: Emerging Technologies

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Electric taxis have the potential to improve urban air quality and save driver's energy expenditure. Although battery electric vehicles (BEVs) have drawbacks such as the limited range and charging inconvenience, technological progress has been presenting promising potential for electric taxis. Many cities around the world including New York City, USA are taking initiatives to replace gasoline taxis with plug-in electric vehicles. This paper extracts ten variables from the trip data of the New York City yellow taxis to represent their spatialtemporal travel patterns in terms of driver-shift, travel demand and dwell, and examines the implications of these driving patterns on the BEV taxi feasibility. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs of a given driving range during a year. It is found that the currently deployed 280 public charging stations in New York City are far from sufficient to support a large BEV taxi fleet. However, adding merely 372 new charging stations at various locations where taxis frequently dwell can potentially make BEVs with 200-and 300-mile ranges feasible for more than half of the taxi fleet. The results also show that taxis with certain characteristics are more suitable for switching to BEV-200 or BEV-300, such as fewer daily shifts, fewer drivers assigned to the taxi, shorter daily driving distance, fewer daily dwells but longer dwelling time, and higher likelihood to dwell at the borough of Manhattan.

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Section: Introductionmentioning

confidence: 99%

Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA

Dong

Lin

et al. 2018

Transportation Research Part C: Emerging Technologies

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“…It found that traditional taxis with experienced and professional drivers tended to operate more efficiently during peak hours and in more congested locations than the often-amateur drivers of ride-hailing services, many of whom rely heavily on GPS rather than personal knowledge. This line of research posits that the emergence of ride-hailing is unlikely to eliminate the demand for traditional taxis, and both services are ultimately required (and desired) in large metropolitan areas [8].…”

Section: The Comparison Of Ride-hailing and Traditional Taxismentioning

confidence: 99%

“…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%

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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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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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“…In many major cities of the world, on-demand shared mobility services are disrupting the business model of traditional street hailing and dispatched taxi services. Ondemand shared mobility services involve popular transportation network companies (TNCs) such as Uber and Lyft and microtransit services such as Ford-owned Chariot [1]. This escalating competition for passengers has been motivating taxi companies to mine dynamic mobility data to reveal insights that could benefit operations [2], locate more customers [3], and forecast demand [4].…”

Section: Introductionmentioning

confidence: 99%

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

Bridgelall

Tolliver

et al. 2018

Journal of Advanced Transportation

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On-demand shared mobility services such as Uber and microtransit are steadily penetrating the worldwide market for traditional dispatched taxi services. Hence, taxi companies are seeking ways to compete. This study mined large-scale mobility data from connected taxis to discover beneficial patterns that may inform strategies to improve dispatch taxi business. It is not practical to manually clean and filter large-scale mobility data that contains GPS information. Therefore, this research contributes and demonstrates an automated method of data cleaning and filtering that is suitable for such types of datasets. The cleaning method defines three filter variables and applies a layered statistical filtering technique to eliminate outlier records that do not contribute to distributions that match expected theoretical distributions of the variables. Chi-squared statistical tests evaluate the quality of the cleaned data by comparing the distribution of the three variables with their expected distributions. The overall cleaning method removed approximately 5% of the data, which consisted of errors that were obvious and others that were poor quality outliers. Subsequently, mining the cleaned data revealed that trip production in Dubai peaks for the case when only the same two drivers operate the same taxi. This finding would not have been possible without access to proprietary data that contains unique identifiers for both drivers and taxis. Datasets that identify individual drivers are not publicly available.

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How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China

Cited by 252 publications

References 15 publications

Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA

Analyzing battery electric vehicle feasibility from taxi travel patterns: The case study of New York City, USA

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

Mining Connected Vehicle Data for Beneficial Patterns in Dubai Taxi Operations

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