How can the taxi industry survive the tide of ridesourcing? Evidence from Shenzhen, China
By Yu (Marco) Nie. 2017
266 citations recorded by the source corpus.
Subfields: Automotive Engineering, Marketing, Transportation
View Paper: https://www.semanticscholar.org/paper/74699c867f9118c3366438fbc8e2c84e981b8e8d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Automotive Engineering
In Marketing
In Transportation
Citations in the corpus
- Urban traffic signal control with connected and automated vehicles: A survey (2019, background): “So far, most stochastic filtering and particle filter techniques are model-based and try to integrate traffic flow models with data; while the pure data-driven methods such as deep-learning based methods depend solely on data (Lv et al., 2015; Ma et al., 2015, 2017; Polson and Sokolov, 2017).”
- Ridesourcing systems: A framework and review (2019, background): “…and fixed-route transit systems (e.g., zone-based and line-based ridesourcing e-hailing services along a fixed-route transit line as in Chen and Nie, 2017 ), schedule and route optimization of public transit (e.g., buses and metro) while considering ridesourcing’s complementary and feeding…”
- Uber, Public Transit, and Urban Transportation Equity: A Case Study in New York City (2019, background): “…with the growing popularity of ridesourcing services, there have been an increasing number of studies and debates looking into the impacts of ridesourcing on urban efficiency, equity, and sustainability (Cramer and Krueger 2016; Schor 2016; Shaheen and Chan 2016; Frenken and Schor 2017; Nie 2017).”
- Does ride-hailing increase or decrease vehicle kilometers traveled (VKT)? A simulation approach for Santiago de Chile (2020, methodology): “The time taxis move without passengers as a rate of the total driving time has been estimated as 41.6% for Berlin (Bischoff, Maciejewski, & Sohr, 2015), 40%–50% for Shenzhen16 (Nie, 2017), 50%–52% for New York (Cramer & Krueger, 2016) and 61%–62% for Seattle (Cramer and Krueger, 2016).”
- Pricing and equilibrium in on-demand ride-pooling markets (2020, background): “…al., 2020b ; Chen et al., 2020 ); government regulations and policies ( Yu et al., 2019 ; Li et al., 2019b ); impacts on conventional taxi markets ( Nie, 2017 ; Wallsten, 2015 ); geometrical matching and order dispatching ( Xu et al., 2015 , 2018 ; Zha et al., 2018 ; Zhang et al., 2017 ; Lyu et…”