Book-ahead & supply management for ridesourcing platforms

Book-ahead ride-hailing trip and its determinants: Findings from large-scale trip records in China

“…Cruising ride-hailing vehicles on urban streets results in additional empty vehicle miles and fuel costs, detrimental to urban mobility (Gao et al, 2022;Wei et al, 2023). Book-ahead ride-hailing services enabling travelers to pre-book rides before their trips can potentially reduce cruising traffic and waiting times for passengers, thereby improving urban mobility (Yahia et al, 2021). Effective management strategies and policy interventions for such services require understanding the relationship between book-ahead ride-hailing demand and its determinants.…”

“…They further formulated an analytical model with a closed form to examine the effects of detours and waiting time restrictions on reservation-based carpooling services. Yahia et al (2021) conducted a simulation experiment to analyze the impact of book-ahead rides on driver supply management for ride-sourcing platforms and found that an increase in book-ahead rides led to a reduction in the total number of drivers required. It's a sign that ride-hailing trip reservations are still lacking in critical aspects despite their attractiveness and promise.…”

“…Currently, there is still a lack of overall understanding of ride-hailing trip reservations on a large scale with real data. While Yahia et al (2021) examined the impacts and benefits of book-ahead rides on driver supply management for ride-sourcing platforms, there have been few empirical studies on book-ahead ride-hailing trips and their determinants. This knowledge gap is relevant to regulators and planners seeking to promote or limit the use of book-ahead ride-hailing services, as the benefits of such services will remain theoretical without a deeper understanding of their determinants.…”

Optimal Curbside Pricing for Managing Ride-Hailing Pick-Ups and Drop-Offs

“…Operations of ride-hailing services have also been studied intensively including location prediction of the next rider pickup (Nair et al, 2020), service matching (Qin et al, 2021) and vehicle rebalancing (Kontou et al, 2020;Guo et al, 2021;Zhu et al, 2022), demand prediction (Battifarano and Qian, 2019), ride reservations (Yahia et al, 2021), impact of expanding service (Beojone and Geroliminis, 2021), shared parking (Gao et al, 2022), and management strategies such as pricing for related parking infrastructure (Liu et al, 2021), service platform and market (Zha et al, 2016;Nourinejad and Ramezani, 2020). A macroscopic fluid framework for operating a large-scale on-demand ride-hailing system was developed and used for decision making (Xu et al, 2021).…”