Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

Tang, Jinjun; Gao, Fan; Han, Chunyang; Cen, Xuekai; Li, Zhitao

doi:10.1016/j.jtrangeo.2021.103134

Cited by 39 publications

(17 citation statements)

References 47 publications

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“…In other words, the GWR model expands the traditional regression framework by relaxing the space-invariant relationship assumption, thereby allowing point-varying parameters to be estimated [37]. It is employed in numerous empirical studies [38][39][40], and its formula can be expressed as…”

Section: Geographically Weighted Regression (Gwr) Modelsmentioning

confidence: 99%

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Yang

Liu

Liang

et al. 2021

IJGI

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Population aging has become a notable and enduring demographic phenomenon worldwide. Older adults’ walking behavior is determined by many factors, such as socioeconomic attributes and the built environment. Although a handful of recent studies have examined the influence of street greenery (a built environment variable readily estimated by big data) on older adults’ walking behavior, they have not focused on the spatial heterogeneity in the influence. To this end, this study extracts the socioeconomic and walking behavior data from the Travel Characteristic Survey 2011 of Hong Kong and estimates street greenery (the green view index) based on Google Street View imagery. It then develops global models (linear regression and Box–Cox transformed models) and local models (geographically weighted regression models) to scrutinize the average (global) and location-specific (local) relationships, respectively, between street greenery and older adults’ walking time. Notably, green view indices in three neighborhoods with different sizes are estimated for robustness checks. The results show that (1) street greenery has consistent and significant effects on walking time; (2) the influence of street greenery varies across space—specifically, it is greater in the suburban area; and (3) the performance of different green view indices is highly consistent.

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Section: Geographically Weighted Regression (Gwr) Modelsmentioning

confidence: 99%

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Yang

Liu

Liang

et al. 2021

IJGI

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“…For example, studies have shown that bike-sharing has a complementary effect on public transit by helping to overcome the last-mile problem (Chu et al. , 2021; Tang et al. , 2021; Tyndall, 2022).…”

Section: Hypotheses Developmentmentioning

confidence: 99%

“…Although a large number of studies have investigated the impact of ride-sharing services on public transit use based on survey data or trip data (Baker, 2020; Barajas and Brown, 2021; Clewlow and Mishra, 2017; Erhardt et al. , 2022; Tang et al. , 2021; Wang et al.…”

Section: Introductionmentioning

confidence: 99%

The complementary effect of ride-sharing on public transit: evidence from a natural experiment

Xu,

Huang,

Zhang

et al. 2023

IMDS

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PurposeIdentifying the complementary effects of ride-sharing on public transit is critical to understanding the potential value of growing partnerships between public transit agencies and ride-sharing platforms. The purpose of this paper is to investigate whether and how ride-sharing services complement public transit.Design/methodology/approachTaking advantage of a natural experiment whereby subway Line 2 opened after the entry of ride-sharing services in Xiamen, this study uses a difference-in-differences approach to identify the complementary effects of ride-sharing on public transit based on a proprietary fine-grained trip-level data set from a large ride-sharing platform.FindingsThis study obtained the encouraging finding that ride-sharing has a significant complementary effect on the subway, as the number of ride-sharing pickups and drop-offs at subway stations increased by 130% and 117.9%, respectively, after the subway opening. Moreover, mechanism analysis shows that the complementary effect of ride-sharing services is stronger when connection distance is short (i.e. under 6 km) and when the transportation availability is limited (i.e. at night or in the areas with low transit supply and low population density).Practical implicationsThe findings provide guidelines for promoting cooperation between public transit agencies and ride-sharing platforms to build an efficient and sustainable urban transport system.Originality/valueThis study is the first to examine the complementary effect of ride-sharing services on public transit via unique fine-grained ride-sharing trips data, and further reveal the underlying mechanism behind this effect.

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“…The benefits have been described in Twumasi-Boakye et al ( 2021) through a simulation for the city of Ann Arbor (USA), the economic and environmental effects have been analyzed for Kuwait in AlKheder (2021), highlighting the reduction in gas emissions in three scenarios, and in New York City by Barann et al (2017) demonstrating the reduction of gas emissions and the total travel distance of all taxi rides. The effects of ridesharing were analyzed for the city of Shenzhen (China) by Tang et al (2021) and the findings indicated that it replaces buses and cabs for short-distance trips in the center of the city, and in surrounding areas, it replaces buses and assists in accessing metro stations. Ridesharing also has an impact in areas where public transport is not fully deployed and driving is almost the only solution to move around the city, as is the case in Thessaloniki (Greece), where Ayfantopoulou et al (2021) have evaluated the initial step of ridesharing implementation in the area.…”

Section: Related Work On Ridesharing Problemsmentioning

confidence: 99%

Combining Survival Analysis and Simheuristics to Predict the Risk of Delays in Urban Ridesharing Operations with Random Travel Times

Herrera

Panadero

Juan³

et al. 2022

2022 Winter Simulation Conference (WSC)

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More sustainable transportation and mobility concepts, such as ridesharing, are gaining momentum in modern smart cities. In many real-life scenarios, travel times among potential customers' locations should be modeled as random variables. This uncertainty makes it difficult to design efficient ridesharing schedules and routing plans, since the risk of possible delays has to be considered as well. In this paper, we model ridesharing as a stochastic team orienteering problem in which the trade-off between maximizing the expected reward and the risk of incurring time delays is analyzed. In order to do so, we propose a simulation-optimization approach that combines a simheuristic algorithm with survival analysis techniques. The aforementioned methodology allows us to generate not only the probability that a given routing plan will suffer a delay, but also gives us the probability that the routing plan experiences delays of different sizes.

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Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

Cited by 39 publications

References 47 publications

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

Spatially Varying Effects of Street Greenery on Walking Time of Older Adults

The complementary effect of ride-sharing on public transit: evidence from a natural experiment

Combining Survival Analysis and Simheuristics to Predict the Risk of Delays in Urban Ridesharing Operations with Random Travel Times

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