Transformation of ridehailing in New York City: A quantitative assessment

Dey, Bibhas Kumar; Tirtha, Sudipta Dey; Eluru, Naveen; Konduri, Karthik C.

doi:10.1016/j.trc.2021.103235

Cited by 16 publications

(9 citation statements)

References 47 publications

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“…Destination attributes that represent nonmotorized and transit infrastructure-such as bike lanes, bus stops, Divvy stations, walk score, and transit-offer positive association with destination choice. Several earlier studies have documented some or all of these relationships (21,22,53,(59)(60)(61). For bus stops and Divvy stations, the impact on destination selection is even higher during the AM peak period.…”

Section: Destination Choice Modelmentioning

confidence: 92%

“…From this broad set of alternatives, destination choice models are developed employing a random sample of 30 alternatives (inclusive of the chosen alternative). A similar random sampling process has been adopted in earlier literature for destination choice models (see Joewono et al, Ryan and Frank, Seong et al, and Dey et al for details) ( 50 – 53 ).…”

Section: Data Preparationmentioning

confidence: 99%

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Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

Parvez

Tirtha

Bhowmik

et al. 2023

Transportation Research Record: Journal of the Transportation R

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In this study, we examine the factors affecting Chicago, U.S., transportation network companies (TNCs) users’ trip fare and destination choice behavior. While trip fare has been examined from various perspectives, earlier fare models have not considered an exhaustive set of independent variables. Further, trip fare decisions are significantly influenced by trip destination. Therefore, in our study a joint model of trip fare and destination choice is proposed. The joint model system—linear regression for fare and multinomial logit model for destination—is developed based on Chicago TNC weekday trip data from January 2019 to December 2019. A wide range of origin- and destination-specific land use and built environment factors, transportation infrastructure attributes, and weather attributes were found to be significant in the model system. Based on log-likelihood and Bayesian information criterion measures, the model performance of the proposed joint model is found to be superior compared with independent fare and destination models. The applicability of our proposed fare and destination choice model is illustrated through fare prediction and destination elasticity analysis. The framework can potentially be employed to generate TNC fare for inclusion in level of service measures for TNC models in the mode choice model.

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Section: Destination Choice Modelmentioning

confidence: 92%

Section: Data Preparationmentioning

confidence: 99%

Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

Parvez

Tirtha

Bhowmik

et al. 2023

Transportation Research Record: Journal of the Transportation R

Self Cite

View full text Add to dashboard Cite

show abstract

“…The demand stored in such a standard form is available for multiple cities, e.g. New York (Dey et al, 2021) or Chicago (Dean and Kockelman, 2021).…”

Section: Inputmentioning

confidence: 99%

Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides

Kucharski¹,

Cats²

2022

Preprint

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We propose Hyper-pool, an analytical, offline, utility-driven ride-pooling algorithm to aggregate individual trip requests into attractive shared rides of high-occupancy.We depart from our ride-pooling ExMAS algorithm where single rides are pooled into attractive door-to-door rides and propose two novel demand-side algorithms for further aggregating individual demand towards more compact pooling. First, we generate stop-to-stop rides, with a single pick up and drop off points optimal for all the travellers. Second, we bundle such rides again, resulting with hyper-pooled rides compact enough to resemble public transport operations. We propose a bottom-up framework where the pooling degree of identified rides is gradually increased, thereby ensuring attractiveness at subsequent aggregation levels.Our Hyper-pool method outputs the set of attractive pooled rides per service variant for a given travel demand. The algorithms are publicly available and reproducible. It is applicable for real-size demand datasets and opens new opportunities for exploiting the limits of ride-pooling potential.In our Amsterdam case-study we managed to pool over 220 travellers into 40 hyper-pooled rides of average occupancy 5.8 pax/veh.

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“…Studies into the role that ridehailing services play in bridging local accessibility gaps have found improvements in equitable access after service introduction in more suburban settings outside regional cores (Acheampong et al 2020;Abdelwahab et al 2021). However, ridehailing services tend to favor urban areas disproportionately due to the inherent demand provided by their greater activity densities (Dey et al 2021), which is also associated with shorter travel and wait times as well as decreased costs (Conway et al 2018). Yu and Peng (2019) further underscored how built environments conducive to high-quality transit service are also likely to be catalysts for ridehailing activity, finding that greater land use entropy in addition to transit stop and sidewalk density were associated with greater ridehailing service utilization.…”

Section: Ridehailing and Built Environmentmentioning

confidence: 99%

“…Greater land use mixing as well as stronger local and regional accessibility have been found to predict ridehailing service use elsewhere (Sabouri et al 2020). Although other studies have found residents in areas of greater mixed-use favor active transportation modes over ridehailing services (Alemi et al 2018b), ridehailing service use continues to be linked to walkable neighborhoods (Malik et al 2021) characterized by greater intensities of restaurants, cafes, and points of interest near an individual's residence (Dey et al 2021).…”

Section: Ridehailing and Built Environmentmentioning

confidence: 99%

Spatial equity implications and neighborhood indicators of ridehailing trip frequency and vehicle miles traveled in the phoenix metro region

Gehrke

Huff

2022

Transportation

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Early optimism for ridehailing services to complement existing public transit services and offer individuals another shared mobility service with reduced travel costs and improved travel times have largely proven to be unsubstantiated. This unwelcomed outcome, in part due to the popularity of ridehailing services among wealthier populations and restrictions on the less-expensive ridesharing service in some urban settings, has likely instead resulted in heightened disparities in access to this on-demand mobility option for historically-marginalized populations and under-resourced communities. This hypothesis is examined by estimating the macro-level socioeconomic and built environment determinants of ridehailing pick-ups and drop-offs in the Phoenix metro region with spatial lag of X modeling. A geographically weighted regression (GWR) model of vehicle miles traveled was then estimated using route-level ridehailing data from a third-party mileage tracking app to identify zonal attributes associated with this measure of vehicle-based exposure. Together, study findings highlight the benefits and drawbacks of greater ridehailing service activity, identifying a need for programs and interventions that safeguard and improve access to affordable high-quality mobility options for transportation disadvantaged neighborhoods.

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Transformation of ridehailing in New York City: A quantitative assessment

Cited by 16 publications

References 47 publications

Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

Joint Econometric Model Framework for Transportation Network Company Users’ Trip Fare and Destination Choice Analysis

Hyper-pool: pooling private trips into high-occupancy transit-like attractive shared rides

Spatial equity implications and neighborhood indicators of ridehailing trip frequency and vehicle miles traveled in the phoenix metro region

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