A review of Ride-Matching strategies for Ridesourcing and other similar services

Chakraborty, Jayita; Pandit, Debapratim; Chan, Félix; Xia, Jianhong

doi:10.1080/01441647.2020.1866096

Cited by 10 publications

(2 citation statements)

References 46 publications

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“…The Transportation Network Companies (TNCs) (e.g., Uber, Ola, or DiDi) use a pool of drivers to address the ride requests generated by the users. The drivers play a significant role as one of the primary stakeholders to fulfill the supply needs catering to the demand for On-Demand En-Route Ride (OnERide) services (Chakraborty et al 2020 ), popularly known as rideosurcing services. As the demand for ridesourcing services is spatio-temporal, it is often difficult for the drivers to get a trip immediately after dropping off a passenger.…”

Section: Introductionmentioning

confidence: 99%

Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India

et al. 2022

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It is often difficult for the ridesourcing drivers to get a trip immediately after dropping off a passenger. The main objective of the drivers is to increase their income by serving more trips. The most prominent options available to the drivers after reaching passengers’ destinations are: (a) park and wait in and around their drop-off location, (b) cruise in and around their drop-off location and (c) drive to another location to receive trip requests quickly. Previous studies were conducted to understand the driver behaviour in a taxi and other similar services. However, the perception of ridesourcing drivers on parking and waiting after dropping off passengers is yet to be explored. The drivers’ decision on waiting can affect users’ waiting time, the number of matched trips by the TNCs, and parking spaces in the city. Moreover, drivers’ waiting time tolerance can also impact other drivers’ total number of trips, total earnings, total distance travelled in the city, and fleet size. The aim of this study is to understand the influence of drivers’ characteristics on drivers’ decision to park and wait after dropping off a passenger. This study estimates and compares the waiting time tolerance of the ridesourcing drivers using a zero-inflated cox spline model between Perth and Kolkata. It is observed that drivers in Kolkata have higher waiting time tolerance than Perth drivers. Moreover, the drivers in both the cities are more likely to wait at high-demand areas urging the urban authorities to determine spatio-temporal parking demand to design the parking infrastructure for such areas.

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Section: Introductionmentioning

confidence: 99%

Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India

et al. 2022

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“…Furthermore, along with other factors (e.g. passenger waiting time, number of trips per driver, vehicle miles travelled, and demand for parking space), demand forecasting also associates in determining appropriate fleet size [6]. While on the other hand, forecasting the supply‐demand gap serves as an indicator of the supply deficit/surplus of ride‐hailing cars in each zone, which is found to be informative for implementing dynamic pricing strategies and incentivizing drivers as a means to deal with the disequilibrium issues [7].…”

Section: Introductionmentioning

confidence: 99%

Using spatio‐temporal deep learning for forecasting demand and supply‐demand gap in ride‐hailing system with anonymised spatial adjacency information

Rahman

Rifaat

2021

IET Intelligent Trans Sys

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To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and supply-demand gap in a ridehailing system, making accurate forecasts for both demand and supply-demand gap is a difficult task. Furthermore, due to confidentiality and privacy issues, ride-hailing data are sometimes released to the researchers by removing spatial adjacency information of the zones, which hinders the detection of spatio-temporal dependencies. To that end, a novel spatio-temporal deep learning architecture is proposed in this paper for forecasting demand and supply-demand gap in a ride-hailing system with anonymised spatial adjacency information, which integrates feature importance layer with a spatio-temporal deep learning architecture containing 1D convolutional neural network (CNN) and zone-distributed independently recurrent neural network (IndRNN). The developed architecture is tested with real-world datasets of Didi Chuxing, which shows that the models based on the proposed architecture can outperform conventional time-series models (e.g. ARIMA) and machine learning models (e.g. gradient boosting machine, distributed random forest, generalized linear model, artificial neural network). Additionally, the feature importance layer provides an interpretation of the model by revealing the contribution of the input features utilized in prediction.

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Genealogy of Shared Mobility in India

Kathait¹,

Agarwal²

2022

Lecture Notes in Civil Engineering

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A review of Ride-Matching strategies for Ridesourcing and other similar services

Cited by 10 publications

References 46 publications

Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India

Modeling the decision of ridesourcing drivers to park and wait at trip ends: a comparison between Perth, Australia and Kolkata, India

Using spatio‐temporal deep learning for forecasting demand and supply‐demand gap in ride‐hailing system with anonymised spatial adjacency information

Genealogy of Shared Mobility in India

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