scholarly journals Path Clustering Based on a Novel Dissimilarity Function for Ride-Sharing Recommenders

2016 ◽  
Author(s):  
Eleonora D'Andrea ◽  
David Di Lorenzo ◽  
Beatrice Lazzerini ◽  
Francesco Marcelloni ◽  
Fabio Schoen
Keyword(s):  
Ride Sharing ◽  
Dissimilarity Function

Deep Reinforcement Learning for Ride-sharing Dispatching and Repositioning

2019 ◽  
Author(s):  
Zhiwei (Tony) Qin ◽  
Xiaocheng Tang ◽  
Yan Jiao ◽  
Fan Zhang ◽  
Chenxi Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Human Computer Interaction ◽  
Ride Sharing ◽  
Simulation Based ◽  
Computer Interaction

In this demo, we will present a simulation-based human-computer interaction of deep reinforcement learning in action on order dispatching and driver repositioning for ride-sharing.  Specifically, we will demonstrate through several specially designed domains how we use deep reinforcement learning to train agents (drivers) to have longer optimization horizon and to cooperate to achieve higher objective values collectively. 

Urban Transport

2018 ◽  
Author(s):  
José van
Keyword(s):  
Urban Areas ◽  
Urban Transport ◽  
Transport Sector ◽  
Public And Private ◽  
Transport Services ◽  
Ride Sharing ◽  
The World ◽  
Private And Public ◽  
Selection Mechanisms

Platformization affects the entire urban transport sector, effectively blurring the division between private and public transport modalities; existing public–private arrangements have started to shift as a result. This chapter analyzes and discusses the emergence of a platform ecology for urban transport, focusing on two central public values: the quality of urban transport and the organization of labor and workers’ rights. Using the prism of platform mechanisms, it analyzes how the sector of urban transport is changing societal organization in various urban areas across the world. Datafication has allowed numerous new actors to offer their bike-, car-, or ride-sharing services online; selection mechanisms help match old and new complementors with passengers. Similarly, new connective platforms are emerging, most prominently transport network companies such as Uber and Lyft that offer public and private transport options, as well as new platforms offering integrated transport services, often referred to as “mobility as a service.”

Working Time under Alternative Pay Contracts in the Ride-Sharing Industry

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Filippo Belloc
Keyword(s):  
Network Effects ◽  
Network Externalities ◽  
Profit Sharing ◽  
Static Model ◽  
Peer To Peer ◽  
Working Time ◽  
Intensive Margin ◽  
Hours Worked ◽  
Ride Sharing ◽  
Transportation Sector

AbstractWe study hours worked by drivers in the peer-to-peer transportation sector with cross-side network effects. Medallion lease (regulated market), commission-based (Uber-like pay) and profit-sharing (“pure” taxi coop) compensation schemes are compared. Our static model shows that network externalities matter, depending on the number of active drivers. When the number of drivers is limited, in the presence of positive network effects, a regulated system always induces more hours worked, while the commission fee influences the comparative incentives towards working time of Uber-like pay versus profit-sharing. When the number of drivers is infinite (or close to it), the influence of network externalities on optimal working time vanishes. Our model helps identifying which is the pay scheme that best remunerates longer working times and offers insights to regulators seeking to improve the intensive margin of coverage by taxi services.

Unpacking Sharing in the Peer-to-Peer Economy: The Impact of Shared Needs and Backgrounds on Ride-Sharing

2020 ◽  
Vol 4 (CSCW1) ◽  
pp. 1-19
Author(s):  
Ning F. Ma ◽  
Benjamin V. Hanrahan
Keyword(s):  
Peer To Peer ◽  
Ride Sharing ◽  
The Impact

scholarly journals A Dynamic Mobility Traffic Model Based on Two Modes of Transport in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 253-270
Author(s):  
Mohammed Bin Hariz ◽  
Dhaou Said ◽  
Hussein T. Mouftah
Keyword(s):  
Data Exchange ◽  
Smart Cities ◽  
Time Data ◽  
School Bus ◽  
The Public ◽  
Ride Sharing ◽  
Dynamic Mobility ◽  
Proposed Model ◽  
Loading System ◽  
Suitable Combination

This paper focuses on transportation models in smart cities. We propose a new dynamic mobility traffic (DMT) scheme which combines public buses and car ride-sharing. The main objective is to improve transportation by maximizing the riders’ satisfaction based on real-time data exchange between the regional manager, the public buses, the car ride-sharing and the riders. OpenStreetMap and OMNET++ were used to implement a realistic scenario for the proposed model in a city like Ottawa. The DMT scheme was compared to a multi-loading system used for a school bus. Simulations showed that rider satisfaction was enhanced when a suitable combination of transportation modes was used. Additionally, compared to the other scheme, this DMT scheme can reduce the stress level of car ride-sharing and public buses during the day to the minimal level.

Navigating Ride-Sharing Regulations

2019 ◽  
Author(s):  
Anita Chen ◽  
Chien-Wen Yuan ◽  
Ning F. Ma ◽  
Chi-Yang Hsu ◽  
Benjamin V. Hanrahan
Keyword(s):  
Ride Sharing

scholarly journals Making markets gendered: Kathmandu’s ride-sharing platforms through a gender lens

2021 ◽  
pp. 1-23
Author(s):  
Pritee Hamal ◽  
Roy Huijsmans
Keyword(s):  
Ride Sharing ◽  
Gender Lens

scholarly journals Uncovering the Providers’ Continuance Intention of Participation in the Sharing Economy: A Moderated Mediation Model

2021 ◽  
Vol 13 (9) ◽  
pp. 5095
Author(s):  
Jiang Jiang ◽  
Rui Feng ◽  
Eldon Y. Li
Keyword(s):  
Sharing Economy ◽  
Economic Benefits ◽  
Peer To Peer ◽  
Partial Least Square ◽  
Least Square ◽  
Mediating Effect ◽  
Utilization Efficiency ◽  
Continuance Intention ◽  
Hedonic Value ◽  
Ride Sharing

The sharing economy has evolved into a promising business concept that enables individuals to share their idle resources, improving resource utilization efficiency commercially. Recently, it has gained enormous academic attention. However, little concern has been given to the behavior of individual providers on the supply side. This paper aims to uncover the motivational and trust-based providers’ continuance intention of participation in the context of peer-to-peer ride-sharing services. Based on the survey data from 202 providers and the partial least-square analysis, we confirm the mediating effect of attitude in the relationships between participation continuance intention; trust; and three motivational dimensions: economic benefits, social–hedonic value, and sustainability. We further confirm the moderating effects of innovativeness using PROCESS. The results show that economic benefits, social–hedonic value, and sustainability significantly affect providers’ participation continuance intention. Moreover, attitudes toward the sharing economy play a complementary partial-mediating role in the relationships from economic benefits and social–hedonic value to participation continuance intention, which is negatively moderated by innovativeness. Trust does not significantly affect providers’ attitude toward the sharing economy and participation continuance intention in the peer-to-peer ride-sharing context.

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