The Value of Knowing Drivers' Reservation Wage in Ride Sharing Systems

2021 ◽  
Author(s):  
Ran I Snitkovsky ◽  
Jim Dai ◽  
Costis Maglaras
Keyword(s):  
Reservation Wage ◽  
Ride Sharing

Paradoxes of reservation wage setting in the Russian labor market. Part I

2016 ◽  
pp. 5-27
Author(s):  
R. Kapeliushnikov ◽  
A. Lukyanova
Keyword(s):  
Labor Market ◽  
Empirical Studies ◽  
Reservation Wage ◽  
Wage Setting ◽  
Reservation Wages ◽  
Monitoring Survey ◽  
Main Determinants ◽  
Subjective Estimates ◽  
Objective Indicators

Using panel data from the Russian Longitudinal Monitoring Survey for 2006-2014, the paper investigates reservation wages setting in the Russian labor market. The sample includes non-employed individuals wishing to get a job (both searchers and non-searchers). The first part of the paper provides a survey of previous empirical studies, describes data and analyzes subjective estimates of reservation wages in comparison with various objective indicators of actual wages. The analysis shows that wage aspirations of the majority of Russian non-employed individuals are overstated. However their wage expectations are rather flexible and decrease rapidly as the search continues that prevents high long-term unemployment. The second part of the paper provides an econometric analysis of main determinants of reservation wage and its impact on probability of re-employment and wages on searchers’ new jobs.

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.

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
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