Research on Dynamic Taxi Ride-Sharing Price Model

2020 ◽  
Author(s):  
Shun Xu ◽  
Changlong Yin ◽  
Qirui Zhang
Keyword(s):  
Price Model ◽  
Ride Sharing

Research on Dynamic Taxi Ride-Sharing Price Model

2020 ◽  
Author(s):  
Xi-Jun ZHANG ◽  
Ye TAO ◽  
Qi-Rui ZHANG ◽  
Meiya DONG ◽  
Jumin ZHAO
Keyword(s):  
Price Model ◽  
Ride Sharing

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.

scholarly journals Markov chain approximation of one-dimensional sticky diffusions

2021 ◽  
Vol 53 (2) ◽  
pp. 335-369
Author(s):  
Christian Meier ◽  
Lingfei Li ◽  
Gongqiu Zhang
Keyword(s):  
Markov Chain ◽  
Approximate Solutions ◽  
Price Model ◽  
One Dimensional ◽  
Markov Chain Approximation ◽  
Alternative Approaches ◽  
Second Order Convergence ◽  
Chain Approximation ◽  
Matrix Exponentials ◽  
Interior Points

AbstractWe develop a continuous-time Markov chain (CTMC) approximation of one-dimensional diffusions with sticky boundary or interior points. Approximate solutions to the action of the Feynman–Kac operator associated with a sticky diffusion and first passage probabilities are obtained using matrix exponentials. We show how to compute matrix exponentials efficiently and prove that a carefully designed scheme achieves second-order convergence. We also propose a scheme based on CTMC approximation for the simulation of sticky diffusions, for which the Euler scheme may completely fail. The efficiency of our method and its advantages over alternative approaches are illustrated in the context of bond pricing in a sticky short-rate model for a low-interest environment and option pricing under a geometric Brownian motion price model with a sticky interior point.

scholarly journals The Premium of Public Perceived Greenery: A Framework Using Multiscale GWR and Deep Learning

2021 ◽  
Vol 18 (13) ◽  
pp. 6809
Author(s):  
Yonglin Zhang ◽  
Xiao Fu ◽  
Chencan Lv ◽  
Shanlin Li
Keyword(s):  
Deep Learning ◽  
Green Spaces ◽  
Development Pattern ◽  
Real Estate Development ◽  
Hedonic Price ◽  
Price Model ◽  
Analytic Framework ◽  
Neighborhood Scale ◽  
Analytical Tools ◽  
Neighborhood Level

Population agglomeration and real estate development encroach on public green spaces, threatening human settlement equity and perceptual experience. Perceived greenery is a vital interface for residents to interact with the urban eco-environment. Nevertheless, the economic premiums and spatial scale of such greenery have not been fully studied because a comprehensive quantitative framework is difficult to obtain. Here, taking advantage of big geodata and deep learning to quantify public perceived greenery, we integrate a multiscale GWR (MGWR) and a hedonic price model (HPM) and propose an analytic framework to explore the premium of perceived greenery and its spatial pattern at the neighborhood scale. Our empirical study in Beijing demonstrated that (1) MGWR-based HPM can lead to good performance and increase understanding of the spatial premium effect of perceived greenery; (2) for every 1% increase in neighborhood-level perceived greenery, economic premiums increase by 4.1% (115,862 RMB) on average; and (3) the premium of perceived greenery is spatially imbalanced and linearly decreases with location, which is caused by Beijing’s monocentric development pattern. Our framework provides analytical tools for measuring and mapping the capitalization of perceived greenery. Furthermore, the empirical results can provide positive implications for establishing equitable housing policies and livable neighborhoods.

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