Auctions Negotiation for Mobile Rideshare Service

Abdel-Naby, Sameh; Fante, Stefano; Giorgini, Paolo

doi:10.1109/icpca.2007.4365444

Cited by 13 publications

(11 citation statements)

References 7 publications

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“…Most of the proposed applications have not been examined for either real-life and/or large-scale datasets. These solutions are developed based on a variety of techniques and approaches such as auction negotiation [21], analyzing social connections in SRSS [22], multi source-destination path planning [23] and even cloud computing [24]. and [8] 2. http://www.numbeo.com that focuses on different criteria.…”

Section: Dynamic Ridesharingmentioning

confidence: 99%

A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

Zhang

Shemshadi²,

Sheng

et al. 2021

IEEE Trans. Big Data

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Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users' efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.

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Section: Dynamic Ridesharingmentioning

confidence: 99%

A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

Zhang

Shemshadi²,

Sheng

et al. 2021

IEEE Trans. Big Data

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“…The system coordinated ride matches and used Bluetooth capabilities to detect nearby users (6). Abdel-Naby et al developed another system that relied on Bluetooth capabilities, wireless communication, and mobile phones (7). The algorithm that the researchers produced was an auction-based negotiation that sorted riders by their particular preferences (7).…”

Section: Integrating Recent Technological Advances Into Ridesharingmentioning

confidence: 99%

Integrating Automated Toll Discounts into a Real-Time Ridesharing Program

Wood

Jones-Meyer²

2016

Transportation Research Record

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A pilot program in Austin, Texas, tested the practicality of integration of a real-time ridesharing application with a toll operator to process toll discounts for carpools. The toll discounts appeared on monthly toll transaction statements. The program lasted for almost a year on the 183A Toll Road and the US-290 Manor Expressway. Travelers used a smartphone application to track, record, and submit their trips for discounts. Two-person carpools that used the application received a 50% discount, and carpools of three or more people could travel toll free. The program was a partnership between the Central Texas Regional Mobility Authority, the local toll system operator, and a private ridesharing vendor. Back-office processes matched trip data from the smartphone application to transactions recorded by the toll systems. A total of 95 unique drivers were provided toll rebates for 2,213 trips during the 10.5-month period of the pilot program. During the pilot program, rebates were provided for most trips by two-person carpools. Individual driver behavior varied considerably. A select few drivers had a high number of carpool trips, whereas others took a sporadic trip or infrequent trips. Drivers took a median of seven trips during the pilot program. Future ridesharing programs should consider showing higher-dollar-value rebates that represent annual savings to incentivize ridesharing behavior. Timely feedback to users was found to be an important goal for success. In addition, program sponsors should provide positive customer service and engage users when problems that are not under their direct purview exist.

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“…For example, BarterCell is an agent-based application which provide the possibility for end-users to exchange similar value objects. ANDIAMO [1] is another agent-based application that enables users of pocket computing devices to do carpooling. Our research effort addresses the possible integration of recent communication technologies in our daily life service applications that are agent-based.…”

Section: Introductionmentioning

confidence: 99%

Investigating agent-based mobile service applications integration with reliable wireless communications

Abdel-Naby

Sandygulova

Zlydareva

et al. 2012

2012 IFIP Wireless Days

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Abstract-In this paper we investigate how recent advances in wireless communications can be integrated with agent-base service applications so that the connectivity between different users of the same environment, or remotely connected ones, is improved. We present two agent-based mobile service applications that employ software agents to represent end-users. We show how these applications are providing essential services for users on-the-run through distributed Bluetooth access points. We then briefly analyze possible future integration once advanced wireless communication is added to one of these services.

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Auctions Negotiation for Mobile Rideshare Service

Cited by 13 publications

References 7 publications

A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

A User-Oriented Taxi Ridesharing System with Large-Scale Urban GPS Sensor Data

Integrating Automated Toll Discounts into a Real-Time Ridesharing Program

Investigating agent-based mobile service applications integration with reliable wireless communications

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