A New Model for a Carpool Matching Service

Curtin, Kevin M.; Li, Weihong; Zhao, Yong‐Long

doi:10.1371/journal.pone.0129257

Cited by 40 publications

(23 citation statements)

References 27 publications

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“…First, the pilot required matching of drivers and riders based on both the origin, and destination, of a given trip. The probability of riders and drivers sharing both an origin and destination within reasonable proximity is a persistent challenge to rideshare matching (Xia et al 2015), which is addressed by profit-oriented models that are more akin to taxi services than true ridesharing.…”

Section: What Are the Affordances Of Ridesharing Platforms Associatedmentioning

confidence: 99%

Co-producing Mobility: Lessons from Ridesharing for a More Just and Sustainable Autonomous Future

Griffin¹

2019

Preprint

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Big changes in technology create big opportunities for sustainability. Decreasing the number of cars on the road through carpooling can mitigate many problems related to transportation, including traffic congestion, emissions, and safety. Mobile information and communication technologies (ICTs) provide communicative and financial capabilities, termed affordances; to make carpooling much more convenient. However, research has yet to distinguish the role of affordances in reaching a critical mass of drivers. This chapter showcases empirical results from an in-depth study of a carpooling app, coupled with an innovative policy pilot to provide toll road discounts for carpool trips registered with the program. Results from the pilot countered with a for-profit model suggest drivers require sufficient reimbursement for travel costs and coordination time, to reach a critical mass needed to support a competitive travel option. However, recruitment tactics such as paid and organic media coverage, face-to-face events and incentives, and driver-focused outreach support growth of the carpool system registration, and use. Additional studies are needed to evaluate different combinations of ridesharing affordances and transportation policies to determine whether communities can realize the benefits of a critical mass of ICT-supported carpooling.

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Section: What Are the Affordances Of Ridesharing Platforms Associatedmentioning

confidence: 99%

Co-producing Mobility: Lessons from Ridesharing for a More Just and Sustainable Autonomous Future

Griffin¹

2019

Preprint

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“…The constraints from 1 to 3 are usual for a commute process [28], while the constraints from 4 to 6 are specific for the proposed dynamic ridesharing system. As mentioned previously, the potential route of a driver is encoded with a polyline α d , which is a matrix with two columns where each row represents the coordinate of each point in the polyline.…”

Section: The Route Matching Algorithmmentioning

confidence: 99%

Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith

et al. 2017

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This paper focuses on dynamic carpooling services in urban areas to address the needs of mobility in real-time by proposing a two-fold contribution: a solution with novel features with respect to the current state-of-the-art, which is named CLACSOON and is available on the market; the analysis of the carpooling services performance in the urban area of the city of Cagliari through emulations. Two new features characterize the proposed solution: partial ridesharing, according to which the riders can walk to reach the driver along his/her route when driving to the destination; the possibility to share the ride when the driver has already started the ride by modeling the mobility to reach the driver destination. To analyze which features of the population bring better performance to changing the characteristics of the users, we also conducted emulations. When compared with current solutions, CLACSOON allows for achieving a decrease in the waiting time of around 55% and an increase in the driver and passenger success rates of around 4% and 10%, respectively. Additionally, the proposed features allowed for having an increase in the reduction of the CO 2 emission by more than 10% with respect to the traditional carpooling service.

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“…Xia et al [25] present matching services for carpooling by applying both optimal and heuristic approaches to test and find solutions. It is verified that a new formulation and related solution processes can authorize the purpose of the optimal carpool teams and their routes.…”

Section: Introductionmentioning

confidence: 99%

An Organizational-Based Model and Agent-Based Simulation for Co-Traveling at an Aggregate Level

et al. 2017

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Abstract:Carpooling is an environmentally friendly and sustainable emerging traveling mode that enables commuters to save travel time and travel expenses. In order to co-travel, individuals or agents need to communicate, interpret information, and negotiate to achieve co-operation to find matching partners. This paper offers the scheme of a carpooling model for a set of candidate carpoolers. The model is interpreted using an agent-based simulation to analyze several effects of agents' interaction and behavior adaptations. Through communication and negotiation processes, agents can reach dynamic contracts in an iterative manner. The start of the negotiation process relies on the agents' intention to emit an invitation for carpooling. The realization of the negotiation process depends significantly on the departure time choice, on the agents' profile, and on route optimization. The schedule or agenda adaptation relies on the preferences among the realistic schedules of the agents and usually depends on both the participation of the trip and on the time of day. From the considerations, it is possible to reveal the actual representation of the possible carpoolers during the simulated period. Experiments demonstrate the nearly-polynomial relationship between computation time and the number of agents.

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A New Model for a Carpool Matching Service

Cited by 40 publications

References 27 publications

Co-producing Mobility: Lessons from Ridesharing for a More Just and Sustainable Autonomous Future

Co-producing Mobility: Lessons from Ridesharing for a More Just and Sustainable Autonomous Future

Dynamic Carpooling in Urban Areas: Design and Experimentation with a Multi-Objective Route Matching Algorith

An Organizational-Based Model and Agent-Based Simulation for Co-Traveling at an Aggregate Level

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