Dynamic ride-sharing: A simulation study in metro Atlanta

Agatz, Niels; Erera, Alan L.; Savelsbergh, Martin; Wang, Xing

doi:10.1016/j.trb.2011.05.017

Cited by 312 publications

(95 citation statements)

References 7 publications

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“…However, none of these works considered the ride-pooling problem of servicing multiple rides with a single trip. The ride-pooling problem is more related to the vehiclerouting problem and the dynamic pickup and delivery problem (139)(140)(141), where spatiotemporally distributed demand must be picked up and delivered within prespecified time windows. A major challenge when addressing this problem is the need to explore a very large decision space while computing solutions fast enough to provide users with the experience of real-time booking and service.…”

Section: Fleet Managementmentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

736

378

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In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning, and decision-making for autonomous vehicles have led to great improvements in functional capabilities, with several prototypes already driving on our roads and streets. Yet challenges remain regarding guaranteed performance and safety under all driving circumstances. For instance, planning methods that provide safe and systemcompliant performance in complex, cluttered environments while modeling the uncertain interaction with other traffic participants are required. Furthermore, new paradigms, such as interactive planning and end-to-end learning, open up questions regarding safety and reliability that need to be addressed. In this survey, we emphasize recent approaches for integrated perception and planning and for behavior-aware planning, many of which rely on machine learning. This raises the question of verification and safety, which we also touch upon. Finally, we discuss the state of the art and remaining challenges for managing fleets of autonomous vehicles. 8.1

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Section: Fleet Managementmentioning

confidence: 99%

Planning and Decision-Making for Autonomous Vehicles

Schwarting

Alonso–Mora

Rus

2018

Annu. Rev. Control Robot. Auton. Syst.

736

378

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“…In the scope of the project, a dynamic ridesharing system was designed, in order to serve real-time transport requests. In Agatz et al [14] is considered the problem of matching drivers and riders for a dynamic ridesharing scenario, presenting a simulation study based on travel demand data for the city of Atlanta. The matching problem is described as the minimization of the total system-wide vehicle miles incurred by users and their individual travel costs.…”

Section: Past Workmentioning

confidence: 99%

“…On the other hand, dynamic ridesharing is a relatively new type of carpooling: it is a system where an automated process employed by a ridesharing provider matches up drivers and riders on a very short notice [9], which can range from a few minutes to a few hours before the departure time. Dynamic ridesharing clearly brings several advantages over the static ridesharing approach.…”

Section: Introductionmentioning

confidence: 99%

“…This approach is shown to be effective for mid/long distance trips, while it is not suitable for short distance trips, which often occur in an urban scenario: in this case, a real-time approach fits better. Dynamic ridesharing is a relatively new type of carpooling: it is a system where an automated process employed by a ridesharing provider matches up drivers and riders on a very short notice [9], which can range from a few minutes to a few hours before departure time. In addition to using communication technologies, dynamic ridesharing systems must establish a procedure that enables travelers to form ridesharing instantaneously [11].…”

Section: Past Work and Introduced Innovationmentioning

confidence: 99%

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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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“…Dynamic planning could be carried out in a time-based or an event-based manner (see Agatz et al (2011)). A well known example of a time-based approach is the rolling horizon approach where optimization is repeated after a given time interval.…”

mentioning

confidence: 99%

Planning of truck platoons: A literature review and directions for future research

Bhoopalam

Agatz

Zuidwijk

2018

Transportation Research Part B: Methodological

237

119

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A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient use of road capacity. To fully reap these benefits in the initial phases of technology deployment, careful planning of platoons based on trucks' itineraries and time schedules is required. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research.

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Dynamic ride-sharing: A simulation study in metro Atlanta

Cited by 312 publications

References 7 publications

Planning and Decision-Making for Autonomous Vehicles

Planning and Decision-Making for Autonomous Vehicles

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

Planning of truck platoons: A literature review and directions for future research

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