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You searched for subject:(ride sharing systems). Showing records 1 – 3 of 3 total matches.

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University of Waterloo

1. Sadeghi Yengejeh, Armin. Multi-robot Coverage and Redeployment Algorithms.

Degree: 2020, University of Waterloo

In this thesis, we focus on two classes of multi-robot task allocation and deployment problems motivated by applications in ride-sourcing transportation networks and service robots: 1) coverage control with multiple robots, and 2) robots servicing tasks arriving sequentially over time. The first problem considers the deployment of multiple robots to cover a domain. The multi-robot problem consists of multiple robots with sensors on-board observing the spatially distributed events in an environment. The objective is to maximize the sensing quality of the events via optimally distributing the robots in the environment. This problem has been studied extensively in the literature and several algorithms have been proposed for different variants of this problem. However, there has been a lack of theoretical results on the quality of the solutions provided by these algorithms. In this thesis, we provide a new distributed multi-robot coverage algorithm with theoretical guarantees on the solution quality, run-time complexity, and communication complexity. The theoretical bound on the solution quality holds for on-board sensors where the sensing quality of the sensors is a sub-additive function of the distance to the event location in convex and non-convex environments. A natural extension of the multi-robot coverage control problem is considered in this thesis where each robot is equipped with a set of different sensors and observes different event types in the environment. Servicing a task in this problem corresponds to sensing an event occurring at a particular location and does not involve visiting the task location. Each event type has a different distribution over the domain. The robots are heterogeneous in that each robot is capable of sensing a subset of the event types. The objective is to deploy the robots into the domain to maximize the total coverage of the multiple event types. We propose a new formulation for the heterogeneous coverage problem. We provide a simple distributed algorithm to maximize the coverage. Then, we extend the result to the case where the event distribution is unknown before the deployment and provide a distributed algorithm and prove the convergence of the approach to a locally optimal solution. The third problem considers the deployment of a set of autonomous robots to efficiently service tasks that arrive sequentially in an environment over time. Each task is serviced when a robot visits the corresponding task location. Robots can then redeploy while waiting for the next task to arrive. The objective is to redeploy the robots taking into account the next N task arrivals. We seek to minimize a linear combination of the expected cost to service tasks and the redeployment cost between task arrivals. In the single robot case, we propose a one-stage greedy algorithm and prove its optimality. For multiple robots, the problem is NP-hard, and we propose two constant-factor approximation algorithms, one for the problem with a horizon of two task arrivals and the other for the infinite…

Subjects/Keywords: distributed control; multi-robot coverage control; mobility-on-demand systems; ride-sharing systems

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Sadeghi Yengejeh, A. (2020). Multi-robot Coverage and Redeployment Algorithms. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/16370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sadeghi Yengejeh, Armin. “Multi-robot Coverage and Redeployment Algorithms.” 2020. Thesis, University of Waterloo. Accessed March 08, 2021. http://hdl.handle.net/10012/16370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sadeghi Yengejeh, Armin. “Multi-robot Coverage and Redeployment Algorithms.” 2020. Web. 08 Mar 2021.

Vancouver:

Sadeghi Yengejeh A. Multi-robot Coverage and Redeployment Algorithms. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/10012/16370.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sadeghi Yengejeh A. Multi-robot Coverage and Redeployment Algorithms. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/16370

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Cornell University

2. Liu, Lan. TOWARD A SYSTEMATIC APPROACH TO DRIVERS' BEHAVIORAL STUDY ON MOBILITY-ON-DEMAND RIDE SHARING SYSTEMS.

Degree: M.S., Civil and Environmental Engineering, Civil and Environmental Engineering, 2017, Cornell University

With continuous growth of urban populations, transportation system faces many challenges related to increasing demand of real time services, limited government investment and sustainability of environment. Ride sharing, as a mobile-internet-based transportation service mode, has gained wide popularity across the world. Instead of operating a fixed fleet, a ride sharing platform consolidates supplies from independent drivers with dynamic and flexible schedules. A ride sharing system provides drivers with a flexible working method and also improves passengers’ trip experiences in respect of easy reservation and convenient access to trip information. Unlike traditional taxi business, where supply is constant, ride sharing systems interact with a dynamic fleet. Therefore, adequate study for drivers’ behaviors is of great research interest, as it not only enriches behavioral study for suppliers in economic activities but also supports design of operation strategy for ride sharing system. This research proposes a comprehensive and data driven method that implements behavioral study based on Multinomial Logit Model (MNL) and Mixed Logit Model, from the family of Random Utility Maximization models. Furthermore, in order to explore operation strategies, a simulation framework of ride sharing system is developed. Operation strategies that involve consideration of drivers’ behaviors are proposed and simulated, which attempt to improve the ride sharing system’s operation efficiency. Advisors/Committee Members: Alvarez Daziano, Ricardo (chair), Renegar, James (committee member).

Subjects/Keywords: Simulation; drivers' behavioral study; Multinomial Logit Model; operation strategy; ride sharing systems; Transportation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Liu, L. (2017). TOWARD A SYSTEMATIC APPROACH TO DRIVERS' BEHAVIORAL STUDY ON MOBILITY-ON-DEMAND RIDE SHARING SYSTEMS. (Masters Thesis). Cornell University. Retrieved from http://hdl.handle.net/1813/56882

Chicago Manual of Style (16th Edition):

Liu, Lan. “TOWARD A SYSTEMATIC APPROACH TO DRIVERS' BEHAVIORAL STUDY ON MOBILITY-ON-DEMAND RIDE SHARING SYSTEMS.” 2017. Masters Thesis, Cornell University. Accessed March 08, 2021. http://hdl.handle.net/1813/56882.

MLA Handbook (7th Edition):

Liu, Lan. “TOWARD A SYSTEMATIC APPROACH TO DRIVERS' BEHAVIORAL STUDY ON MOBILITY-ON-DEMAND RIDE SHARING SYSTEMS.” 2017. Web. 08 Mar 2021.

Vancouver:

Liu L. TOWARD A SYSTEMATIC APPROACH TO DRIVERS' BEHAVIORAL STUDY ON MOBILITY-ON-DEMAND RIDE SHARING SYSTEMS. [Internet] [Masters thesis]. Cornell University; 2017. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/1813/56882.

Council of Science Editors:

Liu L. TOWARD A SYSTEMATIC APPROACH TO DRIVERS' BEHAVIORAL STUDY ON MOBILITY-ON-DEMAND RIDE SHARING SYSTEMS. [Masters Thesis]. Cornell University; 2017. Available from: http://hdl.handle.net/1813/56882


University of Michigan

3. Cai, Hua. Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.

Degree: PhD, Natural Resources and Environment and Environmental Engineering, 2015, University of Michigan

To alleviate fossil fuel use, reduce air emissions, and mitigate climate change, “new mobility” systems start to emerge with technologies such as electric vehicles, multi-modal transportation enabled by information and communications technology, and car/ride sharing. Current literature on the environmental implications of these emerging systems is often limited by using aggregated travel pattern data to characterize personal mobility dynamics, neglecting the individual heterogeneity. Individual travel patterns affect several key factors that determine potential environmental impacts, including charging behaviors, connection needs between different transportation modes, and car/ride sharing potentials. Therefore, to better understand these systems and inform decision making, travel patterns at the individual level need to be considered. Using vehicle trajectory data of over 10,000 taxis in Beijing, this research demonstrates the benefits of integrating individual travel patterns into environmental assessments through three case studies (vehicle electrification, charging station siting, and ride sharing) focusing on two emerging systems: electric vehicles and ride sharing. Results from the vehicle electrification study indicate that individual travel patterns can impact the environmental performance of fleet electrification. When battery cost exceeds 200/kWh, vehicles with greater battery range cannot continuously improve travel electrification and can reduce electrification rate. At the current battery cost of 400/kWh, targeting subsidies to vehicles with battery range around 90 miles can achieve higher electrification rate. The public charging station siting case demonstrates that individual travel patterns can better estimate charging demand and guide charging infrastructure development. Charging stations sited according to individual travel patterns can increase electrification rate by 59% to 88% compared to existing sites. Lastly, the ride sharing case shows that trip details extracted from vehicle trajectory data enable dynamic ride sharing modeling. Shared taxi rides in Beijing can reduce total travel distance and air emissions by 33% with 10-minute travel time deviation tolerance. Only minimal tolerance to travel time change (4 minutes) is needed from the riders to enable significant ride sharing (sharing 60% of the trips and saving 20% of travel distance). In summary, vehicle trajectory data can be integrated into environmental assessments to capture individual travel patterns and improve our understanding of the emerging transportation systems. Advisors/Committee Members: Xu, Ming (committee member), Adriaens, Peter (committee member), Lynch, Jerome P. (committee member), Keoleian, Gregory A. (committee member), Simon, Carl P. (committee member), Zhu, Ji (committee member).

Subjects/Keywords: Environmental assessments of emerging transportation systems; Individual travel patterns; Electric vehicles; Charging station siting; Ride sharing; Vehicle trajectory data; Civil and Environmental Engineering; Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cai, H. (2015). Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments. (Doctoral Dissertation). University of Michigan. Retrieved from http://hdl.handle.net/2027.42/113510

Chicago Manual of Style (16th Edition):

Cai, Hua. “Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.” 2015. Doctoral Dissertation, University of Michigan. Accessed March 08, 2021. http://hdl.handle.net/2027.42/113510.

MLA Handbook (7th Edition):

Cai, Hua. “Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments.” 2015. Web. 08 Mar 2021.

Vancouver:

Cai H. Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments. [Internet] [Doctoral dissertation]. University of Michigan; 2015. [cited 2021 Mar 08]. Available from: http://hdl.handle.net/2027.42/113510.

Council of Science Editors:

Cai H. Big Data for Urban Sustainability: Integrating Personal Mobility Dynamics in Environmental Assessments. [Doctoral Dissertation]. University of Michigan; 2015. Available from: http://hdl.handle.net/2027.42/113510

.