Abstract-Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.
a b s t r a c tLarge-scale urban systems simulations are complex and with a large number of active simulation entities the computational workload is extensive. Workstation computers have only limited capabilities of delivering results for large-scale simulations. This leads to the problem that many researchers and engineers have to either reduce the scope of their experiments or fail to execute as many experiments as they would like in a given time frame. The use of high-performance computing (HPC) infrastructure offers a solution to the problem. Users of such simulations are often domain experts with no or little experience with HPC environments. In addition users do not necessarily have access to an HPC. In this paper we propose an architecture for a cloud-based urban systems simulation platform which specifically aims at making large-scale simulations available to typical users. The proposed architecture also addresses the issue of data confidentiality. In addition we describe the Scalable Electro-Mobility Simulation (SEMSim) Cloud Service that implements the proposed architecture.
The introduction of Autonomous Vehicles (AVs) will have far-reaching effects on road traffic in cities and on highways.The implementation of Automated Highway Systems (AHS), possibly with a dedicated lane only for AVs, is believed to be a requirement to maximise the benefit from the advantages of AVs. We study the ramifications of an increasing percentage of AVs on the traffic system with and without the introduction of a dedicated AV lane on highways. We conduct an analytical evaluation of a simplified scenario and a macroscopic simulation of the city of Singapore under user equilibrium conditions with a realistic traffic demand. We present findings regarding average travel time, fuel consumption, throughput and road usage. Instead of only considering the highways, we also focus on the effects on the remaining road network. Our results show a reduction of average travel time and fuel consumption as a result of increasing the portion of AVs in the system. We show that the introduction of an AV lane is not beneficial in terms of average commute time. Examining the effects of the AV population only, however, the AV lane provides a considerable reduction of travel time (≈ 25%) at the price of delaying conventional vehicles (≈ 7%). Furthermore a notable shift of travel demand away from the highways towards major and small roads is noticed in early stages of AV penetration of the system. Finally, our findings show that after a certain threshold percentage of AVs the differences between AV and no AV lane scenarios become negligible.
In an agent-based traffic simulation the level of detail is crucial to the system's runtime performance as well as the fidelity of the results. Therefore, different model abstractions have been used throughout literature. Macroscopic, mesoscopic and microscopic models have their use-cases and benefits. Microscopic traffic simulations have a high level of detail but at the same time require a large amount of computational resources. In a large traffic network of a mega-city or an entire country, the use of a complete microscopic simulation is just not feasible. The resource required to do so are for most use-cases in no relation to the actual outcome. We propose a hybrid traffic simulation model that uses both, a high-resolution agent-based microscopic simulation alongside a lower resolution flow-based macroscopic simulation for specific road segments. The problem with using different simulation models is the fidelity at the boundary between such simulation models. This fidelity discrepancy is caused by the difficulties with aggregation and disaggregation passing through the boundary. We show, in this paper, that the computational performance (simulation time) can be improved by 20% while maintaining a relative high accuracy of below 5% deviation from a pure microscopic simulation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.