.png){kind=icon}
Return
Evaluating Automated Demand Responsive Transit Using Microsimulation
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2991154.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Recent advancements in automated vehicle technology and the concurrent emergence of ride-hailing services have focused increasing attention on Automated Mobility-on-Demand (AMOD; a system of shared driverless taxis) as a potential solution for sustainable future urban mobility. However, the impacts of an unrestricted deployment of AMOD are as yet uncertain and likely to be context-specific; evidence with existing on-demand services suggests that they may lead to the cannibalization of mass-transit and increased traffic congestion. In this context, automated demand-responsive transit (also termed microtransit), which provides similar on-demand services (stop-to-stop or curbside) through higher capacity vehicles, may prove to be a promising substitute and/or complement. In this study, we evaluate the performance of such an automated demand response transit system (hereafter AMOD minibus) through agent-based simulations of the Singapore network. Towards this end, we extend SimMobility (an agent- and activity-based microsimulation laboratory) with the capability of modeling an AMOD minibus service including demand, supply and their interactions. On the demand side, we use an activity-based model system that draws on data from a stated-preferences survey conducted in Singapore. On the supply side, an insertion heuristic is applied to dynamically perform both the assignment of requests to vehicles and vehicle routing. Scenario simulations on the Singapore network (with an area-wide deployment of the AMOD services) indicate the potential benefits of an automated demand responsive transit service for local circulation, which can result in a reduction of Vehicle Kilometres Traveled of up to 50 & x0025; (compared to the AMOD shared taxis) whilst satisfying the same demand, with a modest increase in average travel times.
Keywords:
Agent-based simulation
automated mobility-on-demand (AMOD)
high-capacity ride-sharing
shareability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
Organization
Cited Papers
Interference of colostral antibodies with response to a Bluetongue serotype 8 inactivated vaccine in lambs born from hyperimmune ewes.png)
Vaccine
VaccineIF0
PREAI
ShareSaveShareSaveBiomolecules Electrochemical Sensing Properties of a PMo11V@N-Doped Few Layer Graphene NanocompositeInorganicsOAAI
InorganicsIF0
OAAIShareSaveShareSaveShareSaveShareSaveShareSaveShareSave
View more