Optimization of reservation parking space configurations in city centers through an agent-based simulation

Mei, Zhenyu; Zhang, Wei; Zhang, Lihui; Wang, Dianhai

doi:10.1016/j.simpat.2019.102020

Cited by 31 publications

(14 citation statements)

References 20 publications

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“…A subset of studies from the third group has investigated how onstreet parking patterns can have an impact on such traffic operations [21]- [23]. To address this issue, these studies used cellular automata models [8], queuing models including the M/M/∞ model [24], M/M/C model [25] and agent-based models [26] to estimate the additional travel cost and traffic congestion caused by on-street parking manoeuvres. Some studies utilised microscopic traffic simulation software, including VISSIM and SUMO (Simulation of Urban Mobility) [19], [27].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Management of On-street Parking Provision for the Autonomous Vehicles Era

Stebbins

Feng

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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The increasing degree of connectivity between vehicles and infrastructure, and the impending deployment of autonomous vehicles (AV) in urban streets, presents unique opportunities and challenges regarding the on-street parking provision for AVs. This study develops a novel simulationoptimisation approach for intelligent curbside management, based on a metaheuristic technique. This approach is tested using an idealised grid layout with a range of flow rates and parking policies. The hybrid method balances curb lanes for driving or parking, aiming to minimise the average traffic delay. Results demonstrate delays decreased by 9%-27% from the benchmark case. Additionally, the delay distribution indicates the trade-offs between expanding road capacity and minimising traffic demand through curb management, demonstrating the interplay between curb parking and traffic management in the AV era.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Management of On-street Parking Provision for the Autonomous Vehicles Era

Stebbins

Feng

et al. 2020

2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)

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“…In this method, an AI algorithm will be integrated with the AB model. Figure 2 shows how the AI algorithm and AB model are related to each other [67]. e AI algorithm gets the current state of the system and, based on this information, finds the best policy for the system.…”

Section: Intelligent Modelingmentioning

confidence: 99%

Optimal Agent Framework: A Novel, Cost-Effective Model Articulation to Fill the Integration Gap between Agent-Based Modeling and Decision-Making

2021

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Making proper decisions in today’s complex world is a challenging task for decision makers. A promising approach that can support decision makers to have a better understanding of complex systems is agent-based modeling (ABM). ABM has been developing during the last few decades as a methodology with many different applications and has enabled a better description of the dynamics of complex systems. However, the prescriptive facet of these applications is rarely portrayed. Adding a prescriptive decision-making (DM) aspect to ABM can support the decision makers in making better or, in some cases, optimized decisions for the complex problems as well as explaining the investigated phenomena. In this paper, first, the literature of DM with ABM is inquired and classified based on the methods of integration. Performing a scientometric analysis on the relevant literature lets us conclude that the number of publications attempting to integrate DM and ABM has not grown during the last two decades, while analysis of the current methodologies for integrating DM and ABM indicates that they have serious drawbacks. In this regard, a novel nature-inspired model articulation called optimal agent framework (OAF) has been proposed to ameliorate the disadvantages and enhance the realization of proper decisions in ABM at a relatively low computational cost. The framework is examined with the Bass diffusion model. The results of the simulation for the customized model developed by OAF have verified the feasibility of the framework. Moreover, sensitivity analyses on different agent populations, network structures, and marketing strategies have depicted the great potential of OAF to find the optimal strategies in various stochastic and unconventional conditions which have not been addressed prior to the implementation of the framework.

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“…It is an agent-based model developed as an ArcGIS extension that is used to simulate the on-street parking behaviour of drivers in Tel Aviv City. Mei, Zhang, Zhang and Wang [22] applied agent-based modelling to manage and configure the parking reservation system by optimising social benefits and parking lot revenue in Hangzhou, China. However, these studies have focused on simulating the dynamics of the whole parking process or the choice of parking facility, but not on car drivers' movement across parking lots.…”

Section: Related Workmentioning

confidence: 99%

An Agent-Based Simulation of an Alternative Parking Bay Choice Strategy

Lall

2020

SAJIE

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Trying to find a parking space in a parking lot is a routine activity for most people in our society. However, in managing the sustained growth rate in the number of vehicle users, the demand on such parking facilities is likely to remain under constant pressure. Both the users and the management of parking lots use various strategies in dealing with the requirement for suitable parking facilities. This paper presents a computational model using an agent-based modelling technique to simulate two commonly used strategies applied to choosing a parking spot under various demand and supply conditions. The model is unique in the current literature, as it is specifically designed to evaluate the effectiveness of the strategies. The analysis of the experimental results shows that, in most conditions, accepting the first available parking spot and then walking to the destination performed better than driving as close as possible to the destination and then searching for an available parking spot.

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Optimization of reservation parking space configurations in city centers through an agent-based simulation

Cited by 31 publications

References 20 publications

Intelligent Management of On-street Parking Provision for the Autonomous Vehicles Era

Intelligent Management of On-street Parking Provision for the Autonomous Vehicles Era

Optimal Agent Framework: A Novel, Cost-Effective Model Articulation to Fill the Integration Gap between Agent-Based Modeling and Decision-Making

An Agent-Based Simulation of an Alternative Parking Bay Choice Strategy

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