Empirical Analysis of Parking Behaviour of Conventional and Electric Vehicles for Parking Modelling: A Case Study of Beijing, China

An empirical study of the parking behaviour of Conventional Vehicles (CVs), Battery Electric Vehicles (BEVs), and Plug-in Hybrid Electric Vehicles (PHEVs) was carried out with the data collected in a paper-based questionnaire survey in Beijing, China. The study investigated the factors that might influence the parking behaviour, with a focus on the maximum acceptable time of walking from parking lot to trip destination, parking fee, the availability of charging posts, the state of charge of EVs and the range anxiety of BEVs. Several Multinomial Logit (MNL) models were developed to explore the relationships between individual attributes and parking choices. The results suggest that (1) the maximum acceptable walking time generally increases with the rise in the amount of saving for parking fee; (2) the availability of charging posts does not influence the maximum acceptable walking time when PHEVs and BEVs have sufficient charge, but the percentage of people willing to walk longer than eight minutes increases from around 35% to 46% when PHEVs are in a low stage of charge; (3) more than half of BEV drivers want the driving range of their vehicles to be one and a half times the driving distance before they depart, given the distance is 50 km. Based on the empirical findings above, a conceptual framework was proposed to explicitly simulate the parking behaviour of both CVs and EVs using agent-based modelling.

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A Comparative Study of En Route Refuelling Behaviours of Conventional and Electric Vehicles in Beijing, China

Sustainability ◽

10.3390/su11143869 ◽

2019 ◽

Vol 11 (14) ◽

pp. 3869 ◽

Cited By ~ 8

Author(s):

Chengxiang Zhuge ◽

Chunfu Shao ◽

Xia Li

Keyword(s):

Comparative Study ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Waiting Times ◽

Level Of Education ◽

Electric Driving ◽

The Difference ◽

Charging Stations ◽

Beijing China

A comparative study is carried out to investigate the differences among conventional vehicles (CVs), battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) in the maximum acceptable time of diverting to a refuelling station, maximum acceptable time of queueing at a refuelling station, refuelling modes and desirable electric driving ranges, using Beijing, China, as a case study. Here, several multinomial logit (MNL) models are developed to relate the diverting and waiting times to individual attributes. The results suggest that, (1) the diverting time roughly follows a normal distribution for both CVs and electric vehicles (EVs), but the difference between them is slight; (2) EVs tend to bear longer waiting time above 10 min; (3) the MNL models indicate that income and the level of education tend to be more statistically significant to both the diverting and waiting times; (4) the most preferred driving ranges obtained for BEVs and PHEVs are both around 50 km, indicating that EV drivers may just prefer to charge for a specific time ranging from 8 to 10 min. Finally, ways to apply the empirical findings in planning refuelling and charging stations are discussed with specific examples.

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Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China

Energy ◽

10.1016/j.energy.2018.12.061 ◽

2019 ◽

Vol 169 ◽

pp. 833-843 ◽

Cited By ~ 12

Author(s):

Jun Bi ◽

Yongxing Wang ◽

Qiuyue Sai ◽

Cong Ding

Keyword(s):

Electric Vehicles ◽

Real World ◽

Real World Data ◽

World Data ◽

Battery Electric Vehicles ◽

Driving Range ◽

Beijing China

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Visualising Ostia’s Processional Landscape Through a Multi-Layered Computational Approach: Case Study of the Cult of the Magna Mater

Open Archaeology ◽

10.1515/opar-2019-0028 ◽

2019 ◽

Vol 5 (1) ◽

pp. 444-467

Author(s):

Katherine A. Crawford

Keyword(s):

Network Analysis ◽

Computational Approach ◽

Urban Network ◽

Agent Based ◽

Agent Based Modelling ◽

Existing Problems ◽

Layered Approach ◽

Religious Landscape ◽

Insight Into

AbstractOstia, the ancient port of Rome, had a rich religious landscape. How processional rituals further contributed to this landscape, however, has seen little consideration. This is largely due to a lack of evidence that attests to the routes taken by processional rituals. The present study aims to address existing problems in studying processions by questioning what factors motivated processional movement routes. A novel computational approach that integrates GIS, urban network analysis, and agent-based modelling is introduced. This multi-layered approach is used to question how spectators served as attractors in the creation of a processional landscape using Ostia’s Campo della Magna Mater as a case study. The analysis of these results is subsequently used to gain new insight into how a greater processional landscape was created surrounding the sanctuary of the Magna Mater.

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A Comparison between Agent-Based and GIS-Based Tsunami Evacuation Simulations: A Case Study for Tofino, BC

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2020-0660 ◽

2021 ◽

Author(s):

Joseph Kim ◽

Tomoyuki Takabatake ◽

Ioan NISTOR ◽

Tomoya Shibayama

Keyword(s):

Information Systems ◽

Geographical Information Systems ◽

Mortality Rates ◽

Geographical Information ◽

Evacuation Planning ◽

Loss Of Life ◽

Agent Based ◽

Agent Based Modelling ◽

Validation Tool

Soft measures such as evacuation planning are recommended to mitigate the loss of life during tsunamis. Two types of evacuation models are widely used: (1) Agent-based modelling (ABM) defines sets of rules that individual agents in a simulation follow during a simulated evacuation. (2) Geographical information systems (GIS) are more accessible to city planners, but cannot incorporate the dynamic behaviours found in ABMs. The two evacuation modelling methodologies were compared through a case study by assessing the state of evacuation preparedness and investigating potential mitigation options. The two models showed different magnitudes for mortality rates and facility demand but had similar trends. Both models agreed on the best solution to reduce the loss of life for the community. GIS may serve as a useful tool for initial investigation or as a validation tool for ABMs. ABMs are recommended for use when modelling evacuation until GIS methodologies are further developed.

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Improving the Efficiency of Large-Scale Agent-Based Models Using Compression Techniques

Multidisciplinary Computational Intelligence Techniques ◽

10.4018/978-1-4666-1830-5.ch018 ◽

2012 ◽

pp. 301-326

Author(s):

Mitchell Welch ◽

Paul Kwan ◽

A.S.M. Sajeev ◽

Graeme Garner

Keyword(s):

Large Scale ◽

Agent Based Models ◽

Agent Based ◽

Agent Based Modelling ◽

Livestock Disease ◽

Interacting Agents ◽

National Model ◽

Complex Phenomena ◽

Modelling System

Agent-based modelling is becoming a widely used approach for simulating complex phenomena. By making use of emergent behaviour, agent based models can simulate systems right down to the most minute interactions that affect a system’s behaviour. In order to capture the level of detail desired by users, many agent based models now contain hundreds of thousands and even millions of interacting agents. The scale of these models makes them computationally expensive to operate in terms of memory and CPU time, limiting their practicality and use. This chapter details the techniques for applying Dynamic Hierarchical Agent Compression to agent based modelling systems, with the aim of reducing the amount of memory and number of CPU cycles required to manage a set of agents within a model. The scheme outlined extracts the state data stored within a model’s agents and takes advantage of redundancy in this data to reduce the memory required to represent this information. The techniques show how a hierarchical data structure can be used to achieve compression of this data and the techniques for implementing this type of structure within an existing modelling system. The chapter includes a case study that outlines the practical considerations related to the application of this scheme to Australia’s National Model for Emerging Livestock Disease Threats that is currently being developed.

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Agent-Based Modelling in Multicellular Systems Biology

Data Analytics in Medicine ◽

10.4018/978-1-7998-1204-3.ch020 ◽

2020 ◽

pp. 369-389

Author(s):

Sara Montagna ◽

Andrea Omicini

Keyword(s):

Developmental Biology ◽

Drosophila Melanogaster ◽

Systems Biology ◽

Computational Models ◽

The Self ◽

Agent Based Simulation ◽

Agent Based ◽

Agent Based Modelling ◽

Multi Agent

This chapter aims at discussing the content of multi-agent based simulation (MABS) applied to computational biology i.e., to modelling and simulating biological systems by means of computational models, methodologies, and frameworks. In particular, the adoption of agent-based modelling (ABM) in the field of multicellular systems biology is explored, focussing on the challenging scenarios of developmental biology. After motivating why agent-based abstractions are critical in representing multicellular systems behaviour, MABS is discussed as the source of the most natural and appropriate mechanism for analysing the self-organising behaviour of systems of cells. As a case study, an application of MABS to the development of Drosophila Melanogaster is finally presented, which exploits the ALCHEMIST platform for agent-based simulation.

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Simulation of Future Electric Vehicle Charging Behavior—Effects of Transition from PHEV to FEV

World Electric Vehicle Journal ◽

10.3390/wevj10020042 ◽

2019 ◽

Vol 10 (2) ◽

pp. 42 ◽

Cited By ~ 2

Author(s):

Igna Vermeulen ◽

Jurjen Rienk Helmus ◽

Mike Lees ◽

Robert van den Hoed

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Hybrid Electric Vehicles ◽

Early Years ◽

Charging Infrastructure ◽

Agent Based ◽

Electric Vehicle Charging ◽

Subsidy Scheme ◽

Infrastructure Performance ◽

Charging Stations

The Netherlands is a frontrunner in the field of public charging infrastructure, having one of the highest number of public charging stations per electric vehicle (EV) in the world. During the early years of adoption (2012–2015), a large percentage of the EV fleet were plugin hybrid electric vehicles (PHEV) due to the subsidy scheme at that time. With an increasing number of full electric vehicles (FEVs) on the market and a current subsidy scheme for FEVs only, a transition of the EV fleet from PHEV to FEV is expected. This is hypothesized to have an effect on the charging behavior of the complete fleet, and is reason to understand better how PHEVs and FEVs differ in charging behavior and how this impacts charging infrastructure usage. In this paper, the effects of the transition of PHEV to FEV is simulated by extending an existing agent-based model. Results show important effects of this transition on charging infrastructure performance.

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