The impact of future cities on commuting patterns: An agent-based approach

2018 ◽  
Vol 46 (6) ◽  
pp. 1079-1096
Author(s):  
Marcello Marini ◽  
Anna P Gawlikowska ◽  
Andrea Rossi ◽  
Ndaona Chokani ◽  
Hubert Klumpner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Simulation Models ◽  
Single Family ◽  
Simulation Framework ◽  
Agent Based ◽  
Commercial Activities ◽  
Transportation Sector ◽  
Commuting Time ◽  
Commuting Patterns ◽  
The Impact

Over the next 35 years, the population of Switzerland is expected to grow by 25%. One possible way to accommodate this larger population is to transform smaller cities in Switzerland through the direct intervention of urban planners. In this work, we integrate agent-based simulation models of people flow, mobility and urban infrastructure with models of the electricity and gas systems to examine the increase of the density of existing residential zones and the creation of new workplaces and commercial activities in these urban areas. This novel simulation framework is used to assess, for the year 2050, two different scenarios of urbanization in a region with small urban areas. It is shown that a densification scenario, with a preference for multi-dwelling buildings, consumes 93% less land than a sprawl scenario, with a preference for single-family houses. The former scenario also accommodates 27% more people than the latter scenario, as there is a higher penetration of battery electric vehicles – and therefore reduced air pollution from the transportation sector – and also a larger shift of commuters to the use of public transport. However, in the former scenario, the commuting time is 20% longer. The outcome of this work demonstrates how this novel simulation framework can be used to support the formulation of policies that can direct the transformation of urban areas.

"Agent-based simulation of restaurant deliveries facilitating cargo-bikes and urban consolidation"

2018 ◽  
Author(s):  
Christian Fikar ◽  
Manfred Gronalt
Keyword(s):  
Urban Areas ◽  
Optimization Procedure ◽  
Simulation Models ◽  
Food Delivery ◽  
Agent Based ◽  
Last Mile ◽  
Last Mile Delivery ◽  
Cargo Bike ◽  
The Impact ◽  
Urban Consolidation

"Last-mile distribution in urban areas is challenged by congestion and restriction for motorized traffic. To support operations, this work investigate the impact of operating urban consolidation points and facilitating cargo-bikes for urban last-mile distribution. Motivated by sample setting originating from the food delivery industry, a decision support system combining agentbased simulation with heuristic optimization procedure is developed. It considers a logistics provider who performs the last-mile delivery for multiple competing restaurants in an urban area. Therefore, both demand and the availability of cargo-bikes, which are operated by freelancers, are subject to randomness. Computational experiments investigate the impact of the available amount of cargo-bike drivers as well as the number of operated consolidation points, highlighting the importance of facilitating simulation models to support operations in highly dynamic and uncertain settings."

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

Developing Priority Index for Managing Utility Disruptions in Urban Areas with Focus on Cascading and Interdependent Effects

2018 ◽  
Vol 2672 (1) ◽  
pp. 101-112 ◽  
Author(s):  
Srijith Balakrishnan ◽  
Zhanmin Zhang
Keyword(s):  
Urban Areas ◽  
Cyber Attacks ◽  
Service Networks ◽  
Urban Network ◽  
Agent Based ◽  
Priority Index ◽  
Infrastructure Network ◽  
Relief Operations ◽  
A Chain ◽  
The Impact

Unanticipated events such as natural disasters, terrorist attacks, cyber-attacks, and so forth, could cause prolonged disruptions in major utility service networks including, for example, water and electricity, in urban areas. Owing to the presence of complex interdependencies among infrastructure systems in an urban network, the disruption of one system may trigger a chain of events that degrades the proper functioning of several other dependent systems. Consequently, many parts of the city may not have access to multiple utility services and amenities. Identifying the most vulnerable communities exposed to such utility disruptions is key to performing immediate relief operations. In this paper, the concept of Priority Index, introduced as a measure of the susceptibility of communities to the event, is presented to rank urban regions based on the extent of the impact of disruptions (both cascading and interdependent impacts) caused by an event, as well as the social vulnerability of communities. Agent-based models are employed to simulate the consequences of a disruptive event on a semi-realistic urban infrastructure network. Later, the extent of impact on communities is evaluated using the simulation results and the American Community Survey data. The proposed Priority Index could help city administrations and utility service agencies identify the regions in a city that require immediate attention after a disruptive event occurs in the infrastructure network. A case study based on a semi-realistic infrastructure network in Austin, Texas is presented to demonstrate the implementation of the concept of Priority Index and the methodological framework.

Behavior Selection Using Utility-Based Reinforcement Learning in Irregular Warfare Simulation Models

2013 ◽  
Vol 4 (3) ◽  
pp. 61-78 ◽  
Author(s):  
Sotiris Papadopoulos ◽  
Francisco Baez ◽  
Jonathan Alt ◽  
Christian Darken
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Learning ◽  
Simulation Models ◽  
The Body ◽  
Irregular Warfare ◽  
Agent Based ◽  
Body Of Knowledge ◽  
Learning Techniques ◽  
Study Results ◽  
The Impact

The Theory of Planned Behavior (TPB) provides a conceptual model for use in assessing behavioral intentions of humans. Agent based social simulations seek to represent the behavior of individuals in societies in order to understand the impact of a variety of interventions on the population in a given area. Previous work has described the implementation of the TPB in agent based social simulation using Bayesian networks. This paper describes the implementation of the TPB using novel learning techniques related to reinforcement learning. This paper provides case study results from an agent based simulation for behavior related to commodity consumption. Initial results demonstrate behavior more closely related to observable human behavior. This work contributes to the body of knowledge on adaptive learning behavior in agent based simulations.

scholarly journals Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors

2019 ◽  
Vol 4 ◽  
pp. 84 ◽  
Author(s):  
Moses Chapa Kiti ◽  
Alessia Melegaro ◽  
Ciro Cattuto ◽  
David James Nokes
Keyword(s):  
Urban Areas ◽  
Respiratory Infections ◽  
Social Contact ◽  
Simulation Models ◽  
Face To Face ◽  
Rural And Urban Areas ◽  
Contact Patterns ◽  
Rural And Urban ◽  
Syncytial Virus ◽  
The Impact

Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.

scholarly journals Modelling Public Transport Accessibility with Monte Carlo Stochastic Simulations: A Case Study of Ostrava

2019 ◽  
Vol 11 (24) ◽  
pp. 7098 ◽  
Author(s):  
Jiri Horak ◽  
Jan Tesla ◽  
David Fojtik ◽  
Vit Vozenilek
Keyword(s):  
Monte Carlo ◽  
Public Transport ◽  
Simulation Models ◽  
Stochastic Simulations ◽  
Time Interval ◽  
Real World Data ◽  
Transport Modelling ◽  
Transport Accessibility ◽  
Commuting Time ◽  
The Impact

Activity-based micro-scale simulation models for transport modelling provide better evaluations of public transport accessibility, enabling researchers to overcome the shortage of reliable real-world data. Current simulation systems face simplifications of personal behaviour, zonal patterns, non-optimisation of public transport trips (choice of the fastest option only), and do not work with real targets and their characteristics. The new TRAMsim system uses a Monte Carlo approach, which evaluates all possible public transport and walking origin–destination (O–D) trips for k-nearest stops within a given time interval, and selects appropriate variants according to the expected scenarios and parameters derived from local surveys. For the city of Ostrava, Czechia, two commuting models were compared based on simulated movements to reach (a) randomly selected large employers and (b) proportionally selected employers using an appropriate distance–decay impedance function derived from various combinations of conditions. The validation of these models confirms the relevance of the proportional gravity-based model. Multidimensional evaluation of the potential accessibility of employers elucidates issues in several localities, including a high number of transfers, high total commuting time, low variety of accessible employers and high pedestrian mode usage. The transport accessibility evaluation based on synthetic trips offers an improved understanding of local situations and helps to assess the impact of planned changes.

scholarly journals Study design and protocol for investigating social network patterns in rural and urban schools and households in a coastal setting in Kenya using wearable proximity sensors

2019 ◽  
Vol 4 ◽  
pp. 84 ◽  
Author(s):  
Moses Chapa Kiti ◽  
Alessia Melegaro ◽  
Ciro Cattuto ◽  
David James Nokes
Keyword(s):  
Urban Areas ◽  
Respiratory Infections ◽  
Social Contact ◽  
Simulation Models ◽  
Face To Face ◽  
Rural And Urban Areas ◽  
Contact Patterns ◽  
Rural And Urban ◽  
Syncytial Virus ◽  
The Impact

Background: Social contact patterns shape the transmission of respiratory infections spread via close interactions. There is a paucity of observational data from schools and households, particularly in developing countries. Portable wireless sensors can record unbiased proximity events between individuals facing each other, shedding light on pathways of infection transmission. Design and methods: The aim is to characterize face-to-face contact patterns that may shape the transmission of respiratory infections in schools and households in Kilifi, Kenya. Two schools, one each from a rural and urban area, will be purposively selected. From each school, 350 students will be randomly selected proportional to class size and gender to participate. Nine index students from each school will be randomly selected and followed-up to their households. All index household residents will be recruited into the study. A further 3-5 neighbouring households will also be recruited to give a maximum of 350 participants per household setting. The sample size per site is limited by the number of sensors available for data collection. Each participant will wear a wireless proximity sensor lying on their chest area for 7 consecutive days. Data on proximal dyadic interactions will be collected automatically by the sensors only for participants who are face-to-face. Key characteristics of interest include the distribution of degree and the frequency and duration of contacts and their variation in rural and urban areas. These will be stratified by age, gender, role, and day of the week. Expected results: Resultant data will inform on social contact patterns in rural and urban areas of a previously unstudied population. Ensuing data will be used to parameterize mathematical simulation models of transmission of a range of respiratory viruses, including respiratory syncytial virus, and used to explore the impact of intervention measures such as vaccination and social distancing.

scholarly journals Impact Review Analysis & Scope of Noise Pollution for Energy Harvesting

2021 ◽  
Author(s):  
Arunesh Kumar Singh ◽  
◽  
Shahida Khatoon ◽  
Kriti Kriti ◽  
Abhinav Saxena ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Urban Areas ◽  
Noise Pollution ◽  
Electrical Energy ◽  
Clean Energy ◽  
Pollution Level ◽  
Energy Scavenging ◽  
Electricity Grid ◽  
Commercial Activities ◽  
The Impact

Process of obtaining energy from the environment can be called as energy scavenging or energy harvesting. In this paper, we explore the scope of scavenging electrical energy from the noise pollution present in environment and review various energy harvesting techniques for this purpose. Basically, noise is an unwanted sound that is loud, unpleasant and unexpected. Very high population, industrial, commercial activities and transportation increase the noise pollution level in the environment. In urban areas, transport related noise is the major cause of noise pollution. We know that electricity requirement is increasing day by day. Clean energy resources can help the electricity grid to fulfill the increased requirement without bad consequences. Clean energy does not produce any waste, which can pollute the environment. The various mathematical expressions have shown to minimize the level of noise pollution. With help of empirical formula more electricity can be produced. We reviewed the impact of transportation noise pollution, avoidance methods and simultaneous opportunity to transform it into electrical energy.

scholarly journals Investigation and prediction of open source software evolution using automated parameter mining for agent-based simulation

2021 ◽  
Vol 28 (1) ◽  
Author(s):  
Daniel Honsel ◽  
Verena Herbold ◽  
Stephan Waack ◽  
Jens Grabowski
Keyword(s):  
Simulation Model ◽  
Software Evolution ◽  
Prediction Models ◽  
Structural Evolution ◽  
Simulation Models ◽  
Agent Based Simulation ◽  
Specific Data ◽  
Agent Based ◽  
Project Data ◽  
The Impact

AbstractTo guide software development, the estimation of the impact of decision making on the development process can be helpful in planning. For this estimation, often prediction models are used which can be learned from project data. In this paper, an approach for the usage of agent-based simulation for the prediction of software evolution trends is presented. The specialty of the proposed approach lies in the automated parameter estimation for the instantiation of project-specific simulation models. We want to assess how well a baseline model using average (commit) behavior of the agents (i.e., the developers) performs compared to models where different amount of project-specific data is fed into the simulation model. The approach involves the interplay between the mining framework and simulation framework. Parameters to be estimated include, e.g., file change probabilities of developers and the team constellation reflecting different developer roles. The structural evolution of software projects is observed using change coupling graphs based on common file changes. For the validation of simulation results, we compare empirical with simulated results. Our results showed that an average simulation model can mimic general project growth trends like the number of commits and files well and thus, can help project managers in, e.g., controlling the onboarding of developers. Besides, the simulated co-change evolution could be improved significantly using project-specific data.

scholarly journals Is Agent-Based Simulation a Valid Tool for Studying the Impact of Nature-Based Solutions on Local Economy? A Case Study of Four European Cities

2021 ◽  
Vol 13 (13) ◽  
pp. 7466
Author(s):  
Rembrandt Koppelaar ◽  
Antonino Marvuglia ◽  
Lisanne Havinga ◽  
Jelena Brajković ◽  
Benedetto Rugani
Keyword(s):  
Social Life ◽  
Local Economy ◽  
Green Spaces ◽  
Purchasing Behavior ◽  
Agent Based ◽  
European Cities ◽  
Commercial Activities ◽  
Potential Capacity ◽  
The Impact

Implementing nature-based solutions (NBSs) in cities, such as urban forests, can have multiple effects on the quality of life of inhabitants, acting on the mitigation of climate change, and in some cases also enhancing citizens’ social life and the transformation of customer patterns in commercial activities. Assessing this latter effect is the aim of this paper. An agent-based model (ABM) was used to assess change in commercial activities by small and midsize companies in retail due to the development of parks. The paper focuses on the potential capacity of NBS green spaces to boost retail companies’ business volumes, thus increasing their revenues, and at the same time create a pleasant feeling of space usability for the population. The type of NBS is not specified but generalized into large green spaces. The simulation contains two types of agents: (1) residents and (2) shop owners. Factors that attract new retail shops to be established in an area are simplified, based on attractor points, which identify areas such as large green spaces within and around which shops can form. The simulations provided insights on the number of retail shops that can be sustained based on the purchasing behavior of citizens that walk in parks. Four European cities were explored: Szeged (Hungary), Alcalá de Henares (Spain), Çankaya Municipality (Turkey) and Milan (Italy). The model allowed analyzing the indirect economic benefit of NBSs (i.e., large green spaces in this case) on a neighborhood’s economic structure. More precisely, the presence of green parks in the model boosted the visits of customers to local small shops located within and around them, such as cafés and kiosks, allowing for the emergence of 5–6 retail shops (on average, for about 800 walking citizens) in the case of Szeged and an average 12–14 retail shops for a simulated population of 2900 persons that walk in parks in the case of Milan. Overall, results from this modeling exercise can be considered representative for large urban green areas usually visited by a substantial number of citizens. However, their pertinence to support for local policies for NBS implementation and other decision-making related activities of socioeconomic nature is hampered by the low representativeness of source data used for the simulations.

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