scholarly journals MODELLING TEMPORAL SCHEDULE OF URBAN TRAINS USING AGENT-BASED SIMULATION AND NSGA2-BASED MULTIOBJECTIVE OPTIMIZATION APPROACHES

2015 ◽  
Vol XL-1-W5 ◽  
pp. 645-650
Author(s):  
M. Sahelgozin ◽  
A. Alimohammadi
Keyword(s):  
Urban Areas ◽  
Proper Solution ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The Real ◽  
Schedule Optimization ◽  
Passenger Travel ◽  
Proposed Model ◽  
Study Results ◽  
Perform Sensitivity Analysis

Increasing distances between locations of residence and services leads to a large number of daily commutes in urban areas. Developing subway systems has been taken into consideration of transportation managers as a response to this huge amount of travel demands. In developments of subway infrastructures, representing a temporal schedule for trains is an important task; because an appropriately designed timetable decreases Total passenger travel times, Total Operation Costs and Energy Consumption of trains. Since these variables are not positively correlated, subway scheduling is considered as a multi-criteria optimization problem. Therefore, proposing a proper solution for subway scheduling has been always a controversial issue. On the other hand, research on a phenomenon requires a summarized representation of the real world that is known as Model. In this study, it is attempted to model temporal schedule of urban trains that can be applied in Multi-Criteria Subway Schedule Optimization (MCSSO) problems. At first, a conceptual framework is represented for MCSSO. Then, an agent-based simulation environment is implemented to perform Sensitivity Analysis (SA) that is used to extract the interrelations between the framework components. These interrelations is then taken into account in order to construct the proposed model. In order to evaluate performance of the model in MCSSO problems, Tehran subway line no. 1 is considered as the case study. Results of the study show that the model was able to generate an acceptable distribution of Pareto-optimal solutions which are applicable in the real situations while solving a MCSSO is the goal. Also, the accuracy of the model in representing the operation of subway systems was significant.

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.

scholarly journals A Novel Crowdsourcing Model for Micro-Mobility Ride-Sharing Systems

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4636
Author(s):  
Mohammed Elhenawy ◽  
Mostafizur R. Komol ◽  
Mahmoud Masoud ◽  
Shiqiang Liu ◽  
Huthaifa I. Ashqar ◽  
...  
Keyword(s):  
User Needs ◽  
Computational Results ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Ride Sharing ◽  
Proposed Model ◽  
Micro Mobility

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.

Evaluating Urban Logistics Schemes Using Agent-based Simulation

2020 ◽  
Vol 54 (3) ◽  
pp. 651-675 ◽  
Author(s):  
W. J. A. van Heeswijk ◽  
M. R. K. Mes ◽  
J. M. J. Schutten ◽  
W. H. M. Zijm
Keyword(s):  
Supply Chains ◽  
Urban Areas ◽  
Mathematical Representation ◽  
Extensive Literature ◽  
Literature Study ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Urban Freight ◽  
Urban Freight Transport ◽  
Urban Consolidation

The domain of urban freight transport is becoming increasingly complex. Many urban supply chains are composed of small and independent actors that cannot efficiently organize their highly fragmented supply chains, thereby negatively affecting the quality of life in urban areas. Both companies and local administrators try to improve transport efficiency and reduce external costs, but the effects of such interventions are difficult to predict, especially when applied in combination with each other (an urban logistics scheme). This paper presents an agent-based simulation model to quantify the effects of urban logistics schemes on multiple actors. We provide a detailed mathematical representation in the form of a Markov decision process. Based on an extensive literature study, we aggregate data to represent various actors in typical Western European cities. We perform numerical experiments to obtain insights into urban logistics schemes. The results show that most schemes yield significant environmental improvements but that achieving long-term financial viability is challenging for urban consolidation centers in particular. We also demonstrate that interventions, such as subsidies and access restrictions, do not always yield the intended effect. In a backcasting experiment, we identify conditions and schemes to achieve a financially viable urban consolidation center.

Realistic Spatial Backcloth is not that Important in Agent Based Simulation Research

2011 ◽  
pp. 19-34 ◽  
Author(s):  
Henk Elffers ◽  
Pieter Van Baal
Keyword(s):  
Simulation Model ◽  
Simulation Studies ◽  
Spatial Simulation ◽  
Agent Based Simulation ◽  
Simulation Research ◽  
Agent Based ◽  
The Real ◽  
Gis Tools ◽  
Perceptual Deterrence ◽  
Real City

This chapter considers whether it is worthwhile and useful to enrich agent based spatial simulation studies in criminology with a real geographical background, such as the map of a real city? Using modern GIS tools, such an enterprise is in principle quite feasible, but we argue that in many cases this course is not only not producing more interesting results, but in fact may well be detrimental for the real reason of doing criminal simulation studies, which is understanding the underlying rules. The argument is first outlined in general, and then illustrated in the context of a given example of the ThESE perceptual deterrence simulation model (Van Baal, 2004), a model that actually is using a simple checkerboard as its spatial backcloth.

Modelling and simulation techniques for transport networks within urban areas applied to logistics and resilience

2021 ◽  
Author(s):  
◽  
Pablo Álvarez
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Smart Cities ◽  
Modelling And Simulation ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Simulation Techniques ◽  
New Methodologies

This thesis investigates the use of modelling and simulation techniques in urban areas of smart cities, also exploring how big data can be used to feed these models. These modelling techniques have been applied to two different fields that have been gaining prominence during the last years but where research is still limited: urban logistics and urban resilience. Through this thesis, the author has expanded the research knowledge in these fields by exploring different methods such as meta-heuristics, transport modelling, and agent-based simulation in order to define new methodologies to be applied to urban areas. Regarding logistics, the author has shown through the use of meta-heuristics that when traffic congestion is considered as a dynamic attribute to optimize delivery routes in urban areas, time can be reduced by 11%, which is crucial for logistics companies in a market that is fiercer every day. This is true not only for urban areas, but this research has also demonstrated that optimizing routes with dynamic congestion attributes is also beneficial at a strategic level for routes between cities. To consider congestion costs in real time, a new approach has been developed in which data from Google is downloaded to feed these meta-heuristic models, although other sources of big data could be also used. In this thesis, a methodology is also presented that has been used to model logistics routes in urban areas considering real-time data and with the flexibility to add different network attributes (gradient, traffic bans, CO2, etc.) to simulate different scenarios. This can be useful for logistics companies to optimize their deliveries (choosing between van or tricycles, selecting the time of the day to deliver, etc.) but also for public authorities to get guidance on different transport and urban policies (pedestrianization of some streets, traffic bans, etc.).As for city resilience, the thesis focuses on evacuation planning. A new methodology has been created in which agent-based simulation is used through interconnected sub-models to model a large-scenario evacuation scenario (flooding event as a consequence of a dam collapse). This research defines the data needed to create these models that can be of great help to improve city resilience, and also analyzes how traffic congestion can affect the evacuation procedures. Through the different research articles that compose this thesis, the author brings light to these fields by developing new methodologies and using real case-studies that can help urban planners, companies, and policy makers to create more efficient, sustainable, and resilient smart cities.

CAN HOT SPOTS POLICING REDUCE CRIME IN URBAN AREAS? AN AGENT-BASED SIMULATION*

Criminology ◽  
2017 ◽  
Vol 55 (1) ◽  
pp. 137-173 ◽  
Author(s):  
DAVID WEISBURD ◽  
ANTHONY A. BRAGA ◽  
ELIZABETH R. GROFF ◽  
ALESE WOODITCH
Keyword(s):  
Hot Spots ◽  
Urban Areas ◽  
Agent Based Simulation ◽  
Hot Spots Policing ◽  
Agent Based

Concept for an Adapted BDI Architecture for Agent-Based Simulation of the Socio-Technical System in Manufacturing

2016 ◽  
Vol 1140 ◽  
pp. 419-426
Author(s):  
Kim Schwake ◽  
Jens Wulfsberg
Keyword(s):  
Technical System ◽  
Agent Based Simulation ◽  
Agent Based ◽  
The Real ◽  
Agent Systems ◽  
Bdi Architecture

The predicted results of production simulations often differ considerably from those of the practice. Among other things, reasons for that can be found in the inadequate consideration of the socio-technical system of the real manufacturing. The paper addresses this problem and shows how even complex manufacturing principles such as semi-autonomous groups can be simulated with the help of agent systems using an adapted Belief-Desire-Intention (BDI) architecture concept.

An Agent-Based Simulation Model for Parking Variable Message Sign Location Problem

2018 ◽  
Vol 2672 (19) ◽  
pp. 135-144 ◽  
Author(s):  
Xun-You Ni ◽  
Daniel (Jian) Sun
Keyword(s):  
Urban Areas ◽  
Supply And Demand ◽  
Agent Based Simulation ◽  
Short Supply ◽  
Agent Based ◽  
Average Travel Time ◽  
Parking Lot ◽  
The One ◽  
Message Signs ◽  
Urban Parking

Parking spaces are often in short supply in urban areas. To balance the supply and demand and alleviate the overconsumption of public spaces, parking variable message signs (parking VMSs) are commonly used to release information on space availability to drivers en route. The aim of this study was to find the optimal positions for parking VMSs. To achieve the objective, we first define the major decision point (MDP) as the intersection where the newly generated path deviates from the previous one. When informed that the target parking lot is fully occupied, the driver would divert to an alternative one. The route to the alternative parking lot is indicated as the newly generated path, while the one leading to the original parking lot is denoted as the previous one. Quantitatively, MDPs with the highest frequency of occurrence are selected as the candidate positions. Then, an agent-based simulation is proposed to identify the MDPs induced by changes of space availability and the selection of routes. The results indicate that the proposed location algorithm slightly outperforms the scheme with the completed parking information in terms of average travel time and average travel distance. The algorithm can be further integrated into a simulation package, which may assist in the design and operation of an urban parking guidance and information system.

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