Real-Time Autonomous Taxi Service: An Agent-Based Simulation

2020 ◽  
pp. 199-207
Author(s):  
Negin Alisoltani ◽  
Mahdi Zargayouna ◽  
Ludovic Leclercq
Keyword(s):  
Real Time ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Taxi Service ◽  
Time Autonomous

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.

scholarly journals An agent-based simulation model of dynamic real-time traffic signal controller

2020 ◽  
Vol 10 (46) ◽  
pp. 1-11
Author(s):  
Ahmad Aljaafreh ◽  
Maen Saleh ◽  
Naeem Al-Oudat ◽  
Murad Alaqtash
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
Traffic Signal ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Real Time Traffic

scholarly journals Smart Method for Self-Organization in Last-Mile Parcel Delivery

2020 ◽  
pp. 036119812097606
Author(s):  
J.H.R. van Duin ◽  
T.S. Vlot ◽  
L.A. Tavasszy ◽  
M.B. Duinkerken ◽  
B. van Dijk
Keyword(s):  
Simulation Model ◽  
Real Time ◽  
New Method ◽  
Self Organization ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Last Mile ◽  
Service Areas ◽  
Delivery Services ◽  
Delivery Vehicles

Parcel delivery operators experience an increasing pressure to meet the strongly growing demand for delivery services, while protecting city livability and the environment. Improving the performance of the last mile of delivery is considered key in meeting this challenge as it forms the most inefficient, expensive, and environmentally unfriendly part of delivery operations. A primary cause is a significant duplication of service areas, resulting in redundant vehicle kilometers traveled. In this paper, a new method is presented that allows for the allocation of parcels to delivery vehicles and construction of vehicle routes in real time through an auctioning system. These tasks are performed in a self-organizing manner by vehicles, parcels, and a supporting platform, to allow for collaborative and intermodal delivery. The performance of this new method is tested and compared against the currently used techniques using an agent-based simulation model. The new method manages to greatly improve the efficiency, robustness, and flexibility of delivery operations.

scholarly journals Real-Time Human-In-The-Loop Simulation with Mobile Agents, Chat Bots, and Crowd Sensing for Smart Cities

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4356 ◽  
Author(s):  
Stefan Bosse ◽  
Uwe Engel
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Real Time ◽  
Virtual Worlds ◽  
Real World ◽  
Smart City ◽  
Mobile Agents ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Crowd Sensing

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods.

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