Agent-based Simulation Model for Long-term Carpooling: Effect of Activity Planning Constraints

AbstractIn order to commute by carpooling, individuals need to communicate, negotiate and coordinate, and in most cases adapt their daily schedule to enable cooperation. Through negotiation, agents (individuals) can reach complex agreements in an iterative way, which meets the criteria for the successful negotiation. The procedure of negotiation and trip execution in the long-term carpooling consists of a number of steps namely; (i) decision to carpool, (ii) exploration and communication, (iii) negotiation, (iv) coordination and schedule adaptation, (v) long term trip execution (carpooling), (vi) negotiation during carpooling and (vii) carpool termination and exploration for new carpool. This paper presents a conceptual design of an agent-based model (ABM) of a set of candidate carpoolers. A proof of concept implementation is presented. The proposed model is used for simulating the interactions between autonomous agents. The model enables communication to trigger the negotiation process; it measures the effect of pick-drop and shopping activities on the carpooling trips. Carpooling for commuting is simulated: we consider a set of two intermediate trips (home-to-work and work-to-home) for the long-term carpooling. Schedule adaptation during negotiation depends on personal preferences. Trip timing and duration are crucial factors. We carried out a validation study of our results with real data (partial) collected in Flanders, Belgium. Simulation results show the effect of constraining activities on the carpooling trips. The future research will mainly focus on enhancing the mechanisms for communication and negotiation between agents

An Alternative Approach to Network Demand Estimation: Implementation and Application in Multi-Agent Transport Simulation (MATSim)

AbstractThis paper introduces a novel network demand estimation framework consistent with the input data structure requirements of Multi-Agent Transport Simulation (MATSim). The sources of data are the American Community Survey, US Census Bureau, National Household Travel Surveys, travel surveys from South East Florida Regional Planning Authority, OpenStreetMap and Florida Statewide Transportation Engineering Warehouse for Archived Regional Database. The developed framework employs mathematical and statistical methods to derive probability density functions and multinomial logit models for activity and location choices. The implementation of demand estimation process resulted into the creation of 1,200,889 agents (only those using cars). The scenario for the estimated agents was configured and simulated in MATSim. The results from the simulated scenario resulted in the expected morning, afternoon and evening traffic patterns as well as the desirable level of agreement between simulated and observed traffic volumes

Multi-Agent Systems

This Special Issue ""Multi-Agent Systems"" gathers original research articles reporting results on the steadily growing area of agent-oriented computing and multi-agent systems technologies. After more than 20 years of academic research on multi-agent systems (MASs), in fact, agent-oriented models and technologies have been promoted as the most suitable candidates for the design and development of distributed and intelligent applications in complex and dynamic environments. With respect to both their quality and range, the papers in this Special Issue already represent a meaningful sample of the most recent advancements in the field of agent-oriented models and technologies. In particular, the 17 contributions cover agent-based modeling and simulation, situated multi-agent systems, socio-technical multi-agent systems, and semantic technologies applied to multi-agent systems. In fact, it is surprising to witness how such a limited portion of MAS research already highlights the most relevant usage of agent-based models and technologies, as well as their most appreciated characteristics. We are thus confident that the readers of Applied Sciences will be able to appreciate the growing role that MASs will play in the design and development of the next generation of complex intelligent systems. This Special Issue has been converted into a yearly series, for which a new call for papers is already available at the Applied Sciences journal’s website: https://www.mdpi.com/journal/applsci/special_issues/Multi-Agent_Systems_2019

Identificiranje relevantnih čimbenika primjene tehnologija za potrebe dinamičkog Carpoolinga

Development and overall application of numerous advanced technologies (and services) affects significantly the improvement and availability of information with the aim of connecting the users of the carpooling system by passenger cars. Dynamic carpooling supported by Information-Communication (ICTS) and Location-Navigation Technologies and Services (LNTS) represents the upgrade of the traditional and casual carpooling and the modern method of connecting the users. In order to realize a better connection of users, the paper analyzes the characteristics of key software applications that can improve carpooling functioning. A systemic approach (according to research phases) allows identification of relevant factors of applying the technology for the needs of dynamic carpooling. The survey method was used to determine the needs of carpooling users within the traffic environment (traffic network). The aim of the carried out research is to identify the relevant factors of applying the technologies in order to connect the carpooling users and the total development of dynamic carpooling. The contribution of the paper is reflected in determining the relevant factors of applying the technologies for the needs of carpooling development as alternative and sustainable transportation mode.Razvoj i sveopća primjena brojnih suvremenih tehnologija (i usluga) znatno utječe na unaprjeđenje i dostupnost informacija u cilju povezivanja korisnika sustava zajedničkih vožnji osobnim vozilima (carpooling). Dinamički carpooling podržan informacijsko-komunikacijskim (ICTS) i lokacijsko-navigacijskim tehnologijama i uslugama (LNTS) predstavlja nadogradnju tradicionalnog i povremenog carpoolinga te suvremeni način povezivanja korisnika. U cilju kvalitetnijeg povezivanja korisnika u radu se analiziraju karakteristike ključnih programskih aplikacija koje mogu poboljšati funkcioniranje carpoolinga. Sustavnim pristupom (prema fazama istraživanja) omogućava se prepoznavanje relevantnih čimbenika primjene tehnologija za potrebe dinamičkog carpoolinga. Metodom anketiranja utvrđuju se potrebe korisnika carpoolinga unutar prometnog okruženja (prometne mreže). Cilj provedenog istraživanja je identificiranje relevantnih čimbenika primjene tehnologija za potrebe povezivanja korisnika carpoolinga i ukupnog razvoja dinamičkog carpoolinga. Doprinos članka ogleda se u utvrđivanju relevantnih čimbenika primjene tehnologija za potrebe razvoja carpoolinga kao alternativnog i održivog načina prijevoza

Managing distributed situation awareness in a team of agents

The research presented in this thesis investigates the best ways to manage Distributed Situation Awareness (DSA) for a team of agents tasked to conduct search activity with limited resources (battery life, memory use, computational power, etc.). In the first part of the thesis, an algorithm to coordinate agents (e.g., UAVs) is developed. This is based on Delaunay triangulation with the aim of supporting efficient, adaptable, scalable, and predictable search. Results from simulation and physical experiments with UAVs show good performance in terms of resources utilisation, adaptability, scalability, and predictability of the developed method in comparison with the existing fixed-pattern, pseudorandom, and hybrid methods. The second aspect of the thesis employs Bayesian Belief Networks (BBNs) to define and manage DSA based on the information obtained from the agents' search activity. Algorithms and methods were developed to describe how agents update the BBN to model the system’s DSA, predict plausible future states of the agents’ search area, handle uncertainties, manage agents’ beliefs (based on sensor differences), monitor agents’ interactions, and maintains adaptable BBN for DSA management using structural learning. The evaluation uses environment situation information obtained from agents’ sensors during search activity, and the results proved superior performance over well-known alternative methods in terms of situation prediction accuracy, uncertainty handling, and adaptability. Therefore, the thesis’s main contributions are (i) the development of a simple search planning algorithm that combines the strength of fixed-pattern and pseudorandom methods with resources utilisation, scalability, adaptability, and predictability features; (ii) a formal model of DSA using BBN that can be updated and learnt during the mission; (iii) investigation of the relationship between agents search coordination and DSA management