Simulating the actions of commuters using a multi-agent system

The activity of commuting to and from a place of work affects not only those travelling but also wider society through their contribution to congestion and pollution. It is desirable to have a means of simulating commuting in order to allow organisations to predict the effects of changes to working patterns and locations and inform decision making. In this paper we outline an agent-based software framework that combines real-world data from multiple sources to simulate the actions of commuters. We demonstrate the framework using data supplied by an employer based in the City of Edinburgh UK. We demonstrate that the BDI-inspired decision making framework used is capable of forecasting the transportation modes to be used. Finally we present a case study, demonstrating the use of the framework to predict the impact of moving staff within the organisation to a new work site

Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data

Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains---all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of $\sim10\%$ leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for analytical modelling when such data are not empirically available, as is often the case.Comment: 10 pages, 5 figures, submitted to International Conference on Computational Science 201

An agent-based approach to assess drivers’ interaction with pre-trip information systems.

This article reports on the practical use of a multi-agent microsimulation framework to address the issue of assessing drivers’ responses to pretrip information systems. The population of drivers is represented as a community of autonomous agents, and travel demand results from the decision-making deliberation performed by each individual of the population as regards route and departure time. A simple simulation scenario was devised, where pretrip information was made available to users on an individual basis so that its effects at the aggregate level could be observed. The simulation results show that the overall performance of the system is very likely affected by exogenous information, and these results are ascribed to demand formation and network topology. The expressiveness offered by cognitive approaches based on predicate logics, such as the one used in this research, appears to be a promising approximation to fostering more complex behavior modelling, allowing us to represent many of the mental aspects involved in the deliberation process

The developing field of integrated vehicle health management

The goals that are being set for aviation growth in the near future, combined with the growth in service provision, are unattainable without active health management of airplanes. Numbers associated with door to door travel time and accident rates, coupled with availability demands to provide cost-effective transport, simply do not allow time for unscheduled maintenance. We are therefore going to experience a step jump in the take up of Integrated Vehicle Health Management (IVHM) on these platforms in order to give accurate warning of sub-system and component degradation, allowing for maintenance to be carried out in a timely, scheduled, manner. This paper describes the development of IVHM, covering emerging services, standards, technology and IVHM as used in various industry sectors. This will lead to the commercial picture of today with the top level goals that are being set, providing the business push for technology and its adoption. Examples of research being conducted in the field will be shown, to support the claim that real progress is being made, with implementation of this technology on the horizon.http://www.aerojournalindia.com/journal.htm

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

The efficiency of individual optimization in the conditions of competitive growth

The paper aims to discuss statistical properties of the multi-agent based model of competitive growth. Each of the agents is described by growth (or decay) rule of its virtual "mass" with the rate affected by the interaction with other agents. The interaction depends on the strategy vector and mutual distance between agents and both are subjected to the agent's individual optimization process. Steady-state simulations yield phase diagrams with the high and low competition phases (HCP and LCP, respectively) separated by critical point. Particular focus has been made on the indicators of the power-law behavior of the mass distributions with respect to the critical regime. In this regime the study has revealed remarkable anomaly in the optimization efficiency