Analysis of the Co-routing Problem in Agent-based Carpooling Simulation

AbstractCarpooling can cut costs and help to solve congestion problems but does not seem to be popular. Behavioral models allow to study the incentives and inhibitors for carpooling and the aggregated effect on the transportation system. In activity based modeling used for travel forecasting, cooperation between actors is important both for schedule planning and revision. Carpooling requires cooperation while commuting which in turn involves co-scheduling and co-routing. The latter requires combinatorial optimization. Agent-based systems used for activity based modeling, contain large amounts of agents. The agent model requires helper algorithms that deliver high quality solutions to embedded optimisation problems using a small amount of resources. Those algorithms are invoked thousands of times during agent society evolution and schedule execution simulation. Solution quality shall be sufficient in order to guarantee realistic agent behavior. This paper focuses on the co-routing problem

Doctor of Philosophy

dissertationData-driven analytics has been successfully utilized in many experience-oriented areas, such as education, business, and medicine. With the profusion of traffic-related data from Internet of Things and development of data mining techniques, data-driven analytics is becoming increasingly popular in the transportation industry. The objective of this research is to explore the application of data-driven analytics in transportation research to improve the traffic management and operations. Three problems in the respective areas of transportation planning, traffic operation, and maintenance management have been addressed in this research, including exploring the impact of dynamic ridesharing system in a multimodal network, quantifying non-recurrent congestion impact on freeway corridors, and developing infrastructure sampling method for efficient maintenance activities. First, the impact of dynamic ridesharing in a multimodal network is studied with agent-based modeling. The competing mechanism between dynamic ridesharing system and public transit is analyzed. The model simulates the interaction between travelers and the environment and emulates travelers' decision making process with the presence of competing modes. The model is applicable to networks with varying demographics. Second, a systematic approach is proposed to quantify Incident-Induced Delay on freeway corridors. There are two particular highlights in the study of non-recurrent congestion quantification: secondary incident identification and K-Nearest Neighbor pattern matching. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level. Lastly, a high-dimensional clustering-based stratified sampling method is developed for infrastructure sampling. The stratification process consists of two components: current condition estimation and high-dimensional cluster analysis. High-dimensional cluster analysis employs Locality-Sensitive Hashing algorithm and spectral sampling. The proposed method is a potentially useful tool for agencies to effectively conduct infrastructure inspection and can be easily adopted for choosing samples containing multiple features. These three examples showcase the application of data-driven analytics in transportation research, which can potentially transform the traffic management mindset into a model of data-driven, sensing, and smart urban systems. The analytic

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