Preface

Intelligent Transportation Systems (ITS) have evolved enormously within the last three decades. More recently the rapid growth in new technologies has allowed for the practical implementation of more interactive and pervasive ITS solutions. Because of the important role the transportation systems play in society and economy, industry has engaged actively as a great promoter of ITS’ technological development and governments all over the world are prioritizing mobility as a key ingredient of their social and economic growth. Increasingly, ITS developments become not solely technological, but are across a wide span of different disciplines.

Despite of the exponential growth in computer power and communication technologies underlying ITS, meeting the future challenges in transportation will require focusing on social and environmental aspects where user preferences are a central concern. Human interactions with ITS solutions have now gained a new meaning. As technology is able to behave more intelligently than before, services become peers of users: they perceive, make decisions and reason about the results of their actions, all the while seeking to benefit all parties. In fact, rather than increasing service capacity, one underlying approach of ITS-based solutions nowadays is to ensure productivity and mobility by making better use of existing infrastructure and services, furnishing them with smarter, greener, safer, and more efficient solutions.

The complexity of contemporary ITS technology and its wider social and environmental impacts demand new modeling paradigms that incorporate cooperation and collaboration among intervening parties, and support the design and practical deployment of future mobility solutions. In response to the need to understand the interplay between technology and social interactions, Prof. Fei-Yue Wang proposed the concept of Artificial Transportation Systems (ATS), just over a decade ago, during the 2003 IEEE International Conference on Intelligent Transportation Systems. With the ability to integrate different transportation models and solutions in a virtual environment, ATS are an extension to traditional modeling and simulation methodologies that deal with transportation issues from the complex systems perspective and in a systematic and synthetic way. They provide a natural platform where new approaches can be experimented while avoiding natural drawbacks of dealing directly with real-life critical domains. Building on the theories and metaphors developed in a wide spectrum of disciplines, spanning from social sciences, artificial intelligence, and multi-agent systems, to distributed computing and virtual reality, many important issues arise in ATS that challenge and motivate researchers and practitioners from multidisciplinary technical and scientific backgrounds.

Inspired by the concept, IEEE ITS Society soon after created and has since hosted the Technical Activities Sub-committee on Artificial Transportation Systems and Simulation (ATSS) with the mission of motivating and promoting research and practice in ATSS. As part of this effort, a series of successful biennial ATSS Workshops has been organized by this committee and integrated in scientific programs of IEEE ITSC, the flagship conference series of the society. In addition, every other year, alternating with ATSS workshops, a series of special sessions have been organized as part of ITSC to consolidate ideas and trends discussed in previous ATSS workshops. This book is a major outcome of the ATSS sub-committee, bringing to the readers a collection of selected papers presented during the ATSS Workshop held in ITSC 2010, in Madeira, Portugal, and during the ATSS Special Session held in ITSC 2011, in Washington-DC, USA. The papers herein included report on important aspects and technological advances underlying the concept of ATS and establishing the basis for the implementation of appropriate simulation methodologies and tools.

Each chapter in this book consists of an extended and updated version of papers previously presented in the above two ATSS events. A total of 12 papers have been selected that cover different aspects of ATS and span across topics such as simulation tools, modeling methodologies, practical applications, alternative data sources, crowd sensing, and participatory simulation. Starting with tools, Bazzan et al. present ITSUMO, an open-source microscopic traffic simulator whose implementation relies on the agent metaphor. Differently from other similar tools, ITSUMO integrates both demand and control perspectives. It follows a bottom-up behavioral approach, offering sufficient flexibility for the development of different algorithms and techniques to test with route assignment and re-planning, driver behavior, and traffic control strategies and coordination.

Netto et al. discuss issues related to the representation of complex domains, and propose a framework as a reusable solution for building decentralized self-organizing systems, based on major architectural patterns found in the literature. Considering a higher level of abstraction, their approach provides extensibility features to develop new interaction and coordination mechanisms between agents and the environment, which is demonstrated in an automated guided vehicles scenario. Passos et al. also tackle complex system analysis from a multi-agent perspective. In their work, authors analyze the adequacy of traditional approaches in the field of Agent-Oriented Software Engineering to create adequate multi-agent systems in the specific domain of transportation. They devise a novel methodology where the concept of services is considered as peer of agents, ambience, and processes, and becomes prominent elements in the modeling phase. The approach is illustrated in a typical transportation domain scenario. Holmgren et al. explore modeling specificities in the supply chain domain. The authors propose a method based on a framework of supply chain roles, responsibilities, and interactions, which can be used to represent different types of organizations involved in providing and using products and transport services. Their method is illustrated through five different supply chain simulation models, which are analyzed to demonstrate validity and generality of their approach. Also talking about modeling issues, Hanif and Holvoet illustrate how design patterns can support the design of complex agent-based solutions to Pickup and Delivery Problems. In particular, authors use the so-called delegate multi-agent-system patterns to build agent interaction behaviors, and show through simulation that a structured and reusable pattern can significantly reduce the system design and implementation complexity, yet achieve interesting quality characteristics.

Reporting on the applications, Machado et al. demonstrate how the ATS concept can be used in practice as means to assess and evolve the organizational structure performance of airline companies. The authors build on the empirical knowledge gained through interviews with airline operators and develop an analytical framework so as to evaluate current as well as hypothetical organizational structures. To illustrate their approach, real pre- and postoperational data is used to support the simulation of different operation scenarios allowing for different metrics to be analyzed. Neme et al. address issues in modeling pedestrian dynamics and study passengers inside high-capacity buses. Through ATS they observe uneven density distributions leading to high discomfort to the passengers. An agent-based model was devised to represent the interactions between passengers and the bus interior, comprising seats, aisle, and access doors. The authors analyze different schemas and policies so as to gain insight into how to improve passenger comfort on buses. In a different perspective, Almeida et al. study pedestrian dynamics in relation to the behavior of crowds in emergency situations, and present ModP, an agent-based pedestrian simulator. The tool is flexible enough to allow different behaviors to be modeled through a simple syntax, providing designers with a productive environment to rapidly prototype and test with different scenarios. The authors carried out preliminary studies to illustrate usability of their tool.

The implementation of cognitive capabilities in intelligent vehicles interfaces is addressed by Barthès and Bonnifait as a means to allow vehicles to interact collaboratively with their drivers in operating conditions. The interactive interface is built on top of a multi-agent system and tested in an Advanced Driving Assistance System scenario providing speed warnings whenever dangerous areas are approached. The authors test their approach in a real-world scenario resorting to an experimental vehicle. Zhu and Li look into new methods to address transportation problems from new perspectives using the Artificial Societies, Computational Experiments and Parallel Execution approach, built upon the ATS premises. The authors emphasize on synthesizing artificial societies and modeling environmental impacts, whereas their architectural approach relies on a cloud computing infrastructure. They illustrate the concept with an ATS case study and present preliminary results of their methodology. In addition, Zlatkovic et al. illustrate one important premise of ATS, namely its ability to allow for software- and hardware-in-the-loop simulations. Their work present and discuss an implementation of software-in-the-loop (SIL) simulation of the Advanced System Controller series 3 (ASC/3) in transit signal priority scenarios. The authors test two options of ASC/3 using a VISSIM simulation model of a bus rapid transit solution in West Valley City, Utah. Results are encouraging and demonstrate...