A Flexible Framework for Developing Integrated Models of Transportation Systems Using an Agent-based Approach

AbstractTravel demand, traffic flow and land-use models are typically modeled in a decoupled way, i.e. each of the components is modeled separately assuming that parameters related to the other components are fixed. Moreover, the models are often developed by different groups for different contexts, requirements, etc. In this paper we present a prototype of a software framework which allows the user to develop an integrated simulation of a transportation system and also to link additional models to the new simulation in a standardized way. We use an agent-based approach as the basis of such a model. Integrated transportation system models allow model users to overcome the limitations of traditional aggregated, independent transportation models, particularly with respect to sensitivity to behavioral aspects of the travelers. Another requirement, which the software is to satisfy, is the interoperability of models developed in the new framework with legacy models. By interoperability we mean, that any component of the of the model can be interchanged by a legacy software and be used for the integrated simulation. This would allow disparate research groups working on modeling different aspects of a transportation model to plugnplay their models into the framework and test those as a part of an integrated model of an entire system, providing a benefit to researchers, modelers and institutional users of such models

Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...

There are indications that the current generation of simulation models in practical, operational uses has reached the limits of its usefulness under existing specifications. The relative stasis in operational urban modeling contrasts with simulation efforts in other disciplines, where techniques, theories, and ideas drawn from computation and complexity studies are revitalizing the ways in which we conceptualize, understand, and model real-world phenomena. Many of these concepts and methodologies are applicable to operational urban systems simulation. Indeed, in many cases, ideas from computation and complexity studies—often clustered under the collective term of geocomputation, as they apply to geography—are ideally suited to the simulation of urban dynamics. However, there exist several obstructions to their successful use in operational urban geographic simulation, particularly as regards the capacity of these methodologies to handle top-down dynamics in urban systems. This paper presents a framework for developing a hybrid model for urban geographic simulation and discusses some of the imposing barriers against innovation in this field. The framework infuses approaches derived from geocomputation and complexity with standard techniques that have been tried and tested in operational land-use and transport simulation. Macro-scale dynamics that operate from the topdown are handled by traditional land-use and transport models, while micro-scale dynamics that work from the bottom-up are delegated to agent-based models and cellular automata. The two methodologies are fused in a modular fashion using a system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of residential location has been developed with a view to hybridization. The model mixes cellular automata and multi-agent approaches and is formulated so as to interface with meso-models at a higher scale

The vocational ID : connecting life design counselling and personality systems interaction theory

We introduce the Vocational ID that integrates linguistic and visual representations of a career counselling client’s self. Based upon findings from the Life Design paradigm and the Personality Systems Interaction theory, the Vocational ID facilitates working on clients' vocational identity. In this article, we present the theoretical framework, its practical applications, and a case study

Models of Transportation and Land Use Change: A Guide to the Territory

Modern urban regions are highly complex entities. Despite the difficulty of modeling every relevant aspect of an urban region, researchers have produced a rich variety models dealing with inter-related processes of urban change. The most popular types of models have been those dealing with the relationship between transportation network growth and changes in land use and the location of economic activity, embodied in the concept of accessibility. This paper reviews some of the more common frameworks for modeling transportation and land use change, illustrating each with some examples of operational models that have been applied to real-world settings.Transport, land use, models, review network growth, induced demand, induced supply

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.Comment: 8 page