Written in English
This thesis presents the conceptual development and an operational prototype of an innovative modelling framework for the transit assignment problem, structured in a multi-agent way and inspired by a learning-based approach. The proposed framework is based on representing passengers and both their learning and decision-making activities explicitly. The underlying hypothesis is that individual passengers are expected to adjust their behaviour according to their knowledge and experience with the transit system performance. An operational prototype was implemented to model the transit assignment process in the morning peak period. Using Reinforcement Learning to represent passenger"s behavioural adaptation and accounting for differences in passenger"s preferences and the dynamics of the transit network, the prototype has demonstrated that the proposed approach can simultaneously predict how passengers dynamically choose their routes and home departure time, and estimate the total passenger travel cost in a congested network, as well as loads on different transit routes.
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A new modelling framework for the transit assignment problem: A multi-agent learning-based approach: Summer Austin Shih: Mode split in the GTA: long range temporal trends and underlying travel behaviour: Summer Mily Parveen: Calibration of the aggregate transit assignment model using Genetic Algorithms: Summer Tao YeAuthor: Keenan Dixon. Abstract. In this chapter, the different basic assumptions for the development of assignment models to transit networks (frequency-based, schedule-based) are presented together with the possible approaches to the simulation of the dynamic system (steady state, Cited by: We propose a new formulation for the assignment problem over congested transit networks. The congestion effects due to insufficient capacity of system elements (transit lines) are considered to be concentrated at transit stops. Waiting times on access links are therefore dependent on passenger flows. A special formulation of the transit network is used in order to model correctly the Cited by: Schedule-based transit assignment: a new dynamic equilibrium model with explicit vehicle capacity constraints. in many modern cities the problem of full transit vehicles is becoming more and more relevant, both for the urban metro system and for the Transit Modelling (SBDTM). The most natural and well established modelling approach.
A Case Study for Travel Demand Management Measures --On-Request Urban Transport Parallel Optimization --A General Multi-agent Modelling Framework for the Transit Assignment Problem --A Learning-Based Approach --OLSIM: Inter-urban Traffic Information. Series Title: Lecture notes in computer science, Responsibility. Schedule-Based Modeling of Transportation Networks: Theory and Applications follows the book Schedule-Based Dynamic Transit Modeling, published in this series in , recognizing the critical role that schedules play in transportation systems.. Conceived for the simulation of transit systems, in the last few years the schedule-based approach has been expanded and applied to operational. Since , there has been an exponential amount of research completed in the field of transport modelling thereby creating a need for an expanded and revised edition of this book. National transport models have taken on the new modelling methods and there have been theoretical and empirical advances in performance measurement. This paper is devoted to the problem of competitive traffic flow assignment in a green transit network consisting of green and nongreen routes. for the problem of traffic assignment was.
facility locations within the framework of an existing distribution new potential plant, warehouse, or distribution center will require a different allocation of shipments, depending on its own production and shipping costs and the costs of each existing facility. The choice of a new loca-. ACTIVITY-BASED MODELING OF TRAVEL DEMAND Chandra R. Bhat and Frank S. Koppelman Introduction and Scope Since the beginning of civilization, the viability and economic success of communities have been, to a major extent, determined by the . Chapter Nine surveys the use of hyperpaths in operations research and proposes a new paradigm of equilibrium in a capacitated network, with an application to transit assignment. Chapter Ten analyzes the transient states of a system moving towards equilibrium, using the mathematical framework of projected dynamical systems. Transportation in the New Millennium 2 of different network coding protocols and assignment methods. Even when cross-modal impacts are explicit, planners and decision makers often ignore or misinterpret the results. Travel demand models are not designed to predict land use changes resulting from transportation improvements.