A parallelized micro-simulation platform for population and mobility behavior. Application to Belgium.

In this book we aim at developing an agent-based micro-simulation framework for (large) population evolution and mobility behaviour. More specifically we focus on the agents generation and the traffic simulation parts of the platform, and its application to Belgium. Hence we firstly develop a synthetic population generator whose main characteristics are its sample-free nature, its ability to cope with moderate data inconsistencies and different levels of aggregation. We then generate the traffic demand forecasting with a stochastic and flexible activity-based model relying on weak data requirements. Finally, a traffic simulation is completed by considering the assignment of the generated demand on the road network. We give the initial developments of a strategic agent-based alternative to the conventional simulation-based dynamic traffic assignment models

Traffic Flow Modeling with Real-Time Data for On-Line Network Traffic Estimation and Prediction

This research addresses the problem of modeling time-dependent traffic flow with real-time traffic sensor data for the purpose of online traffic estimation and prediction to support ATMS/ATIS in an urban transportation network. The fundamental objectives of this study are to formulate and develop a dynamic traffic flow model driven by real-world observations, which is suitable for mesoscopic type dynamic traffic assignment simulation. A dynamic speed-density relation is identified by incorporating the physical concept in continuum and kinetic models, coupled with the structural formulation of the transfer function model which is used to represent dynamic relationship. The model recognizes the time-lagged response of speed to the influential factors (speed relaxation, speed convection and density anticipation) as well as the potential autocorrelated system noise. The procedures adapted from transfer function theory are presented for the model estimation and speed prediction using the real-time data. Speed prediction is performed by means of minimum mean square error and conditional on the past information. In the context of real-time dynamic traffic assignment simulation operation, a framework based on the rolling-horizon methodology is proposed for the adaptive calibration of dynamic speed-density relations to reflect more recent traffic trends. To deal with the different time scales in the data observation interval and the traffic simulation interval, an approximation procedure is proposed to derive proper impulse responses for traffic simulation. Short term correction procedures, based on feedback control theory, are formulated to identify discrepancies between simulation and real-world observation in order to adjust speed periodically. Numerical tests to evaluate the dynamic model are conducted in a standalone manner firstly and then by integrating the model into a real-time DTA system. The overall conclusion from the results is that the proposed dynamic model is preferable in the context of real-time application to the use of conventional static traffic flow models due to its higher responsiveness and accuracy, although many other aspects remain to be investigated in further steps

Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over conventional OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

Vehicle-based modelling of traffic . Theory and application to environmental impact modelling

This dissertation addresses vehicle-based approaches to traffic flow modelling. Having regard to the inherent dynamic nature of traffic, the investigations are mainly focused on the question, how this is captured by different model classes. In the first part, the dynamics of a microscopic car-following model (SKM), presented in, is studied by means of computer simulations and analytical calculations. A classification of the model's behaviour is given with respect to the stability of high-flow states and the outflow from jam. The effects of anticipatory driving on the model's dynamics is explored, yielding results valid in general for this model class. In the second part, a new approach is introduced based on queueing theory. It can be regarded as a microscopic implementation of a state-dependent queueing model, using coupled queues where the service rates additionally depend on the conditions downstream. The concept is shown to reproduce the dynamics of free flow and wide-moving jams. This is demonstrated by comparison with the SKM and real world measurements. An analytical treatment is given as well. The phenomena of boundary induced phase transitions is further addressed, giving the complete phase diagrams of both models. Finally, the application of the queueing approach within simulation-based traffic assignment is demonstrated in regard to environmental impact modelling

Using the general link transmission model in a dynamic traffic assignment to simulate congestion on urban networks

This article presents two new models of Dynamic User Equilibrium that are particularly suited for ITS applications, where the evolution of vehicle flows and travel times must be simulated on large road networks, possibly in real-time. The key feature of the proposed models is the detail representation of the main congestion phenomena occurring at nodes of urban networks, such as vehicle queues and their spillback, as well as flow conflicts in mergins and diversions. Compared to the simple word of static assignment, where only the congestion along the arc is typically reproduced through a separable relation between vehicle flow and travel time, this type of DTA models are much more complex, as the above relation becomes non-separable, both in time and space. Traffic simulation is here attained through a macroscopic flow model, that extends the theory of kinematic waves to urban networks and non-linear fundamental diagrams: the General Link Transmission Model. The sub-models of the GLTM, namely the Node Intersection Model, the Forward Propagation Model of vehicles and the Backward Propagation Model of spaces, can be combined in two different ways to produce arc travel times starting from turn flows. The first approach is to consider short time intervals of a few seconds and process all nodes for each temporal layer in chronological order. The second approach allows to consider long time intervals of a few minutes and for each sub-model requires to process the whole temporal profile of involved variables. The two resulting DTA models are here analyzed and compared with the aim of identifying their possible use cases. A rigorous mathematical formulation is out of the scope of this paper, as well as a detailed explanation of the solution algorithm. The dynamic equilibrium is anyhow sought through a new method based on Gradient Projection, which is capable to solve both proposed models with any desired precision in a reasonable number of iterations. Its fast convergence is essential to show that the two proposed models for network congestion actually converge at equilibrium to nearly identical solutions in terms of arc flows and travel times, despite their two diametrical approaches wrt the dynamic nature of the problem, as shown in the numerical tests presented here

Network emulation focusing on QoS-Oriented satellite communication

This chapter proposes network emulation basics and a complete case study of QoS-oriented Satellite Communication

A hybrid cross entropy algorithm for solving dynamic transit network design problem

This paper proposes a hybrid multiagent learning algorithm for solving the dynamic simulation-based bilevel network design problem. The objective is to determine the op-timal frequency of a multimodal transit network, which minimizes total users' travel cost and operation cost of transit lines. The problem is formulated as a bilevel programming problem with equilibrium constraints describing non-cooperative Nash equilibrium in a dynamic simulation-based transit assignment context. A hybrid algorithm combing the cross entropy multiagent learning algorithm and Hooke-Jeeves algorithm is proposed. Computational results are provided on the Sioux Falls network to illustrate the perform-ance of the proposed algorithm