Dynamic network loading: A stochastic differentiable model that derives link state distributions

Osorio, Carolina; Flötteröd, Gunnar; Bierlaire, Michel

doi:10.1016/j.trb.2011.05.014

Cited by 59 publications

(43 citation statements)

References 37 publications

(41 reference statements)

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“…Yperman (2007) provides some intuition for this rather formal speci cation. The connection to MATSim can now be made explicit by labeling the two bracketed terms in Equation (50.4) and Equation (50.5) as "upstream queue" (UQ) and "downstream queue" (DQ) (Osorio et al, 2011;Osorio and Flötteröd, published online in Articles in Advance):…”

Section: Link Modelmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

479

123

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PrefaceDeveloping complex so ware for over a decade with a heterogeneous group of engineers and scientists, each with widely di erent skill levels and expertise across multiple locations around the world, requires dedication and mechanisms unusual for a university environment. This book is one of these mechanisms. It allows us, collectively, to take stock and present a coherent state-of-the-system: for us and anyone interested in this approach. It highlights basics for the student who wants to undertake a small rst research project as part of his or her degree, provides a description of the main functionalities, in detail, for the engineer setting up MATSim (MultiAgent Transport Simulation) to conduct a policy analysis and, nally, ts the approach into the theoretical background of complex systems in computer science and physics.The choice of the additional e-book format is an advantage, as it allows us to keep the book upto-date with future chapters, revisions and, if necessary, errata. Equally importantly it allows you, the readers, to select those sections relevant to your needs.The book comes at an important time for the system; for most of the rst decade, its use was limited to the original developers and users in Berlin and Zürich. It is now much more widely consulted around the world, as we document in the chapter summarizing contributions on scenarios so far.Scenario: This term will occur again and again. In MATSim context, it is de ned as the combination of speci c agent populations, their initial plans and activity locations (home, work, education), the network and facilities where, and on which, they compete in time-space for their slots and modules, i.e., behavioral dimensions, which they can adjust during their search for equilibrium. Within these scenarios, the user can experiment and explore with behavioral utility function parameters, with the sampling rate of the population between 1 % and 100 %, with algorithm parameters, e.g., the share of the sample engaged in replanning in any iteration, or behavioral dimensions or exact settings necessary to avoid gridlock due to the tra c ow dynamics. The creation of a scenario is a substantial e ort, and the framework makes a number of tools available to accelerate it: population synthesizers, network editors, network converters between popular formats and the MATSim representation, e.g., OSM (OpenStreetMap) or GTFS (General Transit Feed Speci cation), semi-automatic network matching to join information, among others.A large group of colleagues has been involved and many of them are contributors to this book; this is a list of those involved, other than ourselves, in Berlin, Singapore and Zürich. Additional contributors are mentioned as authors of their respective chapters in this book. We hope to acknowledge the contributions of more colleagues from other groups in future versions of this book and in the so ware.Special thanks go to a number of people who greatly helped improving this book beyond their own chapters. Benjamin Kickhöfer's deep knowledge of ...

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Section: Link Modelmentioning

confidence: 99%

The Multi-Agent Transport Simulation MATSim

Axhausen¹

2016

479

123

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“…The double-queue model recently proposed by Osorio et al (2011) relies on the concept of an upstream queue and a downstream queue that may co-exist on the same link. These two concepts corresponds to the first and the second terms in (3.17) respectively; see Ma et al (2014b) for a continuous-time representation of the double-queue concept and its relationship with the variational representation (3.17).…”

Section: The Lax-hopf Formula Approach For Detecting Latent Shocksmentioning

confidence: 99%

Continuous-time link-based kinematic wave model: formulation, solution existence, and well-posedness

Han

Piccoli

Szeto

2015

Transportmetrica B: Transport Dynamics

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We present a continuous-time link-based kinematic wave model (LKWM) for dynamic traffic networks based on the scalar conservation law model (Lighthill and Whitham, 1955;Richards, 1956). Derivation of the LKWM involves the variational principle for the Hamilton-Jacobi equation and junction models defined via the notions of demand and supply. We show that the proposed LKWM can be formulated as a system of differential algebraic equations (DAEs), which captures shock formation and propagation, as well as queue spillback. The DAE system, as we show in this paper, is the continuous-time counterpart of the link transmission model (Yperman et al., 2005). In addition, we present a solution existence theory for the continuous-time network model and investigate continuous dependence of the solution on the initial data, a property known as well-posedness. We test the DAE system extensively on several small and large networks and demonstrate its numerical efficiency.

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“…It appears that the complexity of kinetic models with realistic stochastic dependency structures has so far precluded their implementation in non-trivial network contexts (Helbing;2001). Osorio et al (2011) recently proposed a stochastic formulation of the link-transmission model of Yperman et al (2007), which is an operational instance of Newell's simplified theory of kinematic waves (Newell;1993). Newell's model can in turn be derived from the variational theory of Daganzo (2005).…”

Section: Introductionmentioning

confidence: 99%

“…Newell's model can in turn be derived from the variational theory of Daganzo (2005). The queueing-theoretical model of Osorio et al (2011) accommodates stochastic source terms and flows across nodes. It is a (vehicle-)discretized, stochastic instance of the KWM, whereas the aforementioned stochastic CTMs constitute stochastic instances of (space-)discretized KWMs.…”

Section: Introductionmentioning

confidence: 99%

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Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model

Osorio

Flötteröd

2015

Transportation Science

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This work adds realistic dependency structure to a previously developed analytical stochastic network loading model. The model is a stochastic formulation of the link-transmission model, which is an operational instance of Newell's simplified theory of kinematic waves. Stochasticity is captured in the source terms, the flows, and, consequently, in the cumulative flows. The previous approach captured dependency between the upstream and downstream boundary conditions within a link (i.e. the respective cumulative flows) only in terms of time-dependent expectations without capturing higher-order dependency. The model proposed in this paper adds an approximation of full distributional stochastic dependency to the link model. The model is validated versus stochastic microsimulation in both stationary and transient regimes. The experiments reveal that the proposed model provides a very accurate approximation of the stochastic dependency between the link's upstream and downstream boundary conditions. The model also yields detailed and accurate link state probability distributions.

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Dynamic network loading: A stochastic differentiable model that derives link state distributions

Cited by 59 publications

References 37 publications

The Multi-Agent Transport Simulation MATSim

The Multi-Agent Transport Simulation MATSim

Continuous-time link-based kinematic wave model: formulation, solution existence, and well-posedness

Capturing Dependency Among Link Boundaries in a Stochastic Dynamic Network Loading Model

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