Dynamic Traffic Assignment: Properties and Extensions

Szeto, W.Y.; Lo, Hong Kam

doi:10.1080/18128600608685654

Cited by 168 publications

(127 citation statements)

References 27 publications

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“…It is logical that the reported result for the calibration dataset shows a higher percentage of being correct because the model has been calibrated based on this dataset. Also, due to the nature of dynamic traffic assignment, an equilibrium solution is not necessarily unique if existing at all (Szeto and Hong 2006). Hence, the correct percentage of lane direction can be significantly different as it is related to equilibrium flows.…”

Section: Testing the Modelmentioning

confidence: 99%

Estimation of the Logit Model for the Online Contraflow Problem

2010

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Abstract. Contraflow or lane reversal is an efficient way for increasing the outbound capacity of a network by reversing the direction of in-bound roads during evacuations. Hence, it can be considered as a potential remedy for solving congestion problems during evacuation in the context of homeland security, natural disasters and urban evacuations, especially in response to an expected disaster. Most of the contraflow studies are performed offline, thus strategies are generated beforehand for future implementation. Online contraflow models, however, would be often computationally demanding and time-consuming. This study contributes to the state of the art of contraflow modelling in two regards. First, it focuses on the calibration of a Logit choice model which predicts the online contraflow directions of strategic lanes based on the set of directions obtained from offline scenarios. This is the first effort to adjust offline results to be applied for an online case. The second contribution of this paper is the generation of calibration data set from a novel approach through simulation. The calibrated Logit model is then tested for the network of the City of Fort Worth, Texas. The results show a high performance of this approach to generating beneficial strategies, including an increase in up to 16% in throughput compared to no contraflow case.

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Section: Testing the Modelmentioning

confidence: 99%

Estimation of the Logit Model for the Online Contraflow Problem

2010

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“…Szeto and Lo (2006), Ramadurai and Ukkusuri (2010) reported that (dis)utility functions with sudden change would increase the complexity of the algorithm convergence. However, in Step 1, when we calculate time-dependent ATP disutilities, link arrival time j is allowed to be a noninteger, and for traffic links, linear interpolation is utilized to approximate the link travel time for non-integral arrival times.…”

Section: Flow Constraintsmentioning

confidence: 99%

“…Obviously, weak causality in discrete-time is similar to the causality (suggested by Carey et al, 2003;Szeto and Lo, 2005, et al) in continuous-time counterpart, when the value of interval is chosen to be small enough. FIFO (suggested by Huang and Lam, 2002;Szeto and Lo, 2006, et al) means that the traveler who arrives at a non-activity link earlier will not exit later.…”

Section: Link Duration Disutilitymentioning

confidence: 99%

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Dynamic activity-travel assignment in multi-state supernetworks

Liu

Liao

Huang

et al. 2015

Transportation Research Part B: Methodological

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The integration of activity-based modeling and dynamic traffic assignment for travel demand analysis has recently attracted everincreasing attention. However, related studies have limitations either on the integration structure or the number of choice facets being captured. This paper proposes a formulation of dynamic activity-travel assignment (DATA) in the framework of multi-state supernetworks, in which any path through a personalized supernetwork represents a particular activity-travel pattern (ATP) at a high level of spatial and temporal detail. DATA is formulated as a discrete-time dynamic user equilibrium (DUE) problem, which is reformulated as an equivalent variational inequality (VI) problem. A generalized dynamic link disutility function is established with the accommodation of different characteristics of the links in the supernetworks. Flow constraints and nonuniqueness of equilibria are also investigated. In the proposed formulation, the choice of departure time, route, mode, activity sequence, activity and parking location are all unified into one time-dependent ATP choice. As a result, the interdependences among all these choice facets can be readily captured. A solution algorithm based on the route-swapping mechanism is adopted to find the user equilibrium. A numerical example with simulated scenarios is provided to demonstrate the advantages of the proposed approach.

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“…Later the CTM was used as the network loading component in dynamic traffic assignment models for user equilibrium (e.g. Lo (1999), Lo and Szeto (2002), Szeto and Lo (2004), Carey and Ge (2011) or see reviews of DTA such as Szeto and Lo (2006)). An important reason for using the CTM in this way is that, in traffic assignment models, route choice, and hence the proportions of traffic using the various links, are endogenously determined rather than being prespecified.…”

Section: Introductionmentioning

confidence: 99%

Dynamic traffic assignment approximating the kinematic wave model: System optimum, marginal costs, externalities and tolls

Carey

Watling

2012

Transportation Research Part B: Methodological

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System marginal costs, externalities and optimal congestion tolls for traffic networks are generally derived from system optimizing (SO) traffic assignment models and when these are treated as varying over time they are all referred to as dynamic. In dynamic SO network models the link flows and travel times or costs are generally modelled using so-called "whole link" models. Here we instead develop an SO model that more closely reflects traffic flow theory and derive the marginal costs and externalities from that. The most widely accepted traffic flow model appears to be the LWR (Lighthill, Whitham and Richards) model and a tractable discrete implementation or approximation to that is provided by the cell transmission model (CTM) or a finite difference approximation (FDA). These handles spillbacks, traffic controls and moving queues in a way that is consistent with the LWR model (hence with the kinematic wave model and fluid flow model). An SO formulation using the CTM is already available, assuming a single destination and a trapezoidal flow-density function. We extend the formulation to allow more general nonlinear flow density functions and derive and interpret system marginal costs and externalities. We show that if tolls computed from the DSO solution are imposed on users then the DSO solution would also satisfy the criteria for a dynamic user equilibrium (DUE). We introduce constraints on the link outflow proportions at merges and inflow proportions at diverges. We also extend the model to elastic demands and establish links with previous dynamic traffic assignment (DTA) models.

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Dynamic Traffic Assignment: Properties and Extensions

Cited by 168 publications

References 27 publications

Estimation of the Logit Model for the Online Contraflow Problem

Estimation of the Logit Model for the Online Contraflow Problem

Dynamic activity-travel assignment in multi-state supernetworks

Dynamic traffic assignment approximating the kinematic wave model: System optimum, marginal costs, externalities and tolls

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