Chronology of the Life of Charles Henry Davis

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A three-dimensional macroscopic fundamental diagram for mixed bi-modal urban networks

Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus-Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority

Towards a realistic microscopic description of highway traffic

Simple cellular automata models are able to reproduce the basic properties of highway traffic. The comparison with empirical data for microscopic quantities requires a more detailed description of the elementary dynamics. Based on existing cellular automata models we propose an improved discrete model incorporating anticipation effects, reduced acceleration capabilities and an enhanced interaction horizon for braking. The modified model is able to reproduce the three phases (free-flow, synchronized, and stop-and-go) observed in real traffic. Furthermore we find a good agreement with detailed empirical single-vehicle data in all phases.Comment: 7 pages, 7 figure

Bayesian inference for transportation origin-destination matrices: the Poisson-inverse Gaussian and other Poisson mixtures

Transportation origin–destination analysis is investigated through the use of Poisson mixtures by introducing covariate‐based models which incorporate different transport modelling phases and also allow for direct probabilistic inference on link traffic based on Bayesian predictions. Emphasis is placed on the Poisson–inverse Gaussian model as an alternative to the commonly used Poisson–gamma and Poisson–log‐normal models. We present a first full Bayesian formulation and demonstrate that the Poisson–inverse Gaussian model is particularly suited for origin–destination analysis because of its desirable marginal and hierarchical properties. In addition, the integrated nested Laplace approximation is considered as an alternative to Markov chain Monte Carlo sampling and the two methodologies are compared under specific modelling assumptions. The case‐study is based on 2001 Belgian census data and focuses on a large, sparsely distributed origin–destination matrix containing trip information for 308 Flemish municipalities

A three-objective user equilibrium model:Time surplus maximisation under uncertainty

In this paper, we propose a user equilibrium model considering the three most important factors influencing route choice behaviour in a road network, namely, travel time, travel time reliability and monetary cost. We further develop the time surplus maximisation bi-objective user equilibrium (TSmaxBUE) model and incorporate the concept of travel time budget to model how users might react to uncertainty induced by day-to-day variability in travel time caused by traffic incidents. This results in a three-objective user equilibrium model, which has a possibly infinite set of equilibrium flows. To compute equilibrium flows, we introduce time budget surplus (TBS) defined as the maximum travel time a user is willing to spend minus the actual time budget required for a desired level of travel time reliability. At equilibrium, for each origin-destination (O-D) pair, all individuals are travelling on the path with the highest TBS value among all the efficient paths between this O-D pair. This becomes a time budget surplus maximisation three-objective user equilibrium model (TBSmaxTUE). We show that the TBSmaxTUE model is a special case of three-objective user equilibrium considering minimisation of expected travel time, travel time variance and toll (monetary cost) as objectives. We illustrate the model and our results on a small network