A macroscopic theory for unsignalized intersections

The following paper presents a dynamic macroscopic model for unsignalized intersections which accounts for time-limited disruptions in the minor stream flow, even in free-flow conditions when the average flow demand is satisfied. It introduces a deterministic fictive traffic light to represent an average alternating sequence of available and busy time periods for insertion depending on the major stream flow. Two allocation schemes of the total outflow during green periods are developed to model the influence or non-influence of the minor stream over the major stream flow. The aggregation of the resulting dynamic flow variations gives relevant capacity values. Moreover, the model predicts accurate average vehicle delay and queue length estimates compared to theoretical and empirical data. It has three easy-to-measure parameters and can be integrated into a dynamic macroscopic simulation tool for urban networks.     

Do microscopic merging models reproduce the observed priority sharing ratio in congestion?

A classical way to represent vehicle interactions at merges at the microscopic scale is to combine a gap-acceptance model with a car-following algorithm. However, in congested conditions (when a queue spills back on the major road), outputs of such a combination may be irrelevant if anticipatory aspects of vehicle behaviours are disregarded (like in single-level gap-acceptance models). Indeed, the insertion decision outcomes are so closely bound to the car-following algorithm that irrelevant results are produced. On the one hand, the insertion decision choice is sensitive to numerical errors due to the car-following algorithm. On the other hand, the priority sharing process observed in congestion cannot be correctly reproduced because of the constraints imposed by the car-following on the gap-acceptance model. To get over these issues, more sophisticated gap-acceptance algorithms accounting for cooperation and aggressiveness amongst drivers have been recently developed (multi-level gap-acceptance models). Another simpler solution, with fewer parameters, is investigated in this paper. It consists in introducing a relaxation procedure within the car-following rules and proposing a new insertion decision algorithm in order to loosen the links between both model components. This approach will be shown to accurately model the observed flow allocation pattern in congested conditions at an aggregate scale.     

Improving noise assessment at intersections by modeling traffic dynamics

Three families of road noise prediction models can be distinguished. Static noise models only consider free-flow constant-speed traffic with uniformly distributed vehicles. Analytic noise models assume that all vehicles are isolated from one another but account for their mean kinematic profile over the network. Micro-simulation noise models relax the hypothesis of no interaction between vehicles and fully capture traffic flow dynamic effects such as queue evolution. This study compares the noise levels obtained by these three methodologies at signalized intersections and roundabouts. It reveals that micro-simulation noise models outperform the other approaches. Particularly, they are able to capture the effects of stochastic transient queues in under-saturated conditions as well as stop-and-go behaviors in oversaturated regime. Accounting for traffic dynamics is also shown to improve predictions of noise variations due to different junction layouts. In this paper, a roundabout is found to induce a 2.5 dB(A) noise reduction compared to a signalized intersection in under-saturated conditions while the acoustic contributions of both kinds of junctions balance in oversaturated regime.     

Estimation des impacts atmosphériques des projets de gestion de trafic : de l’application des modèles théoriques sur des cas concrets

In addition to its harmful economical and social effects, road traffic congestion is a key contributor of emissions of CO₂ and local pollutants. Many traffic management policies could be implemented to tackle it. However, the impacts of those strategies on air quality and greenhouse effect are poorly predicted by classical assessment procedures. Indeed, current assessment tools are based on aggregated traffic data, which fail to capture the dynamic effects of traffic management policies on traffic flow. This article presents an estimation tool capable of measuring all the effects of traffic management policies. It is based on a commonly used dynamic traffic flow model (AIMSUN) combined with different emission models, in order to identify modelling features leading or not to differences in results. Different models combination have been applied and compared for two traffic regulation strategies: a HOV lane dedicated only to buses and taxis created on the leftmost lane of A1 between Charles de Gaulle airport and Paris, and a reduction of speed limits on a section of motorway A86 in the northwest of Paris. Substantial differences in predictions of different emission models are highlighted. They cannot be entirely explained by differences in emission laws. Other insights with regard to traffic representation and emission models?? inputs are also investigated to explain them.