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.     

Capacity drops at merges: An endogenous model

The Newell-Daganzo merge model is not only very simple but also accurately reproduces experimental findings. However, the capacity downstream of the merge is an exogenous variable in the model. This is a serious limitation for merges that behave as active bottlenecks because their downstream capacity is a direct consequence of the merging behavior. This paper proposes an analytical model that extends the Newell-Daganzo model by incorporating, endogenously, the capacity drop related to the merging process. Two cases are investigated depending on the traffic states on the on-ramp. The model properties are analyzed and a sensitivity analysis is performed to quantify the relative contribution of the each parameter in the capacity drop. Finally, the extended Newell-Daganzo model is validated with experimental data coming from an active merge bottleneck on the M6 freeway in UK.     

The economics of airport noise: How to manage markets for noise licenses

This paper discusses various issues in the implementation of a local market for aircraft noise licenses to solve the noise externalities harming the residents located near airports. The design of such markets is affected by aircraft heterogeneity, wind contingencies, peak times, runways capacity constraints, hub strategies, and airport planning is discussed.     

Investigating the irregularity of bus routes: highlighting how underlying assumptions of bus models impact the regularity results

A bus route is inherently unstable: when the system is uncontrolled, buses fail to maintain their time‐headways and tend to bunch. Several mathematical bus motion models were proposed to reproduce the bus behavior and assess management strategies. However, no work has established how the choice of a model impacts the irregularity of modeled bus systems, that is, the non‐respect of scheduled headways. Because of this gap, a large body of existing works assumes that the ability of these models to reproduce instability comes only from stochasticity, although the link between stochastic inputs and the level of irregularity remains unknown. Moreover, some recognized phenomena such as a change of travel conditions during a day or delays at signalized intersections are ignored. To address these shortcomings, this paper provides an overview of existing dynamic bus‐focused models and proposes a simple way to classify them. Commonly used deterministic and stochastic models are compared, which allows quantifying the relative influence of stochasticity of each model component on outputs. Moreover, we show that a change in the system equilibrium in a full deterministic system can lead to irregularity. Finally, this paper proposes a refinement of travel time models to account for non‐dynamic signals. In presence of traffic signals, we show that a bus system can be self‐regulated. Especially, these insights could help to calibrate bus model inputs to better reproduce real data. Copyright © 2014 John Wiley & Sons, Ltd.     

一种动态的感官评估实验方法

以二分法为基础,应用自动学习机原理建立一种动态的感官评估实验方法．评估者根据评估过程中获得的信息,动态地调整评估策略．该实验方法分为二方面内容：首先提出应用二分法以较少实验次数完成对样本的评估,其次是应用自动学习机原理优化二分法．仿真结果表明,在不影响实验结果精度的前提下,动态实验方法能够显著地减少评估实验次数．