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Abstract-Classically, fundamental diagrams are estimated from aggregated data at a specific location. Such a measurement method may lead to inconsistency which mainly explain the current controversy about their shape.This paper proposes a new estimation method based on passing rate measurements along moving observer paths. Under specific assumptions, it can be proved that in congestion passing rate is independent of the traffic flow states.This property allows (i) to prove that a linear fundamental diagram is suitable to represent traffic flow behavior involved in the Next Generation Simulation Model (NGSim) dataset; (ii) to fit its two parameters: the congested wave speed and the jam density.

This paper aims to extend the concept of Macroscopic Fundamental Diagram (MFD) to 21 combine different transportation modes. Especially, we propose a unified relationship 22 that accounts for cars and buses because the classical MFD is not sufficient to capture 23 1. INTRODUCTION 32 33 Cities and transit agencies worldwide have to face an accelerating demand for mobility 34 as people continue to flock to urban areas seeking access to greater economic, 35 educational, and social opportunities. This poses a challenge to optimally distribute city 36 space to multiple transportation modes. To this end, management strategies have to be 37 dynamic, multiscale, and simultaneously applied to individual cars and other 38 transportation modes (such as public transport). 39 40 The core element of such management strategies is a global evaluation function of the 41 transportation network. This function must quantify the performance of the whole 42 system that can combine different transportation modes (individual cars, buses, trams, 43 trucks, etc.). This is thus a challenge to capture the traffic dynamics of a complex 44 network mixing these modes. It turns out that cities are complex and intricate systems. 45 Therefore, they are impossible to model in perfect detail. The approach taken in this 46 paper is to look at the transportation network at a macroscopic level. It is important to 47 notice that the approach of the paper is very idealized. Indeed, the challenge here is to 48 propose a modeling framework as general as possible. Then, it could be applied to a 49 relatively wide range of situations and refined based on the characteristics of these 50 situations. 51 52 To this end, we resort to an aggregated and parsimonious model to evaluate the 53 transportation network performance. Such a model provides a better understanding and 54 valuable insights on arterial traffic dynamics. The macroscopic fundamental diagram 55 (MFD) can play this role. Indeed, on their seminal works (

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a b s t r a c tThis paper first presents a method to estimate Newell's car-following model parameters in congestion at a microscopic scale. I-80 NGSim data analysis shows that at this scale driving behaviors vary from one vehicle to the another. Then, relations between stochastic Newell's model with heterogeneous drivers and its associated macroscopic pattern are established. This proves that the mean jam spacing is the arithmetic mean of individual jam spacings whereas the mean wave speed is the harmonic mean of individual wave speeds. These new insights are valuable for (i) calibrating a macroscopic model from individual observations and (ii) establishing the equivalence between the stochastic and the well-calibrated deterministic versions of Newell's model for a sufficiently large number of vehicles.

This study is focused on capacity and travel times in a signalized corridor and bus lanes with intermittent priority (BLIPs). These strategies consist of opening the bus lane to general traffic intermittently when a bus is not using it. Although the benefits of such strategies have been pointed out in the literature, the activation phase has received little attention. In an attempt to fill this gap, the activation of BLIP strategies was studied analytically. To this end, the extended kinematic wave model with bounded acceleration was chosen. BLIP activation reduced capacity and increased the travel time of buses. However, even if this strategy seems to be counterproductive at first, it clearly increases the performance of transit buses on a larger scale.

Capturing variability within flow is an important task for traffic flow models. The linearity of the congested part of the fundamental diagram induces a linear speed-spacing relationship at an individual level, characterized by two parameters. This study assumes that most intervehicle variability can be accounted for by estimating these two parameters for each vehicle. Two methods are presented to quantify individual linear speed-spacing relationships. The first method is based on data: it estimates the speed-spacing relationship by fitting the experimental speed-spacing scatter plot with a straight line. The second method is based on simulation: it computes the optimum parameters so that the simulated trajectories obtained by Newell's car-following algorithm reproduce as closely as possible the experimental vehicle's trajectories. Both proposed methods are implemented on the Next Generation Simulation trajectory data set recorded on I-80. The individual parameters for the speed-spacing relationship are quantified, and their distributions are specified. The need to distinguish driver behavior on a lane-by-lane basis is discussed. The results tend to prove that taking into account individual variability between drivers can improve the accuracy of simulated trajectories.