Nicolas Chiabaut

Fundamental Diagram Estimation Through Passing Rate Measurements in Congestion

Classically, fundamental diagrams are estimated from aggregated data at a specific location. Such a measurement method may lead to inconsistency, which mainly explains 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, the passing rate is independent of the traffic flow states. This property allows 1) proof that a linear fundamental diagram is suitable to represent traffic flow behavior involved in the next generation simulation (NGSim) data set and 2) fitting of its two parameters, i.e., the congested wave speed and the jam density.

Estimating Individual Speed-Spacing Relationship and Assessing Ability of Newell's Car-Following Model to Reproduce Trajectories

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 Newells car-following algorithm reproduce as closely as possible the experimental vehicles 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.

Road Capacity and Travel Times with Bus Lanes and Intermittent Priority Activation

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.

Performance analysis for different designs of a multimodal urban arterial

This article aims to introduce an analysis framework to assess and compare different designs of an urban multimodal arterial before their implementation. Especially, the work focuses on dedicated bus lanes and intermittent bus lanes (IBLs), which are compared to the reference case where the buses and cars are mixed in the same flow. First, analytical considerations highlight the influence of buses and IBLs on traffic dynamics in free-flow conditions. Second, the article resorts to an aggregated and parsimonious model to account for both free-flow and congested traffic states. Such a model provides a better understanding and valuable insights on multimodal traffic dynamics on the arterial. To this end, the concept of passenger fundamental diagram is introduced. With this new relationship, efficiency of the global transport system, i.e. behaviours of cars and buses, is assessed and domains of applications of the different transit strategies are identified.

Wave Velocity Estimation Through Automatic Analysis of Cumulative Vehicle Count Curves

A fundamental diagram of traffic flow (and thus wave velocity) under congested traffic conditions is estimated from cumulative vehicle count curves. A new method estimates jam density and congested wave velocity simultaneously on a macroscopic scale. Based on kinematic wave model properties, this method overcomes the drawbacks of existing estimation processes. The objective is to determine the optimal parameters of the kinematic wave model on field data by focusing on wave propagation. The proposed method is used to automatically estimate parameters of the fundamental diagram for the study site. The fundamental diagram is linear in its congested part at the macroscopic scale. A sensitivity analysis is performed to observe the impact of observation days and loop detector locations on wave velocity under congested traffic conditions.

Clustering Approach for Assessing the Travel Time Variability of Arterials

Travel time variability may significantly influence a drivers route choice. Urban infrastructure managers are interested in reliable estimation of travel time, which could provide better information to drivers, encourage reassignment on the network, and optimize use of the network. This study addresses arterials in undersaturated conditions with known fixed-time traffic signals. The kinematic wave model was chosen to represent traffic dynamics simply. A semianalytical (grid-free) method is applied to determine the separation between vehicle groups at each signal of the arterial. The main contribution of this paper is an aggregated diagram that describes the functioning of this arterial. The aggregated diagram graphically provides a direct assessment of vehicle travel times with respect to their departure and traffic flow. This tool depends only on signal settings and is cyclic and invariant for fixed-time signals. The diagram can be used to generate probabilistic travel time distributions when some input parameters are uncertain. The diagram appears to offer insight when traffic flow is not accurately known.

Macroscopic Fundamental Diagram for Urban Streets and Mixed Traffic: Cross Comparison of Estimation Methods

In the past decade many papers focused on describing the vehicular traffic stream of an arterial on an aggregate level. Unfortunately, in this considerable body of research, only a few papers account for bus systems. This paper tries to fill that gap by investigating two potential methods for estimating macroscopic fundamental diagrams of multimodal transport systems of a signalized arterial. The first approach models the motion of buses endogenously by extending the existing estimation method with the moving bottleneck theory; the second approach proposes to incorporate the effects of buses exogenously. The estimated macroscopic fundamental diagrams were then cross compared with results provided by microsimulation software that finely reproduced the traffic stream. Mean speeds of vehicles and buses produced by the different methods were similar and consistent. Finally, results of the three methods were expressed for levels of service and compared with the levels of service of the Highway Capacity Manual 2010.

Spatiotemporal Partitioning of Transportation Network Using Travel Time Data

Today, the deployment of sensing technology permits the collection of massive amounts of spatiotemporal data in urban areas. These data can provide comprehensive traffic state conditions for an urban network and for a particular day. However, data are often too numerous and too detailed to be of direct use, particularly for applications such as delivery tour planning, trip advisors, and dynamic route guidance. A rough estimate of travel times and their variability may be sufficient if the information is available at the full city scale. The concept of the spatiotemporal speed cluster map is a promising avenue for these applications. However, the data preparation for creating these maps is challenging and rarely discussed. In this study, that challenge is addressed by introducing generic methodologies for mapping the data to a geographic information system network, coarsening the network to reduce the network complexity at the city scale, and estimating the speed from the travel time data, including missing data. This methodology is demonstrated on the large-scale urban network of Amsterdam, Netherlands, with real travel time data. The preprocessed data are used to build the spatiotemporal speed cluster by using three partitioning techniques: normalized cut, density-based spatial clustering of applications with noise, and growing neural gas (GNG). A new posttreatment methodology is introduced for density-based spatial clustering and GNG, which are based on data point clustering, to generate connected zones. A preliminary cross comparison of the clustering techniques shows that GNG performs best in generating zones with minimum internal variance, the normalized cut computes three-dimensional zones with the best intercluster dissimilarity, and GNG has the fastest computation time.

Revealing the day-to-day regularity of urban congestion patterns with 3D speed maps

In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

Replications in Stochastic Traffic Flow Models: Incremental Method to Determine Sufficient Number of Runs

This paper tackles the issues of the minimal and sufficient number of replication needed to evaluate correctly the mean value of a stochastic simulation results but also the shape of the results’ distribution. Indeed, stochasticity is more and more widespread in traffic flow models.