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Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

Automotive traffic monitoring using probe vehicles with Global Positioning System receivers promises significant improvements in cost, coverage, and accuracy. Current approaches, however, raise privacy concerns because they require participants to reveal their positions to an external traffic monitoring server. To address this challenge, we propose a system based on virtual trip lines and an associated cloaking technique. Virtual trip lines are geographic markers that indicate where vehicles should provide location updates. These markers can be placed to avoid particularly privacy sensitive locations. They also allow aggregating and cloaking several location updates based on trip line identifiers, without knowing the actual geographic locations of these trip lines. Thus they facilitate the design of a distributed architecture, where no single entity has a complete knowledge of probe identities and fine-grained location information. We have implemented the system with GPS smartphone clients and conducted a controlled experiment with 20 phone-equipped drivers circling a highway segment. Results show that even with this low number of probe vehicles, travel time estimates can be provided with less than 15% error, and applying the cloaking techniques reduces travel time estimation accuracy by less than 5% compared to a standard periodic sampling approach.

This article is motivated by the practical problem of highway traffic estimation using velocity measurements from GPS enabled mobile devices such as cell phones. In order to simplify the estimation procedure, a velocity model for highway traffic is constructed, which results in a dynamical system in which the observation operator is linear. This article presents a new scalar hyperbolic partial differential equation (PDE) model for traffic velocity evolution on highways, based on the seminal Lighthill-Whitham-Richards (LWR) PDE for density. Equivalence of the solution of the new velocity PDE and the solution of the LWR PDE is shown for quadratic flux functions. Because this equivalence does not hold for general flux functions, a discretized model of velocity evolution based on the Godunov scheme applied to the LWR PDE is proposed. Using an explicit instantiation of the weak boundary conditions of the PDE, the discrete velocity evolution model is generalized to a network, thus making the model applicable to arbitrary highway networks. The resulting velocity model is a nonlinear and nondifferentiable discrete time dynamical system with a linear observation operator, for which a Monte Carlo based ensemble Kalman filtering data

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An extension of the Colombo phase transition model is proposed. The congestion phase is described by a two-dimensional zone defined around a standard fundamental diagram. General criteria for building such a set-valued fundamental diagram are enumerated and instantiated on several standard fluxes with different concavity properties. The solution to the Riemann problem in the presence of phase transitions is obtained through the design of a Riemann solver, which enables the construction of the solution of the Cauchy problem using wavefront tracking. The free-flow phase is described using a Newell-Daganzo fundamental diagram, which allows for a more tractable definition of phase transition compared to the original Colombo phase transition model. The accuracy of the numerical solution obtained by a modified Godunov scheme is assessed on benchmark scenarios for the different flux functions constructed. Introduction.First order scalar models of traffic. Hydrodynamic models of traffic go back to the 1950s with the work of Lighthill and Whitham [31] and Richards [38], who built the first model of the evolution of vehicle density on the highway using a first order scalar hyperbolic partial differential equation (PDE) referred to as the LWR PDE. Their model relies on the knowledge of an empirically measured flux function, also called the fundamental diagram in transportation engineering, for which measurements go back to 1935 with the pioneering work of Greenshields [22]. Numerous other flux functions have since been proposed in the hope of capturing effects of congestion more accurately, in particular, Greenberg [21], Underwood [44], Newell [34], Daganzo [10], and Papageorgiou [47]. The existence and uniqueness of an entropy solution to the Cauchy problem [39] for the class of scalar conservation laws to which the LWR PDE belongs go back to the work of Oleinik [35] and Kruzhkov [27] (see also the seminal article of Glimm [18]), which was extended later to the initial-boundary value problem [2] and specifically instantiated for the scalar case with a concave flux function in [29], in particular for traffic in [40]. Numerical solutions of the LWR PDE go back to the seminal Godunov scheme [20,30], which was shown to converge to the entropy solution of the first order hyperbolic PDE (in particular, the LWR

Traffic state estimation is a challenging problem for the transportation community due to the limited deployment of sensing infrastructure. However, recent trends in the mobile phone industry suggest that GPS equipped devices will become standard in the next few years. Leveraging these GPS equipped devices as traffic sensors will fundamentally change the type and the quality of traffic data collected on large scales in the near future. New traffic models and data assimilation algorithms must be developed to efficiently transform this data into usable traffic information.In this work, we introduce a new partial differential equation (PDE) based on the Lighthill-Whitham-Richards PDE, which serves as a flow model for velocity. We formulate a Godunov discretization scheme to cast the PDE into a Velocity Cell Transmission Model (CTM-v), which is a nonlinear dynamical system with a time varying observation matrix. The Ensemble Kalman Filtering (EnKF) technique is applied to the CTMv to estimate the velocity field on the highway using data obtained from GPS devices, and the method is illustrated in microsimulation on a fully calibrated model of I880 in California. Experimental validation is performed through the unprecedented 100-vehicle Mobile Century experiment, which used a novel privacy-preserving traffic monitoring system to collect GPS cell phone data specifically for this research.

This article proposes a method to quantitatively measure the resilience of transportation systems using GPS data from taxis. The granularity of the GPS data necessary for this analysis is relatively coarse; it only requires coordinates for the beginning and end of trips, the metered distance, and the total travel time. The method works by computing the historical distribution of pace (normalized travel times) between various regions of a city and measuring the pace deviations during an unusual event. This method is applied to a dataset of nearly 700 million taxi trips in New York City, which is used to analyze the transportation infrastructure resilience to Hurricane Sandy. The analysis indicates that Hurricane Sandy impacted traffic conditions for more than five days, and caused a peak delay of two minutes per mile. Practically, it identifies that the evacuation caused only minor disruptions, but significant delays were encountered during the postdisaster reentry process. Since the implementation of this method is very efficient, it could potentially be used as an online monitoring tool, representing a first step toward quantifying city scale resilience with coarse GPS data.In recent years, resilience of city infrastructure has gained a great deal of attention [1]. When disasters and other extreme events occur, infrastructure may fail, incurring large human, economic, and environmental costs. This is especially relevant for transportation infrastructure, since it is crucial for city evacuations and emergency services in post-disaster environments. Methods are needed to quantitatively monitor the transportation infrastructure in terms of its ability to withstand and recover from such events. Measuring the performance of city-scale infrastructure with traditional traffic sensors is cost-prohibitive due to relatively high installation costs, but many cities already have taxi fleets equipped with GPS sensors. Though this analysis could be performed with any GPS data, taxi data is publicly available in some cases. The New York City dataset used in this analysis gives interesting insights about the performance of infrastructure during Hurricane Sandy and other major events.The goal of this article is to develop and implement a method for measuring resilience of city-scale transportation networks using only taxi datasets. The technique is designed with the following characteristics:1. The method can be applied at the city-scale, or larger. Extreme events such as hurricanes have the ability to affect an entire city. For this reason, it is important to examine impacts at a high-level city view, rather than the level of individual vehicles or streets. * bpdonov2@illinois.edu † dbwork@illinois.edu arXiv:1507.06011v1 [physics.soc-ph] 21 Jul 2015 2. The method measures network performance quantitatively, in terms of recovery time and peak pace deviations. Recovery time and peak performance degradation are fairly standard quantities of interest in the resilience literature [2,3]. While travel times are a natural performance m...