Alfredo Nantes

An Experimental Space for Conducting Controlled Driving Behavior Studies based on a Multiuser Networked 3D Virtual Environment and the Scenario Markup Language

We present a new framework for conducting controlled driving behavior studies based on multiuser networked 3-D virtual environments. The framework supports: 1) the simulation of multiuser immersive driving; 2) the visualization of surrounding traffic; 3) the specification and creation of reproducible traffic scenarios; and 4) the collection of meaningful driving behavior data. We use our framework to investigate the “rubbernecking” phenomenon, which refers to the slowing down of a driver due to an accident on the opposite side of the road, and its effect on the following drivers. The main contribution of the paper is the Scenario Markup Language (SML) framework, which is composed of: 1) the SML as a practical tool to specify dynamic traffic situations (e.g., an accident) and 2) the Scenario Control System to ensure the reproducibility of particular traffic situations, so that traffic engineers can obtain comparable data and draw valid conclusions. To demonstrate the effectiveness of our framework, we specified the traffic accident scenario in SML and conducted a study about the rubbernecking phenomenon. We report on the results of our study from two viewpoints: 1) the reproducibility of the traffic accident situation (i.e., state variables of interest are recreated successfully in 78% of the cases); and 2) the interactive car-following behavior of human subjects embedded in the traffic situation of the virtual environment.

Is Bus Overrepresented in Bluetooth MAC Scanner data? Is MAC-ID Really Unique?

One of the concerns about the use of Bluetooth MAC Scanner (BMS) data, especially from urban arterial, is the bias in the travel time estimates from multiple Bluetooth devices being transported by a vehicle. For instance, if a bus is transporting 20 passengers with Bluetooth equipped mobile phones, then the discovery of these mobile phones by BMS will be considered as 20 different vehicles, and the average travel time along the corridor estimated from the BMS data will be biased with the travel time from the bus. This paper integrates Bus Vehicle Identification system with BMS network to empirically evaluate such bias, if any. The paper also reports an interesting finding on the uniqueness of MAC-IDs.

A Cyber-Physical System Simulator for Risk-Free Transport Studies

Traffic operations result from human decision making and complex multidriver interaction at different behavioral levels. Cyber-Physical System Simulator (CPSS) is a novel platform for conducting controlled and risk-free driving and traveling behavior studies. The key features of CPSS are: (1) simulation of multiuser immersive driving in a three-dimensional (3D) virtual environment; (2) integration of traffic and communication simulators with human driving based on dedicated middleware; and (3) accessibility of multiuser driving simulator on popular software and hardware platforms. This combination of features allows for the easy collection of large-scale data on interesting phenomena regarding the interaction between multiple user drivers, which is not possible with current single-user driving simulators. The papers contribution are threefold: (1) to introduce a multiuser driving simulator based on DiVE, the authors original massively multiuser networked 3D virtual environment; (2) to introduce OpenV2X, a middleware for simulating vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication; and (3) to present two experiments based on the CPSS platform. The first experiment investigates the “rubbernecking” phenomenon, where a platoon of four user drivers experiences an accident in the oncoming direction of traffic. Second, the authors report on a pilot study about the effectiveness of a Cooperative Intelligent Transport Systems advisory system with a focus on V2V communications to identify vehicles that drive at high speed.

Estimating link-dependent Origin-Destination matrices from sample trajectories and traffic counts

In transport networks, Origin-Destination matrices (ODM) are classically estimated from road traffic counts whereas recent technologies grant also access to sample car trajectories. One example is the deployment in cities of Bluetooth scanners that measure the trajectories of Bluetooth equipped cars. Exploiting such sample trajectory information, the classical ODM estimation problem is here extended into a link-dependent ODM (LODM) one. This much larger size estimation problem is formulated here in a variational form as an inverse problem. We develop a convex optimization resolution algorithm that incorporates network constraints. We study the result of the proposed algorithm on simulated network traffic.

Comparative analysis of traffic state estimation ─ Cumulative counts-based and trajectory-based methods

The macroscopic fundamental diagram (MFD) relates space–mean density and flow. Because the MFD represents areawide network traffic performance, perimeter control strategies and networkwide traffic state estimation using the MFD concept have been studied. Most previous works used data from fixed sensors, such as inductive loops, to estimate the MFD, which can cause biased estimation in urban networks because of queue spillovers at intersections. To overcome this limitation, recent literature reported on the use of trajectory data obtained from probe vehicles. However, these studies were conducted with simulated data sets; few works have discussed the limitations of real data sets and their impact on variable estimation. This study compares two methods for estimating traffic state variables of signalized arterial sections: a method based on cumulative vehicle counts (CUPRITE) and one based on vehicle trajectory from taxi GPS logs. The comparisons reveal some characteristics of taxi trajectory data available in Brisbane, Queensland, Australia. The current trajectory data have limitations in quantity (i.e., the penetration rate), because of which the traffic state variables tend to be underestimated. Nevertheless, the trajectory-based method successfully captures the features of traffic states, which suggests that the trajectories from taxis can be a good estimator for networkwide traffic states.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

Bluetooth Data in an Urban Context: Retrieving Vehicle Trajectories

Bluetooth sensors have recently been developed throughout the world for traffic information gathering. Primarily designed for travel time analysis, this article presents a method for vehicular trajectories retrieval. After a short description of some of the challenges at hand in using Bluetooth data in an urban network, a procedure to extract trip information from such data is proposed. It is further analyzed and illustrated at work on a real dataset collected in Brisbane. Last, this article shows that using spatially constrained shortest path analysis, this trip information, once extracted, can be used for the reconstruction of the trajectories. The performance of the process is assessed using both a simulated dataset and one from the real-world acquired in Brisbane, showing encouraging results, with up to 84% of accurately recovered trajectories.

A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-destination matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. Taking advantage of probe trajectories, whose capture is made possible by new measurement technologies, the present contribution extends the concept of ODM to that of link-dependent ODM (LODM). LODM also contains the flow distribution on links making specification of assignment models, e.g., by means of routing matrices, unnecessary. An original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimization problem, where the functional to be minimized consists of five convex functions, each modeling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoffs law), similarity of flows having similar origins and destinations, and positivity of traffic flows. A proximal primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations.