Romain Billot

Bio: Romain Billot is an academic researcher from University of Lyon. The author has contributed to research in topics: Traffic flow & Electric vehicle. The author has an hindex of 16, co-authored 59 publications receiving 918 citations. Previous affiliations of Romain Billot include Queensland University of Technology & IFSTTAR.

How to assess the benefits of connected vehicles? A simulation framework for the design of cooperative traffic management strategies

Abstract: Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.

Machine Learning and Natural Language Processing in Mental Health: Systematic Review.

Abstract: Background: Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining. Objective: The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice Methods: This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed. Results: A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform. Conclusions: Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients’ daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.

Efficient Allocation of Electric Vehicles Charging Stations: Optimization Model and Application to a Dense Urban Network

Abstract: The deployment of Electric Vehicles (EVs) needs an optimized and cost-effective implementation of charging stations. As a decision support tool for network design, we define a methodology to allocate charging stations in a real network. This study uses trip OD matrix information from household travel survey coupled with a dynamic vehicle model to evaluate EVs consumption based on realistic trips (urban drive cycles). These trips are computed based on routing tools and supplied with elevation information. This enables an accurate characterization of energy needs in the Lyon Metropolitan Area. All these parameters are used as inputs of an integer linear optimization program for the location of charging stations. The methodology is based on an adaption of the classic fixed charge location model with a p-dispersion constraint. The results indicate that this methodology can help the future implementation of charging stations at an urban scale.

Linear and Weakly Nonlinear Stability Analyses of Cooperative Car-Following Models

Abstract: Stability analyses have been widely used to better understand the mechanism of traffic jam formation In this paper, we consider the impact of cooperative systems (aka connected vehicles) on traffic dynamics and, more precisely, on flow stability Cooperative systems are emerging technologies enabling communication between vehicles and/or with the infrastructure In a distributed communication framework, equipped vehicles are able to send and receive information to/from other equipped vehicles Here, the effects of cooperative traffic are modeled through a general bilateral multianticipative car-following law that improves cooperative drivers' perception of their surrounding traffic conditions within a given communication range Linear stability analyses are performed for a broad class of car-following models They point out different stability conditions in both multianticipative and nonmultianticipative situations To better understand what happens in unstable conditions, information on the shock wave structure is studied in the weakly nonlinear regime by the mean of the reductive perturbation method The shock wave equation is obtained for generic car-following models by deriving the Korteweg de Vries equations We then derive traffic-state-dependent conditions for the sign of the solitary wave (soliton) amplitude This analytical result is verified through simulations Simulation results confirm the validity of the speed estimate The variation of the soliton amplitude as a function of the communication range is provided The performed linear and weakly nonlinear analyses help justify the potential benefits of vehicle-integrated communication systems and provide new insights supporting the future implementation of cooperative systems

Spatiotemporal Analysis of Bluetooth Data: Application to a Large Urban Network

Abstract: The emergence of new technologies allows better monitoring of traffic conditions and understanding of urban network dynamics. Bluetooth technology is becoming widespread, as it represents a cost-effective means for capturing road traffic in both arterials and motorways. Although the extraction of travel time from Bluetooth data is fairly straightforward, data reliability and processing is still challenging with the issues of penetration rate, mode discrimination, and detection quality. This paper presents a methodological contribution to the use of Bluetooth data for the spatiotemporal analysis of a large urban network (Brisbane, Australia). It introduces the concept of the Bluetooth origin–destination (B-OD) matrix, which is built from a network of 79 Bluetooth detectors located within the Brisbane urban area. The B-OD matrix describes the dynamics of a subpopulation of vehicles, between pairs of detectors. The results show that the characteristics of urban networks can be effectively represented through B-OD matrices. A comparison with loop detector data enables an assessment of the results' significance. Then, the spatiotemporal structure of the network is analyzed with two different clustering analyses, namely, latent Dirichlet allocation (LDA) and $K$ -means. While LDA is used to detect a temporal pattern, the $K$ -means algorithm highlights Bluetooth fundamental diagram (BFD) classes. The results show that Bluetooth data has the potential to be a reliable data source for traffic monitoring. By highlighting hidden structures of a large area, the algorithm outputs allow us to provide the road operators with a fine spatiotemporal analysis of their network, in terms of traffic conditions.

Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments

Abstract: 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.

Value of Travel Time Reliability: A Review of Current Evidence

Abstract: Travel time reliability is a fundamental factor in travel behavior. It represents the temporal uncertainty experienced by users in their movement between any two nodes in a network. The importance of the time reliability depends on the penalties incurred by the users. In road networks, travelers consider the existence of a trip travel time uncertainty in different choice situations (departure time, route, mode, and others). In this paper, a systematic review of the current state of research in travel time reliability, and more explicitly in the value of travel time reliability is presented. Moreover, a meta-analysis is performed in order to determine the reasons behind the discrepancy among the reliability estimates.