Improving travel time estimates from inductive loop and toll collection data with dempster-shafer data fusion

Dempster-Shafer data fusion can enhance travel time estimation for motorists and traffic managers. In this paper, travel time data from inductive loop road sensors and toll collection stations are merged through Dempster-Shafer inference to generate an improved estimate of travel time. The technique captures travel time data from the two sources and combines them by using Dempster's rule and belief values (also called probability mass) calculated from a confusion matrix. The most probable travel time over the monitored road section is selected as that with the largest belief. A case study is provided to illustrate application of the fusion technique with data gathered on winter Saturdays for 2 years: 2003 data are used to compute the confusion matrices and belief values, and 2004 data are used for validation

Integrating the impact of rain into traffic management: online traffic state estimation using sequential Monte Carlo techniques

This paper consists in a new step toward the integration of the effects of inclement weather into traffic management strategies. It is well recognized that adverse weather conditions are a critical factor impacting traffic operations and safety. In a previous work (1), a methodology for the analysis of the rain impact has been put forward and this impact on key traffic indicators (e.g. free-flow speed, capacity) has been quantified. Thanks to these quantification studies, a first parameterization of the fundamental diagram according to the rain intensity is proposed. Next, since the fundamental diagram represents the basis of many simulation tools, the goal is to develop weather-responsive traffic state estimation tools, which can be useful for control applications and traffic management. More precisely, the online traffic state estimation takes place within a Bayesian framework with particle filtering techniques (i.e. sequential Monte Carlo simulations) in combination with a parameterized first-order macroscopic model. This approach has already been validated for sensor diagnosis and accident detection. Here, the goal is to show how the integration of the weather effects can improve this efficient tool. The approach is validated with real world data from the Lyon's ring road section (8sensors from a homogeneous section). The results from different scenarios show the benefits of the integration of the rain impact for traffic state estimation. Strategies to detect a rain event in time and in space are also suggested

Citizen science for observing and understanding the Earth

Citizen Science, or the participation of non-professional scientists in a scientific project, has a long history—in many ways, the modern scientific revolution is thanks to the effort of citizen scientists. Like science itself, citizen science is influenced by technological and societal advances, such as the rapid increase in levels of education during the latter part of the twentieth century, or the very recent growth of the bidirectional social web (Web 2.0), cloud services and smartphones. These transitions have ushered in, over the past decade, a rapid growth in the involvement of many millions of people in data collection and analysis of information as part of scientific projects. This chapter provides an overview of the field of citizen science and its contribution to the observation of the Earth, often not through remote sensing but a much closer relationship with the local environment. The chapter suggests that, together with remote Earth Observations, citizen science can play a critical role in understanding and addressing local and global challenges

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Steady-state analysis of second-order traffic models and application to traffic flow control

International audienc

Can safety indicators assess and monitor road traffic risk in real-time? Investigation of two safety indicators on Swiss motorways.

Road safety has become a major concern and many strategies have been introduced to reduce road risk and mitigate severe consequences of unavoidable crashes. This paper presents an innovative methodology which aims at monitoring in real time and even anticipating crash risk on Swiss motorways based on detectors providing individual traffic data. Two safety indicators are studied: from their sensitivity to their results on real-world rear-end crashes. Platoon Braking Time Risk is found to be better than the classic Time-To-Collision indicator

Road Safety Indicators: Swiss Results in Vaud Canton

Road safety has become a major concern and many strategies have been introduced to reduce road risk and mitigate severe consequences of unavoidable crashes. This paper presents an innovative methodology which aims at monitoring in real time and even anticipating crash risk on Swiss motorways based on individual traffic data recorded by detectors devices

Assessing the changes in operating traffic stream conditions due to weather conditions

International audienc