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In Intelligent Transportation Systems (ITS), the Vehicular Ad Hoc Networks (VANETs) paradigm based on the WAVE IEEE 802.11p standard is the main alternative for inter-vehicle communications. Recently, many protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Most of these services rely on location systems and require different levels of accuracy for their full operation. The Global Positioning System (GPS) is an off-the-shelf solution for localization in VANETs and ITS. However, GPS systems present problems regarding inaccuracy and unavailability in dense urban areas, multilevel roads, and tunnels, posing a challenge for protocols, applications, and services that rely on localization. With this motivation, we carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets, and regions around tunnels were selected. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capabilities, an integrated Dead Reckoning aided Geometric Dilution of Precision (GDOP)-based Cooperative Positioning solution was developed and evaluated. Leveraging the potential of vehicular sensors, such as odometers, gyroscopes, and digital compasses, vehicles share their positions and kinematics information using vehicular communication to improve their location estimations. With the assistance of a digital map, vehicles adjust the final estimated position using the road geometry. The situations of GPS unavailability characterized in the datasets were reproduced in a simulation environment to validate the proposed localization solution. The simulation results show average gains in Root Mean Square Error (RMSE) between 97% to 98% in comparison with the stand-alone GPS solution, and 83.00% to 88.00% against the GPS and Dead Reckoning (DR) only solution. The average absolute RMSE was reduced to the range of 3 to 5 m by vehicle. In addition, the proposed solution was shown to support 100% of the GPS unavailability zones on the evaluated scenarios.

The increase in floods and flash floods over the last decades has motivated researchers to develop improved methodologies for flood risk prevention and warning. Flood forecasting models available today have evolved technologically but are subject to limitations due to the lack of data and limited community participation. This paper presents the Hydrological Alert Model with Participatory Basis (HAMPB) model, an approach for integrating water level data reported by citizens, which has the advantage of being inexpensive and potentially highly available, with traditional data to improve flood forecasting. The model assimilates spatiotemporal water levels measured in the field when they are available through a real‐time estimator. We added random perturbations of up to |10| and |15| cm to those data using the Monte Carlo Method to mimic the uncertainty in citizen science data collection. Applying the HAMPB model for urban nested‐scale catchments (0.11 km2 ≤ Area ≤ 21.84 km2) in Brazil shows: (a) significant improvements in flood simulations when field data was assimilated even considering the volunteered data uncertainty; (b) capability to update simulations in more than one point in the semi‐distributed hydrological model by a regionalization method; and (c) flood hazard indexes and their uncertainties show better estimations using field data for updating.

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