Unmanned aerial vehicles (UAVs) play an increasingly vital role in wilderness search and rescue (SAR) operations by enhancing situational awareness and extending the capabilities of human teams. Yet, a lack of standardized benchmarks has impeded the systematic evaluation of single- and multi-agent path-planning algorithms. This paper introduces an open-source dataset and evaluation framework to address this gap. The framework comprises 60 geospatial scenarios across four distinct European environments, featuring high-resolution probability maps. We present a lost person probabilistic model derived from statistical models of lost person behavior. We provide a suite of tools for evaluating search paths against four baseline methods: Concentric Circles, Pizza Zigzag, Greedy, and Random Exploration, using three quantitative metrics: Accumulated probability of detection, time-discounted probability of detection, and lost person discovery score. We provide an evaluation framework to facilitate the comparative analysis of single- and multi-agent path-planning algorithms, supporting both the baseline methods presented and custom user-defined path generators. By providing a structured and extensible framework, this work establishes a foundation for the rigorous and reproducible assessment of UAV search strategies in complex wilderness environments.

The evolution of headways (time intervals between consecutive vehicles) is an important performance indicator of public transportation systems. In this paper, we develop a data processing system that processes the expected arrival time data provided by the corresponding APIs of Madrid and London bus public transport systems with the aim of modeling and supervising their evolution along the time to detect their operational anomalies. By employing only estimates of the time headways between buses, we construct joint statistical modeling along the time of these headways within a stochastic process framework. Then, this model is employed to design a deviation detection procedure that provides a simple and adjustable anomaly detection scheme, as well as a complementary index for assessing the overall Quality of Service (QoS) provided by the operator. The proposed adjustable system can be easily tuned by managers or operators for online anomaly detection, which is an important asset to increase the efficiency of bus transportation services.

Nowadays, public transport is an indispensable urban service, helping to improve urban mobility by making it accessible to everyone, as well as reducing the pollution inside big cities. For this reason, any efforts for characterizing and improving urban public transportation systems are welcome. In the present paper we perform an analysis of the data corresponding to the arrival time estimators of the urban buses provided by the EMT (municipal transport company) of Madrid, with the aim of improving their performance and reliability. To achieve these objectives, we have developed several algorithms that, using the EMT provided data, estimate the position, arrival times and running times between stops. Finally, we estimate the headways between the buses, which can be used to monitor the performance of the service in each line.

Statistical description and prediction of bus arrival times is relevant for public transport users since it allows more timewise efficient journeys. This work is focused on characterizing the real behavior of buses based on past arrival estimation data. The main goal is to estimate real bus pass times by optimally collecting data from an intercity bus arrival time estimation system which is limited in petition handling capacity. This requires to model the server behavior prior to the design of the data collection system. In addition, it also requires the design of an algorithm to estimate the bus real passing time considering that only the provided estimated time of arrival is available. This information can be useful for designing alternative online arrival time estimators based on supervised learning which could potentially improve the estimator efficiency.