Algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks

Andreatta, Giovanni; Francesco, Carla De; Giovanni, Luigi De

doi:10.3390/s21248188

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…Information technology provides hardware and software (e.g., collision detection sensors, routing, and collision avoidance logic) that contribute to safe and efficient operations, with relevant social benefits in terms of improved system performance and reduced accident rates. In this context, the authors of [9] addressed the design of efficient collision-free routes in a minimum-sized routing network. They considered a grid and a set of vehicles, each moving from the bottom of the origin column to the top of the destination column.…”

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confidence: 99%

Sensors and Communications for the Social Good

Palazzi

Gaggi

Manzoni

2023

Sensors

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confidence: 99%

Sensors and Communications for the Social Good

Palazzi

Gaggi

Manzoni

2023

Sensors

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Integer Linear Programming Formulations for the Fleet Quickest Routing Problem on Grids

Francesco

Giovanni

2023

Optimization and Decision Science: Operations Research, Inclusion and Equity

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Bounds for Multistage Mixed-Integer Distributionally Robust OptimizationMultistage mixed-integer distributionally robust optimization (DRO) forms a class of extremely challenging problems since their size grows exponentially with the number of stages. One way to model the uncertainty in multistage DRO is by creating sets of conditional distributions (the socalled conditional ambiguity sets) on a finite scenario tree and requiring that such distributions remain close to nominal conditional distributions according to some measure of similarity/distance (e.g., phi-divergences or Wasserstein distance). In this talk, new bounding criteria for this class of difficult decision problems are provided through scenario grouping using the ambiguity sets associated with various commonly used phi-divergences and the Wasserstein distance. Our approach does not require any special problem structure such as linearity, convexity, stagewise independence, and so forth. Therefore, while we focus on multistage mixed-integer DRO, our bounds can be applied to a wide range of DRO problems including two-stage and multistage, with or without integer variables, convex or nonconvex, and nested or non-nested formulations. Numerical results on a multistage mixed-integer production problem show the efficiency of the proposed approach through different choices of partition strategies, ambiguity sets, and levels of robustness.

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Strengthened Integer Programming Formulations for the Fleet Quickest Routing Problem on Grids

De Francesco,

De Giovanni,

Galeazzo

2024

AIRO Springer Series

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The accurate prediction of train delays is a critical issue for ensuring safe and reliable train operations, as well as for providing passengers with up-to-date and reliable information. Despite an extensive amount of literature on train delay prediction models [1-3], the absence of a reliable standard evaluation framework raises critical questions as posed by [4]: "Are we confident that our models are better, in terms of accuracy, than models developed 30 years ago?" As both research and practice mature, thorough testing and evaluation of prediction algorithms is needed to establish a solid knowledge foundation for the development of future robust real-time train delay prediction algorithms. To address this challenge, this paper proposes a comprehensive evaluation framework for real-time train delay prediction that takes into account three different dimensions: overall, spatial, and temporal performances. The proposed framework aggregates data at various levels, enabling a more comprehensive assessment of prediction models' performance and facilitating the identification of prediction patterns. The study applies the evaluation framework to three different prediction algorithms and evaluates their performance across multiple Swedish train stations over a three-month period. The results demonstrate that the AI solution outperforms both the graph and manual methods. The graph method predicts train delays better on weekends but struggles during peak hours on weekdays. The AI solution exhibits stable performance in predicting train arrival delays, especially during peak hours, and with greater accuracy at busy stations. The study suggests that a combination of AI and graph methods can complement each other in predicting train arrival delays across different time periods and station types. The proposed evaluation framework can be used to assess the effectiveness of different prediction models and can facilitate the development of more reliable and accurate train delay prediction algorithms. This research can have important implications for improving the safety and reliability of train operations, as well as enhancing the overall passenger experience.Machine learning-based evaluation of risk of delay for the real-time Railway Traffic Management Problem

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Algorithms for Smooth, Safe and Quick Routing on Sensor-Equipped Grid Networks

Cited by 4 publications

References 41 publications

Sensors and Communications for the Social Good

Sensors and Communications for the Social Good

Integer Linear Programming Formulations for the Fleet Quickest Routing Problem on Grids

Strengthened Integer Programming Formulations for the Fleet Quickest Routing Problem on Grids

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