Delay Management in Regional Railway Transport

This article is focused on the reliability of transfer connections in regional railway transport. The reliability of the transportation chain in public transport is an essential element for functional, attractive, and long-term sustainable public transport. This article discusses the causes and consequences of railway traffic disruption and related impacts on passenger transfer connections. To reduce the negative impacts of common operational disruptions, the authors present an original approach for determining transfer waiting times between delayed trains based on a modified critical path method (CPM). In addition, an example of the implementation of this method in regional railway transport in the Vysočina Region of the Czech Republic is provided.

Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach

In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.

Tactical runway scheduling for demand and delay management

Purpose Airports are limited in terms of capacity. Particularly, runways can only accommodate a certain number of movements (arrivals and departures) while ensuring safety and determined operational requirements. In such a constrained operating environment, any reduction in system capacity results in major delays with significant costs for airlines and passengers. Therefore, the efficient operation of airports is a critical cornerstone for demand and delay management of the whole air transportation system. Runway scheduling deals with the sequencing of arriving and departing aircraft at airports such that a predefined objective is optimized subject to several operational constraints, like the dependency of separation on the leading and trailing aircraft type or the runway occupancy time. This study aims to develop a model that acts as a tactical runway scheduling methodology for reducing delays while managing runway usage. Design/methodology/approach By considering real airport performance data with scheduled and actual movements, as well as arrival/departure delays, this study presents a robust model together with an optimization algorithm, which incorporates the knowledge of uncertainty into the tactical operational step. The approach transforms the planning problem into an assignment problem with side constraints. The coupled landing/take-off problem is solved to optimality by exploiting a time-indexed (0, 1) formulation for the problem. The Binary Integer Linear Programming approach allows to include multi-criteria and multi-constraints levels and, even with some major simplifications, provides fewer sequence changes and target time updates, when compared to the usual approach in which the plan is simply updated in case of infeasibility. Thus, the use of robust optimization leads to a protection against tactical uncertainties, reduces delays and achieves more stable operations. Findings This model has been validated with real data from a large international European airport in different traffic scenarios. Results are compared to the actual sequencing of flights and show that the algorithm can significantly contribute to the reduction of delay, while adhering as much as possible to the operative procedures and constraints, and to the objectives of the airport stakeholders. Computational experiments performed on the case study illustrate the benefits of this arrival/departure integrated approach: the proposed algorithm significantly reduces weighted aircraft delay and computes efficient runway schedule solutions within a few seconds and with little computational effort. It can be adopted as a decision-making tool in the tactical stage. Furthermore, this study presents operational insights regarding demand and delay management based on the results of this work. Originality/value Scheduling arrivals and departures at runways is a complex problem that needs to address diverse and often competing considerations among involved flights. In the context of the Airport Collaborative Decision Making programme, airport operators and air navigation service providers require arrival and departure management tools that improve aircraft flows at airports. Airport runway optimization, as the main element that combines airside and groundside operations, is an ongoing challenge for air traffic management.

Appraisal of Various Predictors for Complicated Appendicitis in Adults; A Comparative Study Between Complicated and Non-Complicated Appendicitis

Background: Acute appendicitis is the most common non traumatic surgical abdominal emergency, and is the first operation done by most of the general surgeon during their training period. The most important aspect in the management of acute appendicitis, is early diagnosis and intervention to avoid the development of complications.Patients and methods: This prospective study included 184 patients diagnosed with acute appendicitis who were grouped into complicated and non- complicated appendicitis. Both groups were compared to detect predictors for complicated appendicitis to prevent delay management. Results: About 82.6% of our patients were below 30 years (mean: 23.8 years) and 59.2% were females. Histopathology confirmed acute appendicitis in 86.5 %, chronic appendicitis in 12.5%, and normal appendix in 1.1%. About 81.5% have ALVARADO score equal or greater than 7. Complicated appendicitis was diagnosed in 23.37% of patients. There was a significant correlation between complicated appendicitis and gender, rebound tenderness, elevated temperature, elevated WBC, shift to left of WBC and Modified Alvarado Scoring (P values 0.000,0.002,0.001,0.000,0.000, and 0.006), other parameters showed no significant correlations. Conclusion: The rate of complicated appendicitis should be reduced to decrease the associated morbidity, the presence of rebound tenderness, fever, high WBC count and sift to left, a score of 7 or more by modified ALVAADO score, and male sex are highly suggestive. The presence of these factors mandates early and prompt intervention.