Abstract
Accurate traffic state prediction is fundamental to Intelligent Transportation Systems, playing a critical role in optimising traffic management, improving mobility, and enhancing the efficiency of transportation networks. Traditional methods often rely on feature engineering, statistical time-series approaches, and non-parametric techniques to model the inherent complexities of traffic states, incorporating external factors such as weather conditions and accidents to refine predictions. However, the effectiveness of long-term traffic state prediction hinges on capturing spatial-temporal dependencies over extended periods. Current models face challenges in dealing with (i) high-dimensional traffic features, (ii) error accumulation for multi-step prediction, and (iii) robustness to external factors effectively. To address these challenges, this study proposes a novel model with a Dynamic Feature Embedding layer designed to transform complex data sequences into meaningful representations and a Deep Linear Projection network that refines these representations through non-linear transformations and gating mechanisms. These two features make the model more scalable when dealing with high-dimensional traffic features. The model also includes a Spatial-Temporal Positional Encoding layer to capture spatial-temporal relationships, masked multi-head attention-based encoder blocks, and a Residual Temporal Convolutional Network to process features and extract short-and long-term temporal patterns. Finally, a Time-Distributed Fully Connected Layer produces accurate traffic state predictions up to 24 timesteps into the future. The proposed architecture uses a direct strategy for multi-step modelling to help predict timesteps non-autoregressively and thus circumvents the error accumulation problem. The model was evaluated against state-of-the-art baselines using two benchmark datasets. Experimental results demonstrated the model's superiority, achieving up to 21.17% and 29.30% average improvements in Root Mean Squared Error and 3.56% and 32.80% improvements in Mean Absolute Error compared to the baselines, respectively. The Friedman Chi-Square statistical test further confirmed the significant performance difference between the proposed model and its counterparts. The adversarial perturbations and random sensor dropout tests demonstrated its good robustness. On top of that, it demonstrated good generalizability through extensive experiments. The model effectively mitigates error accumulation in multi-step predictions while maintaining computational efficiency, making it a promising solution for enhancing Intelligent Transportation Systems.

Abstract
The rehabilitation of burn patients is essential and is intrinsically linked to conventional rehabilitation; the motivational challenges faced by burn patients in maintaining engagement with these rehabilitation programs are well known. It is understood that the use of other resources, particularly technological ones, associated with conventional rehabilitation could overcome these constraints and thereby optimize the rehabilitation program and health outcomes. The objective of this study is to synthesize the available evidence on the use of exergames in rehabilitation programs for burn patients. This systematic review was developed following the guidelines of the Joanna Briggs Institute (JBI). The search was conducted in the following databases: Medline®, CINAHL®, Sports Discus®, Cochrane®, and Scopus® during May 2025. The PRISMA Checklist Model was used to organize the information from the selected studies. Seven RCTs were included, involving a total of 236 participants. Outcomes related to the use of exergames in the rehabilitation of burn patients were identified, including increased range of motion, functionality, strength, speed of movement, improved balance, reduced fear and pain, and satisfaction with the technological resource used. It is believed that the results of this review, which confirmed the advantage of using exergames, such as Nintendo Wii, PlayStation, Xbox Kinect, or Wii Fit, to optimize the functionality of burn patients, can support clinical decision-making and encourage the integration of exergames to improve rehabilitation programs for burn patients.

Abstract
Balancing supply and demand in free-floating one-way carsharing systems is a critical operational challenge. This paper presents a novel approach that integrates a binary logit model into a mixed integer linear programming framework to optimize short-term pricing and fleet relocation. Demand modeling, based on a binary logit model, aggregates different trips under a unified utility model and improves estimation by incorporating information from similar trips. To speed up the estimation process, a categorizing approach is used, where variables such as location and time are classified into a few categories based on shared attributes. This is particularly beneficial for trips with limited observations as information gained from similar trips can be used for these trips effectively. The modeling framework adopts a dynamic structure where the binary logit model estimates demand using accumulated observations from past iterations at each decision point. This continuous learning environment allows for dynamic improvement in estimation and decision-making. At the core of the framework is a mathematical program that prescribes optimal levels of promotion and relocation. The framework then includes simulated market responses to the decisions, allowing for real-time adjustments to effectively balance supply and demand. Computational experiments demonstrate the effectiveness of the proposed approach and highlight its potential for real-world applications. The continuous learning environment, combining demand modeling and operational decisions, opens avenues for future research in transportation systems.

Abstract

Abstract

Abstract
The Prisoner Transportation Problem is an NP-hard combinatorial problem and a complex variant of the Dial-a- Ride Problem. Given a set of requests for pick-up and delivery and a homogeneous fleet, it consists of assigning requests to vehicles to serve all requests, respecting the problem constraints such as route duration, capacity, ride time, time windows, multi-compartment assignment of conflicting prisoners and simultaneous services in order to optimize a given objective function. In this paper, we present anew solution framework to address this problem that leads to an efficient heuristic. A comparison with computational results from previous papers shows that the heuristic is very competitive for some classes of benchmark instances from the literature and clearly superior in the remaining cases. Finally, suggestions for future studies are presented.