Accurate short-term traffic flow prediction is crucial for effective urban traffic management. However, selecting the most suitable prediction model and relevant features poses a significant challenge. Moreover, predicting traffic flow on a road using only historical data, adjacent roads, or all roads in the study area can compromise the model’s accuracy or execution time. This paper tackles this challenge by proposing an enhanced Vector Auto regression VAR-based prediction method. We suggest selecting relevant roads for the model using spatio-temporal correlation analysis. Subsequently, we conduct a comparative study between our methodology’s results and those obtained from other temporal and spatio-temporal traffic forecasting methods, including historical average, K-nearest neighbors (KNN), support vector machine for regression (SVR), and autoregressive model (AR). Model performance is evaluated by considering both the impact of normal and abnormal traffic conditions, as well as the selected training days: weekdays and weekends. The study utilizes a traffic dataset collected from an area of Xuancheng city in China. The proposed enhanced VAR outperforms the other methods for short-term forecasting horizons ($\approx$ from 5 to 25 minutes), under both normal and abnormal traffic conditions.

Accurate origin-destination (OD) matrices, which represent the travel patterns of individuals or groups between different locations, are vital for traffic planning and logistics. Traditionally, they’re determined through costly surveys, often resulting in outdated data. A newer method involves using traffic sensors, but mapping these counts to OD matrices is challenging, especially when sensor coverage is limited.To tackle this, we propose using traffic simulators like SUMO to create synthetic datasets. Machine learning models are then trained on this data to predict OD matrices from traffic counts. Different machine learning models have been investigated in this paper, we focus on Random Forest models, which consistently outperform existing methods like DFrouter, a SUMO tool for OD matrix estimation, and evaluated on the same test data using the root mean squared error (RMSE) and mean absolute error (MAE) metrics. The proposed Random Forest-based method is shown to consistently outperform DFrouter, achieving average errors three times lower than DFrouter.

Weak student orientation for university students can create several problems such as skill mismatch and a high rate of unemployment. Youth often choose education paths that do not qualify them for employment, mainly because they lack reliable information about job market needs. Despite this issue, no previous research has attempted to connect university curricula to up-to-date job market needs. Youth usually rely on information from their families and friends and sometimes go to career centers to meet with career counselors. However, the resources youth go to do not have an overview of the job market needs. Students need to be provided with information about the job market needs and be aware of universities’ training that they can follow. This paper collects data from multiple sources such as job portals and universities websites to inform students about the best options for their future university training plans based on up-to-date job market needs for each occupation. In this study, we focus on the IT sector because it is the sector recruiting the most. We use machine learning algorithms to classify job ads into their corresponding occupation and then use text analysis techniques to extract requirements and skills from job ads. Furthermore, we connect jobs with universities training to build an orientation path using text analysis and the assistance of HR specialists. Our paper finds that the occupations related to programming are the most in-demand jobs for the IT sector and sometimes don’t require years of experience. We also identified emerging occupations such as data scientists who doubled the number of job openings within a year. When comparing university curricula to our job market findings, we found a small mismatch between the university offerings and the number of job ads in some occupations. Based on the results, a Dashboard will be developed to help students choose a path that will lead them in having a job.

The content-centric networking (CCN) paradigm proposed by PARC holds considerable promise as the architecture for the future Internet but remains incomplete. In this paper we argue that it is necessary to supplement CCN with mechanisms enabling controlled sharing of network bandwidth by concurrent flows. Traffic control is necessary to ensure low latency for conversational and streaming flows and to realize satisfactory bandwidth sharing between elastic flows. These objectives can be realized using “network neutral” traffic controls based on per-flow fair bandwidth sharing. The paper describes necessary CCN mechanisms and discusses the strategies to be implemented by end systems and routers. A simulation-based performance evaluation demonstrates the effectiveness of the proposed mechanisms and and the impact of possible strategies. We describe our experience in implementing some of the mechanisms using the CCNx prototype software.