Decentralization of Metropolitan Campus to Reduce the Discomforts for Students Due to Excessive Travel Times: A Case Study of UFPE In Recife – Brazil
Bibliographic record
Abstract
The Recife Campus of the Federal University of Pernambuco – UFPE (public and free of charge for students) is located in the Northeast of Brazil in a metropolitan area that has a population of about 4 million of inhabitants. Its academic community resides within a radius of 35 km away and commuting in a congested and chaotic urban traffic. The average traffic speed in the peak times is below 18 km/h for individual vehicles (cars and motorcycles) and about 10 km/h for public transport, mainly buses in mixed flows. The travel times are excessive and penalize the population, especially 64% of the students, captive users of public transport. To make the problem even more serious, about 53% of the student public transport users make changes in crowed integrated stations, increasing even further the travel time. Teachers are less penalized because 88.14% of them use private automobiles and the majority spends less than 45 minutes to go to the university. UFPE’s Campus is located in a 149 hectares area in the west of the city of Recife, in a dense area of mixed land use: residential and commercial. Currently, there are 99 undergraduate courses and 128 post-graduate studies courses. UFPE daily attracts trips from 14 cities in the metropolitan area about 40 thousand people, including teachers, technical and administrative staff, undergraduate and graduate students, as well as visitors. Due to the deficiencies in the metropolitan public transport system of Recife and its road system with low traffic capacity, only a quarter of the campus users spend less than 30 minutes of travel time, approximately 38.5% spend more than an hour and about 16.5%, more than two hours. In addition to the current accessibility problems, this UFPE Campus is near its saturation capacity in terms of occupation of available physical space, as there is still local demand for growth, since the relative number of people graduated in superior studies in the region is still low. This imposes the urgent need for planning for future expansions. Based on the present location problems, a question remains to be answered: is it possible to reduce the concentration of academic activities of a public university, providing better accessibility conditions to students? Assuming that it is not feasible to substantially reduce travel times in the current location of UFPE Recife Campus, seen from the transport and road systems structural conditions, the objective of this paper is to study alternatives for decentralization of UFPE activities, limiting the travel times in a comfort boundary of 60 minutes. Through an analysis of accessibility indices based on a gravitational model, including the current location of student housing (demand) and an impedance function based on travel times, to study alternative locations that maximize accessibility for the total demand for the university, considering a demand growth of 50% for the next 20 years. Observing the isochronous of travel times by public transport obtained by a survey with 1,000 interviews (students, teachers and administrative personal) it is noticed that the travel times up to one hour stand at a distance of up to 11 to 12 km. Moreover, it is not reasonable to expect considerable increases in average traffic speed in the city, it is clear that this is the acceptable limit for the home-university travel distance. From this point of view, it proceeds to search alternative places in the south and/or north of the metropolitan area, or in the city center to be more accessible, to implement academic units of the university, shortening distances and/or travel time for residents in the periphery, in order to improve the accessibility conditions for all. As the survey identified the course where the students are currently enrolled and their places of residence is also possible to indicate the optimal locations and associate it to the courses to be offered.The results are expected to support the University Master Development Plan in defining its future strategies, as a public university committed with the quality of life for its students and workers and contribute to a sustainable mobility for the Metropolitan Area of Recife – RMR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".