Modal Integration for Improving Urban Mobility in Dhaka
Bibliographic record
Abstract
About 15 million people live in Dhaka, the capital city of Bangladesh, with a growth rate of 1.8%, which creates huge travel demand as well as numerous transport problems. Lack of effective public transport system and door-to-door service influence the augmentation of private cars, which is causing congestion and deterioration of environment. Though railway is a very popular, safe, and cheap transport system of Bangladesh, in absence of proper initiatives and investments, the railway could not play the much expected role in Dhaka's public transport system. However, Dhaka is surrounded by four rivers providing an inbuilt facility for operation of circular waterways, due to financial constrain and lack of appropriate planning for interconnectivity among other modes, it's not serving effectively. The airport is in the northern part of Dhaka, which does not have any integration with the public transport system, railway stations, and waterway terminal. Through the development of public transport system using Mass Rapid Transit, Bus Rapid Transit, commuter rail service along with proper integration of airway and circular waterway, an effective sustainable integrated transport system can be achieved in Dhaka. In this paper an attempt has been made to develop an effective integrated transport system by integrating and improving systematic, effective, and safe operation of all modes. Besides these, the present scenario of transportation system of Dhaka city has been illustrated in the context of transport demand and supply and also discusses potential initiatives that will lead to a sustainable integrated transportation system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".