# Worldwide city transport typology prediction with sentence-BERT based supervised learning via Wikipedia

arXiv: https://arxiv.org/abs/2204.05193 · HTML: https://mlsanity.com/paper/2204.05193/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 79 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Predicting Economic Development using Geolocated Wikipedia Articles](https://mlsanity.com/paper/1905.01627/) (2019)
- [Towards a comparative science of cities: using mobile traffic records in New York, London and Hong Kong](https://mlsanity.com/paper/1406.4400/) (2014)

## Built on by (most cited first, 3 of 3)

- [A deep real options policy for sequential service region design and timing](https://mlsanity.com/paper/2212.14800/) (2022)
- [Enabling CMF Estimation in Data-Constrained Scenarios: A Semantic-Encoding Knowledge Mining Model](https://mlsanity.com/paper/2311.08690/) (2023)
- [Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports](https://mlsanity.com/paper/2403.17914/) (2024)

## Abstract (arXiv)

An overwhelming majority of the world's human population lives in urban areas and cities. Understanding a city's transportation typology is immensely valuable for planners and policy makers whose decisions can potentially impact millions of city residents. Despite the value of understanding a city's typology, labeled data (city and it's typology) is scarce, and spans at most a few hundred cities in the current transportation literature. To break this barrier, we propose a supervised machine learning approach to predict a city's typology given the information in its Wikipedia page. Our method leverages recent breakthroughs in natural language processing, namely sentence-BERT, and shows how the text-based information from Wikipedia can be effectively used as a data source for city typology prediction tasks that can be applied to over 2000 cities worldwide. We propose a novel method for low-dimensional city representation using a city's Wikipedia page, which makes supervised learning of city typology labels tractable even with a few hundred labeled samples. These features are used with labeled city samples to train binary classifiers (logistic regression) for four different city typologies: (i) congestion, (ii) auto-heavy, (iii) transit-heavy, and (iv) bike-friendly cities resulting in reasonably high AUC scores of 0.87, 0.86, 0.61 and 0.94 respectively. Our approach provides sufficient flexibility for incorporating additional variables in the city typology models and can be applied to study other city typologies as well. Our findings can assist a diverse group of stakeholders in transportation and urban planning fields, and opens up new opportunities for using text-based information from Wikipedia (or similar platforms) as data sources in such fields.