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The urban population are increasingly suffering from rising transport costs, worsening air quality, longer commuting time, and traffic congestion. Although much scholarly attention has focused on modeling urban traffic congestion, news contents about traffic jam have rarely been examined systematically. This study selects 12 large metropolitan cities across Asia (Beijing, Bengaluru, Hong Kong, Jakarta, Kuala Lumpur, Manila, and Singapore), Oceania (Auckland and Sydney), Europe (London) and North America (Los Angeles and Toronto) for an in-depth content analysis. More than 40,000 pieces of congestion-related articles in the 2009–2018 period have been identified in the local news media of these cities. We apply techniques of text analytics to analyze underlying themes in relation to sustainable transport and congestion-mitigation measures. Also, a sentiment analysis is conducted to examine the level of frustration expressed. Results show that traffic congestion is no longer perceived to be primarily an economic issue. Concerns over the environmental impacts of traffic congestion were increasingly discussed. Based on the content analysis, cities in Asia mentioned a lot about congestion-related PM2.5 pollution and climate change was a recurrent theme among non-Asian cities. Economic cost related to traffic congestion has received much more attention in high-income cities. With regard to congestion mitigation measures, terms related to promoting public and active transport was the most popular, followed by restriction and regulation measures, and then intelligent transport system (ITS) or smart mobility adoption. It is noteworthy that road capacity expansion has attracted the lowest coverage. Generally, high-density cities discussed more about public and active transport in alleviating traffic jams; and police enforcement was seen as important in tackling traffic congestion across all cities. In relation to the sentiment, there is a positive association between the overall traffic congestion level and the congestion frustration level expressed in local news.
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Stored only when both papers are in the corpus — 42 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.