ArticleJournal of the Royal Society, Interface2017
Collective benefits in traffic during mega events via the use of information technologies.
Article in Journal of the Royal Society, Interface, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Human Mobility Datasets in the Complex Metro System of Shanghai.Scientific data · 2025Article
- A dataset to assess mobility changes in Chile following local quarantines.Scientific data · 2023Article
- Understanding congestion propagation by combining percolation theory with the macroscopic fundamental diagram.Communications physics · 2023Article
- Quantifying navigation complexity in transportation networks.PNAS nexus · 2022Article
- The impact of digital navigation on travel behaviour.UCL open. Environment · 2022Article
- Deconstructing laws of accessibility and facility distribution in cities.Science advances · 2020Article
- Scaling in the recovery of urban transportation systems from massive events.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Information technologies today can inform each of us about the route with the shortest time, but they do not contain incentives to manage travellers such that we all get collective benefits in travel times. To that end we need travel demand estimates and target strategies to reduce the traffic volume from the congested roads during peak hours in a feasible way. During large events, the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for collective benefits in traffic. In this paper, we integrate, for the first time, big data resources to estimate the impact of events on traffic and propose target strategies for collective good at the urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic. To that end, we integrate data from mobile phones, Airbnb, Waze and transit information, with game schedules and expected attendance in each venue. Next, we evaluate different route choice scenarios for drivers during the peak hours. Finally, we gather information on the trips that contribute the most to the global congestion which could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest times can save more collective travel time than keeping the routine routes used before the event, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent. Results are presented online for evaluation by the public and policymakers (www.flows-rio2016.com (last accessed 3 September 2017)).
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.