Collective benefits in traffic during mega events via the use of information technologies
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Abstract (arXiv)
Information technologies today can inform each of us about the best alternatives for shortest paths from origins to destinations, but they do not contain incentives or alternatives that manage the information efficiently to get collective benefits. To obtain such benefits, we need to have not only good estimates of how the traffic is formed but also to have target strategies to reduce enough vehicles from the best possible roads in a feasible way. The opportunity is that 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 social good. In this paper, we integrate for the first time big data resources to quantify the impact of events and propose target strategies for collective good at 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 information of venues. Next, we evaluate the impact of the Olympic Games to the travel of commuters, and propose different route choice scenarios during the peak hours. Moreover, we gather information on the trips that contribute the most to the global congestion and that could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest path can save more collective travel time than keeping the routine routes, 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 on-line for the evaluation of the public and policy makers.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jul 2016.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.
3 papers.
- 2021PG2Net: Personalized and Group Preferences Guided Network for Next Place Predictioncited by 1
- 2023Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continentscited by 0
- 2024TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network for Mobility Predictioncited by 0
- 2024TrajGEOS: Trajectory Graph Enhanced Orientation-based Sequential Network for Mobility Prediction26 Dec 2024
- 2023Using machine learning to understand causal relationships between urban form and travel CO2 emissions across continents31 Aug 2023
- 2021PG2Net: Personalized and Group Preferences Guided Network for Next Place Prediction15 Oct 2021