Monkeys navigate using mental maps just like us

Duke, Cameron

doi:10.1016/s0262-4079(21)01453-6

Cited by 2 publications

(2 citation statements)

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“…However, most of the previous work is focused on minimising the exhaust emissions and fuel consumption of a vehicle. In recent times, Google Maps has introduced eco-friendly routing, which suggests a path in a similar manner (Dicker 2021). Our work focuses on improving the health of residents who live near frequently used roads by reducing their long term exposure to high levels of pollution i.e., we do not explicitly aim to reduce the pollution but aim to distribute the pollution better.…”

Section: Related Workmentioning

confidence: 99%

“…A solution can be implemented by restricting the set of paths available for a vehicle to take and the number of vehicles on each path but does not restrict a vehicle's choice among the allowed ones. (b) GPS software(s) like Google maps can use to suggest different set of paths to different users on different days (Dicker 2021). (c) Autonomous cars can be routed using our method, where vehicles can be instructed to use different paths.…”

Section: Implementing Policy In Simulation and Real Worldmentioning

confidence: 99%

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City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC

Suriyanarayanan

Paruchuri

Varma

2023

AAAI

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A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We give the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Implementing Policy In Simulation and Real Worldmentioning

confidence: 99%

City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC

Suriyanarayanan

Paruchuri

Varma

2023

AAAI

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City-scale Pollution Aware Traffic Routing by Sampling Max Flows using MCMC

Suriyanarayanan¹,

Paruchuri²,

Varma³

2023

Preprint

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A significant cause of air pollution in urban areas worldwide is the high volume of road traffic. Long-term exposure to severe pollution can cause serious health issues. One approach towards tackling this problem is to design a pollution-aware traffic routing policy that balances multiple objectives of i) avoiding extreme pollution in any area ii) enabling short transit times, and iii) making effective use of the road capacities. We propose a novel sampling-based approach for this problem. We provide the first construction of a Markov Chain that can sample integer max flow solutions of a planar graph, with theoretical guarantees that the probabilities depend on the aggregate transit length. We designed a traffic policy using diverse samples and simulated traffic on real-world road maps using the SUMO traffic simulator. We observe a considerable decrease in areas with severe pollution when experimented with maps of large cities across the world compared to other approaches.

show abstract

Monkeys navigate using mental maps just like us

Cited by 2 publications

References 0 publications

City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC

City-Scale Pollution Aware Traffic Routing by Sampling Max Flows Using MCMC

City-scale Pollution Aware Traffic Routing by Sampling Max Flows using MCMC

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