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Data-Driven Solutions for Addressing Two Pressing Urban Sustainability Challenges : = Air Pollution Reduction and Traffic Management.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data-Driven Solutions for Addressing Two Pressing Urban Sustainability Challenges :/
Reminder of title:	Air Pollution Reduction and Traffic Management.
Author:	Iyer, Shiva Radhakrishnan.
Description:	1 online resource (127 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28865197click for full text (PQDT)
ISBN:	9798426804944

Data-Driven Solutions for Addressing Two Pressing Urban Sustainability Challenges : = Air Pollution Reduction and Traffic Management.
Iyer, Shiva Radhakrishnan.

Data-Driven Solutions for Addressing Two Pressing Urban Sustainability Challenges :Air Pollution Reduction and Traffic Management. - 1 online resource (127 pages)

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--New York University, 2022.

Includes bibliographical references

Data Science and AI-driven solutions are abounding today for a large variety of practical applications. With a continuing focus on urban development and sustainability, in this thesis, I present our attempts in addressing two prominent urban challenges -- urban air pollution control and road traffic congestion management. For both these applications, we have developed novel methods, such as the message-passing recurrent neural network, for predictive analytics and inference in collaboration with economists, public policy experts and ICTD researchers. The city of Delhi has 32 air quality monitors over an area of about 900 sq km, but we do not have information on fine-grained variations in air quality in the city in order to reason about citizen exposure and identify hotspots. We have installed 28 low-cost sensors, many of them concentrated in the south Delhi region. We have identified many hotspots by studying spatio-temporal variations from the data, further motivating the need for fine-grained sensing. And ultimately, we designed a novel model combining geostatistics and deep learning that is able to make spatio-temporal pollution forecasts by the hour with an MAPE of about 10% across all locations.Urban traffic management is another pressing challenge in an era where we observe increasing urbanization and industrialization. Simply building new lanes and larger roads is not enough -- we need to go back to formula and understand how jams happen, and how we can effectively implement traffic control. In the first of our works, we show that road networks can experience traffic jams over prolonged periods, as high as 20 hours sometimes, due to sudden traffic bursts over short time scales. We illustrate this using real data from two different cities -- New York and Nairobi. We provide a formalism for understanding the phenomena of traffic collapse and sudden jams. In the second work, we devise a novel model called the message-passing neural network for modeling the propagation of congestion within a road network and forecasting congestion. The MPRNN achieves the lowest mean error of 0.3 mph when predicting ahead in 10 minute intervals, for up to 3 road segments ahead (message passing across 3 hops). Finally, in the third work, we describe an algorithm for signal control in free-flow road networks, inspired from congestion control in computer networks. Our proposed method significantly enhances the operational capacity of free-flow road networks in the real world by several orders of magnitude (3x-5x) and prevents congestion collapse.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798426804944Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Air pollutionIndex Terms--Genre/Form:

542853
Electronic books.

Data-Driven Solutions for Addressing Two Pressing Urban Sustainability Challenges : = Air Pollution Reduction and Traffic Management.
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502 $a Thesis (Ph.D.)--New York University, 2022.
504 $a Includes bibliographical references
520 $a Data Science and AI-driven solutions are abounding today for a large variety of practical applications. With a continuing focus on urban development and sustainability, in this thesis, I present our attempts in addressing two prominent urban challenges -- urban air pollution control and road traffic congestion management. For both these applications, we have developed novel methods, such as the message-passing recurrent neural network, for predictive analytics and inference in collaboration with economists, public policy experts and ICTD researchers. The city of Delhi has 32 air quality monitors over an area of about 900 sq km, but we do not have information on fine-grained variations in air quality in the city in order to reason about citizen exposure and identify hotspots. We have installed 28 low-cost sensors, many of them concentrated in the south Delhi region. We have identified many hotspots by studying spatio-temporal variations from the data, further motivating the need for fine-grained sensing. And ultimately, we designed a novel model combining geostatistics and deep learning that is able to make spatio-temporal pollution forecasts by the hour with an MAPE of about 10% across all locations.Urban traffic management is another pressing challenge in an era where we observe increasing urbanization and industrialization. Simply building new lanes and larger roads is not enough -- we need to go back to formula and understand how jams happen, and how we can effectively implement traffic control. In the first of our works, we show that road networks can experience traffic jams over prolonged periods, as high as 20 hours sometimes, due to sudden traffic bursts over short time scales. We illustrate this using real data from two different cities -- New York and Nairobi. We provide a formalism for understanding the phenomena of traffic collapse and sudden jams. In the second work, we devise a novel model called the message-passing neural network for modeling the propagation of congestion within a road network and forecasting congestion. The MPRNN achieves the lowest mean error of 0.3 mph when predicting ahead in 10 minute intervals, for up to 3 road segments ahead (message passing across 3 hops). Finally, in the third work, we describe an algorithm for signal control in free-flow road networks, inspired from congestion control in computer networks. Our proposed method significantly enhances the operational capacity of free-flow road networks in the real world by several orders of magnitude (3x-5x) and prevents congestion collapse.
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653 $a Urban sustainability
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