research-article

Analyze NYC Transportation to Mitigate Speeding and Explore New Business Models Using Machine Learning

Authors:

Joe Ma,

Dong SiAuthors Info & Claims

ICCDA '17: Proceedings of the International Conference on Compute and Data Analysis

Pages 75 - 80

https://doi.org/10.1145/3093241.3093291

Published: 19 May 2017 Publication History

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Abstract

Many cities have been releasing their traffic data for companies to do the data analytics for business and other purposes. In this paper, we propose different classification models to analyze the places that most of vehicles speed and the places that most of vehicles visited in specific time range. The result of it can be helpful for the government allocating the police in the correct timing and places to catch vehicles speeding. In addition, by knowing the result of the major places that most of vehicles visited, it's helpful for gas companies to decide where they should build gas stations.

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"ABOUT DOT," New York City Department of Transportation. [Online].

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Index Terms

Analyze NYC Transportation to Mitigate Speeding and Explore New Business Models Using Machine Learning
1. Information systems
  1. Information systems applications
    1. Data mining

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ICCDA '17: Proceedings of the International Conference on Compute and Data Analysis

May 2017

307 pages

ISBN:9781450352413

DOI:10.1145/3093241

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Florida: University of Florida

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Association for Computing Machinery

New York, NY, United States

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Published: 19 May 2017

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ICCDA '17

ICCDA '17: 2017 International Conference on Compute and Data Analysis

May 19 - 23, 2017

FL, Lakeland, USA

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Car-Following-Response-Based Vehicle Classification via Deep Learning

Airborne traffic surveillance systems: video surveillance of highway traffic

Predicting truck parking occupancy using machine learning

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Car-Following-Response-Based Vehicle Classification via Deep Learning

Airborne traffic surveillance systems: video surveillance of highway traffic

Predicting truck parking occupancy using machine learning

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