Variational Inference for Agent-Based Models with Applications to Achieve Fuel Economy

Wen, Dong; Chunming, Qiao; Transportation Informatics University Transportation Center; State University of New York at Buffalo

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Variational Inference for Agent-Based Models with Applications to Achieve Fuel Economy

2017-10-29
By Wen, Dong ; Chunming, Qiao

[PDF-1.02 MB]

English

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Details:

Creators:

Wen, Dong ; Chunming, Qiao

Wen, Dong ; Chunming, Qiao Less -
Corporate Creators:

Transportation Informatics University Transportation Center ; State University of New York at Buffalo

Transportation Informatics University Transportation Center ; State University of New York at Buffalo Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Algorithms Fuel Consumption Machine Learning Probe Vehicles Real Time Information Smartphones Statistical Inference Traffic Data Traffic Simulation Travel Time
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final June 2016 – June 2017
Corporate Publisher:

Transportation Informatics University Transportation Center
Abstract:

In this project, we developed optimization and sampling based inference algorithms to track and predict real-time traffic dynamics in a city-scale transportation network from an agent-based transportation model and isolated observations of the trajectories of several hundred probe vehicles. We demonstrated the value of combining simulation modeling and big data in delivering travel information to drivers and promoting efficient driving through real world road networks and tracking data from mobile phones. This project integrates machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data, and promotes responsible driving by showing how different agent trips are associated with different travel time and fuel consumption.
Format:

PDF
Funding:

DTRT13-G-UTC48
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:1a7894e09bec3cd1b40c21f67c822c3a5524559b8c82f064e6a539486e00a816
Download URL:

https://rosap.ntl.bts.gov/view/dot/63601/dot_63601_DS1.pdf
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No Additional Files

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Variational Inference for Agent-Based Models with Applications to Achieve Fuel Economy

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