Analysis and prediction of spatiotemporal impact of traffic incidents for better mobility and safety in transportation systems.

Shahabi, Cyrus; Demiryurek, Ugur; University of Southern California. Dept. of Computer Science

Advanced Search

Select up to three search categories and corresponding keywords using the fields to the right. Refer to the Help section for more detailed instructions.

Search our Collections & Repository

Advanced Search
Custom Query

All these words:

For very narrow results

This exact word or phrase:

When looking for a specific result

Any of these words:

Best used for discovery & interchangable words

None of these words:

Recommended to be used in conjunction with other fields

Language:

Dates

Publication Date Range:

to

Document Data

Title:

Document Type:

Library

Collection:

Series:

People

Author:

Clear All

Query Builder

Query box

Clear All

For additional assistance using the Custom Query please check out our Help Page

ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

i

Analysis and prediction of spatiotemporal impact of traffic incidents for better mobility and safety in transportation systems.

2015-12-01
By Shahabi, Cyrus ; Demiryurek, Ugur

English

Details You May Also Like

Details:

Creators:

Shahabi, Cyrus ; Demiryurek, Ugur

Shahabi, Cyrus ; Demiryurek, Ugur Less -
Corporate Creators:

University of Southern California. Dept. of Computer Science
Corporate Contributors:

United States. Dept. of Transportation. Research and Innovative Technology Administration
Subject/TRT Terms:

[+]

Accident Data Algorithms Analysis Forecasting Police Reports Traffic Data Traffic Delay Traffic Incidents Traffic Models
Publication/ Report Number:

METRANS Project 14-04
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

METRANS Transportation Center (Calif.)
Abstract:

The goal of this research is to develop a machine learning framework to predict the spatiotemporal impact

of traffic accidents on the upstream traffic and surrounding region. The main objective of the framework

is, given a road accident, to forecast when and how the travel-time delay will occur on transportation

network. Towards this end, we have developed a Dynamic Topology-aware Temporal (DTT) machine

learning algorithm that learns the behavior of traffic in both normal conditions and during accidents from

the historical traffic sensor datasets. This research exploits four years of real-world Los Angeles traffic

sensor data and California Highway Patrol (CHP) accidents logs collected from Regional Integration of

Intelligent Transportation Systems (RIITS) under Archived Traffic Data Management System (ADMS)

project.
Format:

PDF
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:e39177e126f06b805a710c89ba7105c16d7e42f4b43caec39dc1967319387c11
Download URL:

https://rosap.ntl.bts.gov/view/dot/29740/dot_29740_DS1.pdf
File Type:

[PDF-1.01 MB]