pith. sign in

arxiv: 1012.4249 · v1 · pith:AS42QHF3new · submitted 2010-12-20 · 💻 cs.LG

Travel Time Estimation Using Floating Car Data

classification 💻 cs.LG
keywords datafloatingreporttechniquestravelaccuratelyarchitecturecity
0
0 comments X
read the original abstract

This report explores the use of machine learning techniques to accurately predict travel times in city streets and highways using floating car data (location information of user vehicles on a road network). The aim of this report is twofold, first we present a general architecture of solving this problem, then present and evaluate few techniques on real floating car data gathered over a month on a 5 Km highway in New Delhi.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. To each route its own ETA: A generative modeling framework for ETA prediction

    cs.LG 2019-06 unverdicted novelty 4.0

    A route-specific deep generative model learns the probability distribution of bus trip ETAs from historical data alone and conditions updates on real-time trip progress.