Representation learning of rare temporal conditions for travel time prediction

08/09/2022
by   Niklas Petersen, et al.
0

Predicting travel time under rare temporal conditions (e.g., public holidays, school vacation period, etc.) constitutes a challenge due to the limitation of historical data. If at all available, historical data often form a heterogeneous time series due to high probability of other changes over long periods of time (e.g., road works, introduced traffic calming initiatives, etc.). This is especially prominent in cities and suburban areas. We present a vector-space model for encoding rare temporal conditions, that allows coherent representation learning across different temporal conditions. We show increased performance for travel time prediction over different baselines when utilizing the vector-space encoding for representing the temporal setting.

READ FULL TEXT
research
09/02/2020

Travel time prediction for congested freeways with a dynamic linear model

Accurate prediction of travel time is an essential feature to support In...
research
11/17/2022

Self-supervised Trajectory Representation Learning with Temporal Regularities and Travel Semantics

Trajectory Representation Learning (TRL) is a powerful tool for spatial-...
research
04/06/2019

Bus Travel Time Prediction: A Lognormal Auto-Regressive (AR) Modeling Approach

Providing real time information about the arrival time of the transit bu...
research
07/12/2018

Tracking the Evolution of Words with Time-reflective Text Representations

More than 80 unstructured datasets evolving over time. A large part of t...
research
09/16/2023

Test-Time Compensated Representation Learning for Extreme Traffic Forecasting

Traffic forecasting is a challenging task due to the complex spatio-temp...
research
11/15/2017

Predicting vehicular travel times by modeling heterogeneous influences between arterial roads

Predicting travel times of vehicles in urban settings is a useful and ta...

Please sign up or login with your details

Forgot password? Click here to reset