Bayesian inference for link travel time correlation of a bus route

02/19/2022
by   Xiaoxu Chen, et al.
0

Estimation of link travel time correlation of a bus route is essential to many bus operation applications, such as timetable scheduling, travel time forecasting and transit service assessment/improvement. Most previous studies rely on either independent assumptions or simplified local spatial correlation structures. In the real world, however, link travel time on a bus route could exhibit complex correlation structures, such as long-range correlations, negative correlations, and time-varying correlations. Therefore, before introducing strong assumptions, it is essential to empirically quantify and examine the correlation structure of link travel time from real-world bus operation data. To this end, this paper develops a Bayesian Gaussian model to estimate the link travel time correlation matrix of a bus route using smart-card-like data. Our method overcomes the small-sample-size problem in correlation matrix estimation by borrowing/integrating those incomplete observations (i.e., with missing/ragged values and overlapped link segments) from other bus routes. Next, we propose an efficient Gibbs sampling framework to marginalize over the missing and ragged values and obtain the posterior distribution of the correlation matrix. Three numerical experiments are conducted to evaluate model performance. We first conduct a synthetic experiment and our results show that the proposed method produces an accurate estimation for travel time correlations with credible intervals. Next, we perform experiments on a real-world bus route with smart card data; our results show that both local and long-range correlations exist on this bus route. Finally, we demonstrate an application of using the estimated covariance matrix to make probabilistic forecasting of link and trip travel time.

READ FULL TEXT

page 11

page 13

page 14

page 15

page 17

research
06/14/2022

Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model

Accurate forecasting of bus travel time and its uncertainty is critical ...
research
04/23/2020

Inference for travel time on transportation networks

Travel time is essential for making travel decisions in real-world trans...
research
05/01/2020

TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Data

In this paper, we target at recovering the exact routes taken by commute...
research
08/19/2021

Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach

Urban rail transit (URT) system plays a dominating role in many megaciti...
research
05/07/2020

Modelling arterial travel time distribution using copulas

The estimation of travel time distribution (TTD) is critical for reliabl...
research
06/21/2022

Route to Time and Time to Route: Travel Time Estimation from Sparse Trajectories

Due to the rapid development of Internet of Things (IoT) technologies, m...
research
01/15/2022

Big Data Application for Network Level Travel Time Prediction

Travel time is essential in advanced traveler information systems (ATIS)...

Please sign up or login with your details

Forgot password? Click here to reset