scholarly journals Modelling travel time uncertainty in urban networks based on floating taxi data

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Dietmar Bauer ◽  
Mirsad Tulic ◽  
Wolfgang Scherrer
Keyword(s):  
Travel Time ◽  
Prediction Errors ◽  
Travel Times ◽  
Model Errors ◽  
Travel Time Prediction ◽  
Data Set ◽  
Time Uncertainty ◽  
Time Prediction ◽  
Travel Time Uncertainty ◽  
Link Travel Time

Abstract The prediction of the uncertainty of route travel time predictions for all possible routes in an urban road network is of importance for example for logistics. Such predictions need to take the essential features of the data set as well as the underlying traffic dynamics into account.In this paper a large floating taxi data set is used in order to derive predictions of route travel time uncertainty based on link travel time uncertainty predictions. Prediction errors, that is actual travel times minus predicted travel times, are differentiated from model errors, that is measured travel times minus predicted travel times. These two errors are related, but not identical, as model errors contain measurement noise while the prediction errors do not. Detailed models for the variance of the link travel time prediction errors as well as the correlation between the model errors for different links are derived. The models are validated in depth using two different validation data sets.Estimates for the variance of prediction errors are obtained. The standardized model error distributions show a remarkable stability, such that modelling the variance appears to be sufficient for quantifying the uncertainty of the model errors.Furthermore we show that the model errors for adjacent links are highly correlated but correlations fade with increasing distance. Additionally usage of the road network plays a role with high correlation for links along common routes and low correlations for links along seldom used routes. We assume identical features for the prediction errors which is partly validated based on additional data.The paper provides a way to estimate the complete distribution of route travel time prediction errors for any given route in the street network.

Day-to-Day Travel-Time Trends and Travel-Time Prediction from Loop-Detector Data

2000 ◽  
Vol 1717 (1) ◽  
pp. 120-129 ◽  
Author(s):  
Jaimyoung Kwon ◽  
Benjamin Coifman ◽  
Peter Bickel
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Prediction Errors ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Loop Detectors ◽  
Stepwise Variable Selection ◽  
Planning Models ◽  
Current Implementation

An approach is presented for estimating future travel times on a freeway using flow and occupancy data from single-loop detectors and historical travel-time information. Linear regression, with the stepwise-variable-selection method and more advanced tree-based methods, is used. The analysis considers forecasts ranging from a few minutes into the future up to an hour ahead. Leave-a-day-out cross-validation was used to evaluate the prediction errors without underestimation. The current traffic state proved to be a good predictor for the near future, up to 20 min, whereas historical data are more informative for longer-range predictions. Tree-based methods and linear regression both performed satisfactorily, showing slightly different qualitative behaviors for each condition examined in this analysis. Unlike preceding works that rely on simulation, real traffic data were used. Although the current implementation uses measured travel times from probe vehicles, the ultimate goal is an autonomous system that relies strictly on detector data. In the course of presenting the prediction system, the manner in which travel times change from day to day was examined, and several metrics to quantify these changes were developed. The metrics can be used as input for travel-time prediction, but they also should be beneficial for other applications, such as calibrating traffic models and planning models.

Vehicle Travel Time Prediction in Spatio-Temporal Space

2012 ◽  
Vol 253-255 ◽  
pp. 1662-1665
Author(s):  
Qing Li ◽  
William H. K. Lam ◽  
Mei Lam Tam
Keyword(s):  
Travel Time ◽  
Travel Times ◽  
Spatial Correlations ◽  
Travel Time Prediction ◽  
Time Data ◽  
Time Prediction ◽  
Temporal Space ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Link Travel Time

It is recognized that travel times on a link are temporally correlated with its travel times of previous time periods. Also, the link travel time are spatially correlated by travel times on its neighboring links. Based on such temporal and spatial correlations, a new method is proposed for travel time prediction in urban roads. The proposed method is capable of rapidly predicting the link travel time in the near future. For validation of the proposed method, the temporal and spatial variance-covariance of travel times on related links are employed together with historical travel time data. It is found that the proposed method is able to provide more accurate travel time prediction.

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

scholarly journals A dynamic shortest path algorithm using multi-step ahead link travel time prediction

2005 ◽  
Vol 39 (1) ◽  
pp. 5-18 ◽  
Author(s):  
Young-Ihn Lee ◽  
Seungjae Lee ◽  
Shinhae Lee ◽  
Jeunggyu Chon
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Shortest Path Algorithm ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Link Travel Time

Resampling Methods for Estimating Travel Time Uncertainty: Application of the Gap Bootstrap

2018 ◽  
Vol 2672 (42) ◽  
pp. 137-147 ◽  
Author(s):  
Bhaven Naik ◽  
Laurence R. Rilett ◽  
Justice Appiah ◽  
Lubinda F. Walubita
Keyword(s):  
Travel Time ◽  
Bootstrap Method ◽  
Point Estimate ◽  
Data Sets ◽  
Block Bootstrap ◽  
Travel Time Prediction ◽  
Resampling Methods ◽  
Time Uncertainty ◽  
Uncertainty Estimates ◽  
Travel Time Uncertainty

To a large extent, methods of forecasting travel time have placed emphasis on the quality of the forecasted value—how close is the forecast point estimate of the mean travel time to its respective field value? However, understanding the reliability or uncertainty margin that exists around the forecasted point estimate is also important. Uncertainty about travel time is a fundamental factor as it leads end-users to change their routes and schedules even when the average travel time is low. Statistical resampling methods have been used previously for uncertainty modeling within the travel time prediction environment. This paper applies a recently developed nonparametric resampling method, the gap bootstrap, to the travel time uncertainty estimation problem, especially as it pertains to large (probe) data sets for which common resampling methods may not be practical because of the possible computational burden and complex patterns of inhomogeneity. The gap bootstrap partitions the original data into smaller groups of approximately uniform data sets and recombines individual group uncertainty estimates into a single estimate of uncertainty. Results of the gap bootstrap uncertainty estimates are compared with those of two popular resampling methods—the traditional bootstrap and the block bootstrap. The results suggest that, for the datasets used in this research, the gap bootstrap adequately captures the dependent structure when compared with the traditional and block bootstrap methods and may thus yield more credible estimates of uncertainty than either the block bootstrap method or the traditional bootstrap method.

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