An adaptive model for highway travel time prediction

Liu, Xiaobo; Chien, Steven; Chen, Mei

doi:10.1002/atr.1216

Cited by 12 publications

(8 citation statements)

References 37 publications

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“…Regression analysis was attempted by many researchers to predict traffic parameters in the near future . The time series analysis involves the examination of historical data, extracting essential data characteristics and effectively projecting these characteristics into the future . ANN is one of the machine learning techniques that has been extensively used for the prediction of traffic parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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Summary The travel decisions made by road users are more affected by the traffic conditions when they travel than the current conditions. Thus, accurate prediction of traffic parameters for giving reliable information about the future state of traffic conditions is very important. Mainly, this is an essential component of many advanced traveller information systems coming under the intelligent transportation systems umbrella. In India, the automated traffic data collection is in the beginning stage, with many of the cities still struggling with database generation and processing, and hence, a less‐data‐demanding approach will be attractive for such applications, if it is not going to reduce the prediction accuracy to a great extent. The present study explores this area and tries to answer this question using automated data collected from field. A data‐driven technique, namely, artificial neural networks (ANN), which is shown to be a good tool for prediction problems, is taken as an example for data‐driven approach. Grey model, GM(1,1), which is also reported as a good prediction tool, is selected as the less‐data‐demanding approach. Volume, classified volume, average speed and classified speed at a particular location were selected for the prediction. The results showed comparable performance by both the methods. However, ANN required around seven times data compared with GM for comparable performance. Thus, considering the comparatively lesser input requirement of GM, it can be considered over ANN in situations where the historic database is limited. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Literature Reviewmentioning

confidence: 99%

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Badhrudeen

Raj

Vanajakshi

2016

J of Advced Transportation

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“…3. Kalman Filtering Models: (Chen and Chien, 2002;Cathey and Dailey, 2003;Chien and Kuchipudi, 2003;Shalaby and Farhan, 2004;Vanajakshi et al, 2008;Yu et al, 2010;Liu et al, 2012;Mazloumi et al, 2012;Jang, 2013;Gurmu and Fan, 2014). Kalman Filter is a recursive procedure that uses linear quadratic estimation model to estimates the future states of system.…”

Section: Research Surveymentioning

confidence: 99%

Prediction of Bus Travel Time Using Artificial Neural Network

Johar¹,

Singh²,

Kumar³

2015

IJTTE

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The objective of this study is to apply artificial neural network (ANN) for development of bus travel time prediction model. The bus travel time prediction model was developed to give real time bus arrival information to the passenger and transit agencies for applying proactive strategies. For development of ANN model, dwell time, delays and distance between the bus stops was taken as input data. Arrivals/departure times, delays, average speed between the bus stop and distance between the bus stops were collected for two urban routes in Delhi. Model was developed, validated and tested using GPS (Global Positioning System) data collected from field study. Comparative study reveals that ANN model outperformed the regression model in terms of accuracy and robustness.

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“…Li et al (2008) used an Adaptive Simple Exponential Model (ASES) that could detect bias in forecasts and give an indication to re-calibrate the parameters. An ASES model was developed for highway travel time prediction using Kalman Filter to update the parameter weights (Liu et al 2012). Also, various models have been developed to incorporate multiple factors into the forecasts.…”

Section: Exponential Smoothing (Es) Modelsmentioning

confidence: 99%

An Adaptive Long-Term Bus Arrival Time Prediction Model with Cyclic Variations

Balasubramanian¹,

Rao²

2015

JPT

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Real-time bus arrival information systems at transit stops can be useful to passengers for efficient trip planning and reducing waiting times. The accuracy of such systems depends upon the ability of the model to account for variations in the data series and to adjust according to changing traffic conditions. Many of the existing studies on passenger information systems have modeled the system based on stationary relations, not taking into account the cyclic variations in data, which is often suitable for demonstration purposes but not for long-term implementation. The present study models the changing relationships using Double-Seasonal Holt-Winter's Exponential Smoothing approach, which allows for self-updating parameters at four levels. It accounts for both long-term and short-term seasonal fluctuations in data while maintaining the dynamic treatment of real-time bus information. The model also takes into account delays using the real-time running information of the bus and incorporates it into subsequent forecasts for better accuracy. Real-time data from GPS transmitters in buses were used for validation of the proposed model. The results show that the proposed model performs better than the currently-used elementary field methods and is able to forecast bus travel times with a reasonable accuracy.The value of n is chosen based on the nature of the data series. For a relatively stationary data series, a large value of n is desirable. For a data series which is volatile, a small value of n is chosen. Kalman FilterKalman filter is an efficient recursive procedure that estimates the future states of dependent variables using a linear quadratic estimation model. It uses modern control theory to apply state space representations in the prediction scheme. Several studies have been carried out in the past using the Kalman Filter for bus travel time predictions using three components-a track, a filter, and a predictor (Chien and

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An adaptive model for highway travel time prediction

Cited by 12 publications

References 37 publications

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Short‐term prediction of traffic parameters—performance comparison of a data‐driven and less‐data‐required approaches

Prediction of Bus Travel Time Using Artificial Neural Network

An Adaptive Long-Term Bus Arrival Time Prediction Model with Cyclic Variations

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