Transportation research. part c, emerging technologies

This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.

A bayesian dynamic linear model approach for real-time short-term freeway travel time prediction

Survey on traffic prediction in smart cities

Due to the increase in vehicle transit and congestion in road networks, providing information about the state of the traffic to commuters has become a critical issue for Advanced Traveller Information Systems. These systems should assist users in making pre-trip and en-route decisions and, for this purpose, delivering travel time information is very useful because it is very intuitive and easily understood by all travellers. The aim of this paper is to present a global view of the literature on the modelling of travel time, introducing essential concepts and giving a thorough classification of the existing techniques. Most of the attention will focus on travel time estimation and travel time prediction, which are two of the most relevant challenges in travel time modelling. The definition and goals of these two modelling tasks along with the methodologies used to carry them out will be further explored and categorised.

A review of travel time estimation and forecasting for Advanced Traveller Information Systems

講座 highway capacity manual 2000(3)2車線道路と多車線道路

Although there is an increasing demand for travel time prediction methods, the efforts put on urban streets are until now less than on freeways In this paper, a macroscopic model of urban link travel time prediction is developed based on measurements collected by single loop detectors This method divides travel times into two components: link cruising times and intersection delays For each component the mean prediction and prediction interval were investigated in order to provide a reliable prediction Prediction intervals around the mean prediction show the plausible range of travel times a vehicle is likely to encounter Based on simulation data, it is demonstrated that urban link travel time prediction can be executed by this promising method

Prediction of urban travel times with intersection delays

Traffic accident is one of the main causes of the increasing congestion in traffic networks. Due to the fact that traffic accidents reduce the capacity of a freeway and that their effect and duration are rather unpredictable at the moment they occur, it is expected that they contribute for great proportion to less reliable travel times. In this paper we present an empirical travel time reliability analysis on a basis of a large dataset of registered traffic accident data and empirical traffic flow data. The preliminary results show that travel time accidents result in both higher travel time variability and higher probability of traffic breakdown on freeways and thereby higher travel time unreliability.

The Effects of Traffic Accidents on Travel Time Reliability

Travel times on freeways are variable due to variations both in demand and in capacity. For example, the volume of traffic which a road can handle on one day may cause severe congestion on the next due to for example adverse weather affecting capacity. One group of factors affecting capacity are geometrical characteristics of freeways, such as the number of ramps (on-ramp or off-ramp) and weaving sections per unit road length and their physical characteristics. It is expected that these characteristics also influence travel time variability. Empirical analysis on the basis of a large database of travel times on various freeways shows that travel time variability is strongly affected by these geometrical aspects. Above a certain threshold of ramps per unit road length, travel time variability increases sharply as their number increases. It is also found that too short acceleration, deceleration lanes and weaving sections increase travel time variability significantly. As a result of this study, there are thresholds found for designing reliable infrastructure networks with respect to putting clear constraints on the number of ramps and weaving sections and their physical properties

Travel Time Variability versus Freeway Characteristics

In this paper we demonstrate two ways of assigning confidence intervals to real-time neural network based on freeway travel time predictions, which express the uncertainty in our model parameters. Both exploit a bootstrap mechanism, but of different sizes. The non-parameterized bootstrap approach estimates model variance by the variance of the ensemble prediction, while the approximate Bayesian approach utilizes a combination of the ensemble variance and the variance that can be calculated analytically through a Bayesian approach toward neural network training. Both methods yield plausible results, albeit that the Bayesian method requires less (computational) effort, since it suffices with a much smaller ensemble of neural networks. The Bayesian approach, however, does seem to overestimate variance in freeflow conditions. Both methods can be straight forwardly extended to calculate prediction intervals, given that we are able to model the noise inherent to the data.

http://ieeexplore.ieee.org/iel5/8866/28034/01252724.pdf

Confidence intervals for real-time freeway travel

Short-term forecasting of travel time is one of the central topics in current ITS research and practice. The most widely applied travel time forecasting approach is the neural network. Usually many candidate neural networks are trained and the network performing best on an independent validation dataset is selected. However, the training data then needs to be divided in two, leading to less well trained networks. Using Bayesian inference theory, a selection criterion called the `evidence' can be derived for each network without the need for a validation set. This results in higher prediction accuracy as more data can be used for training, Moreover, a committee of neural networks can be constructed using the evidence. A case of forecasting travel times on the A12 motorway in the Netherlands shows that the committee approach indeed leads to improved travel time forecasting accuracy, and that the evidence should be preferred over the validation set approach when constructing the committee.

H. van Lint

Papers

Prediction of urban travel times with intersection delays

The Effects of Traffic Accidents on Travel Time Reliability

Travel Time Variability versus Freeway Characteristics

Confidence intervals for real-time freeway travel

Neural Network Committee to Predict Travel Times: Comparison of Bayesian Evidence Approach to the Use of a Validation Set