A review of travel time estimation and forecasting for Advanced Traveller Information Systems A review of travel time estimation and forecasting for Advanced Traveller Information Systems (original) (raw)
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A review of travel time estimation and forecasting for Advanced Traveller Information Systems
2014
Providing on line travel time information to commuters has become an important issue for Advanced Traveler Information Systems and Route Guidance Systems in the past years, due to the increasing traffic volume and congestion in the road networks. Travel time is one of the most useful traffic variables because it is more intuitive than other traffic variables such as flow, occupancy or density, and is useful for travelers in decision making. The aim of this paper is to present a global view of the literature on the modeling of travel time, introducing crucial 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 generally not presented together. The main goals of these models, the study areas and methodologies used to carry out these tasks will be further explored and categorized.
Empirical Analysis of a Travel-Time Forecasting Model
Geographical Analysis, 2007
The purpose of this article is to analyze empirically a travel-time forecasting model that estimates link travel times on congested road networks. In separate studies by You and Kim, a nonparametric regression model has been developed as a core forecasting algorithm to reduce computation time and increase forecasting accuracy. In this article, the sensitivity of model parameters is evaluated so that the proposed travel-time forecasting model could be utilized in transportation information and management systems such as location-based services and intelligent transportation systems applications.
Travel Time Prediction: Issues and Benefits
2004
This paper addresses the issues and benefits pertaining to travel time prediction and incorporates an on-line survey of the Tokyo Metropolitan Expressways (MEX) users. While travel time has the potential to mitigate congestion spatially and temporally, little is known about how the travel time information is used. The MEX survey found that 78% of drivers would change route or departure time if there is time savings. However, the amount of time savings to prompt drivers taking such action depends on the characteristic of the drivers. Not surprisingly drivers rate pre-trip information higher than on-route information as a more desired information because pre-trip information allows drivers to make a more informed travel decision. The MEX survey clearly shows that users’ acceptance of prediction accuracy is dependent on the amount of time gain or lost and is insensitive to the trip length. Approximately 70% of the survey participants acknowledge that ±5 minutes as an acceptable level o...
Survey of Best Practices in Real Time Travel Time Estimation and Prediction
2000
Over the last decade, there has been a push towards the development and deployment of Intelligent Transportation Systems (ITS) because of the many benefits that these systems can provide. One of the important components of ITS is Advanced Traveler Information Systems (ATIS). These systems aim to provide the users with pre-trip or en route travel information so that users can
A review of travel-time prediction in transport and logistics
… of the Eastern Asia Society for …, 2005
Travel-time information could be applied in various fields and purposes. From the travellers' viewpoints, the travel-time information helps to save travel-time and improve reliability through the selection of travel routes pre-trip and en-route. In the application of logistics, travel-time information could reduce the delivery costs, increase the reliability of delivery, and improve the service quality. For traffic managers, travel-time information is an important index for traffic system operation.
Bayesian predictive travel time methodology for advanced traveller information system
Journal of Advanced Transportation, 2012
Travellers can benefit from the availability of point-to-point driving time estimates on a real time basis for making travel decisions such as route choice at strategic locations (e.g. junctions of major routes). This paper reports a predictive travel time methodology that features a Bayesian approach to fusing and updating information for use in advanced traveller information system. The methodology addresses the issue that data captured in real time on travel conditions becomes obsolete and has archival value only unless it is used as an input to a predictive travel time method for updating the information. The need for fusing real time data with other factors that influence travel time is defined and the concept of predictive travel time is discussed. The methodological framework and its components are advanced and an example application is provided for illustrating the fusion of data captured by infrastructure-based and mobile technology with model-based predictions in order to produce expected travel times.
Towards Developing a Travel Time Forecasting Model for Location-Based Services: A Review
Advances in Spatial Science, 2005
Travel time forecasting models have been studied intensively as a subject of Intelligent Transportation Systems (ITS), particularly in the topics of advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Now, the interests for the travel time forecasting models have been revived, particularly since the market for location-based services (LBS) are foreseen to be rapidly increasing. While the concept of travel time forecasting is relatively simple, it involves a notably complicated task to implement even a simple model. Thus, existing forecasting models are diverse in their original formulations, including mathematical optimizations, computer simulations, statistics, and artificial intelligence. A comprehensive literature review, therefore, would assist in formulating a more reliable travel time forecasting model.
Performance evaluation of an adaptive travel time prediction model
2005
This paper presents a travel time prediction model and evaluates its performance and transferability. Advanced Travelers Information Systems (ATIS) are gaining more and more importance, increasing the need for accurate, timely and useful information to the travelers. Travel time information quantifies the traffic condition in an easy to understand way for the users. The proposed travel time prediction model is based on an efficient use of nearest neighbor search. The model is calibrated for optimal performance using Genetic Algorithms. Results indicate better performance by using the proposed model than the presently used naïve model.
Development of a Data-Driven Framework for Real-Time Travel Time Prediction
Computer-aided Civil and Infrastructure Engineering, 2016
Travel time prediction is one of the most important components in Intelligent Transportation Systems implementation. Various related techniques have been developed, but the efforts for improving the applicability of long-term prediction in a real-time manner have been lacking. Existing methods do not fully utilize the advantages of the state-of-the-art cloud system and large amount of data due to computation issues. We propose a new prediction framework for real-time travel time services in the cloud system. A distinctive feature is that the prediction is done with the entire data of a road section to stably and accurately produce the longterm (at least 6-hour prediction horizon) predicted value. Another distinctive feature is that the framework uses a hierarchical pattern matching called Multilevel k-nearest neighbor (Mk-NN) method which is compared with the conventional k-NN method and Nearest Historical average method. The results show that the method can more accurately and robustly predict the long-term travel time with shorter computation time.
Travel time prediction for urban networks is an important issue in Advanced Traveler Information Systems (ATIS) since drivers can make individual decisions, choose the shortest route, avoid congestions and improve network efficiency based on the predicted travel time information. In this research, two algorithms are proposed to estimate and predict travel time for urban networks, the simulation-based and time-series models. The simulation-based model, DynaTAIWAN, designed and developed for mixed traffic flows, is adopted to simulate the traffic flow patterns. The Autoregressive Integrated Moving Average (ARIMA) model, calibrated with vehicle detector (VD) data, is integrated with signal delay to predict travel time for arterial streets. In the numerical analysis, an arterial street in Kaohsiung city in Taiwan is conducted to illustrate these two models. The empirical and historical data are used to predict and analyze travel time, including: travel time data from survey and historic...