Investigating the effects of daily travel time patterns on short-term prediction (original) (raw)

Short-term Travel-time Prediction on Highway: A Review of the Data-driven Approach

Near future travel-time information is one of the most critical factors that travellers consider before making trip decisions. In efforts to provide more reliable future travel-time estimations, transportation engineers have examined various techniques developed in the last three decades. However, there have not been sufficiently systematic and through reviews so far. In order to effectively support various transportation strategies and applications including Intelligent Transportation Systems (ITS), it is necessary to apply appropriate forecasting methods for matching circumstances in a timely manner. This paper conducts a comprehensive review study focusing on literatures, including modern techniques proposed recently, related to travel time and traffic condition predictions that are based on ‘data-driven’ approaches. Based on the underlying mechanisms and theoretical principles, different approaches are categorized as parametric (linear regression and time series) and non-parametric approaches (artificial intelligence and pattern searching). Then, the approaches are analysed for their strengths, potential weaknesses, and performances from five main perspectives that are prediction range, accuracy, efficiency, applicability, and robustness.

Dynamic Travel Time Prediction using Pattern Recognition 1 2

2013

21 Travel-time information is an essential part of Advanced Traveler Information Systems (ATISs) 22 and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the 23 prediction of travel times. From the perspective of travelers such information may assist in 24 making better route choice and departure time decisions. For transportation agencies these data 25 provide criteria with which to better manage and control traffic to reduce congestion. This study 26 proposes a dynamic travel time prediction algorithm that matches current traffic patterns to 27 historical data. Unlike previous approaches that use travel time as the control variable, the 28 approach uses the temporal-spatial traffic state evolution to match traffic states and predict travel 29 times. The approach first identifies candidate historical time intervals by matching real-time 30 traffic state data against historical data for use in prediction purposes. Subsequently, the selected 31 candidat...

Predicting Travel Times for the South Jersey Real-Time Motorist Information System

Transportation Research Record, 2003

A dynamic travel-time prediction model was developed for the South Jersey (southern New Jersey) motorist real-time information system. During development and evaluation of the model, the integration of traffic flow theory, measurement and application of collected data, and traffic simulation were considered. Reliable prediction results can be generated with limited historical real-time traffic data. In the study, acoustic sensors were installed at potential congested places to monitor traffic congestion. A developed simulation model was calibrated with the data collected from the sensors, and this was applied to emulate traffic operations and evaluate the proposed prediction model under time-varying traffic conditions. With emulated real-time information (travel times) generated by the simulation model, an algorithm based on Kalman filtering was developed and applied to forecast travel times for specific origin-destination pairs over different periods. Prediction accuracy was evaluated by the simulation model. Results show that the developed travel-time predictive model demonstrates satisfactory performance.

Day-wise travel time pattern analysis under heterogeneous traffic conditions

Traffic parameters in general follow some patterns and identifying them is important since it will lead to more efficient end applications. With the introduction of various automated sensors, huge amounts of data are being collected, which can be used for identifying traffic patterns. In general, traffic patterns can be classified as yearly, monthly, weekly, daily and hourly. Travel time has been recognized as one of the most important parameters to fully facilitate many of the Intelligent Transportation Systems (ITS) applications. However, to predict the travel time, its weekly, daily and hourly patterns need to be analyzed. The present study analyzes the pattern followed by Global Positioning System (GPS) based bus travel time data. The travel time pattern may be different for different days of the week and hence the analysis was carried out separately for each day of the week. A systematic statistical analysis was carried out to rank these patterns in the order of significance for each day of the week separately. The statistical test namely, the Z-test for the mean of a population of differences for 'paired' samples data, was used for hypothesis testing at 5% level of significance using one month's data (with a total sample size of 5700). It was observed that all days have a similar pattern except Sunday.

Statistical Method for Pre-Trip Traffic Conditions Prediction

Traffic information on the Metropolitan Expressway network is supplied via various media. Current available real time information is associated with present traffic condition. However, current real time information is not suitable for pre-trip decisions as the information does not factor in the time interval to joining the expressway network and this is subject to considerable prediction errors. This research involved the proposal of a method for matching historic traffic data with real-time traffic data as a means of predicting the travel times and bottleneck status immediately prior to departure. The method was tested on major routes and producing satisfactory accuracy, and therefore demonstrating that the method has attained a deployable level of accuracy. We further propose an effective method of supplying this data to facilitate informed departure time decisions.

Travel Time Prediction on Highways

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015

We describe the development of a predictive model for vehicle journey time on highways. Accurate travel time prediction is an important problem since it enables planning of cost effective vehicle routes and departure times, with the aim of saving time and fuel while reducing pollution. The main information source used is data from roadside double inductive loop sensors which measure vehicle speed, flow and density at specific locations. We model the spatiotemporal distribution of travel times by using local linear regression. The use of real-time data is very accurate for shorter journeys starting now and less reliable as journey times increase. Local linear regression can be used to optimally balance the use of historical and real time data. The main contribution of the paper is the extension of local linear models with higher order autoregressive travel time variables, namely vehicle flow data, and density data. Using two years of UK Highways Agency (HA) loop sensor data we found that the extended model significantly improves predictive performance while retaining the main benefits of earlier work: interpretability of linear models as well as computationally simple predictions.

Simple and Efficient Prediction of Near Future State of Traffic Using Only Past Speed Information

PROMET - Traffic&Transportation

Intelligent traffic systems attempt to solve the problem of traffic congestion, which is one of the most important environmental and economic issues of urban life. In this study, we approach this problem via prediction of traffic status using past average traveler speed (ATS). Five different algorithms are proposed for predicting the traffic status. They are applied to real data provided by the Traffic Control Center of Istanbul Metropolitan Municipality. Algorithm 1 predicts future ATS on a highway section based on the past speed information obtained from the same road section. The other proposed algorithms, Algorithms 2 through 5, predict the traffic status as fluent, moderately congested, or congested, again using past traffic state information for the same road segment. Here, traffic states are assigned according to predetermined intervals of ATS values. In the proposed algorithms, ATS values belonging to past five consecutive 10-minute time intervals are used as input data. Per...

Univariate short-term prediction of road travel times

Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005., 2005

This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison includes linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over linear regression, the only non-linear method that could consistently

Travel Time Prediction Modelling in Mixed Traffic Conditions

INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING, 2018

Over the past few decades, exponential increase in vehicle ownership resulted in issues of traffic control and management. Intelligent Transportation System (ITS) is one of the solutions for the intelligent management of traffic. ITS applications like Advanced Traveler Information Systems and Advanced Traffic Management Systems need travel time as a major input. The estimation of travel time in urban network became more complicated because of the rapid change in the system and traffic. This study is done in order to assess the impact of different travel modes on travel time. Data has been collected on a stretch of 14 km length in Warangal city, India using GPS probe vehicle along with video camera. Different private modes of transportation such as 2 wheeler, passenger car and 3 wheeler have been used as test vehicles for the collection of data in different traffic flow scenarios. Artificial Neural Network and a multi linear regression model have been developed to compare the estimated travel times with the field data. Two combinations of ANN model using single hidden layer, different numbers of neurons and epochs have been compared. The travel time of different modes has been compared and the effect of vehicle composition on travel time has been analyzed. The ANN model perform better than the regression model.

Prediction of traveller information and route choice based on real-time estimated traffic state

Transportmetrica B: Transport Dynamics, 2015

4 34-40 University Road, Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, 5 United Kingdom. tsaa@leeds.ac.uk 6 2 Prediction of traveller information and route choice based on real-time 7 estimated traffic state 8 Accurate depiction of existing traffic states is essential to devise effective real-time 9 traffic management strategies using Intelligent Transportation Systems (ITS). Existing 10 applications of Dynamic Traffic Assignment (DTA) methods are mainly based on 11 either the prediction from macroscopic traffic flow models or measurements from the 12 sensors and do not take advantage of the traffic state estimation techniques, which 13 produce estimate of the traffic states which has less uncertainty than the prediction or 14 measurement alone. On the other hand, research studies which highlight estimation of 15 real-time traffic state are focused only on traffic state estimation and have not utilized 16 the estimated traffic state for DTA applications. In this paper we propose a framework 17 which utilizes real-time traffic state estimate to optimize network performance during 18 an incident through traveller information system. The estimate of real-time traffic states 19 is obtained by combining the prediction of traffic density using Cell Transmission 20 Model (CTM) and the measurements from the traffic sensors in Extended Kalman 21 Filter (EKF) recursive algorithm. The estimated traffic state is used for predicting 22 travel times on alternative routes in a small traffic network and the predicted travel 23 times are communicated to the commuters by a variable message sign (VMS). In 24 numerical experiments on a two-route network, the proposed estimation and 25 information method is seen to significantly improve travel times and network 26 performance during a traffic incident.