Travel Time Prediction Model Research Articles

Freeway Short-Term Travel Time Prediction Based on LightGBM Algorithm

In order to realize the prediction of freeway travel time, a short-term travel time prediction model based on LightGBM (Light Gradient Boosting Machine) is proposed under the influence of weather factors, time period factors, and traffic factors. These factors are called as the features used for increase prediction accuracy. The travel time of a single vehicle is determined by license plate recognition data of two adjacent video monitors in Shaoxing section of Shanghai-Hangzhou-Ningbo Freeway, and a better travel time data set is constructed by data preprocessing. The feature data are determined by Pearson correlation. Based on the analysis of main optimization parameters in LightGBM, the short-term average travel time is predicted, the MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error) obtained by experiments are satisfying, indicating that LightGBM model has high accuracy and good fit. Finally, through comparison with KNN (K-Nearest Neighbor) model and GBDT (Gradient Boosting Decision Tree) model, the prediction accuracy and training speed both show that LightGBM has good advantages in predicting short-term freeway travel time.

Predicting Travel Times of Bus Transit in Washington, D.C. Using Artificial Neural Networks

This study aimed to develop travel time prediction models for transit buses to assist decision-makers improve service quality and patronage. Six-months’ worth of Automatic Vehicle Location and Automatic Passenger Counting data for six Washington Metropolitan Area Transit Authority bus routes operating in Washington, DC was used for this study. Artificial Neural Network (ANN) models were developed for predicting travel times of buses for different peak periods. The analysis included variables such as length of route between stops, average dwell time and number of intersections between bus stops amongst others. Quasi-Newton algorithm was used to train the data to obtain the ideal number of perceptron layers that generated the least amount of error for all peak models. Comparison of the Normalized Squared Errors generated during the training process was done to evaluate the models. Travel time equations for buses were obtained for different peaks using ANN. The results indicate that the prediction models can effectively predict bus travel times on selected routes during different peaks of the day with minimal percentage errors. These prediction models can be adapted by transit agencies to provide patrons with more accurate travel time information at bus stops or online. Doi: 10.28991/cej-2020-03091615 Full Text: PDF

Travel Time Prediction Model of Freeway Corridor Based on Real-Time Safety Reliability

By considering the feature of vehicle driving on the event management unit of the freeway corridor, according to the system target, a method to divide the management unit of the road network was put forward. The relative safety braking deceleration was taken as the evaluation index of single-vehicle driving risk. The reliability graph relationship and structure-function between the management unit and subunit were analyzed. Then, dynamic safety reliability and real-time safety reliability were determined on the basis of driving risk. In addition, the queuing and dissipating characteristics of the management unit under traffic incidents were analyzed based on the wave theory. The incident duration and dissipation time were also calculated. At the same time, the travel time prediction model of the incident management unit was set up when the real-time safety reliability was taken as a road resistance function. Finally, an improved travel time prediction model established in this paper is of great significance to improve traffic safety and efficiency, and the research results will provide an important theoretical foundation in the freeway corridor route decision.

Examining the Impact of Different Periodic Functions on Short-Term Freeway Travel Time Prediction Approaches

Freeway travel time prediction is a key technology of Intelligent Transportation Systems (ITS). Many scholars have found that periodic function plays a positive role in improving the prediction accuracy of travel time prediction models. However, very few studies have comprehensively evaluated the impacts of different periodic functions on statistical and machine learning models. In this paper, our primary objective is to evaluate the performance of the six commonly used multistep ahead travel time prediction models (three statistical models and three machine learning models). In addition, we compared the impacts of three periodic functions on multistep ahead travel time prediction for different temporal scales (5-minute, 10-minute, and 15-minute). The results indicate that the periodic functions can improve the prediction performance of machine learning models for more than 60 minutes ahead prediction and improve the over 30 minutes ahead prediction accuracy for statistical models. Three periodic functions show a slight difference in improving the prediction accuracy of the six prediction models. For the same prediction step, the effect of the periodic function is more obvious at a higher level of aggregation.

Freeway Travel Time Prediction Using Deep Hybrid Model – Taking Sun Yat-Sen Freeway as an Example

As the population keeps growing, traffic congestion happens more and more often. Consequently, travel time has become an important indicator of driving experience. Accurate travel time information helps drivers plan their route more wisely and thus effectively alleviate traffic congestion. In this research, we propose a vehicle travel time prediction model for freeway traffic. The data used in this research are derived from the traffic dataset of the Taiwan Freeway Bureau, and the travel time prediction is made for the Sun Yat-sen Freeway between Taipei and Hsinchu. First, the missing value of the raw data is imputed by Autoencoder. The data are then segmented according to time series and are used to build the prediction model. To effectively capture the hidden features required to predict the travel time for the vehicle traveling on the freeway, a deep learning architecture is adopted in our system, which includes the GRU neural network model, the XGBoost model, and the Hybrid model that combines the GRU and XGBoost through linear regression. To increase computational efficiency, the travel time predictions for consecutive toll gates every 5 minutes apart are pre-computed offline, so that the online travel time prediction of the whole trip can be obtained by simply summing up a few numbers. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time.

Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys.

Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

Research on Expressway Travel Time Prediction Based on Exclusive Disjunctive Soft Set

In order to study the prediction problem of expressway travel time, due to the ambiguity and uncertainty in the road traffic system, the travel time prediction model is established based on the exclusive disjunctive soft set theory. Through the parameter reduction theory of soft set, the main influence factors are extracted, and the mapping relationship between the influence factors and the travel time is obtained through the exclusive disjunctive soft set decision system. The travel time model is established based on the soft set theory, and the travel time is calculated through the mapping relationship. The experimental results show that, compared with the BPR function model, the travel time model based on the exclusive disjunctive soft set theory reduces the prediction error and effectively improves the calculation accuracy of the travel time.

Bus Travel Time Prediction Model Based on Profile Similarity.

In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

Research on Evaluation and Prediction Method of Link Travel Time Based on Floating Car Data by Simulation

Using the floating car data obtained by simulation to carry out theoretical research on link travel time evaluation and prediction, it has the advantage of conveniently setting different situations and providing reference for practical applications. In this paper, we have acquired the floating car data based on microscopic simulation, and then the complex trapezoidal quadrature formula is used to obtain link travel time of a single vehicle. When setting different ratios of floating cars, the average link travel time is obtained by the mean-median method. The results show that the effect is better than the arithmetic average method, and the higher the ratio of floating cars, the higher the accuracy of evaluation. The markov model has been used to correct the gray prediction model, with that we establish travel time prediction model based on the grey markov chain. The experimental results show that the prediction accuracy is high.

Research on Travel Time Prediction Model of Freeway Based on Gradient Boosting Decision Tree

To improve the prediction accuracy of traffic flow, a travel time prediction model based on gradient boosting decision tree (GBDT) is proposed. In order to test the applicability of GBDT, models with different prediction horizons (5 min ahead, 10 min ahead, and 15 min ahead) are established. The 11 variables are viewed as candidates in this paper. Different from other machine learning algorithms as black boxes, GBDT can provide interpretable results through variable importance. In the proposed model, the variable importance shows that for different prediction horizons, the most important influence variable is uniform, which is travel time in the current period. Traffic conditions in the current period have the greatest influence on the predicted travel time. Compared with the back propagation neural network model and the support vector machine model, the proposed GBDT model can produce more accurate prediction results, especially in multi-step prediction, indicating that GBDT is a promising method in travel time prediction.

Travel time prediction for highway network based on the ensemble empirical mode decomposition and random vector functional link network

Travel time prediction supplies data support for the management and operation of the highway network. To deal with this problem, a model based on ensemble empirical mode decomposition and random vector functional link network is proposed in this paper. Ensemble empirical mode decomposition is firstly employed to decompose the complex travel time data series into several simple functions, which are then represented by the same number of random vector functional link networks. Finally, the outputs of all networks are combined by linear addition as the prediction results. A historical travel time data series (from 1 August 2016 to 1 November 2016) of two highways in China is investigated by the proposed models. For comparison, five individual prediction models and their respective ensemble variants are implemented for the same task. The results show that the proposed model outperforms all the other models in terms of symmetric mean absolute percentage error and normalized root mean square error. As for computational speed, the proposed model ranks the first among all the ensemble models. Moreover, the ensemble empirical mode decomposition is better than the empirical mode decomposition. The Friedman statistical test also confirms the results of the comparison. Experimental results reveal that the proposed model reaches the best overall performance and is a very promising model for complex travel time prediction.

Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment

Aiming at reducing per person delay, this paper presents an optimization method for Transit Signal Priority (TSP) considering multirequest under connected vehicle environment, which is based on the travel time prediction model. Conventional arrival time of transit depended on the detection information and the front road state, which restricted the effect of priority seriously. According to the bidirectional and real-time information transmission under connected vehicle environment, this paper establishes a more accurate forecasting model of bus travel time. Based on minimizing the total person delay at the intersection, the decision mechanism of multirequest is devised to meet the priority needs of buses with different arrival times. And the green time compensation algorithm is developed after considering the arrival information of the buses in the next cycle of compensational phase. Finally, the paper combines the COM interface of VISSIM and Matlab to achieve the proposed method under connected vehicle environment. Four control methods were tested when the VCR was 0.5, 0.7, and 0.9. The results illustrated that the proposed method reduced per person delay by 18.57%, 11.88%, and 18.96% and decreased the private vehicle delay by 3.73%, 7.62%, and 13.10%, respectively.

Travel time predictions: should one model speeds or travel times?

PurposeTravel time predictions are of importance for individual trip planning as well as for logistics applications. Since travel time and travel speed have a one-one correspondence, the modeller has the choice to model travel times directly or model the corresponding travel speeds and infer the associated time from the speed predictions. A priori it is not clear which of these is the superior approach. In this paper we investigate the implications of the choice of the methodology for the accuracy of the travel time predictions.MethodsFor a selection of links, travel time prediction models, both in a direct way as well as indirectly via the implied link travel speeds, are obtained. The respective predictions are compared on a validation data set with respect to their accuracy as measured by mean error, root mean square error, mean percentage error as well as mean absolute percentage error. Additionally, the accuracy of route travel time predictions is evaluated based on the raw GPS data from the floating taxis.ResultsThe empirical results overwhelmingly make the case for using direct modelling if the goal of prediction is to obtain a RMSE-optimal prediction. If the MAPE is to be minimized, however, the indirect method provides the better results.ConclusionThus the goal of the prediction determines the better method of modelling: if one is interested in minimizing the RMSE, then, for the data investigated in this paper, the direct method should be selected. However, if one is interested in obtaining a small MAPE, the indirect method achieves better results.

Performance Comparison of Bus Travel Time Prediction Models across Indian Cities

Road traffic congestion has become a global worry in recent years. In many countries congestion is a major factor, causing noticeable loss to both economy and time. The rapid increase in vehicle ownership accompanied by slow growth of infrastructure has resulted in space constraints in almost all major cities in India. To mitigate this issue, authorities have shifted to more sustainable management solutions like Intelligent Transport System (ITS). Advanced Public Transportation System (APTS) is an important area in ITS which could considerably offset the growing ownership of private vehicles as public transport holds a noticeable mode share in several major cities in India. Getting access to real-time information about public transport would certainly attract more users. In this regard, this work aims at developing a reliable structure for predicting arrival/travel time of various public transport systems under heterogeneous traffic conditions existing in India. The data used for the study is collected from three cities—Surat, Mysore, and Chennai. The data is analyzed across space and time to extract patterns which are further utilized in prediction models. The models examined in this paper are k-NN classifier, Kalman Filter and Auto-Regressive Integrated Moving Average (ARIMA) techniques. The performance of each model is evaluated and compared to understand which methods are suitable for different cities with varying characteristics.

A hybrid method for short-term freeway travel time prediction based on wavelet neural network and Markov chain

Short-term travel time prediction is an essential input to intelligent transportation systems. Timely and accurate traffic forecasting is necessary for advanced traffic management systems and advanced traveler information systems. Despite several short-term travel time prediction approaches have been proposed in the past decade, especially for hybrid models that consist of machine learning models and statistical models, few studies focus on the over-fitting problem brought by hybrid models. The over-fitting problem deteriorates the prediction accuracy especially during peak hours. This paper proposes a hybrid model that embraces wavelet neural network (WNN), Markov chain (MAR), and the volatility (VOA) model for short-term travel time prediction in a freeway system. The purpose of this paper is to provide deeper insights into underlining dynamic traffic patterns and to improve the prediction accuracy and robustness. The method takes periodical analysis, error correction, and noise extraction into consideration and improve the forecasting performance in peak hours. The proposed methodology predicts travel time by decomposing travel time data into three components: a periodic trend presented by a modified WNN, a residual part modeled by Markov chain, and the volatility part estimated by the modified generalized autoregressive conditional heteroscedasticity model. Forecasting performance is investigated with freeway travel time data from Houston, Texas and examined by three measures: mean absolute error, mean absolute percentage error, and root mean square error. The results show that the travel times predicted by the WNN-MAR-VOA method are robust and accurate. Meanwhile, the proposed method is able to capture the underlying periodic characteristics and volatility nature of travel time data.

Forecasting journey time distribution with consideration to abnormal traffic conditions

Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records.

Travel Time Prediction Models and Reliability Indices for Palestine Urban Road in Baghdad City

Abstract
 Travel Time estimation and reliability measurement is an important issues for improving operation efficiency and safety of traffic roads networks. The aim of this research is the estimation of total travel time and distribution analysis for three selected links in Palestine Arterial Street in Baghdad city. Buffer time index results in worse reliability conditions. Link (2) from Bab Al Mutham intersection to Al-Sakara intersection produced a buffer index of about 36% and 26 % for Link (1) Al-Mawall intersection to Bab Al- Mutham intersection and finally for link (3) which presented a 24% buffer index. These illustrated that the reliability get worst for link (2), (1) and (3) respectively during the peak period. Extra delay is observed on link(1), (2) and (3) in terms of 95% percentile travel time of about (301.9, 219.4, and 193.8)sec. for Link (1, 2 and 3) respectively. Higher value for 95% travel time is obtained for link (1). Travel time index (TTI) of 4.2 %, 4.9% and 4% is obtained for Link (1, 2 and 3) respectively. Maximum value for delay per km that obtained for link (1) which is about 266 sec/km and 268 sec./km for link (3) and 244 sec/km for link(2). Different predicted model for the three studied links of Palestine street have been developed based on the obtained field data. A best fit is presented as compared the predicted models with the observed field travel time data for all the models of studied links which illustrated that the predicted model can present the actual field data.
 Keywords: Delay, Buffer Index, Travel Time, predicted model, Reliability, Urban Arterial.

Effective long-term travel time prediction with fuzzy rules for tollway

Advanced traveller information system is an important intelligent transportation systems application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long-term travel time prediction (i.e. predicting greater than 60 min ahead). Long-term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long-term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead.

Urban Link Travel Time Prediction Based on a Gradient Boosting Method Considering Spatiotemporal Correlations

The prediction of travel times is challenging because of the sparseness of real-time traffic data and the intrinsic uncertainty of travel on congested urban road networks. We propose a new gradient–boosted regression tree method to accurately predict travel times. This model accounts for spatiotemporal correlations extracted from historical and real-time traffic data for adjacent and target links. This method can deliver high prediction accuracy by combining simple regression trees with poor performance. It corrects the error found in existing models for improved prediction accuracy. Our spatiotemporal gradient–boosted regression tree model was verified in experiments. The training data were obtained from big data reflecting historic traffic conditions collected by probe vehicles in Wuhan from January to May 2014. Real-time data were extracted from 11 weeks of GPS records collected in Wuhan from 5 May 2014 to 20 July 2014. Based on these data, we predicted link travel time for the period from 21 July 2014 to 25 July 2014. Experiments showed that our proposed spatiotemporal gradient–boosted regression tree model obtained better results than gradient boosting, random forest, or autoregressive integrated moving average approaches. Furthermore, these results indicate the advantages of our model for urban link travel time prediction.

A space–time delay neural network model for travel time prediction

Research on space–time modelling and forecasting has focused on integrating space–time autocorrelation into statistical models to increase the accuracy of forecasting. These models include space–time autoregressive integrated moving average (STARIMA) and its various extensions. However, they are inadequate for the cases when the correlation between data is dynamic and heterogeneous, such as traffic network data. The aim of the paper is to integrate spatial and temporal autocorrelations of road traffic network by developing a novel space–time delay neural network (STDNN) model that capture the autocorrelation locally and dynamically. Validation of the space–time delay neural network is carried out using real data from London road traffic network with 22 links by comparing benchmark models such as Naïve, ARIMA, and STARIMA models. Study results show that STDNN outperforms the Naïve, ARIMA, and STARIMA models in prediction accuracy and has considerable advantages in travel time prediction.