Dynamic Router Real-Time Travel Time Prediction Based on a Road Network

The State Railway of Thailand provides passengers with train location information on their Web site, which includes the name of the last station that each train arrives at or departs from, along with the timestamps and the accumulative train delay (in minutes) from the train timetable. This information allows passengers to intuitively predict the arrival time at their station by adding the last known train delay to the scheduled arrival time. This paper aims at providing a more accurate prediction of passenger train's arrival times using the historical travel times between train stations. Two algorithms that use train location information and historical travel times are proposed and evaluated. The first algorithm uses the moving average of historical travel times. The second algorithm utilizes the travel times of the k-nearest neighbors (k-NN) of the last known arrival time. To evaluate the proposed algorithms, we collected six months of data for three different trains and calculated prediction errors using mean absolute error (MAE). The prediction errors of the proposed algorithms are compared to the prediction errors of the baseline algorithm that predicts the arrival time by adding the last known train delay to the scheduled train arrival time. Both algorithms outperform the baseline prediction. The algorithm based on moving average travel time improves the prediction error by 22.9 percent on average, and the algorithm based on k-NN improves the prediction error by 23.0 percent on average (k=16).

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

Travel time prediction plays a significant role in traffic data analysis. In the area of travel time prediction, traditional methods such as linear regression and time series models have been widely applied to predict travel times using historical travel time data. However, with the development of artificial intelligence technologies, these traditional models may become replaceable. In recent years, various novel machine learning-based data prediction methods have been developed accordingly. These types of approaches aim to increase the efficiency and accuracy of the prediction. This study intends to employ ensemble learning-based models (including the XGBoost model and RF model) to predict freeway travel time using information such as time of day, day of the week, and weather conditions. The models are validated on a freeway corridor in the area of Charlotte, North Carolina. The temporal and spatial correlations between each segment are also considered in the model. The effects of different feature combinations in the model are investigated and quantified. The modelling results indicate that the XGBoost model has considerable advantages in terms of both prediction accuracy and efficiency. The research findings demonstrate the feasibility of applying XGBoost in travel-time prediction and prove that the XGBoost model is applicable and performs well.

Decision reliability is becoming an important issue for both researchers and transportation service providers; different models and methods have been proposed to improve decision reliability. A method is proposed for improving decision reliability by analyzing the moments and central moments of historical travel time data. Because of the existence of traffic congestion—especially nonrecurrent traffic congestion—travel time is generally a random variable with an asymmetric historical distribution. Statistical methods are applied to obtain the higher-order moments and central moments of historical travel time, which provide quantitative information about the variability, asymmetry, and leptokurtosis of travel time. This information is useful in making reliable decisions and avoiding scenarios that can potentially lead to extremely high costs. The method is based on advanced traveler information systems, which provide useful historical travel time information for traffic networks. The new method and other me...

Recent advances in traffic monitoring systems have made real-time traffic velocity data ubiquitously accessible for drivers. This paper develops a traffic data-enabled predictive energy management framework for a power-split plug-in hybrid electric vehicle (PHEV). Compared with conventional model predictive control (MPC), an additional supervisory state of charge (SoC) planning level is constructed based on real-time traffic data. A power balance-based PHEV model is developed for this upper level to rapidly generate battery SoC trajectories that are utilized as final-state constraints in the MPC level. This PHEV energy management framework is evaluated under three different scenarios: 1) without traffic flow information; 2) with static traffic flow information; and 3) with dynamic traffic flow information. Numerical results using real-world traffic data illustrate that the proposed strategy successfully incorporates dynamic traffic flow data into the PHEV energy management algorithm to achieve enhanced fuel economy.

Many intelligent transportation systems (ITS) often rely on prediction of traffic variables in the near future to provide useful information for the users. Therefore, accurate traffic prediction is essential in the development of ITS. In this study, we are comparing prediction models for traffic speed and travel time on urban traffic network. We present three ways to build travel time predictors: by using recent travel time and speed of neighboring links, by using recent travel time of individual links, and by using speed predictors. Model-tree ensemble is implemented for both traffic speed and travel time predictions. Experimental result shows that the predictors perform better on travel time prediction than on traffic speed prediction. Among the three discussed travel time prediction methods and two baseline predictors, travel time prediction via speed predictors achieves best accuracy in all tested paths and prediction horizons.

Accurate real-time bus arrival information is essential for an efficient public transport network, as it significantly impacts passenger experience, system reliability, reduced waiting times, dwell times, and operational efficiency. In Sri Lanka's public transport system, current bus arrival time computations primarily rely on static data, neglecting real-time information and critical factors influencing travel times. This highlights the need to identify the unique variables affecting bus arrival times within the Sri Lankan context and to develop robust prediction models that account for these influences. While traditional methods such as Historical Average Models, Regression, Time Series Analysis, and Kalman Filtering have been used in previous research for short-term travel time predictions, Machine Learning (ML) approaches have proven to deliver superior accuracy. ML models are regarded as the most effective for heterogeneous, lane-less traffic conditions with varying traffic volumes, such as those found in Sri Lanka. ML techniques excel in processing large, high-quality datasets and provide accurate predictions by accounting for all relevant variables influencing travel times. Although research has been conducted on developing various basic ML models for travel time prediction, there is a noticeable gap in studies comparing these models to determine the most suitable one for the Sri Lankan context. A Long- Short Term Memory (LSTM) neural network is a deep learning model that is capable of handling long-term dependencies. In the context of bus travel time prediction, LSTMs can leverage historical traffic and travel data to capture temporal patterns and fluctuations that influence travel times. By evaluating LSTMs against basic machine learning models, this study seeks to explore the advantages of applying deep learning techniques to transportation forecasting, ultimately contributing to more accurate and efficient predictive systems in transit planning. The ML models selected in this study include two basic traditional models K- Nearest Neighbours (KNN) and Support Vector Regression (SVR) and four advanced models that utilize ensemble techniques and advanced optimization as Random Forest Regression (RFR), Ada Boost, XG Boost and Gradient Boosting Machine (GBM). The performance of these models was compared with the LSTM model to identify the gap in their accuracies.

Travel time data is an important factor for evaluating the performance of a public transport system. In terms of time and space within the nature of uncertainty, bus travel time is dynamic and flexible. Since the change of traffic status is periodic, contagious or even sudden, the changing mechanism of that is a hidden mode. Therefore, bus travel time prediction is a challenging problem in intelligent transportation system (ITS). Allowing for a large amount of traffic data can be collected at present but lack of precisely-conducting, it is still worth exploring how to extract feature sets that can accurately predict bus travel time from these data. Hence, a feature extraction framework based on the deep learning models were developed to reflect the state of bus travel time. First, the study introduced different historical stages of bus signaling time, taxi speed, the stop identity (ID) of spatial characteristics, and real-time possible arrival time, signified by fourteen spatiotemporal characteristic values. Then, an embedding network is proposed to leverage a wide and deep structure to mate the spatial and temporal data. In order to meet the temporal dependence requirements, an attention mechanism for a Recurrent Neural Network (RNN) was designed in this research in order to capture the temporal information. Finally, a Deep Neural Networks (DNN) was implemented in this research in order to achieve the dynamic bus travel time prediction. Two case studies of Guangzhou and Shenzhen were tested. The results showed that the performance of the algorithm was more efficient than that of the traditional machine-learning model and promoted by 4.82% compared to the deep neural network applied to the initial feature space. Moreover, the study visualized the weighted cost of attention on the bus’s travel time features during a certain running state. Therefore, the study demonstrated the proposed model enabled to understand the characteristic data of transit travel time with visualization.

We present a method to predict the time that will be needed to traverse a given section of a freeway when the departure is at a given time in the future. The prediction is done on the basis of the current traffic situation in combination with historical data. We argue that, for our purposes, the current traffic situation of a section of a freeway is well summarized by the current status travel time. This is the travel time that would result if one were to depart immediately and no significant changes in traffic would occur. This current status travel time can be estimated from single- or double-loop detectors, video data, probe vehicles, or any other means. Our prediction method arises from the empirical observation that there exists a linear relationship between any future travel time and the current status travel time. The slope and intercept of this relationship may change subject to the time of day and the time until departure, but linearity persists. This observation leads to a prediction scheme by means of linear regression with time-varying coefficients.

This paper presents a new historical road network data set, capturing conditions in 1950 and covering large part of the world. Its generation is documented in detail, its features are reported and Nigeria is employed as an example of a geographical information system used to generate historical travel times. This exercise should serve as a starting point for further (historical) transport studies. The main sources of this data set are former US Army Map Service (AMS) maps and a range of historical statistics. As far as the authors can ascertain, such comprehensive and detailed historical transport data has not been available until this point, but it now enables examination of various historical and current transport-related research questions using spatial and time-variant data. The purpose of this paper is: first, to introduce this historical road network data and to position it in the existing literature and research tradition; second, to explain how the network was generated; third, to present the authors' approach to model historical speeds and travel times and; fourth, show how to use such information by applying it in the Nigeria case study.

Ensemble machine learning for travel time prediction: An Active learning-assisted approach

The accuracy of travel time prediction is not only decided by the state of the current journey but also affected by that of the transfer journey. This paper focuses on the transit transfer problem of urban public transport and aims to improve the performance of multi-route bus travel time prediction. In this paper, two kinds of infect factors, which are macro infect factors and local infect factors, are defined. Then, a pre-processing approach is proposed to construct a travel chain, which is used to compensate the shortage of raw data set. Multi-route bus travel time is divided into four parts, which are travel time of the current driving segment (CDS), bus stop dwelling time module, stop-stop travel time, and transfer point waiting time. A parrel processing architecture, which includes four learning blocks, is designed to fulfill the prediction process of the above four time parts. The classic LSTM model is used to predict the CDS travel time and transfer point waiting time. An improved DA-RNN model, the MDARNN model, is proposed to predict the bus stop dwelling time module, stop-stop travel time. Moreover, a real-time traffic flow model is used to calibrate the prediction time of all four sub-models. Experiment results prove the validity of the proposed methods.

Travel Time Prediction (TTP) has become an essential service that people use in daily commutes. With the precise TTP, individuals, logistic companies, and transport authorities can better manage their activities and operations. This paper presents a novel Hybridized Deep Feature Space (HDFS) based TTP ensemble model (HDFS-TTP) for accurate travel time prediction. In the first step, extensive endogenous and exogenous data sources are augmented with traffic data obtained using sensors. Next, we used Principal Component Analysis (PCA) and Deep Stacked Auto-Encoder (DSAE) for feature reduction. We generated feature spaces of deep learning models, namely Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU), and fed them to a model based on Support Vector Regressor (SVR) for predicting travel times. Two best-performing models are selected, and their feature spaces are hybridized to boost feature space. On this boosted feature space, we employed SVR for final prediction. Our proposed HDFS-TTP ensemble can learn complex non-linearities in traffic data with the varying architectural design. The performance of our proposed HDFS-TTP ensemble using hybridized and boosted feature spaces showed significant improvement in test data in terms of Root Mean Square Error (62.27± 1.58), Mean Absolute Error (13.38± 1.09), Maximum Absolute Error (104.66± 2.77), Mean Absolute Percentage Error (2.50± 0.03), and Coefficient of determination (0.99714± 0.00044).

Accurate travel time prediction for major freeways and corridors is crucial but challenging when road incidents happen. Data-driven models require a large set of historical data to estimate the spatial and temporal correlations between road incidents and traffic dynamics. More often than not, the amount of historical data under non-recurring conditions is limited when it comes to training the models. This paper investigates the application of data-driven models on an enriched database with simulated travel times. A well-calibrated traffic simulation is used to capture the artificial incident’s impact on a major urban corridor in Sydney, Australia. This procedure is repeated for multiple created incidents, resulting in a synthetic dataset validated by the available actual historical data. Several machine learning models, such as Regression Tree, Support Vector Regression, Extreme Gradient Boosting, and Recurrent Neural Networks are trained and tested based on the simulated travel time and incident information. As a baseline model for comparison, the measured travel time at the prediction time is considered equal to multi-step ahead travel time. Based on the results, the data-driven models developed with the simulated data outperformed the baseline, indicating that our approach can be effectively employed in the travel time prediction.

Forecasting journey time distribution with consideration to abnormal traffic conditions