Computer Aided Civil and Infrastructure Engineering

An important aspect of Intelligent Public Transportation Systems (IPTS) is providing accurate travel time information. Knowing arrival times of public vehicles in advance can reduce waiting times of passengers and attract more people to take public transport. Existing approaches have two main limitations in the field of bus travel time prediction. First, influenced by increasingly complex real-time traffic factors and sparsity of real-time data, bus travel times can be difficult to predict accurately in modern cities. Second, bus dwelling and transit times are predominantly affected by different factors and hence have different patterns, but little research focuses on how to divide dwelling and transit areas and to build independent models for them. Consequently, we propose a novel segment-based approach to predict bus travel times using a combination of real-time taxi and bus datasets, that can automatically divide bus routes into dwelling and transit segments. Two models are built to predict them separately by incorporating different impact traffic factors. We evaluate our approach using real-world trajectory datasets, collected in Xi’an, China during June 2017. Compared to existing methods, the experimental results reveal that our approach improves the accuracy of bus travel time prediction, especially under abnormal traffic conditions.

Bus travel time prediction with real-time traffic information

The accuracy of travel time information given to passengers plays a key role in the success of any Advanced Public Transportation Systems (APTS) application. In order to improve the accuracy of such applications, one should carefully develop a prediction method. A majority of the available prediction methods considered the variation in travel time either spatially or temporally. The present study developed a prediction method that considers both temporal and spatial variations in travel time. The conservation of vehicles equation in terms of flow and density was first re-written in terms of speed in the form of a partial differential equation using traffic stream models. Then, the developed speed based equation was discretized using the Godunov scheme and used in the prediction scheme that was based on the Kalman filter. From the results, it was found that the proposed method was able to perform better than historical average, regression, and ANN methods and the methods that considered either temporal or spatial variations alone. Finally, a formulation was developed to check the effect of side roads on prediction accuracy and it was found that the additional requirement in terms of location based data did not result in an appreciable change in the prediction accuracy. This clearly demonstrated that the proposed approach based on using vehicle tracking data is good enough for the considered application of bus travel time prediction.

Bus travel time prediction using a time-space discretization approach

Bus arrival prediction has important implications for public travel, urban dispatch, and mitigation of traffic congestion. The factors affecting urban traffic conditions are complex and changeable. As the predicted distance increases, the difficulty of traffic prediction becomes more difficult. Forecast based on historical data responds quite slowly for changes under the short-term conditions, and vehicle prediction based on real-time speed is not sufficient to predict under long-term conditions. Therefore, an arrival prediction method based on temporal vector and another arrival prediction method based on spatial vector is proposed to solve the problems of remote dependence of bus arrival and road incidents, respectively. In this paper, combining the advantages of the two prediction models, this paper proposes a long short-term memory (LSTM) and Artificial neural networks (ANN) comprehensive prediction model based on spatial-temporal features vectors. The long-distance arrival-to-station prediction is realized from the dimension of time feature, and the short-distance arrival-to-station prediction is realized from the dimension of spatial feature, thereby realizing the bus-to-station prediction. Besides, experiments were conducted and tested based on the entity dataset, and the result shows that the proposed method has high accuracy among bus arrival prediction problems.

/pdf/bus-arrival-time-prediction-based-on-lstm-and-spatial-3xn5k5a605.pdf

Bus Arrival Time Prediction Based on LSTM and Spatial-Temporal Feature Vector

Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches.

Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

Accurate prediction of bus arrival time is of great significance to improve passenger satisfaction and bus attraction. This paper presents the prediction model of bus arrival time based on Support Vector Machine with genetic algorithm (GA-SVM). The character of the time period, the length of road, the weather, the bus speed and the rate of road usage are adopted as input vectors in Support Vector Machine (SVM), and the genetic algorithm search algorithm is combined to find the best parameters. Finally, the data from Bus No. 249 in Shenyang, china are used to check the model. The experimental results show that the forecasting model is superior to the traditional SVM model and the Artificial Neural Network (ANN) model in terms of the same data, and is of higher accuracy, which verified the feasibility of the model to predict the bus arrival time.

/pdf/bus-arrival-time-prediction-using-support-vector-machine-idxzft9235.pdf

Bus Arrival Time Prediction using Support Vector Machine with Genetic Algorithm

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Bus Arrival Time Prediction using Support Vector Machine with Genetic Algorithm