Multiple-Factor Based Sparse Urban Travel Time Prediction

Abstract

Abstract:The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

Comparative Assessment of Dynamic Travel Time Prediction Models in the Developing Countries Cities

“…Datadriven methods have been used by different researchers to predict both freeway and urban travel. The methods have shown impressive results in terms of travel time prediction in both urban road networks and freeways (Zhu et al, 2018). The data-driven method includes the Average Historical Model, Regression Analysis Model, Artificial Neural Network and Kalman Filter models.…”

“…The sigmoidal function, such as logistic and tangent hyperbolic is common because of its ability to normalize the input values to the range of -1 to 1. Most of the studies dealing with urban traffic prediction prefer to use logistic and tangent hyperbolic because they produce positive and negative value and are faster in training (Fan and Gurmu 2015;Amita et al, 2016;Čelan and Lep, 2017;Zhu et al, 2018).…”

Travel Time Reliability of Bus Operation in Heterogeneous Traffic Conditions of Dar es Salaam City, Tanzania

“…Most researchers have revealed that travel time reliability is affected by several factors such as weather conditions, natural disasters, congestion, road maintenance, traffic control, road accidents, in-vehicle travel time, waiting time at the intersections and bus stops [4][5][6]. However, this study focuses on in-vehicle travel time, waiting time at the intersections and bus stops data to determine travel time reliability in Dar es Salaam city.…”

Evaluation of the Change in Undrained Shear Strength in Cohesive Soils due to Principal Stress Rotation Using an Artificial Neural Network

“…Artificial neural networks (ANNs) are tools of information technology, often used in science and various fields of technology for modeling complex phenomena (e.g., [13][14][15][16]). ANNs are alternatively used for statistical analysis methods, as they are often more useful compared to traditional methods [17].…”