Efficient traffic speed forecasting based on massive heterogenous historical data

Abstract

Drivers dream of foreseeing traffic condition to enjoy efficient driving experience at all times. Given the historical patterns for different locations and different time, people should be able to guess the possible traffic speed in a near future moment. What is difficult and interesting for this task is that we need to filter the useful data that could help us for the next moment traffic speed prediction from a massive amount of historical data. On the other hand, the traffic condition could be highly dynamic and we can only give a reliable traffic prediction by using the most updated model for prediction. This implies that frequent retraining is necessary. To conquer the task, we propose a lazy learning approach for traffic speed prediction given massive historical data. The approach integrates the kNN and Gaussian process regression for efficient and robust traffic speed prediction. kNN can help us to select the most informative data for Gaussian process Regression using a big data framework. Thanks for the most recent progress of big data research, the processing of massive data for prediction in close to real time has become possible now compared to any time in the past. We aim at using a Hadoop framework for the prediction given heterogeneous data including traffic data such as speed, flow, occupancy, and weather data.

A comprehensive study of speed prediction in transportation system: From vehicle to traffic

“…With regard to traffic-speed prediction, this method is mostly employed in the prediction with traffic information fusion, because it can take high-dimensional data, data heterogeneity, uncertainty, and ambiguity into account. For example, the weather information and traffic properties were considered in prediction through Gaussian process ( Chen et al., 2014 ), and the social mediadata and car trajectory data were taken into account in the same way in ( Lin et al., 2018 ). However, the cubic learning computation and quadratic space requirement are the major limitations of the Gaussian process ( Le et al., 2017 ).…”

Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach

“…Recently some studies have focused on hybrid approaches in an attempt to improve prediction accuracy considering the merits and application associated with each prediction method. Few studies that have utilized hybrid models are; the Bayesian-neural network approach [61]; hybrid fuzzy rule-based approach [62]; state-space approach coupled with least-squares support vector machine (LS-SVM) [63]; KNN-Gaussian regression process [64]; and chaos-wavelet analysis support vector machine approach (CWSVM) [65]. Intuitively, hybrid models provide better prediction accuracy compared to single prediction models [66][67][68].…”

A distributed platform for big data analysis in smart cities: combining Intelligent Transportation Systems and socioeconomic data for Montevideo, Uruguay

“…Chen et al [10] proposed a model that aims to efficiently predict traffic speed on a given location using historical data from various sources including ITS data, weather conditions, and special events taking place in the city. To obtain accurate results the prediction model needs to be re-trained frequently in order to incorporate the most up-to-date data.…”