Short-term traffic speed forecasting based on data recorded at irregular intervals

As demand for proactive real-time transportation management systems has grown, major developments have been seen in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, genetic algorithms, etc., to short-time traffic forecasting to make forecasts more reliable, efficient and accurate. However, most of these methods can only deal with data recorded at regular time intervals, thereby restricting the range of data collection tools to loop detectors or other equipment that generate regular data. The study reported here represents an attempt to expand on several existing time series forecasting methods to accommodate data recorded at irregular time intervals, thus ensuring these methods can be used to obtain predicted traffic speeds through intermittent data sources such as the GPS. The study tested several methods using the GPS data from 480 Hong Kong taxis. The results show that the best performance is obtained using a neural network model with acceleration information predicted by ARIMA model. ©2010 IEEE.published_or_final_versionThe 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 19-22 September 2010. In Proceedings of the 13th IEEE ITSC, 2010, p. 1541-154

Short-term traffic speed forecasting based on data recorded at irregular intervals

Recent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term traffic forecasting to make forecasts more reliable, efficient, and accurate. However, most of these methods can only deal with data recorded at regular time intervals, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments.published_or_final_versio

Networkwide Traffic State Forecasting Using Exogenous Information: A Multi-Dimensional Graph Attention-Based Approach

Traffic state forecasting is crucial for traffic management and control strategies, as well as user- and system-level decision making in the transportation network. While traffic forecasting has been approached with a variety of techniques over the last couple of decades, most approaches simply rely on endogenous traffic variables for state prediction, despite the evidence that exogenous factors can significantly impact traffic conditions. This paper proposes a multi-dimensional spatio-temporal graph attention-based traffic prediction approach (M-STGAT), which predicts traffic based on past observations of speed, along with lane closure events, temperature, and visibility across the transportation network. The approach is based on a graph attention network architecture, which also learns based on the structure of the transportation network on which these variables are observed. Numerical experiments are performed using traffic speed and lane closure data from the California Department of Transportation (Caltrans) Performance Measurement System (PeMS). The corresponding weather data were downloaded from the National Oceanic and Atmospheric Administration (NOOA) Automated Surface Observing Systems (ASOS). For comparison, the numerical experiments implement three alternative models which do not allow for the multi-dimensional input. The M-STGAT is shown to outperform the three alternative models, when performing tests using our primary data set for prediction with a 30-, 45-, and 60-minute prediction horizon, in terms of three error measures: Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). However, the model's transferability can vary for different transfer data sets and this aspect may require further investigation.Comment: Transportation Research Board Annual Meeting 202

A Unified Graph Formulation for Spatio-Temporal Wind Forecasting

publishedVersio

Traffic Flow Forecast based on Vehicle Count

Real-time traffic predictions have now become a time-being need for efficient traffic management due to the exponentially increasing traffic congestion In this paper a more pragmatic traffic management system is introduced to address traffic congestion especially in countries such as Sri Lanka where there is no proper traffic monitoring database Here the real-time traffic monitoring is performed using TFmini Plus light detection and ranging LiDAR sensor and vehicle count for next five minutes will be predicted by feeding consecutively collected data into the LSTM neural network More than ten separate prediction models were trained varying both window size and the volume of input data delivered to train the models Since the accuracy results of all prediction models were above 70 it demonstrates that this system can produce accurate predictions even if it is trained using less input data collection Similarly the sensor accuracy test also resulted in 89 7 accurac

An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio