A Kind of Urban Road Travel Time Forecasting Model with Loop Detectors

Abstract

Urban road travel time is an important parameter to reflect the traffic flow state. Besides, it is one of the important parameters for the traffic management department to formulate guidance measures, provide traffic information service, and improve the efficiency of the detectors group. Therefore, it is crucial to improve the forecast accuracy of travel time in traffic management practice. Based on the analysis of the change-point and the ARIMA model, this paper constructs a model for the massive data collected by loop detectors to forecast travel time parameters. Firstly, the preprocessing algorithm for the data of loop detectors is given, and the calculating model of the travel time is studied. Secondly, a change-point detection algorithm is designed to classify the sequence of large number of travel time data items into several patterns. Then, this paper establishes a forecast model to forecast travel time in different patterns using the improved ARIMA model. At last, the model is verified by simulation and the verification results of several groups of examples show that the model has high accuracy and practicality.

Roads Development Optimization for All-Season Service Accessibility Improvement in Rural Nepal using a Novel Cost-Time Model and Evolutionary Algorithm

“…Geospatial cost-time models represent travel in terms of the minimum time to cross each cell in a grid (raster). These times are calculated based on some combination of the underlying roads and road speeds, landcover, walking speeds, terrain, and other factors (Zhu et al, 2016). The advantage of this approach is that grid cells can model travel as on-network, off-network (e.g.…”