Summary
Forecasting arrival times of a vehicle at many downstream stops is very important in many cases. For multi-stop arrival time prediction, direct approaches and iterative approaches possess respective merits. Therefore, a hybrid method that has both direct and iterative modeling abilities is presented to forecast arrival times at multiple stops. The hybrid method consists of an iterative support vector machine (SVM)-based prediction model and a direct SVM-based prediction model. In hybrid model, output from the iterative model is a rough prediction and it also needs to be adjusted, based on output from the direct model. The proposed model is assessed with the data of transit route number 3 in Guiyang city, China. Results show that the hybrid model seems to be a powerful tool for multi-stop arrival time prediction.
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Extract
Hybrid Model for Multi-Stop Arrival Time Prediction
1. Introduction
Recently, a growing interest has been developing in emerging technologies, such as the use of automatic vehicle location (AVL) or identification (AVI) systems and automatic passenger counters (APC), in transportation. This makes it possible to treat the control strategies as deterministic over a short forecast interval, increase the satisfaction of transit users and maximize transit system efficiency and productivity. Many of these controls require, directly or indirectly, timely, reliable prediction of arrival times at stops. Furthermore, effective prediction of bus arrival times can attract additional ridership and improve transit service level, while transit operators can identify vehicles and effectively operate their systems.Accurate prediction of bus arrival time is critical to advanced public transportation systems (APTS) and advanced traveler information systems (ATIS). Since transit operations are often disrupted by randomness in passenger arrivals and interference from other traffic, etc., the deploym...See the full content of this document
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