Abstract
The primary objective of this paper is to develop models to predict bus arrival time at a target stop using actual multi-route bus arrival time data from previous stop as inputs. In order to mix and fully utilize the multiple routes bus arrival time data, the weighted average travel time and three Forgetting Factor Functions (FFFs) – F1, F2 and F3 – are introduced. Based on different combinations of input variables, five prediction models are proposed. Three widely used algorithms, i.e. Support Vector Machine (SVM), Artificial Neutral Network (ANN) and Linear Regression (LR), are tested to find the best for arrival time prediction. Bus location data of 11 road segments from Yichun (China), covering 12 bus stops and 16 routes, are collected to evaluate the performance of the proposed approaches. The results show that the newly introduced parameters, the weighted average travel time, can significantly improve the prediction accuracy: the prediction errors reduce by around 20%. The algorithm comparison demonstrates that the SVM and ANN outperform the LR. The FFFs can also affect the performance errors: F1 is more suitable for ANN algorithm, while F3 is better for SVM and LR algorithms. Besides, the virtual road concept in this paper can slightly improve the prediction accuracy and halve the time cost of predicted arrival time calculation.
Highlights
Intelligent Transportation System (ITS) is becoming increasingly popular
In some other cities with heavy mixed traffic flow, bicycles and pedestrians have a great influence on the transit system, making it difficult to accurately predict the arrival time of buses
Multiple transit routes running along one road segment will affect the accuracy of arrival time prediction
Summary
Intelligent Transportation System (ITS) is becoming increasingly popular. In the field of public transit system, a couple of new technologies, such as Automatic Passenger Collection (APC), Automatic Vehicle Location (AVL) and Automatic Vehicle Identification (AVI), are used to provide better service to bus rides and enhance the Level Of Service (LOS) of public transit system. With the help of these technologies, bus rides can get transit-related information in real time. Some incidents as well as the other running vehicles along public transit routes will affect the bus operation and leading to inaccurate information. The agencies are trying to provide passengers with more accurate predicted bus arrival information at stops using new algorithms and more precise data. In well-developed cities, the traffic conditions, traffic flow patterns and characters, and the spatial and temporal distribution of bus rides stay stable. In some other cities with heavy mixed traffic flow, bicycles and pedestrians have a great influence on the transit system, making it difficult to accurately predict the arrival time of buses. Choose and process traffic data to predict bus arrival time more accurately, especially under the condition of heavy mixed traffic flow and multiple transit routes?
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