An Adaptive Long-Term Bus Arrival Time Prediction Model with Cyclic Variations

Abstract

Real-time bus arrival information systems at transit stops can be useful to passengers for efficient trip planning and reducing waiting times. The accuracy of such systems depends upon the ability of the model to account for variations in the data series and to adjust according to changing traffic conditions. Many of the existing studies on passenger information systems have modeled the system based on stationary relations, not taking into account the cyclic variations in data, which is often suitable for demonstration purposes but not for long-term implementation. The present study models the changing relationships using Double-Seasonal Holt-Winter's Exponential Smoothing approach, which allows for self-updating parameters at four levels. It accounts for both long-term and short-term seasonal fluctuations in data while maintaining the dynamic treatment of real-time bus information. The model also takes into account delays using the real-time running information of the bus and incorporates it into subsequent forecasts for better accuracy. Real-time data from GPS transmitters in buses were used for validation of the proposed model. The results show that the proposed model performs better than the currently-used elementary field methods and is able to forecast bus travel times with a reasonable accuracy.The value of n is chosen based on the nature of the data series. For a relatively stationary data series, a large value of n is desirable. For a data series which is volatile, a small value of n is chosen. Kalman FilterKalman filter is an efficient recursive procedure that estimates the future states of dependent variables using a linear quadratic estimation model. It uses modern control theory to apply state space representations in the prediction scheme. Several studies have been carried out in the past using the Kalman Filter for bus travel time predictions using three components-a track, a filter, and a predictor (Chien and

SVM‐based hybrid approach for corridor‐level travel‐time estimation

“…500 m) there is a chance that there may be the possibility of two bus stops located closely within a segment or there are no bus stops. Therefore, it is advisable to segregate the segments based on the bus stop location to understand the variation in travel time between the two modes as suggested by [7, 94].…”

Bus arrival time prediction and measure of uncertainties using survival models

“…500 m, 100 m) there is a chance that there may be the possibility of two bus stops located closely within a segment or there are no bus stops at all. Therefore, it is advisable to segregate the segments based on the bus stop locations as suggested by Balasubramanian and Rao [41]. A total of 32 segments were created on the study corridor.…”

Bus arrival time prediction using mixed multi-route arrival time data at previous stop

“…Patnaik et al (2004) developed a set of regression models that estimate arrival times for buses traveling between two points along a route. Balasubramanian and Rao (2015) took cyclic variations in data into account, and proposed a NPR-based long-term bus arrival time prediction model. Some details of previous studies that most related to bus arrival time prediction are listed in Table 1.…”