A Dynamic Bus-Arrival Time Prediction Model Based on APC Data

Abstract

Automatic passenger counter (APC) systems have been implemented in various public transit systems to obtain bus occupancy along with other information such as location, travel time, etc. Such information has great potential as input data for a variety of applications including performance evaluation, operations management, and service planning. In this study, a dynamic model for predicting bus-arrival times is developed using data collected by a real-world APC system. The model consists of two major elements: the first one is an artificial neural network model for predicting bus travel time between time points for a trip occurring at given time-of-day, day-ofweek, and weather condition; the second one is a Kalman filter-based dynamic algorithm to adjust the arrival-time prediction using up-to-the-minute bus location information. Test runs show that this model is quite powerful in modeling variations in bus-arrival times along the service route.

A Hybrid Model Based on Support Vector Machine for Bus Travel-Time Prediction

“…The past decades have witnessed an increasing passion for the research of bus running time prediction. The literature focuses on time series [1] artificial neural network or support vector machine (SVM) [2][3][4][5][6][7][8] and Kalman filtering techniques [9,10], etc.…”

An algorithm for city transport arrival time estimation using adaptive elementary predictions composition

“…Также в оценке времени прибытия широко использу-ются модели, основанные на фильтрации Калмана [8,9,10]. Хотя основной функцией моделей такого рода является прогноз текущего состояния системы, они могут служить основой для оценки будущих значений или для исправления предыдущих прогнозов.…”

“…Популярность этих моделей объясняется их спо-собностью моделировать сложные нелинейные отно-шения между временем прохождения сегментов сети и независимыми переменными, характеризующими дорожную ситуацию [10,12].…”

Artificial Neural Network Travel Time Prediction Model for Buses Using Only GPS Data

“…Kalman filtering models have elegant mathematical representations (e.g., linear state-space equation) and the potential to adequately accommodate traffic fluctuations with time-dependent parameters (e.g., Kalman gain) (Chien et al 2002). These models have been used extensively for predicting bus arrival time (Chien et al 2002;Chen et al 2004;Shalaby and Farhan 2003). Their basic function is to provide estimates of the current state of the system, but they also serve as the basis for predicting future values or for improving estimates of variables at earlier times, i.e., they have the capacity to filter noise (Kalman 1960).…”

“…Therefore, a model of this kind is reliable only when the traffic pattern in the area of interest is relatively stable or where congestion is minimal, e.g., rural areas. (Jeong and Rilett 2004;Ramakrishna et al 2006;Patnaik et al 2004;Chien et al 2002;Chen et al 2004;Shalaby and Farhan 2003). Regression models predict and explain a dependent variable with a linear function formed by a set of independent variables.…”