Predicting Bus Arrival Time on the Basis of Global Positioning System Data

A number of studies have been initiated in the past to address the bus arrival time prediction problem. These efforts have resulted in three types of prediction models: (a) models based on historical data, (b) multilinear regression models, and (c) artificial neural network models. The first type of prediction models infers the current and future travel time of a bus based on the historical travel time of the same bus or other buses. Lin and Zeng (4) proposed a set of bus arrival time prediction algorithms for a transit traveler information system implemented in Blacksburg, Virginia. Four algorithms were introduced with different assumptions on input data and were shown to outperform several algorithms from the literature. Their algorithms, however, did not consider the effect of traffic congestion and dwell time at bus stations. Kidwell (5) presented an algorithm for predicting bus arrival times based on real-time vehicle location. The algorithm worked by dividing each route into zones and recording the time that each bus passed through each zone. Predictions were based on the most recent observation of a bus passing through each zone. However, this algorithm was not suitable for large cities where both travel time and dwell time could be subject to large variations. Generally speaking, these models are reliable only when the traffic pattern in the area of interest is relatively stable. One of their main limitations is that it requires an extensive set of historical data, which may not be available in practice, especially when the traffic pattern varies significantly over time. The second approach is applying mathematical models to predict the expected travel times between stops and then the expected bus arrival times at individual stops. These models are usually established by regressing travel times against a set of independent variables, such as traffic conditions, ridership, number of intermediate stops, and weather condition. Patnaik et al. (6) developed a set of regression models to estimate bus arrival times with data collected by automatic passenger counters installed on buses. The results obtained were promising and indicated that the developed models could be used to estimate bus arrival times under various conditions. However, this approach is reliable only when such equations can be established, which may not be possible for many application environments where many of the system variables are typically correlated. The third approach is applying artificial neural networks (ANN) that are capable of capturing complex nonlinear relationships. Jeong and Rilett (7 ) proposed an ANN model for predicting bus arrival times and demonstrated its superior performance as compared with other methods. However, ANN models require extensive training Predicting Bus Arrival Time on the Basis of Global Positioning System Data