Real-time Bus Arrival Time Prediction: An Application to the Case of Chinese Cities Shandong University, China & University of Maryland at College Park, USA AbstractAbstract  This paper presents two models for the real-time bus arrival time prediction. The proposed basic model uses the Artificial Neural Networks (ANN) to predict bus arrival time according to the historical GPS data.  To contend with the difficulty of capturing traffic fluctuations over different day of week, this study further subdivides the prediction problem into a bunch of clusters, based on the historical bus travel time data from the city of Jinan, China. Sub ANN models are then developed for each cluster and further integrated into the Hierarchical ANN model.  Using the GPS dataset from Jinan, six different scenarios, are selected to evaluate the accuracy and effectiveness of the proposed models. Research Background  In most cities, buses are equipped with GPS and AFC. Since only some of passengers use the smart card to pay bus tickets, the AFC system fails to offer a reliable passenger demands.  The bus route network is intensive, revealing by the overlap of multiple bus routes along an arterial. Therefore, these features consequently requires a quick-response prediction model.  The bus schedule is varying over time. For concerns of the day-to-day passenger demand variation, most transit systems don’t provide a fixed timetable to passengers, especially for those high frequency routes. Case Study ConclusionsConclusions  On the basis of the characteristics of bus operations, the paper proposed Artificial Neural Network model and Hierarchical Artificial Neural Network to predict the short-term bus arrival time, which includes four types of variables, time index, the levels of bus delay, arrival time, and headway distribution.  With field data from GPS, the developed models outperformed existing KF models, especially for predicting bus arrival between neighboring stops. Under recurrent traffic condition, the prediction error within a 10-min prediction time window is less than 20% with the reliability probability more than 85%, while the probability to have more than 40% prediction errors is no more than 7%. A set of candidate variables is selected based on a comprehensive data analysis: headway distribution, time index, the levels of bus delay, arrival time. by Yongjie Lin, Xianfeng Yang, Nan Zou, and Lei Jia Flow chart of the solution for the proposed HANN model ScenarioDateTime of daySample SizeDescription s1 Nov 29, MondayAM Peak hours163 Stops 3 to 4 (0.36km) s2 Nov 24, WednesdayPM Peak hours147 s3 Nov 27, SaturdayAll day415 s4 Nov 29, MondayAM Peak hours217 Stops 3 to 8 (2.3km) s5 Nov 24, WednesdayNon-peak hours416 s6Nov 28, SundayAll day330 Actual headway distribution at stop 8 The field data of bus route 63 in the city of Jinan, China, is used for analysis and performance evaluation. The road network information is obtained through onsite investigation.  There are 15 stops at the each direction along this route, with the total length of 8.1 km;  The bus operating time is from 6:00am to 9:00 pm;  Departure headway is around 4 minutes during peak hours and about 10 minutes in non-peak hours;  No bus exclusive lane is available along this route. Model Development Impact of Signalized Intersection on Travel Time Note that data source is from GPS and AFC system, AM Peak hours are from 7:00 to 9:00, and PM peak hours are from 17:00 to 19:00.