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 BackgroundResearch 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 StudyCase 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

Scenario Date Time of day Sample Size Description

s1 Nov 29, Monday AM Peak hours 163Stops 3 to 4

(0.36km)s2 Nov 24, Wednesday PM Peak hours 147

s3 Nov 27, Saturday All day 415

s4 Nov 29, Monday AM Peak hours 217Stops 3 to 8

(2.3km)s5 Nov 24, Wednesday Non-peak hours 416

s6 Nov 28, Sunday All day 330

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 DevelopmentModel 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.