Abstract

The arrival time of bus at the stop is critical data in design of bus operational strategies. Especially for real-time control strategies (i.e., signal priority system), bus arrival time is usually predicted to assess the next bus operation condition in the future (i.e., bus bunching and reliability of transit service) and then can be used as a decision basis of current control actions. The signalized intersection and surrounding traffic are the key factors in bus travel time prediction, but most previous approaches focus on the impact of signal control on bus delay only. This paper proposes a prediction model for arrival time at bus stop under the influence of both upstream signalized intersection and surrounding traffic flow. Considering the affected range of signalized intersection and the dynamic variation of bus speed, bus running processes are evaluated separately (including processes from a given detection point to stop line, through intersection, and from intersection to bus stop). In the proposed models, bus speed is deduced according to the change of traffic density at different locations to reflect the micro-impact of surrounding traffic flow on bus operation. The observed bus travel time is collected from actual investigations at two bus stops incorporating signalized intersection in Jinan City and compared with the predicted travel time in the proposed model. The results show that the proposed model has a low mean relative error. In addition, through the analysis of the maximum relative error, it also can be seen that vehicle queuing with random arrival of vehicles at stop line makes a gap between the prediction and the actual situation, which will be the focus of further research.

Highlights

  • Numerous intelligent transportation technologies have been widely applied in public transportation system recently

  • This paper presents a bus arrival time prediction model which takes into account the influence of signalized intersection

  • To reflect the change of bus speed under the surrounding traffic flow, the speed is constantly updated according to the traffic density at different locations

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Summary

Introduction

Numerous intelligent transportation technologies have been widely applied in public transportation system recently. This enables an access to the real-time acquisition of information, such as vehicle location, bus speed, and number of passengers, which facilitates transit management. Because of the randomness of traffic flow and the influence of signal control at intersection, the travel time of buses fluctuates randomly, which reduces the stability of public transport. Passengers can perceive the arrival time of buses well and. As an important part of real-time control strategy, it is necessary to obtain accurate real-time bus arrival time. Based on the existing researches, prediction methods of bus travel time or arrival time can be classified as machine learning approaches, regression models and surrounding traffic based approaches

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