Abstract
Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data of Harbin to evaluate the headway performance and proposes a statistical method to identify the abnormal headways. Association mining is used to dig deeper and recognize six causes of bus bunching. The AHP, embedded data analysis, is applied to determine the weight of each cause in the case of that these causes are combined with each other constantly. Results show that the front bus has a greater effect on bus bunching than the following bus, and the traffic condition is the most critical factor affecting bus headway.
Highlights
Headway is one of the important indicators to evaluate the level of transit service, which reflects the service intervals of transit and the rationality of the allocation of public transportation resources
The headway performance of the selected Route 104 is evaluated and it is found that lower transit service levels emerge in rush hour, especially at stop No 14 to No 21
A method to identify the abnormal headways is proposed based on the two parameters: expectation and standard deviation
Summary
Headway is one of the important indicators to evaluate the level of transit service, which reflects the service intervals of transit and the rationality of the allocation of public transportation resources. A great deal of knowledge exists in the objective bus GPS data, which is extremely useful to evaluate and improve the service level of public transit within different densities or departure frequencies of the public transportation network. In part, these observations motivated our study. An effective method to identify and deeply analyze the irregular headway, which is an important indicator of transit service, is proposed These findings would provide significant help to improving the urban public transport service. Data mining and AHP are integrated to find the essential reasons for headway abnormity, and the final section concludes this paper
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