Importance of user perception in evaluating level of service for bus transit for a developing country like India: a review

Das, Shreya; Pandit, Debapratim

doi:10.1080/01441647.2013.789571

Cited by 84 publications

(25 citation statements)

References 33 publications

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“…Wen et al [7] used variable stepwise elimination and determined that the predominant factors were passengers' waiting time and passenger waiting behaviour. Das and Pandit [8] proved that shortening of passenger waiting time could indirectly improve passenger satisfaction and ultimately improve bus capacity. While the time cost indicated that customers preferred to wait rather than to pay for more frequent service, the management's goal was to provide the most suitable service for passengers, thus a more frequent bus timetable was necessary.…”

Section: Introductionmentioning

confidence: 99%

Factors of Perceived Waiting Time and Implications on Passengers’ Satisfaction with Waiting Time

Shen

Sun³

et al. 2016

PROMET

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In order to explore the influence factors on perceived waiting time, a multiple linear regression model has been used to quantitatively describe the relationship between perceived waiting time and various factors. The model is established with 234 data, which is surveyed with questionnaire at three stops in Harbin, China. The results show that several certain factors (“trip purpose - where to”, “presence of a companion - whether one has a companion or not”, “having a timing device - whether one has a timing device or not”, “riding frequency - how many times one takes one line per week” and “waiting behaviour - what one does while waiting for a bus”) have significant influence on perceived waiting time, which confirms previous findings and supports transferability of results. The significance of “waiting mood - howabout the mood while waiting for a bus” and “reserved waiting time - how long one will wait” are confirmed for the first time in this study. In contrast to previous studies, “waiting time interval - for how long in one day” is a negative variable and socioeconomic variables are non-significant. And it is found that the relationship between perceived waiting time and passengers’ satisfaction with the waiting time follows a decreasing exponential distribution. With this model, the variation trend of the section, where passenger satisfaction value is larger than 0 is obviously steeper than the section smaller than 0. Such result proves that passenger mood with short waiting time is more sensitive than with longer waiting time. And the borderline perceived waiting time, distinguishing satisfied from dissatisfied passengers is proven to be 7.87 minutes when assignment interval of satisfaction is (-25.25], when satisfaction is positive (larger than 0), the accuracy being 70.30%, while the accuracy is 82.71% fornegative satisfaction (less than 0).

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Section: Introductionmentioning

confidence: 99%

Factors of Perceived Waiting Time and Implications on Passengers’ Satisfaction with Waiting Time

Shen

Sun³

et al. 2016

PROMET

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“…Accordingly, the definition of transit service attributes should be refined from the passenger perspective. In turn, this means that operators should have a good understanding of the relationship between manageable attributes of transit services and customer satisfaction (Das and Pandit 2013;Yilmaz and Celik 2008;Fu and Xin 2007). Therefore, an investigation of key influential service factors is of great significance to optimize transit service from a customer perspective, resulting in policies that could be formulated to influence traveler behavior and attract more transit users.…”

Section: Introductionmentioning

confidence: 99%

Exploring Passenger Assessments of Bus Service Quality Using Bayesian Networks

Wu¹,

Yang²,

Rasouli³

et al. 2016

JPT

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Section: Conclusion and Recommendationsmentioning

confidence: 99%

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2016

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There has been a worldwide growing public concern regarding transit bus fires, mainly because of their association with severe loss of human life and property. As a result, numerous studies have been carried out to investigate the causes, factors, and features of such accidents, along with research focusing on the simulation of bus fire scenarios. However, a detailed analysis on the causes of bus fires and the interrelationships of risk factors is lacking. This study identified 17 risk factors associated with bus fires through an analysis of accident records from China using the Delphi approach. An integrated interpretative structural modeling (ISM) was adopted to explore the interactions among risk factors associated with bus fires, providing a useful hierarchy of risk factors whose individual relationships are unambiguous but whose group relationships are too complex to organize intuitively. This can help practitioners better understand risk dependencies and prioritize risk mitigation efforts. Results show that a lack of safety education and safety knowledge popularization and inadequate laws and regulations are the two most important risk factors associated with bus fires. Drivers also play an important role in preventing accidents. The analysis can be extended to risk analysis in other types of accidents, i.e., railway accidents and coach accidents.

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Importance of user perception in evaluating level of service for bus transit for a developing country like India: a review

Cited by 84 publications

References 33 publications

Factors of Perceived Waiting Time and Implications on Passengers’ Satisfaction with Waiting Time

Factors of Perceived Waiting Time and Implications on Passengers’ Satisfaction with Waiting Time

Exploring Passenger Assessments of Bus Service Quality Using Bayesian Networks

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