Bus arrival time calculation model based on smart card data

Zhou, Yiren; Lin, Yuan; Chen, Yanyan; Yi, Gong; Lai, Jun

doi:10.1016/j.trc.2016.11.014

Cited by 50 publications

(26 citation statements)

References 11 publications

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“…Investigation of the use of smart mobile devices for journey planning is timely, as other research has shown that smartphone-based real-time information reduces the actual and perceived wait times experienced by transit riders (Watkins et al 2011;Chowdhury and Giacaman 2015); and can assist with trips in unfamiliar areas (Chowdhury and Giacaman 2015). There are also emerging studies on the use of smart card data for bus arrival time computation enhancement (Zhou et al 2017), and for understanding passenger-to-rail assignment and crowding cost to develop business cases and inform pricing policies (Hörcher et al 2017). It has been shown that existing travel information systems in the UK, Sweden, and Germany tend to be biased towards planning in the pre-travel stage of the journey, leading to limited functionalities on the on-trip/in-trip as well as post-trip stages of the journey (Kramers 2014).…”

Section: Introductionmentioning

confidence: 99%

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

et al. 2019

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This paper investigates factors influencing public transport passengers' pre-travel information-seeking behaviours in a British urban environment. Public transport traveller surveys were conducted to better understand the journey stages at which information was sought and the information sources used. A multivariate explanatory model of pre-travel information-seeking behaviour was developed using binomial logistic regression. Explanatory factors considered include socio-demographics, trip context, frequency of public transport use, information sources used, and smartphone ownership and use. Findings suggest that travel behaviour (5 + trips weekly, and < 1 trip weekly), socio-demographics (unemployment/unknown employment), trip context (journey planning stages, mode of transport), and preferred information sources (Internet site, word-of-mouth, visits to travel shop/centre/library) were significant predictors of pre-travel information-seeking behaviours among surveyed travellers. While the final model found that bus users are significantly associated with the use of Internet sites as a source of pre-travel information, rail users rely significantly on a multiplicity of sources comprising Internet sites, word-of-mouth, and visits to a travel shop/centre/library. The final model suggests that metro (light rail) users tend not to seek pre-travel information. The odds of seeking pre-travel public transport information are 2.512 times greater for respondents who reported < 1 trip per week as opposed to those who reported 5 + trips per week. These findings are relevant for passenger information strategies deployed by operators and authorities and can be used to caution against a "one size fits all" strategy for travel information service provision. Implications and suggestions for future research are discussed.

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Section: Introductionmentioning

confidence: 99%

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

et al. 2019

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“…Statistical methods such as regression and time series predicts the future values by developing relationship among the variables affecting travel time or from the series of data points listed in time order. A few studies have implemented regression methods [41,42,43,44,45,46,47,48,49,50] for arrival time prediction. [51] integrated regression methods with adaptive algorithms to make it suitable for real time implementations.…”

Section: Literature Reviewmentioning

confidence: 99%

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Bharathi

Kumar

Achar³

et al. 2020

Transportmetrica A: Transport Science

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Providing real-time arrival time information of the transit buses has become inevitable in urban areas to improve the efficiency of the public transportation system. However, accurate prediction of arrival time of buses is still a challenging problem in dynamically varying traffic conditions especially under heterogeneous traffic condition without lane discipline. One broad approach researchers have adopted over the years is to divide the entire bus route into sections and model the correlations of section travel times either spatially or temporally. The proposed study adopts this approach of working with section travel times and developed two predictive modelling methodologies namely (a) classical time-series approach employing a seasonal AR model with possible integrating non-stationary effects and (b) linear non-stationary AR approach, a novel technique to exploit the notion of partial correlation for learning from data to exploit the temporal correlations in the bus travel time data. Many of the reported studies did not explore the distribution of travel time data and incorporated their effects into the modelling process while implementing time series approach. The present study conducted a detailed analysis of the marginal distributions of the data from Indian conditions (that we use for testing in this paper). This revealed a predominantly log-normal behaviour which was incorporated into the above proposed predictive models. Towards a complete solution, the study also proposes a multi-section ahead travel time prediction algorithm based on the above proposed classes of temporal models learnt at each section to facilitate real time implementation. Finally, the predicted travel time values were corroborated with the actual travel time values. From the results, it was found that the proposed method was able to perform better than historical average, exponential smoothing, ARIMA, and ANN methods and the methods that considered either temporal or spatial variations alone.

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“…The majority of the existing contributions focused on developing methodologies for PT performance assessment. In the reviewed articles, big data was used to estimate regular performance measures such as quality of PT service using GSM data 1 , physical and schedule-based connections of metro user using quadruple 33 , bus arrival time using smart card data 79 , left behind passenger using smart card data and AVL data 80 , accessibility to PT service using mobile phone data 43 , passenger waiting time using smart card data 64 , and spatial variations of urban PT ridership using GPS trajectories and smart card data 66 . Further, Min et al 50 proposed a method to recover the arrival times of trains from the exit times of metro passengers.…”

Section: Measurement Of Performance Assessment Indicatorsmentioning

confidence: 99%

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

Zannat

Choudhury

2019

J Indian Inst Sci

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Introduction Rapid urbanization and associated increase in population are resulting in a higher growth of motorized traffic flow in urban areas. As a consequence, cities are experiencing different problems such as air pollution, road accidents, and congestions. In response to these problems, public transportation (PT) could help to reduce air pollution, road congestion and travel time, and dependency on non-renewable energy, which benefit both

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Bus arrival time calculation model based on smart card data

Cited by 50 publications

References 11 publications

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

Understanding factors influencing public transport passengers’ pre-travel information-seeking behaviour

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

Emerging Big Data Sources for Public Transport Planning: A Systematic Review on Current State of Art and Future Research Directions

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