A Dynamic Model for Bus Arrival Time Estimation based on Spatial Patterns using Machine Learning

B, P, Ashwini; Sudhira, Honnudike Satyanarayana

doi:10.14445/22315381/ijett-v70i9p219

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kolmogorov-Smirnov test [40] was used to check if a data sample is drawn from a population that fitted a given theoretical distribution.…”

Section: Modelling the Datamentioning

confidence: 99%

See 1 more Smart Citation

Public Transit Bus Travel Time Variability Analysis Using Limited Datasets: A Case Study

Ashwini Bukanakere Prakash,

Sumathi,

Satyanarayana Sudhira

2024

Curr. Appl. Sci. Technol.

View full text Add to dashboard Cite

Public transit service is a sustainable and eco-friendly alternative for commuting, and promoting its usage is the need of the day. An understanding of the variability of travel time can aid service operators to improve the reliability and ridership of public transport. Gaining insights into the variability of travel time is a data-intensive task, and most of the existing studies utilize multiple traffic-related datasets. However, most cities lack the infrastructure to collect multiple data sets, hence in the current study, the location data of public transit buses were used for the analysis. The study was conducted in Tumakuru city, India at two spatial levels, namely route and segment, and further at temporal levels such as the day-of-the-week and departure time window. Wilcoxon signed-rank test was applied to identify similar spatial-temporal aggregations, and a few aggregations demonstrated similarity. Consistent with the existing literature, six statistical distributions were selected to fit the data through the Kolmogorov-Smirnov test. The results emphasized that the Logistic distribution is the best fit at all spatial-temporal aggregation levels, and the lognormal and GEV distributions offered better fit for a few aggregation levels. Logistic distribution is recommended for operations planners and researchers to conduct reliability analysis and travel time forecasting in the future.

show abstract

“…Kolmogorov-Smirnov test [40] was used to check if a data sample is drawn from a population that fitted a given theoretical distribution.…”

Section: Modelling the Datamentioning

confidence: 99%

“…In the current study the kurtosis value was high for the travel time of segment 4. Further in the study, Kolmogorov-Smirnov tests[40] were conducted to find the best fit distribution for the travel time data. Based on probability densities, a histogram of travel time data of Mondays is shown in Figure2.Figure 2.…”

mentioning

confidence: 99%