Evaluation of passenger waiting time in public transport by using the Monte Carlo method

Pencheva, Velizara; Georgiev, Ivan; Asenov, Asen

doi:10.1063/5.0040097

Cited by 19 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Waiting time at bus terminal is the time interval spent at transit terminal between the moments of passenger's arrival and boarding the desired vehicle [22,23]. Waiting at bus terminal is mostly perceived as a negative attribute by the transit users, with attendant negative effect on the overall assessment of the system's service quality [10].…”

Section: Passenger's Waiting Timementioning

confidence: 99%

Development of passenger’s waiting time model at Bus Public Transit Terminal

Ibrahim,

Ede

2024

Nig. J. Tech.

View full text Add to dashboard Cite

Passenger’s waiting time at terminal is a key constituent of travel time, as long waiting time increases the overall journey time. In Nigeria, urban-rural bus transit system is associated with long waiting time. This study evaluates bus transit users’ waiting times at terminal with perceived and actual waiting times (PWT and AWT) along with associated bus service frequency (BSF). Bus public transit operating from Enugu city (as origin) to 6 local government areas (Awgu, Isi-Uzo, Nkanu west, Nsukka, Oji River and Udenu) of the state as destinations was studied. Data were collected on AWT and PWT at Old Park, Enugu for 30 days from 8 am to 6 pm (5 days per route). Data on AWT and PWT were collected based on passenger’s observation and oral interview, respectively. The study discovered that transit users incurred long waiting times, with 80 – 90% of the users incurring AWT of 21 minutes to 1 hour and more, indicating a poor service quality. Also, 80 – 90% of the users overestimated their PWT by 25 – 60%. At 95% confidence level, PWT is significantly longer than AWT (P < 0.05) for all the routes. A passenger’s perceived waiting time model was developed for predicting PWT based on AWT. The model developed showed that PWT is strongly correlated with AWT, with an R2 = 0.9591 and F-significance < 0.05. This suggests that that AWT accounts for 96% variability in PWT. Consequently, the model developed exhibits a reasonable accuracy, hence, can be applied for prediction of passenger’s perceived waiting time. The mean BSF for Enugu – Nsukka and Enugu – Oji River routes were 30 and 21 buses per day, respectively. While, the other 4 routes recorded lower values of 7 – 9 buses per day, resulting in longer waiting times than the other 2 routes. The implication of long passengers’ waiting time suggests the need for shifting from unscheduled to scheduled operation and improved BSF by shortening bus headways to minimize waiting time.

show abstract

Section: Passenger's Waiting Timementioning

confidence: 99%

Development of passenger’s waiting time model at Bus Public Transit Terminal

Ibrahim,

Ede

2024

Nig. J. Tech.

View full text Add to dashboard Cite

show abstract

“…Conway et al (2018) utilised MCS to account for variation and uncertainty in accessibility metrics when planning PT sketches [24]. Furthermore, Pencheva et al (2021) applied MCS to determine the waiting time of passenger vehicles in PT areas [25]. Research shows that despite the increased optimisation and uncertainty analysis of PT, the existing studies focus more on single aspects of PT.…”

Section: Monte Carlo Simulation For Managing Uncertaintymentioning

confidence: 99%

Navigating Uncertainty: A Framework for Optimising Public Transport Networks’ Performance

Lin,

Xu,

Wang

et al. 2024

Sustainability

View full text Add to dashboard Cite

Public transport (PT) networks face significant challenges in achieving optimal outcomes due to the presence of risk and uncertainty. Despite the importance of optimising PT networks’ performance, limited research has applied risk management tools to tackle this issue. In response, this study presents a three-stage framework to optimise PT networks’ performance in uncertain conditions. First, we establish a PT criteria matrix using an analytic hierarchy process to develop a criteria model and calculate the criteria weightings. Second, we propose a multi-aspiration-level goal programming approach to optimise a PT network’s performance based on the weighted results. To manage uncertainty, we use Monte Carlo simulation to analyse the probability of the optimal solution. Finally, to validate our approach, we apply the three-stage framework to three case study areas in Australia. The results of this research offer significant insights into identifying the likelihood of criteria optimisation scenarios, thereby assisting decision makers in allocating resources for optimising the delivery of PT network performance solutions in accordance with government requirements.

show abstract

“…Adaptive strategy [1], [3], [4], [7] is well known method for evaluation of multidimensional integrals, especially when the integrand function has peculiarities and peaks. Let p j and I Ωj be the following expressions: p j = Ωj p(x) dx and I Ωj = Ωj f (x)p(x) dx.…”

Section: A Adaptive Approachmentioning

confidence: 99%