Modeling of public transport waiting time indicator for the transport network of a large city

Abstract

The need to develop and improve public passenger transport in major cities was noted. It was reflected that waiting time at bus stops is one of the factors that have a big impact on the passenger quality assessment of transport services. The results of an empirical study of the actual and anticipated waiting time at bus stops were given. It was noted that the reliability functions were used in the field of ride duration modeling, traffic restoration time after an accident, and length of making the decision to travel. The waiting time distribution functions using the lognormal function and the Weibull function were chosen. The results of modeling were objective, the dependent variables in it were the expected waiting time of passengers and the difference between the anticipated and the actual waiting time. The explanatory variables were sex, age, time period, purpose of the trip and the actual waiting time. The results of the research showed that the age, purpose of the trip and the time period influence the waiting time perception, prolong it and lead to its reassessment.

Modeling, equilibrium, and demand management for mobility and delivery services in Mobility‐as‐a‐Service ecosystems

“… $$$$\begin{equation} \Psi _{i}(n_{i})=\gamma ^{M} e^{\frac{n_{i}}{Q}}+\vartheta ,\quad \forall i\in \mathcal {I}, \end{equation}$$$$ $$$$\begin{equation} \tau _{i}(n_{i}^{D})=\gamma ^{D} e^{\frac{n_{i}^{D}}{Q}}+\kappa ,\quad \forall i\in \mathcal {I}, \end{equation}$$$$where the value of Q can be obtained through Table 3, the parameters $$\gamma ^{M}$$ and $$\gamma ^{D}$$ are set as 10 and 8, ϑ and κ are set as 10 and 6. The above parameters are calibrated based on the data provided in Lebedeva and Kripak (2018).…”

“…where the value of 𝑄 can be obtained through Table 3, the parameters 𝛾 𝑀 and 𝛾 𝐷 are set as 10 and 8, 𝜗 and 𝜅 are set as 10 and 6. The above parameters are calibrated based on the data provided in Lebedeva and Kripak (2018).…”

Estimate Speed and Travel Time by Public Transport

“…где Т -время в пути, Т0 -время движения в свободном потоке, Q -интенсивность транспортного потока, Ср -пропускная способность, α, β, γ -поправочные коэффициенты [2]. При оценке времени в пути и скорости общественного транспорта для моделирования принимаются данные согласно расписанию движения, либо оцениваются на основе средних временных затрат в пути в соответствии с техническим классом дороги [3]. Но этот подход не учитывает интенсивности движения и других факторов, которые могут оказать влияние на условия.…”