Abstract
Accurately modeling travel time of road-based public transport can help directly improve current passenger service and operating efficiency. Moreover, it paves the way for control of future high technology automated vehicles, which will share the same characteristics of sharing the road infrastructure with other vehicles; carrying multiple passengers, i.e. having a non-negligible dwell process, and run not completely demand-responsive but in general following a schedule or a target frequency. Recent advances in sensor and communications technology, leading eventually to comprehensive vehicle connectivity, have significantly increased the amount and quality of travel time data available making it possible to better model distributions of travel time of current buses. We assume that the choice of those distributions with regards to transport performance will hold also in the near future. This paper explains definitions of travel time components and explains how they contribute to variability. It focuses on the description of day-to-day variability and systematically reviews the current state of the art for statistically modeling bus travel, running, and dwell time distributions. It considers statistical distributions developed based on empirical data from the research literature. Statistical distributions are powerful tools, as they can describe the inherent variability in data with a limited number of parameters. The review finds that both spatial and temporal data aggregation have an important influence on the statistics as well as the choice of the most appropriate probability distribution. This influence is still not well understood and remains a question for further studies. Furthermore, the review finds that mixture distributions provide good fitting performance, however, it is important to improve the description of components in such distributions, in order to get meaningful and understandable distributions. The methodologies for fitting distributions, for proving if a distribution is suited, and for identifying best fitting, robust and reproducible distribution should be reconsidered. Such a distribution will enable reporting, controlling operations, and disseminating information to operators and travelers. Finally, this review proposes directions for further work.
Highlights
The observed travel time of road-based public transport vehicles, as well as its components, are subject to variability, caused by the stochastic nature of various factors, including traffic congestion
The first step of this paper is to report on an extensive review of the relevant literature on statistical modeling of bus travel time variability
Parametric statistical approaches are a comprehensive approach to model these factors (Anastasopoulos et al, 2017). This systematic literature review aims to report the state of the field of modeling statistical distributions to bus travel time and components of bus travel time observations
Summary
The observed travel time of road-based public transport vehicles, as well as its components (i.e., dwell time and running time), are subject to variability, caused by the stochastic nature of various factors, including traffic congestion. Travel time variability causes uncertainty, increasing costs for travelers and operators (Li et al, 2010). Travel Time Variability Modeling Review of a planned public transport service and the expected travel time complicates their decisions regarding departure time, route choice, or even mode choice. Research has shown that reducing travel time variability is even more valuable to passengers than reducing travel time (Bates et al, 2001). This variability reduces on-time performance and increases operating costs, for example by requiring the addition of recovery times to schedules. Many strategic and operational decisions are affected by variability and impact the cost of service
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