Transit Delay Estimation Using Stop-Level Automated Passenger Count Data

Wong, Eugene; Khani, Alireza

doi:10.1061/jtepbs.0000118

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…Bus operation consists out of alternating running and dwelling processes while a bus travels along a route. Both running and dwell times are subject to stochastic variability (i.e., statistical dispersion), which can be attributed to different factors [4]. Among others, running times are affected by traffic conditions, infrastructure, and driver behavior [5]; dwell times are affected, for instance, by boarding and alighting passengers and vehicle characteristics [6].…”

Section: A Transit Operationsmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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Predictions of transit delays are crucial to passengers and operators. Passengers utilize the predictions to decide on departure time, route choice, and mode choice, whereas operators decide on schedules, timetables, rolling stock allocation, and control actions. We introduce the concept of predictability of transit travel times as the study of the reduction of the predicted variability with the temporal approaching of a predicted event.We evaluate predictability on a real-life test case in Zurich, Switzerland, spanning multiple transit lines over one year of operations. The concept is shown based on predictions obtained by a state-of-the-art Bayesian network approach, where we show how predictability (in general) can be modeled as an exponential decay phenomenon. The study of predictability of transit operations leads to additional insights for control actions and system analysis compared to other complementary concepts such as punctuality or regularity, for instance, concerning bunching, identification of bottlenecks, and passenger routing.

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Section: A Transit Operationsmentioning

confidence: 99%

What Do We Know When? Modeling Predictability of Transit Operations

Büchel

Corman

2022

IEEE Trans. Intell. Transport. Syst.

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“…As dwell times and running times are affected by different factors, it is meaningful to investigate them separately (see Wong and Khani, 2018). Mazloumi et al (2010) explain this in a practical example: Early running buses wait at timing points until their predefined scheduled departure time.…”

Section: Components Of Travel Times and Their Variabilitymentioning

confidence: 99%

“…Typically, running and dwell times and the variations in these times are higher during peak periods. Given this variation, most of the studies reviewed for this research only consider one period or treat periods of the day separately (Xue et al, 2011;Cats et al, 2014;Kieu et al, 2014;Durán-Hormazabal and Tirachini, 2016;Ma et al, 2016;Yan et al, 2016;Chen and Sun, 2017;Chepuri et al, 2018;Rahman et al, 2018;Wong and Khani, 2018). Some studies further aggregate travel time observations into departure time windows (DTW).…”

Section: Travel Timementioning

confidence: 99%

Review on Statistical Modeling of Travel Time Variability for Road-Based Public Transport

Büchel

Corman

2020

Front. Built Environ.

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Accurately modeling the 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; 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 current buses' travel time. 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-today 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 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.

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“…In the existing works, the bus travel times are predicted using manually collected data [14], GPS logs of the buses [15], automatic passenger counters [16], and mobile phone footprints [17]. GPS-based location data is the most common data source used for bus travel time prediction.…”

Section: Introductionmentioning

confidence: 99%

Hybrid travel time estimation model for public transit buses using limited datasets

Ashwini¹,

Sumathi²,

Sudhira³

2023

IJ-AI

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A reliable transit service can motivate commuters to switch their traveling mode from private to public. Providing necessary information to passengers will reduce the uncertainties encountered during their travel and improve service reliability. This article addresses the challenge of predicting dynamic travel times in urban areas where real-time traffic flow information is unavailable. In this perspective, a hybrid travel time estimation model (HTTEM) is proposed to predict the dynamic travel time using the predicted travel times of the machine learning model and the preceding trip details. The proposed model is validated using the location data of public transit buses of, Tumakuru, India. From the numerical results through error metrics, it is found that HTTEM improves the prediction accuracy, finally, it is concluded that the proposed model is suitable for estimating travel time in urban areas with heterogeneous traffic and limited traffic infrastructure.

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Transit Delay Estimation Using Stop-Level Automated Passenger Count Data

Cited by 10 publications

References 7 publications

What Do We Know When? Modeling Predictability of Transit Operations

What Do We Know When? Modeling Predictability of Transit Operations

Review on Statistical Modeling of Travel Time Variability for Road-Based Public Transport

Hybrid travel time estimation model for public transit buses using limited datasets

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