Identifying the Bias: Evaluating Effectiveness of Automatic Data Collection Methods in Estimating Details of Bus Dwell Time

Grisé, Emily; El-Geneidy, Ahmed

doi:10.3141/2647-05

Cited by 9 publications

(4 citation statements)

References 20 publications

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“…The bus dwell time is crucial to estimate bus station capacity [38], [39], and it is, moreover, a key component of bus travel time [40]- [42]. Furthermore, the dwell time functions play a critical role in the transit assignment models and analysis for transit reliability [43]- [45]. Consequently, the estimation of bus dwell time is vital for public transport designers and bus operators.…”

Section: Dwell Time Variationmentioning

confidence: 99%

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

et al. 2020

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Service quality is a significant concern for both providers and users of public transportation. It is crucial for transit agencies to clearly recognize the causes of unreliability before adapting any improvement strategy. However, evaluation of main causes of bus service unreliability has not been investigated well. Existing studies have three main limitations in context of recognizing causes of service unreliability. First, public transport networks and traffic condition are highly complex systems and most of the existing models are not capable to accurately determine the relationship between service irregularity and impact factors. Second, definition of "Big data" has been neglected and most of the studies only focused on one source of large scale data set to determine the causes of unreliability. Third, bus service unreliability can impact the users" perception toward the public transport, significantly. It has been recommended by number of studies that bus service reliability should be evaluated from both service providers" and users" perspective. However, the impact of service unreliability from passengers" perception is not well investigated, yet. Consequently, we proposed a novel simulation-based sensitivity analysis to evaluating main causes of bus service unreliability using a combination of three different sources of big data. Moreover, for the first time we developed a simulation model in R studio which is an open source and powerful coding environment. According to the results, the level of reliability in Route U32 showed the highest sensitivity to headway variations. Waiting time can be decreased by 61% if only bus operators can reduce the headway variation by 25% of the actual observed data. Big gap and bus bunching could be almost disappeared by decreasing headway variations. Moreover, the terminal departure policy could significantly improve the passenger waiting time. Waiting time can be decreased by 36% when almost all the buses depart the terminal on-time.

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Section: Dwell Time Variationmentioning

confidence: 99%

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

et al. 2020

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“…Bus dwell time studies are more commonly found in the literature, in which automatic vehicle location (AVL) and automatic passenger counting (APC) data were the main data sources. However, AVL and APC data tend to overestimate passenger service (or activity) time compared with manual data ( 9 ). The first empirical study that investigated the impact of crowding and stop design on tram dwell time was carried out in Melbourne ( 3 ).…”

Section: Literature Reviewmentioning

confidence: 99%

The Impact of Various Streetcar Types on Passenger Activity and Running Times

Diab

Aboudina

et al. 2018

Transportation Research Record: Journal of the Transportation R

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Transit agencies that operate streetcars use vehicles of varied sizes and characteristics to accommodate demand and service requirements. While some studies have been conducted on the influence of different bus types on service operations, there is limited research on streetcar types. Therefore, this paper examines the impacts of three different types of streetcar operating in the City of Toronto on passenger service time and running time using statistical models. Field data was collected in July 2017 for three types of streetcars serving the busiest corridor in Toronto. Descriptive statistics and two linear regression models were employed to compare their performance. The analysis results support the use of low-floor vehicles with wider doors as well as larger number of doors. Results also indicate a negative impact of the current stop design on the usage of longer streetcars. This study offers transit agencies new insights into the effects of distinct types of streetcars on door usage, passenger service time, and travel time, which are important components of transit service efficiency and attractiveness.

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“…Even the manually collected data, often used in the past [i.e., prior to the proliferation AVL/APC systems (Levinson 1983)], have found much use in applications. It is also worth noting that although AVL/APC system data processing is more cost-effective, the data extracted from these systems tend to mislead analysts into overestimating waiting times, in particular for buses (Grisé and El-Geneidy 2017).…”

Section: Introductionmentioning

confidence: 99%

Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis

Kang

Chung

et al. 2019

J. Big Data Anal. Transp.

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Monitoring transit system "health" by extracting and tracking such quantities as travel time, transfer time, number of passengers, etc., is critical to the benefit of travelers, planners and operators within a transit system. Most of the data typically available to and useful for analysts are generated by tracking vehicles instead of individual passengers/travelers-these data are useful, albeit within certain limits. This paper presents methods for obtaining system-level transit information from a new type of data-that coming from an Automated Fare Collection (AFC) system,-which provides hour-to-hour, day-today transit information, such as the value and reliability in both travel time and traveler count, and the location of congested road clusters in a city. The AFC data of public transit system in Seoul, South Korea is used as an example to illustrate the proposed data extraction methods and analysis. This paper is structured and detailed so as to provide both methodological and practical guidance for researchers and data-handling analysts.

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Identifying the Bias: Evaluating Effectiveness of Automatic Data Collection Methods in Estimating Details of Bus Dwell Time

Cited by 9 publications

References 20 publications

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

Simulation-Based Sensitivity Analysis for Evaluating Factors Affecting Bus Service Reliability: A Big and Smart Data Implementation

The Impact of Various Streetcar Types on Passenger Activity and Running Times

Monitoring Multimodal Travel Environment Using Automated Fare Collection Data: Data Processing and Reliability Analysis

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