國立虎尾科技大學 |

Improving Public Transit Resilience by Leveraging Smartcard Data for Rapid Decision-Making Under Highly Dynamic Conditions.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Improving Public Transit Resilience by Leveraging Smartcard Data for Rapid Decision-Making Under Highly Dynamic Conditions./
作者:	Caicedo Castro, Juan David.
面頁冊數:	1 online resource (117 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
標題:	Transportation. -
電子資源:	click for full text (PQDT)
ISBN:	9798380382083

Improving Public Transit Resilience by Leveraging Smartcard Data for Rapid Decision-Making Under Highly Dynamic Conditions.
Caicedo Castro, Juan David.

Improving Public Transit Resilience by Leveraging Smartcard Data for Rapid Decision-Making Under Highly Dynamic Conditions. - 1 online resource (117 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2023.

Includes bibliographical references

The COVID-19 pandemic presented an unprecedented challenge to public transit systems worldwide, testing the ability of transit agencies to make swift and difficult decisions. Balancing the need to provide essential workers with reliable transportation options while dealing with suddenly reduced revenue and increased operational costs forced many agencies to reduce transit services, making life difficult for countless commuters relying on transit. While the pandemic spotlighted the importance of decisive action during times of crisis, disruptions to public transit systems are a regular occurrence. Disruptions vary significantly in time, duration, location, scope, frequency, and impact. Terrorist attacks, the aftermath of violent protests, and natural disasters are a few examples that can drastically change the transit system and our travel needs. Responding quickly and effectively to these disruptions by making informed decisions is crucial not only for minimizing the impact of the disruption, but also for maintaining the reliability of public transit systems for those who depend on them, ensuring they remain available and accessible when needed most.In recent years, transit agencies have increasingly begun to adopt automated fare collections (AFC) systems, also known as smartcards, to collect fares, making valuable data readily available for analysis. In response, the research community has proposed a number of inferential methods and predictive models to analyze transit patterns. More than 300 research papers have been published on short-term ridership prediction models in the last decade. Despite the abundance of literature, we, the research community, have yet to generate practical knowledge for decision-making. Lack of reproducibility due to inadequate documentation, inaccessible tools and resources, and code and data-sharing issues are prevalent; hindering our ability to create collective wisdom. The problem is further compounded by the lack of rigorous statistical analysis in many studies, making it difficult to identify relevant research, particularly when metrics across studies are incomparable.Adding to this chaos, there is also a significant gap in inference methods and predictive models to account for disruptions in general. Current inference methods, which are mostly used to enrich transaction data with other data sources, are designed for stable periods and do not capture the changing transit behaviors of transit users during disruptions, such as those caused by the COVID-19 pandemic. Beyond the pandemic, station closures, a common cause of disruptions, are not accounted for in any predictive model. This further hinders the ability of transit agencies to utilize such models for decision-making during disruptions.This dissertation demonstrates how to use readily available smartcard data to quickly support decision-making in highly dynamic conditions. The objectives of the dissertation are to:1. Enhance the value of smartcard data by integrating it with other data sources to provide more comprehensive, near-real-time insights during disruptions. 2. Create an open-source repository for short-term ridership prediction models to facilitate accurate and reliable comparisons, and to accelerate advancements of the models. 3. Compare and assess the performance of state-of-the-art methods for short-term ridership prediction in a highly dynamic condition. 4. Improve the accuracy and reliability of short-term ridership prediction models during disruptions by integrating station closure information. All work is done using the transaction data of 147 stations in the Bus Rapid Transit (BRT) system in Bogota, Colombia, from August 2015 to May 2021. This dataset includes two highly dynamic conditions: a month-long protest that occurred in November-December 2019 and the COVID-19 pandemic that began in March 2020. Both conditions drastically changed the travel needs of the users and the transit system. Additionally, the dataset covers several station closures, which are not necessarily associated with these two primary events.In the study described in Chapter 2, I examined the changes in demand for the BRT system in Bogota during the COVID-19 pandemic. Despite the disruptions caused by the pandemic, transit agencies had to continue providing services as it was a crucial transportation option for many essential workers. However, a comprehensive understanding of the remaining transit users, as well as how the demand adjusted during the recovery period across different population segments, was lacking, despite being critical information to inform the prioritization of service changes, station cleanings, and the effective implementation of safety measures. I enriched smartcard data transactions with block-level demographic data from the 2018 census to infer the socio-economic strata of BRT users, a variable that can be used as a proxy for income. I present a methodology that combines behavioral modeling techniques with well-established inference methods to capture the dynamic heterogeneity of transit use by strata at different points in time, and use this information to estimate a probability vector that assigns frequent transit users to a stratum. At the beginning of the pandemic. (Abstract shortened by ProQuest).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380382083Subjects--Topical Terms:

558117
Transportation.
Subjects--Index Terms:

Highly dynamic conditionsIndex Terms--Genre/Form:

554714
Electronic books.

Improving Public Transit Resilience by Leveraging Smartcard Data for Rapid Decision-Making Under Highly Dynamic Conditions.
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Responding quickly and effectively to these disruptions by making informed decisions is crucial not only for minimizing the impact of the disruption, but also for maintaining the reliability of public transit systems for those who depend on them, ensuring they remain available and accessible when needed most.In recent years, transit agencies have increasingly begun to adopt automated fare collections (AFC) systems, also known as smartcards, to collect fares, making valuable data readily available for analysis. In response, the research community has proposed a number of inferential methods and predictive models to analyze transit patterns. More than 300 research papers have been published on short-term ridership prediction models in the last decade. Despite the abundance of literature, we, the research community, have yet to generate practical knowledge for decision-making. Lack of reproducibility due to inadequate documentation, inaccessible tools and resources, and code and data-sharing issues are prevalent; hindering our ability to create collective wisdom. The problem is further compounded by the lack of rigorous statistical analysis in many studies, making it difficult to identify relevant research, particularly when metrics across studies are incomparable.Adding to this chaos, there is also a significant gap in inference methods and predictive models to account for disruptions in general. Current inference methods, which are mostly used to enrich transaction data with other data sources, are designed for stable periods and do not capture the changing transit behaviors of transit users during disruptions, such as those caused by the COVID-19 pandemic. Beyond the pandemic, station closures, a common cause of disruptions, are not accounted for in any predictive model. This further hinders the ability of transit agencies to utilize such models for decision-making during disruptions.This dissertation demonstrates how to use readily available smartcard data to quickly support decision-making in highly dynamic conditions. The objectives of the dissertation are to:1. Enhance the value of smartcard data by integrating it with other data sources to provide more comprehensive, near-real-time insights during disruptions. 2. Create an open-source repository for short-term ridership prediction models to facilitate accurate and reliable comparisons, and to accelerate advancements of the models. 3. Compare and assess the performance of state-of-the-art methods for short-term ridership prediction in a highly dynamic condition. 4. Improve the accuracy and reliability of short-term ridership prediction models during disruptions by integrating station closure information. All work is done using the transaction data of 147 stations in the Bus Rapid Transit (BRT) system in Bogota, Colombia, from August 2015 to May 2021. This dataset includes two highly dynamic conditions: a month-long protest that occurred in November-December 2019 and the COVID-19 pandemic that began in March 2020. Both conditions drastically changed the travel needs of the users and the transit system. Additionally, the dataset covers several station closures, which are not necessarily associated with these two primary events.In the study described in Chapter 2, I examined the changes in demand for the BRT system in Bogota during the COVID-19 pandemic. Despite the disruptions caused by the pandemic, transit agencies had to continue providing services as it was a crucial transportation option for many essential workers. However, a comprehensive understanding of the remaining transit users, as well as how the demand adjusted during the recovery period across different population segments, was lacking, despite being critical information to inform the prioritization of service changes, station cleanings, and the effective implementation of safety measures. I enriched smartcard data transactions with block-level demographic data from the 2018 census to infer the socio-economic strata of BRT users, a variable that can be used as a proxy for income. I present a methodology that combines behavioral modeling techniques with well-established inference methods to capture the dynamic heterogeneity of transit use by strata at different points in time, and use this information to estimate a probability vector that assigns frequent transit users to a stratum. At the beginning of the pandemic. (Abstract shortened by ProQuest).
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