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Sustainable Urban Public Transport Management and Planning with Big Data

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 21171

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Special Issue Editor

Prof. Dr. Sangho Choo

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Guest Editor

Department of Urban Design & Planning, Hongik University, Seoul 04066, Republic of Korea
Interests: travel behavior; transportation planning; urban public transport; mobility as a service; big data analysis; discrete choice modeling
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Special Issue Information

Dear Colleagues,

Recently, information and communication technology (ICT) has been widely used in the field of public transport operation and planning. As a result, various big data such as passenger OD (origin/destination) data and bus operation data have been generated, and much interest in public transportation policies using them has increased. This Special Issue invites papers related to public transport management and planning utilizing various big data generated by public transport systems. We encourage new methodologies and various applications of data analysis for sustainable urban public transport management and planning.

Prof. Sangho Choo
Guest Editor

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Keywords

public transport management
public transport planning
public transport operation
big data
sustainable urban transport
ICT

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Published Papers (7 papers)

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Research

20 pages, 2089 KiB

Open AccessArticle

Does the Inclusion of Spatio-Temporal Features Improve Bus Travel Time Predictions? A Deep Learning-Based Modelling Approach

by Gyeongjae Lee, Sangho Choo, Sungtaek Choi and Hyangsook Lee

Sustainability 2022, 14(12), 7431; https://doi.org/10.3390/su14127431 - 17 Jun 2022

Cited by 7 | Viewed by 2757

Abstract

With the abundance of public transportation in highly urbanized areas, it is common for passengers to make inefficient or flawed transport decisions due to a lack of information. The exact arrival time of a bus is an example of such information that can aid passengers in making better decisions. The purpose of this study is to provide a method for predicting path-based bus travel time, thereby assisting accurate bus arrival and departure time predictions at each bus stop. Specifically, we develop a Geo-conv Long Short-term Memory (LSTM) model that (1) extracts subsequent spatial features through a 1D Convolution Neural Network (CNN) for the entire bus travel sequence and (2) captures the temporal dependencies between subsequences through the LSTM network. Additionally, this study utilizes additional variables that affect two components of bus travel time (dwelling time and transit time) to precisely predict travel time. The constructed model is then evaluated by the practical application to two bus lines operating in Seoul, Korea. The results show that our model outperforms three other baseline models. Two bus lines with different types of operation show different model performance patterns that are dependent on travel distance. Interestingly, we find that the variable related to the link of the stop location appears to play an important role in predicting bus travel time. We believe that these novel findings will contribute to the literature on transportation and, in particular, on deep learning-based travel time prediction. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

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18 pages, 541 KiB

Open AccessArticle

Identifying the Causal Relationship between Travel and Activity Times: A Structural Equation Modeling Approach

by Jahun Koo, Jiyoon Kim, Sungtaek Choi and Sangho Choo

Sustainability 2022, 14(8), 4615; https://doi.org/10.3390/su14084615 - 12 Apr 2022

Viewed by 1831

Abstract

This study aims to identify the causal relationship between travel and activity times using the dataset collected from the 2019 Time Use Survey in Korea. As a statistical solution, a structural equation model (SEM) was developed. A total number of 31,177 and 20,817 cases were used in estimating the weekday and weekend models, respectively. Three types of activities (subsistence, maintenance, and leisure), 13 socio-demographic variables, and a newly proposed latent variable (vitality) were incorporated in the final model. Results showed that (1) the magnitude of indirect effects were mostly greater than that of direct effects, (2) all types of activities affected travel time regardless of what the travel purpose was, (3) travel can be treated as both a utility and disutility, and (4) personal status could affect the travel time ratio. It indicates the significance of indirect effects on travel time, thereby suggesting a broad perspective of activities when establishing a transportation policy in practical areas. It also implies that unobserved latent elements could play a meaningful role in identifying travel time-related characteristics. Lastly, we believe that this study contributes to literature by clarifying a new perspective on the lively debated issue discussing whether travel time is wasted or productive. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

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26 pages, 5235 KiB

Open AccessEditor’s ChoiceArticle

Investigating the Potential of Data Science Methods for Sustainable Public Transport

by Christine Keller, Felix Glück, Carl Friedrich Gerlach and Thomas Schlegel

Sustainability 2022, 14(7), 4211; https://doi.org/10.3390/su14074211 - 1 Apr 2022

Cited by 10 | Viewed by 3792

Abstract

The planning and implementation of public transport involves many data sources. These data sources in turn generate a high volume of data, in a wide variety of formats and data rates. This phenomenon is reinforced by the ongoing digitization of public transport; new data sources have continuously emerged in public transport in recent years and decades. This results in a great potential for the application and utilization of data science methods in public transport. Using big data methods and sources can, or in some cases already does, contribute to a better understanding and the further optimization of public transport networks, public transport service and public transport in general. This paper classifies data sources in the field of public transport and examines systematically for which use cases the data are used or can be used. These steps contribute by structuring ongoing discussions about the application of data science in the public transport domain and illustrate the potential of the application of data science for public transport. We present several use cases in which we applied data science methods, such as machine learning and visualization to public transport data. Several of these projects use data from automated passenger information systems, a data source that has not been widely studied to date. We report our findings for these use cases and discuss the lessons learned, to inform future research on these use cases and discuss their potential. This paper concludes with a summary of the typical problems that occur when dealing with big public transport data and a discussion of solutions for these problems. This discussion identifies future work and topics worth investigating for public transport companies as well as for researchers. Working on these topics will, in our opinion, support the improvement of public transport towards the efficiency and attractiveness that is needed for public transport to play its essential role in future sustainable mobility. The application of these methods in public transport requires the collaboration of domain experts with researchers and data scientists, calling for a mutual understanding. This paper also contributes to this understanding by providing an overview of the methods that are already used, potential new use cases, data sources, challenges and possible solutions. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

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17 pages, 3096 KiB

Open AccessArticle

An Application of a Deep Q-Network Based Dynamic Fare Bidding System to Improve the Use of Taxi Services during Off-Peak Hours in Seoul

by Yunji Cho, Jaein Song, Minhee Kang and Keeyeon Hwang

Sustainability 2021, 13(16), 9351; https://doi.org/10.3390/su13169351 - 20 Aug 2021

Cited by 3 | Viewed by 2389

Abstract

The problem of structural imbalance in terms of supply and demand due to changes in traffic patterns by time zone has been continuously raised in the mobility market. In Korea, unlike large overseas cities, the waiting time tolerance increases during the daytime when supply far exceeds demand, resulting in a large loss of operating profit. The purpose of this study is to increase taxi demand and further improve driver’s profits through real-time fare discounts during off-peak daytime hours in Seoul, Korea. To this end, we propose a real-time fare bidding system among taxi drivers based on a dynamic pricing scheme and simulate the appropriate fare discount level for each regional time zone. The driver-to-driver fare competition system consists of simulating fare competition based on the multi-agent Deep Q-Network method after developing a fare discount index that reflects the supply and demand level of each region in 25 districts in Seoul. According to the optimal fare discount level analysis in the off-peak hours, the lower the OI Index, which means the level of demand relative to supply, the higher the fare discount rate. In addition, an analysis of drivers’ profits and matching rates according to the distance between the origin and destination of each region showed up to 89% and 65% of drivers who actively offered discounts on fares. The results of this study in the future can serve as the foundation of a fare adjustment system for varying demand and supply situations in the Korean mobility market. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

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15 pages, 793 KiB

Open AccessArticle

What Factors Affect Commuters’ Utility of Choosing Mobility as a Service? An Empirical Evidence from Seoul

by Sujae Kim, Sangho Choo, Sungtaek Choi and Hyangsook Lee

Sustainability 2021, 13(16), 9324; https://doi.org/10.3390/su13169324 - 19 Aug 2021

Cited by 18 | Viewed by 3166

Abstract

Mobility as a Service (MaaS), which integrates public and shared transportation into a single service, is drawing attention as a travel demand management strategy aimed at reducing automobile dependency and encouraging public transit. In particular, there have been few studies that recognize traffic congestion during peak hours and identify related factors for practical application. The purpose of this study is to explore what factors affect Seoul commuters’ mode choice including MaaS. A web-based survey that 161 commuters participated in was conducted to collect information about personal, household, and travel attributes, together with their mode preference for MaaS. A latent class model was developed to classify unobserved latent groups based on trip frequency by means and to identify factors influencing mode-specific utilities (in particular, MaaS service) for each class. The result shows that latent classes are divided into two groups (public transit-oriented commuters and balanced mode commuters). Most variables have significant impacts on choice for MaaS. The coefficient of MaaS choice of Class 1 and Class 2 were different. These findings suggest there is a difference between the classes according to trip frequency by means as an influencing factor in MaaS choice. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

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11 pages, 232 KiB

Open AccessArticle

The Effect of COVID-19 on the Efficiency of Intercity Bus Operation: The Case of Chungnam

by Wonchul Kim and Sung Hyo Hong

Sustainability 2021, 13(11), 5958; https://doi.org/10.3390/su13115958 - 25 May 2021

Cited by 2 | Viewed by 2236

Abstract

This paper estimates the efficiency of operating intercity bus lines in Chungnam province over the period 2017–2020, and then empirically analyzes the determinants of the efficiency. In particular, it evaluates to what extent the efficiency in 2020 decreased due to COVID-19 and which characteristics of lines (length of a line, frequency of operation, whether a line operates via highways and includes the capital region or not) affected the efficiency during the pandemic through a tobit model. The empirical results show that the efficiency in the operation of intercity bus lines in Chungnam was higher in 2018 and 2019 compared to 2017, but dropped in 2020 by 15.8%. It appears that the efficiency is higher when a line operates more frequently and covers a longer distance, but the efficiency increases at a decreasing rate as the operating distance becomes longer. In addition, the difference in the efficiency according to operating distance due to COVID-19 seems to be statistically significant. Given that intercity bus lines are heavily dependent upon a (local) government’s financial support and the amount of this support needs to reflect the degree to which the efficiency has decreased due to COVID-19 as an external shock, it is important to precisely estimate the magnitude of the efficiency reduction from both a policy and academic standpoint. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

46 pages, 5858 KiB

Open AccessArticle

Multiple Utility Analyses for Sustainable Public Transport Planning and Management: Evidence from GPS-Equipped Taxi Data in Haikou

by Jiawei Gui and Qunqi Wu

Sustainability 2020, 12(19), 8070; https://doi.org/10.3390/su12198070 - 30 Sep 2020

Cited by 1 | Viewed by 3607

Abstract

The transportation utility values calculated by traditional utility methods are not comprehensive. Some objects and factors are ignored in traditional utility methods, and this narrow perspective is their primary drawback. In intelligent transportation systems, it is necessary to calculate transportation utility for promoting public traffic planning and management. To build a sustainable intelligent transportation system, modified utility methods are essential to analyze transportation utility in a comprehensive way with innovative technologies and efficient communication systems. To solve the disadvantages of traditional utility methods, it is necessary to establish a new method to build sustainable public transport in the future. In this study, the Multiple Utility Method and Transportation Utility Method are proposed for public transport planning and management from multiple perspectives. A sample is presented to provide a better description, and 69,174 GPS-equipped taxi data in Haikou are adopted for the application of the Transportation Utility Method. The results show that the transportation utility values calculated by the Transportation Utility Method are more comprehensive than the transportation utility calculated by traditional utility methods. This indicates that it is necessary to calculate transportation utility from multiple perspectives based on the Transportation Utility Method. Future directions could include improving the methods, considering more factors, expanding the data used, and extrapolating this research to other cities around the world with similar urban metrics and urban form. Full article

(This article belongs to the Special Issue Sustainable Urban Public Transport Management and Planning with Big Data)

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