Exploring Equity in Public Transportation Planning Using Smart Card Data
Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates....
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/9/3039 |
| _version_ | 1827694041453559808 |
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| author | Kiarash Ghasemlou Murat Ergun Nima Dadashzadeh |
| author_facet | Kiarash Ghasemlou Murat Ergun Nima Dadashzadeh |
| author_sort | Kiarash Ghasemlou |
| collection | DOAJ |
| description | Existing public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments. |
| first_indexed | 2024-03-10T11:56:43Z |
| format | Article |
| id | doaj.art-20cca5e444fc42caa9ec515e7af6186e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T11:56:43Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-20cca5e444fc42caa9ec515e7af6186e2023-11-21T17:14:54ZengMDPI AGSensors1424-82202021-04-01219303910.3390/s21093039Exploring Equity in Public Transportation Planning Using Smart Card DataKiarash Ghasemlou0Murat Ergun1Nima Dadashzadeh2Graduate School of Science, Engineering and Technology, Istanbul Technical University, 34467 Istanbul, TurkeyCivil Engineering Faculty, Istanbul Technical University, 34467 Istanbul, TurkeyFaculty of Civil and Geodetic Engineering, University of Ljubljana, 1000 Ljubljana, SloveniaExisting public transport (PT) planning methods use a trip-based approach, rather than a user-based approach, leading to neglecting equity. In other words, the impacts of regular users—i.e., users with higher trip rates—are overrepresented during analysis and modelling because of higher trip rates. In contrast to the existing studies, this study aims to show the actual demand characteristic and users’ share are different in daily and monthly data. For this, 1-month of smart card data from the Kocaeli, Turkey, was evaluated by means of specific variables, such as boarding frequency, cardholder types, and the number of users, as well as a breakdown of the number of days traveled by each user set. Results show that the proportion of regular PT users to total users in 1 workday, is higher than the monthly proportion of regular PT users to total users. Accordingly, users who have 16–21 days boarding frequency are 16% of the total users, and yet they have been overrepresented by 39% in the 1-day analysis. Moreover, users who have 1–6 days boarding frequency, have a share of 66% in the 1-month dataset and are underrepresented with a share of 22% in the 1-day analysis. Results indicated that the daily travel data without information related to the day-to-day frequency of trips and PT use caused incorrect estimation of real PT demand. Moreover, user-based analyzing approach over a month prepares the more realistic basis for transportation planning, design, and prioritization of transport investments.https://www.mdpi.com/1424-8220/21/9/3039public transportationsmart card dataequitycost benefit analysistravel behaviormobility pattern |
| spellingShingle | Kiarash Ghasemlou Murat Ergun Nima Dadashzadeh Exploring Equity in Public Transportation Planning Using Smart Card Data Sensors public transportation smart card data equity cost benefit analysis travel behavior mobility pattern |
| title | Exploring Equity in Public Transportation Planning Using Smart Card Data |
| title_full | Exploring Equity in Public Transportation Planning Using Smart Card Data |
| title_fullStr | Exploring Equity in Public Transportation Planning Using Smart Card Data |
| title_full_unstemmed | Exploring Equity in Public Transportation Planning Using Smart Card Data |
| title_short | Exploring Equity in Public Transportation Planning Using Smart Card Data |
| title_sort | exploring equity in public transportation planning using smart card data |
| topic | public transportation smart card data equity cost benefit analysis travel behavior mobility pattern |
| url | https://www.mdpi.com/1424-8220/21/9/3039 |
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