Exploring Equity in Public Transportation Planning Using Smart Card Data
Abstract
:1. Introduction
2. Background
2.1. Equity in PT Planning
- Temporal equity: equitable access to transport services considering the time-critical nature of accessibility needs [24], such as transport accessibility for users with low trip frequency or high trip frequency (regular users) during peak-hour or off-peak-hour.
2.2. Smart Card Data Application in PT Planning
3. Material and Method
3.1. Data and Study Area
3.2. Data Analysis Method
- 1.1.
- Extracting data from SQL server of KMM;
- 1.2.
- Converting extracted data to daily basis Excel file;
- 2.1.
- Identifying the data without coordination information;
- 2.2.
- Identifying the data with false coordination information;
- 2.3.
- Modifying data coordination information according to General Transit Feed Specification (GTFS) data coordination (PT schedules and associated geographic information provided by Google);
- 2.4.
- Categorizing card holders into six user groups with similar characteristics to come across more meaningful analysis.
- 3.1.
- Filtering the dataset based on ID numbers;
- 3.2.
- Extracting the following information for each ID (card holder) and add to ID dataset: daily number of boarding, trip-day information (which day and total number of days in 1 month), and card type. This refers to the calculation of group characteristics of each cardholder ID per day (frequency of PT use, boarding rate per workdays and weekends) and the calculation of its average values per user (including the average frequency of use and boardings per workday etc.);
- 3.3.
- Clustering natural cardholder groups according to their average values;
- 3.4.
- Clustering cardholder frequency groups (30 groups according to number of day that cardholder used PT);
- 3.5.
- Creating PT route-based dataset and extracting the boarding data of bus routes.
- 4.1.
- CARD ID-based data analysis and filtering
- 4.1.1.
- Identifying both workday (weekday) trips and weekend trips in ID-based dataset, and assign a dummy variable value (0 and 1) to them;
- 4.1.2.
- Identifying the number of days commuted by every card holder ID in entire dataset, determine the average daily trips, average monthly trips, and their standard deviation (STD);
- 4.1.3.
- Identifying the workday trips by every card holder ID, in entire dataset, determine the average daily trips, average monthly trips, and their STD;
- 4.1.4.
- Identifying the weekend trips by every card holder ID, in entire dataset, determine the average daily trips, average monthly trips, and their STD;
- 4.1.5.
- Identifying the number of days commuted by PT based on the day commuted (all days, workday, and weekend) and modified user groups (6 clustered user groups), determining the average daily trips, average monthly trips, and their STD.
- 4.2.
- PT Route-based data analysis and filtering
- 4.2.1.
- Filter the data based on PT routes;
- 4.2.2.
- For each PT route, identify the workday trips and weekend trips in entire ID dataset, and assign a dummy variable value (0 and 1) to them;
- 4.2.3.
- For each PT route, identify the number of days commuted by every card holder ID in entire dataset, determine the average daily trips, average monthly trips, and their STD;
- 4.2.4.
- For each PT route, identify the workday trips by every card holder ID in entire data set, determine the average daily trips, average monthly trips, and their STD;
- 4.2.5.
- For each PT route, identify the weekend trips by every card holder ID in entire dataset, determine the average daily trips, average monthly trips, and their STD;
- 4.2.6.
- For each PT route, identify the number of days commuted based on the day commuted (all days, workday, and weekend) and modified user groups (i.e., six similar card holder groups), determine the average daily trips, average monthly trips, and their STD.
- WD: notation for weekday or weekend/holiday; whether the day of boarding (boarding) is weekend or holiday; WD = 0, else WD = 1;
- SCg,i,WD: number of card holder of group “g”, which have boarding in “i” days in weekday or weekend/holiday;
- TCg,i,WD: number of boarding of card holder of group “g”, which have boarding in “i” days in weekday or weekend/holiday;
- n: number of days; for 1 month, n = 30; for weekday, n = 21; and for weekend, n = 9.
4. Results and Discussions
4.1. Group Categorization Analysis
4.2. Monthly’s Trip-Frequency Analysis on User Groups
4.3. Workdays’ Trip-Frequency Analysis on User Groups
4.4. Bus Line-Based Analysis on Workday Trips
- Bus lines no. 118 and 23 have on average 6348 and 6802 boarding per workday, respectively.
- Average daily card holder numbers of these lines are 5014 and 5531 per workday, respectively.
- The total number of card holders per workdays are 38,628 and 45,921 for bus lines no. 118 and no. 23, respectively.
5. Conclusions
5.1. Research Highlights
- Card holders who have a 1–day boarding frequency represent 66% of the whole dataset, while they represent 22% in a single workday.
- Card holders with a 16–21 days boarding frequency represent 16% of the whole dataset, while they represent 39% in 1 workday.
- Regular users also have a higher boarding rate per day and will be much more overrepresented in single-day data.
- The elderly, those with disabilities, the elderly and disabled: in terms of the average number of boardings, elderly card holders (group 3), and PwD card holders (group 4) have higher (c. 10–15%) boarding rates on weekdays and weekends compared to other card holder groups. This shows that these users need more travel access. Another finding shows that PT routes and lines are not planned based on their travel needs (medical centers, elderly house, organizations for PwD, etc.), consequently, this increases their number of transfers between lines.
- Some trip routes are used by more people, even though the number of users is rarer on a daily basis, such as PT routes to medical centers (medical trips).
- Since the smart card data are boarding (transaction) based, more boardings may not really mean more trips. In other words, more boardings are likely to result from more transfer due to PT network limitations.
- Monthly users and boarding frequencies, instead of daily data, can be examined in public transportation planning, investments, improvements, and evaluated as a performance criterion.
- Travel behavior and mobility pattern of PT users varies on weekends compared to workdays due to reduced PT vehicles’ frequency (headway) and reduced number of active PT lines.
5.2. Limitations and Directions for Further Studies
- We proposed a novel approach using 1-month data, which can be paved the way for further studies using long-period data like yearly dataset.
- Some users use different cards or a card belong to somebody else (e.g., family members, relatives, or friends) as there is no card control (verification) or enforcement system in KMM’s PT network. Moreover, some PT users do not have a smart card or enough charge while boarding, consequently, they have to use the driver’s card or other passengers’ cards. Thus, one of the limitations of our study is the possibility of the mis-grouping of those users having low boarding frequency. This issue needs further development in the proposed method.
- It can be seen that the elderly and PwD have significantly higher boarding rates on weekdays and weekends. Using the PT boarding data for OD estimation, one may study whether this boarding rate is due to their high travel tendency or the transfer between bus lines. If it was due to their high transfer rate between the bus lines, it means transport infrastructure is not designed/planned based on vulnerable users’ group.
- In this study, 1-month PT-SCFC data has been used to analyze the travel behavior of users. The study can be extended using more than 1-month data or 1-year data to include the effects of holidays, summer seasons, etc. on the travel behavior of PT users.
- A simple and effective statistical analysis was conducted to come to the research hypothesis; however, advanced data analysis techniques could be employed to improve the proposed methodology. More advanced analyses may reveal more details about the nature of the phenomenon.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Trip Freq. Group | # of Days Which Users Have a Min. 1 Boarding | Share of Card Holder | Share of Boarding | Number of Card Holders | Share of Card holders in Entir Dataset | Avg. Boarding Per Card Holder |
---|---|---|---|---|---|---|
1-day | 212,901 | 3.5% | 2.7% | 212,901 | 26.3% | 1.57 |
2-days | 192,544 | 3.2% | 2.9% | 96,272 | 11.9% | 1.87 |
3-days | 210,165 | 3.5% | 3.2% | 70,055 | 8.7% | 1.88 |
4-days | 214,556 | 3.6% | 3.3% | 53,639 | 6.6% | 1.90 |
5-days | 211,370 | 3.5% | 3.3% | 42,274 | 5.2% | 1.91 |
6-days | 205,074 | 3.4% | 3.2% | 34,179 | 4.2% | 1.92 |
7-days | 197,015 | 3.3% | 3.1% | 28,145 | 3.5% | 1.92 |
8-days | 188,800 | 3.1% | 3.0% | 23,600 | 2.9% | 1.94 |
9-days | 176,418 | 2.9% | 2.8% | 19,602 | 2.4% | 1.95 |
10-days | 172,460 | 2.9% | 2.7% | 17,246 | 2.1% | 1.95 |
11-days | 164,780 | 2.7% | 2.6% | 14,980 | 1.9% | 1.96 |
12-days | 162,276 | 2.7% | 2.6% | 13,523 | 1.7% | 1.98 |
13-days | 158,288 | 2.6% | 2.6% | 12,176 | 1.5% | 1.98 |
14-days | 158,466 | 2.6% | 2.6% | 11,319 | 1.4% | 1.98 |
15-days | 159,615 | 2.6% | 2.6% | 10,641 | 1.3% | 2.00 |
16-days | 168,112 | 2.8% | 2.7% | 10,507 | 1.3% | 2.00 |
17-days | 177,548 | 2.9% | 2.9% | 10,444 | 1.3% | 2.01 |
18-days | 196,038 | 3.2% | 3.2% | 10,891 | 1.3% | 2.01 |
19-days | 216,201 | 3.6% | 3.6% | 11,379 | 1.4% | 2.02 |
20-days | 251,860 | 4.2% | 4.2% | 12,593 | 1.6% | 2.04 |
21-days | 317,583 | 5.3% | 5.3% | 15,123 | 1.9% | 2.05 |
22-days | 304,788 | 5.0% | 5.2% | 13,854 | 1.7% | 2.09 |
23-days | 295,688 | 4.9% | 5.1% | 12,856 | 1.6% | 2.12 |
24-days | 273,936 | 4.5% | 4.8% | 11,414 | 1.4% | 2.16 |
25-days | 268,800 | 4.5% | 4.8% | 10,752 | 1.3% | 2.18 |
26-days | 242,554 | 4.0% | 4.4% | 9329 | 1.2% | 2.24 |
27-days | 189,945 | 3.1% | 3.5% | 7035 | 0.9% | 2.26 |
28-days | 149,324 | 2.5% | 2.8% | 5333 | 0.7% | 2.33 |
29-days | 114,057 | 1.9% | 2.2% | 3933 | 0.5% | 2.39 |
30-days | 85,200 | 1.4% | 1.8% | 2840 | 0.4% | 2.57 |
Monthly Dataset Considered | 1-Day Data Considered | |||||
---|---|---|---|---|---|---|
Trip-Frequency Group | # of Boarding | # of Persons | Share of Boarding Per Workday | Real Share of Persons in Entire Dataset | Average Daily Boarding | Average Daily Persons |
1-day | 324,499 | 199,101 | 3.4% | 27.7% | 3.4% | 4.3% |
2-days | 349,716 | 93,754 | 3.7% | 13.1% | 3.7% | 4.0% |
3-days | 367,098 | 65,030 | 3.9% | 9.1% | 3.9% | 4.2% |
4-days | 364,439 | 47,898 | 3.8% | 6.7% | 3.8% | 4.1% |
5-days | 355,237 | 36,995 | 3.7% | 5.2% | 3.7% | 4.0% |
6-days | 331,553 | 28,614 | 3.5% | 4.0% | 3.5% | 3.7% |
7-days | 315,060 | 23,289 | 3.3% | 3.2% | 3.3% | 3.5% |
8-days | 307,429 | 19,625 | 3.2% | 2.7% | 3.2% | 3.4% |
9-days | 297,678 | 16,887 | 3.1% | 2.4% | 3.1% | 3.3% |
10-days | 294,019 | 14,819 | 3.1% | 2.1% | 3.1% | 3.2% |
11-days | 283,139 | 13,075 | 3.0% | 1.8% | 3.0% | 3.1% |
12-days | 283,032 | 11,881 | 3.0% | 1.7% | 3.0% | 3.1% |
13-days | 298,710 | 11,421 | 3.1% | 1.6% | 3.1% | 3.2% |
14-days | 309,643 | 10,970 | 3.3% | 1.5% | 3.3% | 3.3% |
15-days | 350,036 | 11,405 | 3.7% | 1.6% | 3.7% | 3.7% |
16-days | 398,111 | 12,002 | 4.2% | 1.7% | 4.2% | 4.1% |
17-days | 464,997 | 13,113 | 4.9% | 1.8% | 4.9% | 4.8% |
18-days | 564,845 | 14,880 | 5.9% | 2.1% | 5.9% | 5.7% |
19-days | 699,227 | 17,351 | 7.4% | 2.4% | 7.4% | 7.1% |
20-days | 966,314 | 22,257 | 10.2% | 3.1% | 10.2% | 9.5% |
21-days | 1,572,397 | 33,214 | 16.6% | 4.6% | 16.6% | 15.0% |
Trip Freq. Group | 1 Normal | 2 Student | 3 Elderly | 4 PwD | 5 Limited-Use | 6 Others | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Board. | Person | Board. | Person | Board. | Person | Board. | Person | Board. | Person | Board. | Person | |
1-day | 5.44% | 5.77% | 1.08% | 1.28% | 2.73% | 3.37% | 1.65% | 2.12% | 98.38% | 98.70% | 2.42% | 2.89% |
2-days | 6.79% | 6.98% | 1.32% | 1.57% | 4.01% | 4.78% | 2.48% | 3.08% | 1.30% | 1.08% | 3.26% | 3.72% |
3-days | 6.87% | 7.06% | 1.44% | 1.67% | 4.79% | 5.54% | 3.00% | 3.57% | 0.22% | 0.17% | 3.68% | 4.13% |
4-days | 6.56% | 6.69% | 1.50% | 1.71% | 5.34% | 5.98% | 3.26% | 3.82% | 0.02% | 0.02% | 3.94% | 4.33% |
5-days | 6.12% | 6.21% | 1.57% | 1.77% | 5.50% | 6.08% | 3.44% | 3.90% | 0.00% | 0.00% | 4.11% | 4.45% |
6-days | 5.44% | 5.51% | 1.62% | 1.81% | 5.32% | 5.77% | 3.44% | 3.82% | 0.00% | 0.00% | 3.94% | 4.23% |
7-days | 4.84% | 4.89% | 1.67% | 1.88% | 5.59% | 6.00% | 3.76% | 4.14% | 0.00% | 0.00% | 3.77% | 4.04% |
8-days | 4.36% | 4.36% | 1.93% | 2.14% | 5.20% | 5.42% | 3.64% | 3.95% | 0.00% | 0.00% | 3.93% | 4.14% |
9-days | 3.98% | 4.02% | 2.08% | 2.27% | 5.01% | 5.13% | 3.62% | 3.86% | 0.00% | 0.00% | 3.65% | 3.85% |
10-days | 3.71% | 3.74% | 2.22% | 2.37% | 4.91% | 4.91% | 3.72% | 3.88% | 0.00% | 0.00% | 3.64% | 3.81% |
11-days | 3.43% | 3.50% | 2.28% | 2.45% | 4.61% | 4.63% | 3.67% | 3.83% | 0.00% | 0.00% | 3.41% | 3.55% |
12-days | 3.16% | 3.23% | 2.53% | 2.67% | 4.33% | 4.30% | 3.50% | 3.56% | 0.00% | 0.00% | 3.54% | 3.68% |
13-days | 3.13% | 3.17% | 2.90% | 3.01% | 4.23% | 4.12% | 3.93% | 3.94% | 0.00% | 0.00% | 3.52% | 3.62% |
14-days | 3.07% | 3.13% | 3.32% | 3.40% | 3.67% | 3.47% | 3.81% | 3.91% | 0.00% | 0.00% | 3.40% | 3.53% |
15-days | 3.23% | 3.27% | 4.02% | 4.04% | 3.86% | 3.67% | 4.17% | 4.05% | 0.00% | 0.00% | 3.70% | 3.79% |
16-days | 3.42% | 3.43% | 4.87% | 4.84% | 3.92% | 3.60% | 4.22% | 4.10% | 0.08% | 0.04% | 4.41% | 4.39% |
17-days | 3.60% | 3.62% | 6.11% | 5.99% | 3.80% | 3.50% | 4.70% | 4.77% | 0.00% | 0.00% | 5.50% | 5.40% |
18-days | 4.05% | 3.93% | 7.92% | 7.79% | 4.03% | 3.66% | 5.34% | 5.10% | 0.00% | 0.00% | 5.73% | 5.63% |
19-days | 4.63% | 4.48% | 10.40% | 10.19% | 4.12% | 3.63% | 6.24% | 5.97% | 0.00% | 0.00% | 6.17% | 5.87% |
20-days | 5.62% | 5.30% | 14.95% | 14.39% | 5.68% | 4.89% | 9.05% | 8.38% | 0.00% | 0.00% | 8.69% | 7.82% |
21-days | 8.55% | 7.69% | 24.25% | 22.76% | 9.35% | 7.52% | 19.36% | 16.27% | 0.00% | 0.00% | 15.60% | 13.13% |
Date of November 2018 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
Share of boarding (trip) | 7% | 5% | 5% | 5% | 6% | 9% | 9% | 5% | 4% | 4% | 5% | 5% | 8% | 9% | 5% |
Date of November 2018 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 |
Share of boarding (trip) | 4% | 5% | 5% | 6% | 8% | 9% | 5% | 5% | 5% | 5% | 7% | 9% | 10% | 10% | 9% |
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Transportation Modes | Home-Based Work Trips | Home-Based School Trips | Home-Based Other Trips | Non-Home-Based Trips | Total Trips | |||||
---|---|---|---|---|---|---|---|---|---|---|
No. of Trips | % | No. of Trips | % | No. of Trips | % | No. of Trips | % | No. of Trips | % | |
Walking | 135,085 | 21% | 539,252 | 70% | 223,257 | 27% | 76,573 | 39% | 974,168 | 40% |
Private car | 172,874 | 21% | 29,605 | 70% | 296,496 | 27% | 66,062 | 39% | 565,037 | 23% |
Shuttle service | 201,878 | 31% | 108,173 | 14% | 13,135 | 2% | 7397 | 4% | 330,583 | 14% |
Public transport | 149,105 | 23% | 89,976 | 12% | 285,815 | 35% | 48,230 | 24% | 573,125 | 23% |
Total | 658,943 | 100% | 767,005 | 100% | 818,703 | 100% | 198,262 | 100% | 2,442,913 | 100% |
Org. Card Type | Rev. Card Type | Unique Card | # of Boarding | Org. Card Type | Rev. Card Type | Unique Card | # of Boarding |
---|---|---|---|---|---|---|---|
1 | 1 | 382,442 | 2,271,551 | 73 | 6 | 564 | 6750 |
10 | 1 | 16,030 | 79,413 | 78 | 6 | 521 | 4307 |
4 | 2 | 216,333 | 2,641,065 | 76 | 6 | 306 | 3475 |
65 | 3 | 57,788 | 425,234 | 79 | 6 | 225 | 1687 |
16 | 3 | 605 | 3804 | 67 | 6 | 168 | 2798 |
13 | 5 | 73,333 | 73,896 | 70 | 6 | 128 | 1750 |
5 | 6 | 22,883 | 148,718 | 72 | 6 | 87 | 1124 |
6 | 6 | 9950 | 104,110 | 77 | 6 | 46 | 264 |
69 | 6 | 3664 | 35,078 | 66 | 6 | 2 | 13 |
68 | 6 | 2203 | 25,918 | 74 | 4 | 16,254 | 170,436 |
87 | 6 | 1855 | 14,785 | 75 | 4 | 3447 | 20,185 |
Clustered User Groups | Boarding | Unique Card ID | Boarding Rate | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Workday | STD | Weekend | STD | Workday | STD | Weekend | STD | Workday | STD | Weekend | STD | |
1: Normal | 36.6% | 0.6% | 42.3% | 0.8% | 38.0% | 0.5% | 42.2% | 0.4% | 1.96 | 0.01 | 2.01 | 0.04 |
2: Student | 45.9% | 0.8% | 40.3% | 1.0% | 44.9% | 0.7% | 39.7% | 0.8% | 2.08 | 0.02 | 2.04 | 0.03 |
3: Elderly | 7.7% | 0.3% | 7.4% | 0.4% | 7.0% | 0.3% | 7.3% | 0.3% | 2.21 | 0.03 | 2.04 | 0.05 |
4: PwD | 3.5% | 0.1% | 3.8% | 0.2% | 3.1% | 0.1% | 3.5% | 0.2% | 2.31 | 0.02 | 2.19 | 0.02 |
5: Limited Use | 0.5% | 0.1% | 1.0% | 0.2% | 1.0% | 0.1% | 1.9% | 0.3% | 1.04 | 0.01 | 1.03 | 0.01 |
6: Others | 5.8% | 0.1% | 5.1% | 0.3% | 5.9% | 0.1% | 5.3% | 0.3% | 1.97 | 0.01 | 1.93 | 0.02 |
Trip Freq. Group | Boarding | Person | ||
---|---|---|---|---|
Workday | SDT | Workday | SDT | |
1-day | 3.42% | 0.67% | 4.27% | 0.74% |
2-days | 3.68% | 0.37% | 4.02% | 0.38% |
3-days | 3.87% | 0.29% | 4.18% | 0.30% |
4-days | 3.84% | 0.23% | 4.11% | 0.23% |
5-days | 3.74% | 0.21% | 3.96% | 0.19% |
6-days | 3.49% | 0.13% | 3.68% | 0.13% |
7-days | 3.32% | 0.09% | 3.49% | 0.08% |
8-days | 3.24% | 0.09% | 3.36% | 0.09% |
9-days | 3.13% | 0.07% | 3.26% | 0.07% |
10-days | 3.10% | 0.08% | 3.18% | 0.09% |
11-days | 2.98% | 0.12% | 3.08% | 0.12% |
12-days | 2.98% | 0.14% | 3.06% | 0.15% |
13-days | 3.14% | 0.16% | 3.18% | 0.17% |
14-days | 3.26% | 0.20% | 3.29% | 0.19% |
15-days | 3.69% | 0.23% | 3.67% | 0.22% |
16-days | 4.19% | 0.25% | 4.12% | 0.23% |
17-days | 4.90% | 0.24% | 4.78% | 0.23% |
18-days | 5.95% | 0.22% | 5.74% | 0.22% |
19-days | 7.36% | 0.20% | 7.07% | 0.20% |
20-days | 10.18% | 0.20% | 9.54% | 0.19% |
21-days | 16.56% | 0.21% | 14.95% | 0.18% |
Card Type | Bus Line No. 23 | Bus Line No. 118 | ||||
---|---|---|---|---|---|---|
Person Day | TRIP DAY | Real # of Users | Person Day | Trip Day | Real # of Users | |
1 Normal | 23.3% | 24.4% | 34.0% | 50.1% | 52.5% | 55.7% |
2 Students | 70.7% | 70.1% | 56.8% | 32.5% | 31.1% | 25.4% |
3 Elderly | 2.3% | 2.1% | 4.0% | 9.1% | 8.6% | 10.5% |
4 PwD | 1.2% | 1.1% | 1.6% | 3.4% | 3.3% | 3.1% |
5 Limited Use | 0.1% | 0.1% | 0.4% | 0.1% | 0.1% | 0.3% |
6 Others | 2.3% | 2.1% | 3.4% | 4.8% | 4.4% | 5.1% |
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Ghasemlou, K.; Ergun, M.; Dadashzadeh, N. Exploring Equity in Public Transportation Planning Using Smart Card Data. Sensors 2021, 21, 3039. https://doi.org/10.3390/s21093039
Ghasemlou K, Ergun M, Dadashzadeh N. Exploring Equity in Public Transportation Planning Using Smart Card Data. Sensors. 2021; 21(9):3039. https://doi.org/10.3390/s21093039
Chicago/Turabian StyleGhasemlou, Kiarash, Murat Ergun, and Nima Dadashzadeh. 2021. "Exploring Equity in Public Transportation Planning Using Smart Card Data" Sensors 21, no. 9: 3039. https://doi.org/10.3390/s21093039
APA StyleGhasemlou, K., Ergun, M., & Dadashzadeh, N. (2021). Exploring Equity in Public Transportation Planning Using Smart Card Data. Sensors, 21(9), 3039. https://doi.org/10.3390/s21093039