Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus Article Swipe
Rafidah Md Noor
,
Ng Seong Yik
,
Raenu Kolandaisamy
,
Ismail Ahmedy
,
Md. Asif Hossain
,
Kok‐Lim Alvin Yau
,
Wahidah Md Shah
,
Tarak Nandy
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202002.0197.v1
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202002.0197.v1
The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.
Related Topics
Concepts
Support vector machine
Computer science
Convergence (economics)
Arrival time
Kernel (algebra)
Data set
Set (abstract data type)
Algorithm
Time of arrival
Data mining
Machine learning
Artificial intelligence
Engineering
Transport engineering
Mathematics
Telecommunications
Programming language
Economics
Combinatorics
Economic growth
Wireless
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202002.0197.v1
- https://www.preprints.org/manuscript/202002.0197/v1/download
- OA Status
- green
- Cited By
- 10
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3015464208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3015464208Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202002.0197.v1Digital Object Identifier
- Title
-
Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart BusWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-02-15Full publication date if available
- Authors
-
Rafidah Md Noor, Ng Seong Yik, Raenu Kolandaisamy, Ismail Ahmedy, Md. Asif Hossain, Kok‐Lim Alvin Yau, Wahidah Md Shah, Tarak NandyList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202002.0197.v1Publisher landing page
- PDF URL
-
https://www.preprints.org/manuscript/202002.0197/v1/downloadDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.preprints.org/manuscript/202002.0197/v1/downloadDirect OA link when available
- Concepts
-
Support vector machine, Computer science, Convergence (economics), Arrival time, Kernel (algebra), Data set, Set (abstract data type), Algorithm, Time of arrival, Data mining, Machine learning, Artificial intelligence, Engineering, Transport engineering, Mathematics, Telecommunications, Programming language, Economics, Combinatorics, Economic growth, WirelessTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2023: 2, 2021: 2, 2020: 5Per-year citation counts (last 5 years)
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3015464208 |
|---|---|
| doi | https://doi.org/10.20944/preprints202002.0197.v1 |
| ids.doi | https://doi.org/10.20944/preprints202002.0197.v1 |
| ids.mag | 3015464208 |
| ids.openalex | https://openalex.org/W3015464208 |
| fwci | 1.24438019 |
| type | preprint |
| title | Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11344 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9994000196456909 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2215 |
| topics[0].subfield.display_name | Building and Construction |
| topics[0].display_name | Traffic Prediction and Management Techniques |
| topics[1].id | https://openalex.org/T10698 |
| topics[1].field.id | https://openalex.org/fields/33 |
| topics[1].field.display_name | Social Sciences |
| topics[1].score | 0.9872999787330627 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/3313 |
| topics[1].subfield.display_name | Transportation |
| topics[1].display_name | Transportation Planning and Optimization |
| topics[2].id | https://openalex.org/T12406 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9729999899864197 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2210 |
| topics[2].subfield.display_name | Mechanical Engineering |
| topics[2].display_name | IoT and GPS-based Vehicle Safety Systems |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C12267149 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7980158925056458 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[0].display_name | Support vector machine |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6522472500801086 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2777303404 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6382817029953003 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q759757 |
| concepts[2].display_name | Convergence (economics) |
| concepts[3].id | https://openalex.org/C3017552255 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5957527756690979 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q4135208 |
| concepts[3].display_name | Arrival time |
| concepts[4].id | https://openalex.org/C74193536 |
| concepts[4].level | 2 |
| concepts[4].score | 0.539958655834198 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q574844 |
| concepts[4].display_name | Kernel (algebra) |
| concepts[5].id | https://openalex.org/C58489278 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5241739749908447 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1172284 |
| concepts[5].display_name | Data set |
| concepts[6].id | https://openalex.org/C177264268 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5146192312240601 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[6].display_name | Set (abstract data type) |
| concepts[7].id | https://openalex.org/C11413529 |
| concepts[7].level | 1 |
| concepts[7].score | 0.44964727759361267 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[7].display_name | Algorithm |
| concepts[8].id | https://openalex.org/C163150518 |
| concepts[8].level | 3 |
| concepts[8].score | 0.42714303731918335 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q4135208 |
| concepts[8].display_name | Time of arrival |
| concepts[9].id | https://openalex.org/C124101348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4006944000720978 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[9].display_name | Data mining |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3900153934955597 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3536873757839203 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.19599035382270813 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C22212356 |
| concepts[13].level | 1 |
| concepts[13].score | 0.17025578022003174 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q775325 |
| concepts[13].display_name | Transport engineering |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.14635857939720154 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C76155785 |
| concepts[15].level | 1 |
| concepts[15].score | 0.11691319942474365 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q418 |
| concepts[15].display_name | Telecommunications |
| concepts[16].id | https://openalex.org/C199360897 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[16].display_name | Programming language |
| concepts[17].id | https://openalex.org/C162324750 |
| concepts[17].level | 0 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[17].display_name | Economics |
| concepts[18].id | https://openalex.org/C114614502 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q76592 |
| concepts[18].display_name | Combinatorics |
| concepts[19].id | https://openalex.org/C50522688 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q189833 |
| concepts[19].display_name | Economic growth |
| concepts[20].id | https://openalex.org/C555944384 |
| concepts[20].level | 2 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q249 |
| concepts[20].display_name | Wireless |
| keywords[0].id | https://openalex.org/keywords/support-vector-machine |
| keywords[0].score | 0.7980158925056458 |
| keywords[0].display_name | Support vector machine |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6522472500801086 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/convergence |
| keywords[2].score | 0.6382817029953003 |
| keywords[2].display_name | Convergence (economics) |
| keywords[3].id | https://openalex.org/keywords/arrival-time |
| keywords[3].score | 0.5957527756690979 |
| keywords[3].display_name | Arrival time |
| keywords[4].id | https://openalex.org/keywords/kernel |
| keywords[4].score | 0.539958655834198 |
| keywords[4].display_name | Kernel (algebra) |
| keywords[5].id | https://openalex.org/keywords/data-set |
| keywords[5].score | 0.5241739749908447 |
| keywords[5].display_name | Data set |
| keywords[6].id | https://openalex.org/keywords/set |
| keywords[6].score | 0.5146192312240601 |
| keywords[6].display_name | Set (abstract data type) |
| keywords[7].id | https://openalex.org/keywords/algorithm |
| keywords[7].score | 0.44964727759361267 |
| keywords[7].display_name | Algorithm |
| keywords[8].id | https://openalex.org/keywords/time-of-arrival |
| keywords[8].score | 0.42714303731918335 |
| keywords[8].display_name | Time of arrival |
| keywords[9].id | https://openalex.org/keywords/data-mining |
| keywords[9].score | 0.4006944000720978 |
| keywords[9].display_name | Data mining |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.3900153934955597 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.3536873757839203 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.19599035382270813 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/transport-engineering |
| keywords[13].score | 0.17025578022003174 |
| keywords[13].display_name | Transport engineering |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.14635857939720154 |
| keywords[14].display_name | Mathematics |
| keywords[15].id | https://openalex.org/keywords/telecommunications |
| keywords[15].score | 0.11691319942474365 |
| keywords[15].display_name | Telecommunications |
| language | en |
| locations[0].id | doi:10.20944/preprints202002.0197.v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S6309402219 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Preprints.org |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.preprints.org/manuscript/202002.0197/v1/download |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.20944/preprints202002.0197.v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5017116281 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-6266-2390 |
| authorships[0].author.display_name | Rafidah Md Noor |
| authorships[0].countries | MY |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[0].affiliations[0].raw_affiliation_string | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[0].institutions[0].id | https://openalex.org/I33849332 |
| authorships[0].institutions[0].ror | https://ror.org/00rzspn62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I33849332 |
| authorships[0].institutions[0].country_code | MY |
| authorships[0].institutions[0].display_name | University of Malaya |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Rafidah Md Noor |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[1].author.id | https://openalex.org/A5097244068 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Ng Seong Yik |
| authorships[1].countries | MY |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[1].affiliations[0].raw_affiliation_string | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[1].affiliations[1].institution_ids | https://openalex.org/I84339108 |
| authorships[1].affiliations[1].raw_affiliation_string | Department of Computing and Information Systems, Sunway University, Selangor, Malaysia |
| authorships[1].institutions[0].id | https://openalex.org/I84339108 |
| authorships[1].institutions[0].ror | https://ror.org/04mjt7f73 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I84339108 |
| authorships[1].institutions[0].country_code | MY |
| authorships[1].institutions[0].display_name | Sunway University |
| authorships[1].institutions[1].id | https://openalex.org/I33849332 |
| authorships[1].institutions[1].ror | https://ror.org/00rzspn62 |
| authorships[1].institutions[1].type | education |
| authorships[1].institutions[1].lineage | https://openalex.org/I33849332 |
| authorships[1].institutions[1].country_code | MY |
| authorships[1].institutions[1].display_name | University of Malaya |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ng Seong Yik |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Computing and Information Systems, Sunway University, Selangor, Malaysia, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[2].author.id | https://openalex.org/A5103239222 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Raenu Kolandaisamy |
| authorships[2].countries | MY |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I55285296 |
| authorships[2].affiliations[0].raw_affiliation_string | Faculty of Business & Information Science, UCSI University, Jalan Menara Garding, Kuala Lumpur, Malaysia |
| authorships[2].institutions[0].id | https://openalex.org/I55285296 |
| authorships[2].institutions[0].ror | https://ror.org/019787q29 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I55285296 |
| authorships[2].institutions[0].country_code | MY |
| authorships[2].institutions[0].display_name | UCSI University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Raenu Kolandaisamy |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Faculty of Business & Information Science, UCSI University, Jalan Menara Garding, Kuala Lumpur, Malaysia |
| authorships[3].author.id | https://openalex.org/A5022212729 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-4324-8965 |
| authorships[3].author.display_name | Ismail Ahmedy |
| authorships[3].countries | MY |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[3].affiliations[0].raw_affiliation_string | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[3].institutions[0].id | https://openalex.org/I33849332 |
| authorships[3].institutions[0].ror | https://ror.org/00rzspn62 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I33849332 |
| authorships[3].institutions[0].country_code | MY |
| authorships[3].institutions[0].display_name | University of Malaya |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ismail Ahmedy |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[4].author.id | https://openalex.org/A5003296992 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-8685-3113 |
| authorships[4].author.display_name | Md. Asif Hossain |
| authorships[4].countries | MY |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[4].affiliations[0].raw_affiliation_string | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[4].institutions[0].id | https://openalex.org/I33849332 |
| authorships[4].institutions[0].ror | https://ror.org/00rzspn62 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I33849332 |
| authorships[4].institutions[0].country_code | MY |
| authorships[4].institutions[0].display_name | University of Malaya |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Mohammad Asif Hossain |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[5].author.id | https://openalex.org/A5055349217 |
| authorships[5].author.orcid | https://orcid.org/0000-0003-3110-2782 |
| authorships[5].author.display_name | Kok‐Lim Alvin Yau |
| authorships[5].countries | MY |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[5].affiliations[0].raw_affiliation_string | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[5].affiliations[1].institution_ids | https://openalex.org/I84339108 |
| authorships[5].affiliations[1].raw_affiliation_string | Department of Computing and Information Systems, Sunway University, Selangor, Malaysia |
| authorships[5].institutions[0].id | https://openalex.org/I84339108 |
| authorships[5].institutions[0].ror | https://ror.org/04mjt7f73 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I84339108 |
| authorships[5].institutions[0].country_code | MY |
| authorships[5].institutions[0].display_name | Sunway University |
| authorships[5].institutions[1].id | https://openalex.org/I33849332 |
| authorships[5].institutions[1].ror | https://ror.org/00rzspn62 |
| authorships[5].institutions[1].type | education |
| authorships[5].institutions[1].lineage | https://openalex.org/I33849332 |
| authorships[5].institutions[1].country_code | MY |
| authorships[5].institutions[1].display_name | University of Malaya |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Kok-Lim Alvin Yau |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Computing and Information Systems, Sunway University, Selangor, Malaysia, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[6].author.id | https://openalex.org/A5073075008 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-6693-846X |
| authorships[6].author.display_name | Wahidah Md Shah |
| authorships[6].countries | MY |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I32589535 |
| authorships[6].affiliations[0].raw_affiliation_string | Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. |
| authorships[6].institutions[0].id | https://openalex.org/I32589535 |
| authorships[6].institutions[0].ror | https://ror.org/01xb6rs26 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I32589535 |
| authorships[6].institutions[0].country_code | MY |
| authorships[6].institutions[0].display_name | Technical University of Malaysia Malacca |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Wahidah Md Shah |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. |
| authorships[7].author.id | https://openalex.org/A5002386592 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-6370-0640 |
| authorships[7].author.display_name | Tarak Nandy |
| authorships[7].countries | MY |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[7].affiliations[0].raw_affiliation_string | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| authorships[7].institutions[0].id | https://openalex.org/I33849332 |
| authorships[7].institutions[0].ror | https://ror.org/00rzspn62 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I33849332 |
| authorships[7].institutions[0].country_code | MY |
| authorships[7].institutions[0].display_name | University of Malaya |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Tarak Nandy |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.preprints.org/manuscript/202002.0197/v1/download |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11344 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9994000196456909 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2215 |
| primary_topic.subfield.display_name | Building and Construction |
| primary_topic.display_name | Traffic Prediction and Management Techniques |
| related_works | https://openalex.org/W2765924402, https://openalex.org/W2920514105, https://openalex.org/W2550892593, https://openalex.org/W4288481730, https://openalex.org/W4387960969, https://openalex.org/W3003184106, https://openalex.org/W3028083027, https://openalex.org/W1581082085, https://openalex.org/W2321909426, https://openalex.org/W2949636136 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2023 |
| counts_by_year[1].cited_by_count | 2 |
| counts_by_year[2].year | 2021 |
| counts_by_year[2].cited_by_count | 2 |
| counts_by_year[3].year | 2020 |
| counts_by_year[3].cited_by_count | 5 |
| locations_count | 1 |
| best_oa_location.id | doi:10.20944/preprints202002.0197.v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S6309402219 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Preprints.org |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.preprints.org/manuscript/202002.0197/v1/download |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.20944/preprints202002.0197.v1 |
| primary_location.id | doi:10.20944/preprints202002.0197.v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S6309402219 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Preprints.org |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.preprints.org/manuscript/202002.0197/v1/download |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.20944/preprints202002.0197.v1 |
| publication_date | 2020-02-15 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W3015464208, https://openalex.org/W1987520464, https://openalex.org/W2782823708, https://openalex.org/W2765635256, https://openalex.org/W655895022, https://openalex.org/W2545793358, https://openalex.org/W2054188095, https://openalex.org/W2344399997, https://openalex.org/W6641163509, https://openalex.org/W2593156067, https://openalex.org/W1992341494, https://openalex.org/W2101234009, https://openalex.org/W2479611522, https://openalex.org/W2945685727, https://openalex.org/W1964357740, https://openalex.org/W4402378005 |
| referenced_works_count | 16 |
| abstract_inverted_index.a | 101, 208 |
| abstract_inverted_index.By | 16, 126 |
| abstract_inverted_index.as | 67, 98, 117, 195 |
| abstract_inverted_index.at | 109 |
| abstract_inverted_index.by | 13, 52 |
| abstract_inverted_index.in | 39, 89, 119, 137 |
| abstract_inverted_index.is | 10, 30, 61, 87, 151 |
| abstract_inverted_index.it | 22 |
| abstract_inverted_index.of | 2, 7, 27, 79, 103, 122, 157, 198 |
| abstract_inverted_index.on | 63, 180 |
| abstract_inverted_index.or | 111 |
| abstract_inverted_index.to | 20, 47, 95, 191 |
| abstract_inverted_index.Bus | 75 |
| abstract_inverted_index.ETA | 19 |
| abstract_inverted_index.SVR | 124, 145, 169 |
| abstract_inverted_index.The | 0, 149, 164 |
| abstract_inverted_index.all | 134 |
| abstract_inverted_index.and | 32, 34, 107, 130, 143, 203 |
| abstract_inverted_index.are | 140 |
| abstract_inverted_index.bus | 14, 28, 49 |
| abstract_inverted_index.has | 187 |
| abstract_inverted_index.its | 176 |
| abstract_inverted_index.low | 177 |
| abstract_inverted_index.not | 188 |
| abstract_inverted_index.set | 156 |
| abstract_inverted_index.the | 3, 25, 40, 54, 64, 68, 80, 104, 120, 123, 135, 144, 154, 161, 168, 181, 192, 196, 199 |
| abstract_inverted_index.City | 74 |
| abstract_inverted_index.Jaya | 82 |
| abstract_inverted_index.This | 43 |
| abstract_inverted_index.aims | 46 |
| abstract_inverted_index.area | 83 |
| abstract_inverted_index.been | 115, 189 |
| abstract_inverted_index.data | 76, 158, 186, 206 |
| abstract_inverted_index.from | 160 |
| abstract_inverted_index.good | 172 |
| abstract_inverted_index.have | 114 |
| abstract_inverted_index.hour | 113 |
| abstract_inverted_index.long | 41 |
| abstract_inverted_index.name | 85 |
| abstract_inverted_index.part | 78 |
| abstract_inverted_index.peak | 110 |
| abstract_inverted_index.run. | 42 |
| abstract_inverted_index.same | 65 |
| abstract_inverted_index.show | 207 |
| abstract_inverted_index.such | 97 |
| abstract_inverted_index.test | 155 |
| abstract_inverted_index.them | 24 |
| abstract_inverted_index.this | 35, 90 |
| abstract_inverted_index.time | 6, 51 |
| abstract_inverted_index.used | 88, 116 |
| abstract_inverted_index.with | 153, 175, 202 |
| abstract_inverted_index.(ETA) | 9 |
| abstract_inverted_index.(SVR) | 58 |
| abstract_inverted_index.PJ03) | 86 |
| abstract_inverted_index.Urban | 73 |
| abstract_inverted_index.based | 62 |
| abstract_inverted_index.error | 179 |
| abstract_inverted_index.gives | 23 |
| abstract_inverted_index.input | 118 |
| abstract_inverted_index.model | 59, 146, 150, 170, 194, 200 |
| abstract_inverted_index.often | 11 |
| abstract_inverted_index.road, | 105 |
| abstract_inverted_index.split | 159 |
| abstract_inverted_index.trick | 129 |
| abstract_inverted_index.using | 53, 127 |
| abstract_inverted_index.which | 60 |
| abstract_inverted_index.work. | 92 |
| abstract_inverted_index.(SVM). | 72 |
| abstract_inverted_index.(route | 84 |
| abstract_inverted_index.Vector | 56, 70 |
| abstract_inverted_index.higher | 37, 138 |
| abstract_inverted_index.impact | 1 |
| abstract_inverted_index.kernel | 128 |
| abstract_inverted_index.model. | 125 |
| abstract_inverted_index.result | 166 |
| abstract_inverted_index.theory | 66 |
| abstract_inverted_index.travel | 99 |
| abstract_inverted_index.Machine | 71 |
| abstract_inverted_index.Support | 55, 69 |
| abstract_inverted_index.ability | 174 |
| abstract_inverted_index.arrival | 8, 50 |
| abstract_inverted_index.average | 178 |
| abstract_inverted_index.optimum | 132 |
| abstract_inverted_index.predict | 48 |
| abstract_inverted_index.project | 45 |
| abstract_inverted_index.related | 94 |
| abstract_inverted_index.result. | 183 |
| abstract_inverted_index.results | 197 |
| abstract_inverted_index.riders, | 21 |
| abstract_inverted_index.traffic | 96 |
| abstract_inverted_index.trained | 201 |
| abstract_inverted_index.weather | 106, 185, 205 |
| abstract_inverted_index.without | 204 |
| abstract_inverted_index.Features | 93 |
| abstract_inverted_index.However, | 184 |
| abstract_inverted_index.Petaling | 81 |
| abstract_inverted_index.accurate | 4, 18 |
| abstract_inverted_index.achieves | 147 |
| abstract_inverted_index.covering | 77 |
| abstract_inverted_index.dataset. | 163 |
| abstract_inverted_index.displays | 171 |
| abstract_inverted_index.distance | 102 |
| abstract_inverted_index.features | 136 |
| abstract_inverted_index.non-peak | 112 |
| abstract_inverted_index.original | 162 |
| abstract_inverted_index.promotes | 36 |
| abstract_inverted_index.reliable | 33 |
| abstract_inverted_index.research | 44, 91 |
| abstract_inverted_index.services | 29 |
| abstract_inverted_index.training | 121 |
| abstract_inverted_index.duration, | 100 |
| abstract_inverted_index.efficient | 31 |
| abstract_inverted_index.estimated | 5 |
| abstract_inverted_index.evaluated | 152 |
| abstract_inverted_index.indicates | 167 |
| abstract_inverted_index.operation | 108 |
| abstract_inverted_index.providing | 17 |
| abstract_inverted_index.ridership | 38 |
| abstract_inverted_index.Regression | 57 |
| abstract_inverted_index.calculated | 142 |
| abstract_inverted_index.dimensions | 139 |
| abstract_inverted_index.impression | 26 |
| abstract_inverted_index.negligible | 209 |
| abstract_inverted_index.operators. | 15 |
| abstract_inverted_index.overlooked | 12 |
| abstract_inverted_index.prediction | 173, 182, 193 |
| abstract_inverted_index.specifying | 131 |
| abstract_inverted_index.difference. | 210 |
| abstract_inverted_index.efficiently | 141 |
| abstract_inverted_index.influential | 190 |
| abstract_inverted_index.parameters, | 133 |
| abstract_inverted_index.convergence. | 148 |
| abstract_inverted_index.experimental | 165 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.8100000023841858 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.76622498 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |