research-article

Business Intelligence Dashboard for Driver Performance in Fleet Management

Authors:

Aulia Syahirah Khalid,

Noor Hafizah Hassan,

Nur Azaliah Abu Bakar Razak,

Ahmad Fazreen BaharudenAuthors Info & Claims

IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

Pages 347 - 351

https://doi.org/10.1145/3377571.3377642

Published: 03 May 2020 Publication History

Abstract

Transportation is at the center of logistics as it represents the physical movement of materials between points in a supply chain. The problem involve in the transportation industry is fleet management. Fleet management is the broad topic that involve vehicles maintenance, operation capacity, driver selection and so on. This project focus on performance of the driver in fleet management. Imbalance driver contribution in fleet management decline the productivity of the organization especially in transportation industry. Each driver should be evaluated and analysed on their productivity and contribution towards the organization based on their performance. The aim of this project is to identify the factors influencing driver performance in logistic transportation and provide business intelligence dashboard for visualize driver performance for organization in their decision making. One transportation industry has been selected for the case study relying on business intelligence framework and tools for the development of dashboard. As the finding of this project, a conceptual model representing factors influencing driver performance is proposed. A dashboard was developed to provide business insight and help the organization in decision making based on the conceptual model proposed. The dashboard comprises of four main components which are summary, delivery, driver profile and driver behaviour. The dashboard was evaluated with respondents who involved in fleet management.

References

[1]

Gitahi, P. M., & Ogollah, K. 2014. Influence of Fleet Management Practices on Service Delivery to Refugees in United Nations High Commissioner for Refugees Kenya Programme. European Journal of Business Management Vol.2, Issue 1, 2014, 2(1), 1--18.

[2]

Saghaei, H. (2016). Design and Implementation of a Fleet Management System Using Novel GPS / GLONASS Tracker and Web-Based Software. In Proceedings of 1st International Conference on New Research Achievements in Electrical and Computer Engineering, IEEE, At Tehran, Iran, Volume: 1

[3]

Pierro, M. J., & Schneider, W. R. (2001). US 6301531 B1. United States. Retrieved from https://patentimages. storage.googleapis.com/e9/b8/72/e435779cd8cbb8/US6301531.pdf

[4]

Chou, P. B.-L., Iyer, B. S., Lai, J., Levas, A., Lieberman, L. I., Lui, T.-K., ... Kim, E. (2001). US 6330499 B1. United States.

[5]

Molin, H. M., & Au, K. D. (2018). US 9922567 B2. United States.Retrieved from: https://patentimages.storage.googleapis.com/29/d3/92/433b04-3d290f6f/US9922567.pdf

[6]

Benjamin G. Bates. (2000). US006085805A. United States. Retrieved from https://patentimages.storage.googleapis.com/a2/9e/c2/e8b8f2d 1230f96/US6085805.pdf

[7]

Chien, C. F., & Chen, L. F. 2008. Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Systems with Applications. 34,1 (Jan. 2008), 280--290. DOI = https://doi.org/10.1016/j.eswa.2006.09.003

Digital Library

[8]

Vivaldini, M., Pires, S. R. I., & Souza, F. B. De. (2012b). Improving Logistics Services Through The Technology Used in Fleet Management, Journal of Information Systems and Technology Managemnt. 9,3, 541--562. DOI= https://doi.org/10.4301/S1807-17752012000300006

[9]

Sundar Kumar. 2013. Are you measuring the right metrics to optimize logistics processes? Genpact Intelligent Power by Process, 5.

[10]

Júnior, J. F., Carvalho, E., Ferreira, B. V., De Souza, C., Suhara, Y., Pentland, A., & Pessin, G. 2017. Driver behavior profiling: An investigation with different smartphone sensors and machine learning. PLoS ONE. 12,4.: e0174959. DOI= https://doi.org/10.1371/journal.pone.0174959

[11]

Simsekoglu, Ö. 2018. Socio-demographic characteristics, psychological factors and knowledge related to electric car use: A comparison between electric and conventional car drivers. Transport Policy, 72 (June 2017), 180--186. DOI = https://doi.org/10.1016/j.tranpol.2018.03.009

[12]

Padilla, J. L., Doncel, P., Gugliotta, A., & Castro, C. (2018a). Which drivers are at risk? Factors that determine the profile of the reoffender driver. Accident Analysis and Prevention. 119, 237--247. DOI = https://doi.org/10.1016/j.aap.2018.07.021

[13]

del Valle, C. H. C. 2019. Cognitive profile of optimistic and pessimistic drivers attending re-education courses. Transportation Research Part F: Traffic Psychology and Behaviour, 65 (Aug. 2019), 598--609, DOI=. https://doi.org/10.1016/j.trf.2018.04.018

[14]

Hamblin, P. 1987. Lorry driver ' s time habits in work and their involvement in traffic accidents, 30, 9 (May 2007), 1323--133, DOI= https://doi.org/10.1080/00140138708966026

[15]

Simões, A., Bianchi Piccinini, G. F., Rôla, S., & Ferreira, A. L. 2013. Gender and age-related differences in the perception of in-vehicle mobile phone usage among Portuguese drivers. IET Intelligent Transport Systems, 7, 2, 223--229. DOI = https://doi.org/10.1049/iet-its.2012.0149

[16]

Vivoli, Roberto, Bergomi, M., Rovesti, S., Bussetti, P., & Guaitoli, G. M. 2006. Biological and behavioral factors affecting driving safety. Journal of Preventive Medicine and Hygiene, 47, 2, 69--73, DOI = https://doi.org/10.1029/2006WR005449

[17]

Yang, Y., Wong, A., & McDonald, M. 2015. Does gender make a difference to performing in-vehicle tasks? IET Intelligent Transport Systems, 9, 4, 359--365. DOI = https://doi.org/10.1049/iet-its.2013.0117

[18]

Stahl, P., Donmez, B., & Jamieson, G. A. 2014. Anticipation in driving: The role of experience in the efficacy of pre-event conflict cues. IEEE Transactions on Human- Machine Systems, 44, 5, 603--613, DOI= https://doi.org/10.1109/THMS.2014.2325558

[19]

Liao, Y., Li, G., & Chen, F. 2017. Context-Adaptive support information for truck drivers: An interview study on its contents priority. In Proceeding of IEEE Intelligent Vehicles Symposium, Proceedings, 1268--1273. DOI = https://doi.org/10.1109/IVS.2017.7995886

Digital Library

[20]

Jin, J., & Deng, Y. 2017. A comparative study on traffic violation level prediction using different models. In Proceedings of 2017 4th International Conference on Transportation Information and Safety, ICTIS 2017 - Proceedings, 1134--1139. DOI = https://doi.org/10.1109/ICTIS.2017.8047913

[21]

Gayathri, N. & Chandrakala, K. (2014). Embedded driver assistance system for effective dynamic vehicle routing. In Proceeding of International Conference on Embedded Systems, ICES 2014. 182--187. DOI= 10.1109/EmbeddedSys.2014.6953153

[22]

Shattell, M., Apostolopoulos, Y., Snmez, S., & Griffin, M. 2010. Occupational stressors and the mental health of truckers. Issues in Mental Health Nursing. 31,9, 561--568 DOI = https://doi.org/10.3109/01612840.2010.488783

[23]

Vujanović, D., Mijailović, R., Momčilović, V., & Papić, V. 2010. Energy efficiency as a criterion in the vehicle fleet management process. Thermal Science, 14, 4, 865--878. DOI= https://doi.org/10.2298/TSCI090719010V

[24]

Amiama, C., Pereira, J. M., Carpente, L., & Salgado, J. 2015. Spatial decision support system for the route management for milk collection from dairy farms. Transportation Letters, 7,5, 279--288.DOI= https://doi.org/10.1179/1942787515Y.0000000001

[25]

Andrejić, M., Bojović, N., & Kilibarda, M. 2016. A framework for measuring transport efficiency in distribution centers. Transport Policy, 45, 99--106. DOI= https://doi.org/10.1016/j.tranpol.2015.09.013

[26]

Factor, R. 2018. An empirical analysis of the characteristics of drivers who are ticketed for traffic offences. Transportation Research Part F: Traffic Psychology and Behaviour, 53, 1--13.DOI= https://doi.org/10.1016/j.trf.2017.12.001

[27]

Surbakti, H., & Ta'a, A. 2018. Tacit knowledge for business intelligence framework: A part of unstructured data? Journal of Theoretical and Applied Information Technology, 96, 3, 616--625.

[28]

Fruhling, A., & Lee, S. 2005. Assessing the Reliability, Validity and Adaptability of PSSUQ and Adaptability of PSSUQ. In AMCIS 2005 Proceedings, 2394--2402.

Cited By

Lê L(2024)P-R-Dash: A Dashboard-Constructing Tool for Twinning Business ProcessesSN Computer Science10.1007/s42979-024-02850-05:5Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02850-0
Soares GBrito M(2023)Business Intelligence Over and Above Apache Superset2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211907(1-6)Online publication date: 20-Jun-2023
https://doi.org/10.23919/CISTI58278.2023.10211907
Halim SRovanelli AWidyadana I(2023)Designing Performance Dashboard for Monitoring Post-harvest Loss in Transportation2023 1st IEEE International Conference on Smart Technology (ICE-SMARTec)10.1109/ICE-SMARTECH59237.2023.10461952(37-41)Online publication date: 17-Jul-2023
https://doi.org/10.1109/ICE-SMARTECH59237.2023.10461952
Show More Cited By

Index Terms

Business Intelligence Dashboard for Driver Performance in Fleet Management
1. Applied computing
  1. Enterprise computing
    1. Business process management
      1. Business intelligence

Recommendations

Driver behavior analysis during ACC activation and deactivation in a real traffic environment

For the development of a traffic-simulation model to estimate the effect of adaptive cruise control (ACC) systems on traffic safety, throughput, and environment, data of a field operational test (FOT) were analyzed, in which vehicles were equipped with ...
Centralized fleet management system for cybernetic transportation

In this article, we present a centralized fleet management system (CFMS) for cybernetic vehicles called cybercars. Cybercars are automatically guided vehicles for passenger transport on dedicated networks like amusement parks, shopping centres etc. The ...
Celer: A Smart Fleet Management System (Optimizing Traffic Flow in New York City)
SIGCSE 2022: Proceedings of the 53rd ACM Technical Symposium on Computer Science Education V. 2

As society moves closer to fully autonomous vehicles, it must eventually make vehicles work together. This would reduce traffic jams, reduce cost of trips, reduce overall travel time, reduce the environmental impact, and reduce the number of casualties ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

January 2020

441 pages

ISBN:9781450372947

DOI:10.1145/3377571

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Ritsumeikan University: Ritsumeikan University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 May 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IC4E 2020

IC4E 2020: 2020 the 11th International Conference on E-Education, E-Business, E-Management, and E-Learning

January 10 - 12, 2020

Osaka, Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
549
Total Downloads

Downloads (Last 12 months)81
Downloads (Last 6 weeks)2

Reflects downloads up to 21 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lê L(2024)P-R-Dash: A Dashboard-Constructing Tool for Twinning Business ProcessesSN Computer Science10.1007/s42979-024-02850-05:5Online publication date: 16-May-2024
https://dl.acm.org/doi/10.1007/s42979-024-02850-0
Soares GBrito M(2023)Business Intelligence Over and Above Apache Superset2023 18th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI58278.2023.10211907(1-6)Online publication date: 20-Jun-2023
https://doi.org/10.23919/CISTI58278.2023.10211907
Halim SRovanelli AWidyadana I(2023)Designing Performance Dashboard for Monitoring Post-harvest Loss in Transportation2023 1st IEEE International Conference on Smart Technology (ICE-SMARTec)10.1109/ICE-SMARTECH59237.2023.10461952(37-41)Online publication date: 17-Jul-2023
https://doi.org/10.1109/ICE-SMARTECH59237.2023.10461952
Mulerikkal JThandassery SDixon K DRejathalal VAyyappan B(2022)JP-DAP: An Intelligent Data Analytics Platform for Metro Rail Transport SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.309154223:7(9146-9156)Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1109/TITS.2021.3091542
Nguyen NLe L(2022)Toward Code Generation for Process-oriented, Role-based Dashboards : An Example of Digital Advertising in Vietnam2022 International Conference on Advanced Computing and Analytics (ACOMPA)10.1109/ACOMPA57018.2022.00010(20-26)Online publication date: Nov-2022
https://doi.org/10.1109/ACOMPA57018.2022.00010

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents