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.
- 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.Google Scholar
- 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: 1Google Scholar
- Pierro, M. J., & Schneider, W. R. (2001). US 6301531 B1. United States. Retrieved from https://patentimages. storage.googleapis.com/e9/b8/72/e435779cd8cbb8/US6301531.pdfGoogle Scholar
- 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.Google Scholar
- 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.pdfGoogle Scholar
- Benjamin G. Bates. (2000). US006085805A. United States. Retrieved from https://patentimages.storage.googleapis.com/a2/9e/c2/e8b8f2d 1230f96/US6085805.pdfGoogle Scholar
- 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.2006.09.003Google ScholarDigital Library
- 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/S1807-17752012000300006Google Scholar
- Sundar Kumar. 2013. Are you measuring the right metrics to optimize logistics processes? Genpact Intelligent Power by Process, 5.Google Scholar
- 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.0174959Google Scholar
- 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.2018.03.009Google Scholar
- 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.2018.07.021Google ScholarCross Ref
- 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.2018.04.018Google Scholar
- 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/10.1080/00140138708966026Google Scholar
- 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.2012.0149Google ScholarCross Ref
- 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/10.1029/2006WR005449Google Scholar
- 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.2013.0117Google ScholarCross Ref
- 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.2014.2325558Google ScholarCross Ref
- 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.2017.7995886Google ScholarDigital Library
- 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.2017.8047913Google ScholarCross Ref
- 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.2014.6953153Google ScholarCross Ref
- 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/10.3109/01612840.2010.488783Google ScholarCross Ref
- 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/TSCI090719010VGoogle ScholarCross Ref
- 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.0000000001Google ScholarCross Ref
- 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.2015.09.013Google ScholarCross Ref
- 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.2017.12.001Google ScholarCross Ref
- 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.Google Scholar
- Fruhling, A., & Lee, S. 2005. Assessing the Reliability, Validity and Adaptability of PSSUQ and Adaptability of PSSUQ. In AMCIS 2005 Proceedings, 2394--2402.Google Scholar
Index Terms
- Business Intelligence Dashboard for Driver Performance in Fleet Management
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. 2As 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