TRANSPORT SERVICE SYSTEM DESIGN USING MODIFIED APRIORI ALGORITHM

  • Godwin A. Otu
  • Philip Achimugu
  • Adeyemi Owolabi Nigerian Defence Academy
  • Ugbe U. Raphael
  • Monday J. Abdullahi
  • Oyebanji Modupeola Shukurah
  • Nachamada Vachaku Blamah
  • Sakinate L. Usman
Keywords: Apriori, scheduling, assigning, viability.

Abstract

This research is carried-out to evaluate the effectiveness of data mining in determining viability of routes, efficient scheduling and assigning of vehicles to commuters. The study was guided by the following objectives: modification of Apriori algorithm, implementation using C programming language, analyses and deductions from the results to determine if a given route is feasible. Data Mining (association rule) technique has been used to identify geographical locations where accidents have occurred and their characteristics, in road management to develop effective accident preventive measures, to determine estimated travel time and in market basket analysis applied in grocery stores. Data was collected from three transport companies for each route. The data was inputted into the program implemented using modified Apriori algorithm. The study findings revealed the volume of commuters per route and how vehicles can be assigned and scheduled. Using the above findings, effective transport service system is designed using routes viability

References

Akash, R. and Mahendra K.G.(2019). Association Rule Mining in Medical Diagnosis. Association Rule Mining: Applications in various Areas. International Conference on Data Management 1(8), 151-207 ISSN 201206.

Akash, R. and Mahendra K.G (2017). Association Rule Mining in Market Basket Analysis.

Xiaofeng, Z and Shu, W. (2017). Study on the Method of Road Transport Management Information Data Mining Based on Pruning Eclat Algorithm and MapReduce. Social and Behavioural Sciences 138(2014): 757-766.

Jianfeng, .X, Gao, .Z Niu., Ding, T and Ning, G. (2020). A Hybrid Algorithm of Traffic Accident Data Mining on Cause Analysis. Hindawi Publishing Corporation Mathematical Problems in Engineering volume. Article ID 302627.

Xiao, L., Peng, X. and Hong, P (2014).Research on Traffic Monitoring Network and its Traffic Flow Forecast and Congestion Control Model Based on wireless Sensor Networks.Published in International Conference on Measuring Technology.

Shaw, K., Tu, S., and Flanagin, M. (2010). Design Strategies to Improve performance of GIS Web service. International Conference on Information.

Marco, Z. (2017).Data mining Techniques for Design Pattern Detection.UniversitadegliStudi di Milano Bicocca Di Partimento di Informatica, Sistemistica e Communicazione.Dottorato di Ricerca in Informatica-XXIV Circo.

Ryo, N., Zhou, H. and Hirasawa, K. (2010).Fuzzy class association Rule Mining for traffic Prediction Using Genetic Network Programming with Multi-Paths.

Zhang, X. and Rice. (2003): Short Term Travel time prediction Transportation research Part C: Emerging Technologies. 11. 3-4. Pp. 18-210.

Kwon, J. and Petty, K. F. (2018). Travel Time Prediction Algorithm Scalable to Freeway Networks with many Nodes with Arbitrary travel Routes. Transportation Research record Journal of the Transportation Research Board. 30(1935).

Yin-Fu .H, Jian-Ying .C and Chich-Ming .W. (2020). Privacy Preserving Association Rules by Using Greedy Approach. CSIE 2009 proceedings of the WRI world Congress on Computer Science and Information Engineering 4 pp. 61-65.

Fabrizi.,V and Ragona.,R.(2016). A Pattern Matching Approach to Speed Forecasting of Traffic Networks. European Transport research Review 6(3), 333-334.

Certiner, B., Sari, M. and Oguz, B. (2010). A Neural Network Based Traffic-Flow Prediction Model. Mathematical and Computational Applications 15(2) pp 269-278. Association of Scientific Research.

Ozkurt, C and Camci, F. (2009). Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks. Mathematical and Computational Applications. 14(3) pp. 187-196.

Jiang and Wah (2018). Constructing and Training Feed-Forward Neural Networks for pattern Classification. ScienceDirect. 36(4) pp. 853-867.

Drakopoulous and abdulkadir(2005). Neural Network Training.International Journal of Computer Science and Emerging Technologies 1(4).ISSN 2044-6004(online).

Zheng and Zuylen (2020). Urban Link Travel time Estimation Based on Sparse Probe Data. Transportation Research part C EmergingTechnologies31:pp.145-157

.

Published
2022-08-18
How to Cite
OtuG. A., AchimuguP., OwolabiA., RaphaelU. U., AbdullahiM. J., ShukurahO. M., BlamahN. V., & UsmanS. L. (2022). TRANSPORT SERVICE SYSTEM DESIGN USING MODIFIED APRIORI ALGORITHM. FUDMA JOURNAL OF SCIENCES, 6(4), 25 - 36. https://doi.org/10.33003/fjs-2022-0604-845

Most read articles by the same author(s)