IDENTIFICATION OF LEAST CONGESTED AND SHORTEST DISTANCE PATH IN TRAFFIC NETWORK

TING KIEN HUA; NORAINI ABDULLAH

doi:10.51200/jaaab.v0i0.1783

Authors

TING KIEN HUA
NORAINI ABDULLAH

DOI:

https://doi.org/10.51200/jaaab.v0i0.1783

Keywords:

degree of saturation, distance, least congested, shortest path, Dijkstra’s algorithm, traffic network

Abstract

Traffic congestion, a mobility problem that exists as the traffic volume demand exceeds the capacity of the existing intersections. Conventional approaches such as building flyover and road widening might not be very effective in solving traffic congestion. Therefore, a suggested approach in this study was to change the driver’s behaviour in selecting the best path. The shortest path may not be the best path. The added criterion in selecting the best path was the level of service of the path, while the included parameter that determines the level of service was the degree of saturation. Dijkstra’s algorithm was applied to solve the network graph. Excel solver was used to find the solution. Percentage of difference between paths was carried out to find out the difference and significant of the paths. In conclusion, the path with combined criteria of shortest distance and least congested (degree of saturation) would be the best path, instead of solely based on the shortest distance or minimum degree of saturation.

Author Biographies

TING KIEN HUA

Faculty of Science & Natural Resources,

Universiti Malaysia Sabah,

88400 Kota Kinabalu,

Sabah, Malaysia

NORAINI ABDULLAH

Faculty of Science & Natural Resources,

Universiti Malaysia Sabah,

88400 Kota Kinabalu,

Sabah, Malaysia

References

Akcelik, R. (1989). On the estimation of lane flows for intersection analysis. Australian Road Research, 19 (1), 51 – 57.

Bernama. (2016, December 28). Traffic woes haunt KK residents daily. The Borneo Post. Retrieved from https://www.theborneopost.com/2016/12/28/traffic-woes-haunt-kk-residents-daily/

Gupta, N., Mangla, K., Jha, A. K., & Umar, M. (2016). Applying Dijkstra’s algorithm in routing process. International Journal of New Technology and Research (IJNTR), 2 (5), 122 – 124.

Mathew, T. V., & Rao, K. V. K. (2006). Introduction to transportation engineering. National Programme on Technology Enhanced Learning (NPTEL). Retrieved from https://nptel.ac.in/downloads/105101087/

Morpi, M. (2015, July 3). World Bank report highlights worsening KK traffic woes. The Borneo Post. Retrieved from http://www.theborneopost.com/2015/07/03/world-bank-report-highlightsworsening-kk-traffic-woes/

Neumann, T. (2014). Method of path selection in the graph-case study. International Journal on Marine Navigation and Safety of Sea Transportation, 8 (4), 557 – 562.

Rao, A. M., & Rao, K. R. (2012). Measuring urban traffic congestion: A review. International Journal for Traffic & Transport Engineering, 2 (4).

Rewadkar, D. N., & Ratnaparkhi, P. M. (2015). Traffic estimation and least congested alternate route finding GPS and non-GPS vehicles through real time data on Indian roads. International Journal of Innovative Research in Computer and Communication Engineering, 3 (7), 6686 – 6691.

Rodegerdts, L. A., Nevers, B., Robinson, B., Ringert, J., Koonce, P., Bansen, J., ... Courage, K. (2004). Signalized intersections: Informational guide (Technical report No. FHWAHRT-04-091). Washington, DC: Federal Highway Administration.

Rohila, K., Gouthami, P., & Priya, M. (2014). Dijkstra’s shortest path algorithm for road network. International Journal of Innovative Research in Computer and Communication Engineering, 2 (10), 6139 – 6144.

Sangaiah, A. K., Han, M., & Zhang, S. (2014). An investigation of Dijkstra and Floyd Algorithms in National City Traffic Advisory Procedures. International Journal of Computer Science and Mobile Computing, 3 (2), 124 – 138.