Ideal Flow Traffic Analysis: A Case Study on a Campus Road Network
Kardi Teknomo1, Roselle Wednesday Gardon2*, and Czarina Saloma3
1Department of Information Systems and Computer Science,
Ateneo de Manila University, Quezon City 1108 Philippines
2School of Computing and Information Technologies,
Asia Pacific College, Makati City 1232 Philippines
3Department of Sociology and Anthropology,
Ateneo de Manila University, Quezon City 1108 Philippines
Traditional traffic assignment models often use historical travel demand, such as the costly origin-destination flow distribution and actual flow distribution, as inputs in determining the most efficient distribution of flow on a road network. In this paper, the authors examine the ideal flow network (IFN) model, a novel and alternative traffic assignment model. The IFN model is compared with a traditional traffic assignment model using a generic model comparison method. The application of the method is presented using a campus road network as a case study to examine the importance of understanding the road network structure – by making a comparison between the results of a traditional traffic assignment model and the IFN model to gain nuanced insights into the distribution of the traffic flow. The authors suggest that – while both models can yield almost the same result – the IFN model has the advantage of using a stochastic matrix, which is more readily available than demand data. The IFN model is likewise more geared toward evaluating the ideas of solving the traffic problem through simulation modeling, which – as a form of social engineering – is easier to stabilize into traffic management.
Traffic congestion has always been a perennial problem, especially on important roads such as those located in densely populated metropolitan areas. The problem of congestion is not easy to handle, and varied solutions have been suggested and implemented to address the challenge. These solutions necessitate the creation of, or the improvement of, existing traffic assignment models. Traditional traffic assignment models make use of historical travel demand data, specifically from the costly origin-destination (OD) survey. This is a difficulty for low-income societies, especially those with limited OD data or minimal budget for data collection. It is beneficial, therefore, to view traffic assignment from both sides of supply (road infrastructure) and demand rather than merely from the demand side. This is the uniqueness and novelty of the Ideal Flow Network (IFN) model (Teknomo 2017, Teknomo & Gardon 2017). The IFN models the ideal flow matrix with which one can measure the efficiency of the current traffic flow. In other words, one can use the flow matrix generated by the model as a guide to how the current traffic flow should be managed. . . . . read more
BELL MG, LIDA Y. 1997. Transportation Network Analysis. s.l.: John Wiley & Sons, Ltd.
[BPR] Bureau of Public Roads. 1964. Traffic assignment manual for application with a large, high speed computer. Washington: U.S. Dept. of Commerce, Bureau of Public Roads, Office of Planning, Urban Planning Division.
DAGANZO CF, SHEFFI Y. 1977. On Stochastic Models of Traffic Assignment. Transportation Science 11: 253–274.
FRANCO E, SALOMA C, TEKNOMO K, BATAC A, FAVIS AM. 2012. Towards Efficient Mobility in a Sustainable Campus (A Report by the Ateneo Traffic Group). s.l.: s.n.
GARDON RW, TEKNOMO K. 2017. Analysis of the Distribution of Traffic Density Using the Ideal Flow Method and the Principle of Maximum Entropy. Proceedings of the 17th Philippine Computing Science Congress. University of San Carlos, Cebu, Philippines.
GOLUB G, KAHAN W. 1965. Calculating the singular values and pseudo-inverse of a matrix. Journal of the Society for Industrial and Applied Mathematics, Series B: Numerical Analysis 2(2): 205–224.
HÄGGSTRÖM O. 2002. Finite Markov chains and algorithmic applications. s.l.: Cambridge University Press.
JAYNES ET. 1957. Information theory and statistical mechanics. Physical Review 106(4): 620.
KITAMURA R, YOSHII T, YAMAMOTO T. 2009. The Expanding Sphere of Travel Behavior Research: Selected Papers from the 11th International Conference on Travel Behavior Research. s.l.: Emerald Group Publishing.
KRYLATOV A, ZAKHAROV V, MALYGIN I. 2016. Competitive traffic assignment in road networks. Transport and Telecommunication 17(3): 212–221.
[NRC] National Research Council. 2010. HCM 2010: Highway capacity manual. Washington, DC: Transportation Research Board.
ORTÚZAR JDD, WILLUMSEN LG. 2011. Modelling Transport. 4th ed. s.l.: Wiley.
PATRIKSSON M. 2015. The Traffic Assignment Problem: Models and Methods. s.l.: Courier Dover Publications.
SALOMA C, AKPEDONU E. 2016. Eating in vertical neighborhoods. Food consumption in the city: Practices and patterns in Urban Asia and the Pacific. London: Routledge.
SALOMA C, PEREZ GJ, TAPANG G, LIM M, PALMES-SALOMA C. 2003. Self-organized queuing and scale-free behavior in real escape panic. Proceedings of the National Academy of Sciences 100(21): 11947–52.
SENETA E. 2006. Non-negative matrices and Markov chains. s.l.: Springer Science & Business Media.
SEO T, KUSAKABE T. 2015. Probe vehicle-based traffic flow estimation method without fundamental diagram. Transportation Research Procedia 9: 149–163.
SHINAR D. 2017. Traffic Safety and Human Behavior 2nd ed. s.l.: Emerald Publishing Limited.
TEKNOMO K. 2008. Modeling Mobile Traffic Agents on Network Simulation. Proceedings of the 16th Annual Conference of Transportation Science Society of the Philippines. Metro Manila, Philippines.
TEKNOMO K. 2017. Ideal Relative Flow Distribution on Directed Network. Journal of the Eastern Asia Society for Transportation Studies 12: 939–958.
TEKNOMO K, FERNANDEZ P. 2014. A theoretical foundation for the relationship between generalized origin-destination matrix and flow matrix based on ordinal graph trajectories. Journal of Advanced Transportation 48(6): 608–626.
TEKNOMO K, GARDON RW. 2017. Intersection Analysis Using the Ideal Flow Model. Proceedings of the IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama, Japan.
TOTH C, SUH W, ELANGO V, SADANA R, ANGSHUMAN G, HUNETER M, GUENSLER R. 2013. Tablet-Based Traffic Counting Application Designed to Minimize Human Error. Transportation Research Record 2339(1): 39–46.
WARDROP JG. 1952. Some Theoretical Aspects of Road Traffic Research. Proceedings of the Institution of Civil Engineers 1: 325–362.
YANG X, LU Y, HAO W. 2017. Origin-Destination Estimation Using Probe Vehicle Trajectory and Link Counts. Journal of Advanced Transportation 2017.
YUAN J, ZHENG Y, XIE X, SUN G. 2013. T-drive: Enhancing driving directions with taxi drivers' intelligence. IEEE Transactions on Knowledge and Data Engineering 25(1): 220–232.