Making Index for the intercity transportation network based on the modeling and preparation of mathematical method
Subject Areas : Mathematical Optimization
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Keywords: Transportation systems management, The safety transportation networks and traffic management, The system capacity and transportation planning,
Abstract :
The necessary condition but not the enough one in order to enhance productivity and optimizing the operation of the transportation network lines is to give a proper index to any of these routes. The common lines of the distinct routes, therefore, would give more complexity and importance to the matter. So, through modeling and using the mathematical method based on the preparation of software we have tried to solve the problem. First, the routes of the journeys between all origins and destinations on the network concerning they sometimes had no specific and constant moving plan were identified. The number of journey demands between them were processed by this software in which for any route and number of journeys a quantitative figure between zero and one with the name of index was calculated. The more the index is closer to the number one, the more valuable the activity of the route will be. In this way the capacity of the routes must be regarded as independent variables. It is notable that by using this mathematical method it would be possible to predict the critical situations; that is, with an omission of one or more routes from the network we can calculate the traffic load imposed on the other routes as well as their respective index variations. The evaluations of the said indices are the basic tools for the safety and traffic management as well as the analysis of the system capacity and transportation planning.
[1] An, K. and Lo, H. (2016). Two-phase stochastic program for transit network design under demand uncertainty. Transportation Research B, 84:157–181. Journal of Urban Planning and Development . Volume 146, Issue 3. https://doi.org/10.1061/(ASCE)UP.1943-5444.000060
[2] Badia, H., Estrada, M., and Robusté, F. (2014). Competitive transit network design in cities with radial street patterns. Transportation Research Part B: Methodological, 59:161–181. https://doi.org/10.1016/j.trb.2013.11.006
[3] Bel, G. and Holst, M. (2018). Evaluation of the impact of bus rapid transit on air pollution in mexico city. Transport Policy, 63, 209–220. https://doi.org/10.1016/j.tranpol.2018.01.001
[4] Beltran, B., Carrese, S., Cipriani, E., and Petrelli, M. (2009). Transit network design with allocation of green vehicles: A genetic algorithm approach. Transportation Research Part C: Emerging Technologies, 17(5), 475–483. https://doi.org/10.1016/j.trc.2009.04.008
[5] B. Barabino.(2009). Transit Bus Route Network Design: a model and its application in a real network. WIT Transactions on The Built Environment, 107, 369-382. https://doi.org/10.2495/UT090331
[6] Y. Jiang, W.Y. Szeto, and T.M. Ng.(2013). Transit Network Design: A Hybrid Enhanced Artificial Bee Colony Approach and a Case Study. International Journal of Transportation Science and Technology, 2, no. 3, 243-260. https://doi.org/10.1260/2046-0430.2.3.243
[7] Y. Kuo, C. Luo, and C. Wang, “Bus route design with limited travel time.” Transport, 28, no. 4, 368-373. https://doi.org/10.3846/16484142.2013.865103
[8] S. Ngamchai, D.J. Lovell, and A.M. ASCE, (2005). Optimal Time Transfer in Bus Transit Route Network Design Using a Genetic Algorithm. Proceedings of the Eastern Asia Society for Transportation Studies, 5, pp, 477 - 489. https://www.researchgate.net/publication/228960835
[9] Sharma, SK, & George, SA. (2018). Modelling resilience of truckload transportation industry. Benchmarking An International Journal, 25, 2531–2545. https://doi.org/10.1108/BIJ-07- 2017-0188
[10] Twumasi-Boakye, R., and J. O. Sobanjo. 2017. Evaluation of transportation user costs based on simulated regional network models. Journal of the Transportation Research Board. 2612, 1. https://doi.org/10.3141/2612-14
[11] Twumasi-Boakye, R, & Sobanjo, JO. (2018). Resilience of Regional Transportation Networks Subjected to Hazard-Induced Bridge Damages. Journal of Transportation Engineering, Part A: Systems, 144, 10 .https://doi.org/10.1061/JTEPBS.0000186
[12] Wang, C, Ye, Z, & Wang, W. (2020). A Multi-Objective Optimization and Hybrid Heuristic Approach for Urban Bus Route Network Design. IEEE Access, 8, 12154–12167. https://doi.org/10.1109/ACCESS.2020.2966008.
