Comparing System Optimum-based and User Decision-based Traffic Models in an Autonomous Transport System
Abstract
The paper introduces a framework to perform the demand management and route planning tasks of a highly developed transport system managing scheme, assuming an autonomous transport system. Two types of autonomous transport system managing models have been introduced. In case of the first model, the assigned number of trips is assumed to be the modified variable related to the optimization problem. In case of the second model, the decision process is directly influenced by the travel prices defined by the optimization method. These approaches represent different demand management strategies. The first model aims to directly assign the incoming user demands to the system, while the second procedure lets the users make the decision. However, in the second case the system can strongly influence the users’ choices through the values of the travel prices. Accordingly, it seems to be a reasonable assumption that the firstly presented model has significantly higher efficiency in distributing the load on the network. On the other hand, the method of the second model would be much more tolerable and acceptable from a social point of view. Therefore, the aim of the paper is to introduce the developed models and to compare their efficiencies.
References
Apronti D, Ksaibati K, Gerow K, Hepner JJ. Estimating traffic volume on Wyoming low volume roads using linear and logistic regression methods. Journal of Traffic and Transportation Engineering (English Edition). 2016;3(6): 493-506. Available from: doi:10.1016/j.jtte.2016.02.004
Ansari S, Başdere M, Li X, Ouyang Y, Smilowitz K. Advancements in continuous approximation models for logistics and transportation systems: 1996–2016. Transportation Research Part B: Methodological. 2017;107: 229-252. Available from: doi:10.1016/j.trb.2017.09.019
Kumar P, Rosenberger JM, Iqbal GMD. Mixed integer linear programming approaches for land use planning that limit urban sprawl. Computers & Industrial Engineering. 2016;102: 33-43. Available from: doi:10.1016/j.cie.2016.10.007
Přibyl O, Svítek M. System-oriented Approach to Smart Cities. In: Proceedings of the 1st IEEE International Smart Cities Conference. IEEE International Smart Cities Conference (ISC2-15), 2015, Guadalajara, Mexico. Available from: doi:10.1109/ISC2.2015.7428760
Hensher DA, Button KJ. (eds.) Handbook of transport modelling. 2nd ed. Emerald Group Publishing Limited; 2007.
Friesz TL, Kim T, Kwon C, Rigdon MA. Approximate network loading and dual-time-scale dynamic user equilibrium. Transportation Research Part B: Methodological. 2011;45(1): 176-207. Available from: doi:10.1016/j.trb.2010.05.003
Nishiyama H, Tada Y, Kato N, Yoshimura N, Toyoshima M, Kadowaki N. Toward Optimized Traffic Distribution for Efficient Network Capacity Utilization in Two-Layered Satellite Networks. IEEE Transactions on Vehicular Technology. 2013;62(3): 1303-1313. Available from: doi:10.1109/TVT.2012.2227861
Zhang X, Waller ST. Implications of link-based equity objectives on transportation network design problem. Transportation. 2018; 1-31. Available from: doi:10.1007/s11116-018-9888-1
Gonzales EJ, Daganzo CF. Morning commute with competing modes and distributed demand: User equilibrium, system optimum, and pricing. Transportation Research Part B: Methodological. 2012;46(10): 1519-
Available from: doi:10.1016/j.trb.2012.07.009
Li ZC, Lam WH, Wong SC, Sumalee A. Environmentally sustainable toll design for congested road networks with uncertain demand. International Journal of Sustainable Transportation. 2012;6(3):127-155. Available from: doi:10.1080/15568318.2011.570101
Hu TC, Kahng AB. Linear and Integer Programming Made Easy. Springer International Publishing; 2017. Available from: doi:10.1007/978-3-319-24001-5 [Accessed 16th June 2018].
Pauer G, Török Á. Static System Optimum of Linear Traffic Distribution Problem Assuming an Intelligent and Autonomous Transportation System. Periodica Polytechnica Transportation Engineering. 2017;47(1): 64-67. Available from: doi:10.3311/PPtr.11548
Cavone G, Dotoli M, Epicoco N, Seatzu C. A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis. Applied Mathematical Modelling. 2017;52: 255-273. Available from: doi:10.1016/j.apm.2017.07.030
Pauer G. Defining the Optimization Process of Traffic Distribution Problem with Linear Programming Approach in case of Autonomous Transportation System. In: MOSATT 2017 Modern Safety Technologies in Transportation: Proceedings of the International Scientific Conference, 2017, Herl’any, Faculty of Aeronautics of Kosice, Slovakia; 2017. p. 124-130. Available from: https://mosatt.lf.tuke.sk/PDF/2017.pdf
Török Á, Pauer G. Optimization of Linear Traffic Distribution Problem in terms of the Road Toll Structure Assuming an Autonomous Transportation System. International Journal for Traffic and Transport Engineering. 2018;8(1): 112-124. Available from: doi:10.7708/ijtte.2018.8(1).08
Yakimov M. Optimal Models used to Provide Urban Transport Systems Efficiency and Safety. Transportation Research Procedia. 2017;20: 702-708. Available from: doi:10.1016/j.trpro.2017.01.114
Kurczveil T, Becker IU. Optimization of novel charging Infrastructures using linear programming. IFAC-PapersOnLine. 2016;49(3): 227-230. Available from: doi:10.1016/j.ifacol.2016.07.038
Ma W, Qian Z. On the variance of recurrent traffic flow for statistical traffic assignment. Transportation Research Part C: Emerging Technologies. 2017;81: 57-82. Available from: doi:10.1016/j.trc.2017.05.009
Ryu S, Chen A, Choi K. Solving the combined modal split and traffic assignment problem with two types of transit impedance function. European Journal of Operational Research. 2017;257(3): 870-880. Available from: doi:10.1016/j.ejor.2016.08.019
Ma J, Li X, Zhou F, Hao W. Designing optimal autonomous vehicle sharing and reservation systems: A linear programming approach. Transportation Research Part C: Emerging Technologies. 2017;84: 124-141. Available from: doi:10.1016/j.trc.2017.08.022
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