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Optimization Techniques in Intelligent Transportation Systems

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Metaheuristics and Optimization in Computer and Electrical Engineering

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 696))

Abstract

Intelligent Transportation Systems (ITS) refer to a range of transportation applications based on communication and information technology. These systems by the aid of modern ideas, provide comfortable, efficient and safe services for transportation users. They are located in the linkage of information technology, computer science, electrical engineering, system analysis, civil engineering, and optimization. They form a main branch of the smart cities and are fundamental for the development of countries. ITS applications, usually, use the capabilities of sensor networks, electrical devices, and computer processing units to deliver a service, however, they are not limited to the hardware devices. Instead, the modeling of transportation problems and solving them efficiently are more challenging problems. Especially, when they support good ideas for controlling the transportation systems or guiding some users. In this chapter, the application of optimization models for the transportation context are discussed. To this end, the models for data collection by a sensor network are needed. Then, for mining these data and extracting the necessary knowledge for transportation context awareness, some fundamental models such as regression analysis, frequent pattern mining, clustering or classification can be applied. These backgrounds are used to extend the appropriate models of ITS in an integrated architecture. To solve these models, the classical network and combinatorial optimization methods, simulation-optimization techniques, and metaheuristic algorithms will be explained. Then, we categorize the applications of these optimization models in the different subsystems of ITS architecture. The output of this investigation can be used to develop different ITS services for the urban and inter-cities networks.

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References

  1. Intelligent transportation systems—national ITS architecture [Online]. Available: https://www.its.dot.gov/research_archives/arch/index.htm. Accessed: 01 Oct 2019

  2. National Iranian ITS plan—ITSRI-Amirkabir University of Technology [Online]. Available: http://its.aut.ac.ir/nits/. Accessed: 10 Oct 2019

  3. Abpeykar S, Ghatee M (2014) Supervised and unsupervised learning DSS for incident management in intelligent tunnel: a case study in Tehran Niayesh tunnel. Tunn Undergr Space Technol 42:293–306

    Article  Google Scholar 

  4. Dabove P (2019) Smartphones: recent innovations and applications. Nova Science Publishers, Incorporated

    Google Scholar 

  5. Bessis N, Dobre C (2014) Big data and internet of things: a roadmap for smart environments, vol 546. Springer

    Google Scholar 

  6. Eftekhari HR, Ghatee M (2016) An inference engine for smartphones to preprocess data and detect stationary and transportation modes. Transp Res Part C Emerg Technol 69:313–327

    Article  Google Scholar 

  7. Eftekhari HR, Ghatee M (2018) Hybrid of discrete wavelet transform and adaptive neuro fuzzy inference system for overall driving behavior recognition. Transp Res Part F Traffic Psychol Behav 58:782–796

    Article  Google Scholar 

  8. Han J, Pei J, Kamber M (2011) Data mining: concepts and techniques. Elsevier

    Google Scholar 

  9. Bejani MM, Ghatee M (2018) A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data. Transp Res Part C Emerg Technol 89:303–320

    Article  Google Scholar 

  10. Vlahogianni EI (2015) Computational intelligence and optimization for transportation big data: challenges and opportunities. In: Engineering and applied sciences optimization. Springer, pp 107–128

    Google Scholar 

  11. Mehmood R, Meriton R, Graham G, Hennelly P, Kumar M (2017) Exploring the influence of big data on city transport operations: a Markovian approach. Int J Oper Prod Manag 37(1):75–104

    Article  Google Scholar 

  12. Abpeykar S, Ghatee M, Zare H (2019) Ensemble decision forest of RBF networks via hybrid feature clustering approach for high-dimensional data classification. Comput Stat Data Anal 131:12–36

    Article  MathSciNet  MATH  Google Scholar 

  13. Abpeykar S, Ghatee M (2019) Neural trees with peer-to-peer and server-to-client knowledge transferring models for high-dimensional data classification. Expert Syst Appl 137:281–291

    Article  Google Scholar 

  14. Fakhri A, Ghatee M (2016) Application of benders decomposition method in solution of a fixed-charge multicommodity network design problem avoiding congestion. Appl Math Model 40(13–14):6468–6476

    Article  MathSciNet  MATH  Google Scholar 

  15. Fakhri A, Ghatee M (2014) Fractional multi-commodity flow problem: duality and optimality conditions. Appl Math Model 38(7–8):2151–2162

    Article  MathSciNet  MATH  Google Scholar 

  16. Fakhri A, Ghatee M (2016) Minimizing the sum of a linear and a linear fractional function applying conic quadratic representation: continuous and discrete problems. Optimization 65(5):1023–1038

    Article  MathSciNet  MATH  Google Scholar 

  17. Fakhri A, Ghatee M (2013) Solution of preemptive multi-objective network design problems applying benders decomposition method. Ann Oper Res 210(1):295–307

    Article  MathSciNet  MATH  Google Scholar 

  18. Niksirat M, Ghatee M, Hashemi SM (2012) Multimodal K-shortest viable path problem in Tehran public transportation network and its solution applying ant colony and simulated annealing algorithms. Appl Math Model 36(11):5709–5726

    Article  MathSciNet  MATH  Google Scholar 

  19. Ghatee M (2011) QoS-based cooperative algorithm for integral multi-commodity flow problem. Comput Commun 34(7):835–846

    Article  Google Scholar 

  20. Davis L (1991) Handbook of genetic algorithms. CumInCAD

    Google Scholar 

  21. Glover F (1989) Tabu search—part I. ORSA J Comput 1(3):190–206

    Article  MATH  Google Scholar 

  22. Glover F (1990) Tabu search—part II. ORSA J Comput 2(1):4–32

    Article  MATH  Google Scholar 

  23. Moscato P (1999) Memetic algorithms: a short introduction. In: New ideas in optimization. McGraw-Hill Ltd., UK, pp 219–234

    Google Scholar 

  24. Van Laarhoven PJ, Aarts EH (1987) Simulated annealing. In: Simulated annealing: theory and applications. Springer, pp 7–15

    Google Scholar 

  25. Dorigo M, Birattari M (2010) Ant colony optimization. Springer

    Google Scholar 

  26. Kennedy J (2010) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning, Springer, Boston, MA, pp 760–766. https://doi.org/10.1007/978-0-387-30164-8_630

  27. Yang X-S (2010) Nature-inspired metaheuristic algorithms. Luniver Press

    Google Scholar 

  28. Hopfield JJ, Tank DW (1985) ‘Neural’ computation of decisions in optimization problems. Biol Cybern 52(3):141–152

    Article  MATH  Google Scholar 

  29. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  30. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76(2):60–68

    Article  Google Scholar 

  31. Atashpaz-Gargari E, Lucas C (2007) Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In: 2007 IEEE congress on evolutionary computation, IEEE. pp 4661–4667

    Google Scholar 

  32. Ahuja RK, Magnanti TL, Orlin JB, Weihe K (1995) Network flows: theory, algorithms and applications. ZOR-Methods Models Oper Res 41(3):252–254

    Google Scholar 

  33. Tu C-J, Chuang L-Y, Chang J-Y, Yang C-H (2007) Feature selection using PSO-SVM. Int J Comput Sci 33(1):1–18

    Google Scholar 

  34. Abpeykar S, Ghatee M (2018) Decent direction methods on the feasible region recognized by supervised learning metamodels to solve unstructured problems. J Inf Optim Sci 39(6):1245–1262

    MathSciNet  Google Scholar 

  35. Asadi R, Ghatee M (2015) A rule-based decision support system in intelligent hazmat transportation system. IEEE Trans Intell Transp Syst 16(5):2756–2764

    Article  Google Scholar 

  36. Eftekhari HR, Ghatee M (2019) A similarity-based neuro-fuzzy modeling for driving behavior recognition applying fusion of smartphone sensors. J Intell Transp Syst 23(1):72–83

    Article  Google Scholar 

  37. Pashaei A, Ghatee M, Sajedi H (2019) Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images. J Real-Time Image Process 17:1–16

    Google Scholar 

  38. Ghatee M (2019) Smartphone-based systems for driving evaluation. In: Dabove P (ed) Smartphones: recent innovations and applications. Nova Science Publishers, Incorporated, 2019, pp 143–222

    Google Scholar 

  39. Pressl B, Wieser M (2006) A computer-based navigation system tailored to the needs of blind people. In: International conference on computers for handicapped persons. Springer, Berlin, Heidelberg, pp 1280–1286

    Google Scholar 

  40. Ghatee M, Hashemi M (2009) An expert system for network control problems and its applications in large scale network design under uncertainty. In: International network optimization conference (INOC 2009), Pisa, Italy

    Google Scholar 

  41. Ghatee M, Hashemi SM (2007) Descent direction algorithm with multicommodity flow problem for signal optimization and traffic assignment jointly. Appl Math Comput 188(1):555–566

    MathSciNet  MATH  Google Scholar 

  42. Ghatee M, Hashemi SM, Zarepisheh M, Khorram E (2009) Preemptive priority-based algorithms for fuzzy minimal cost flow problem: an application in hazardous materials transportation. Comput Ind Eng 57(1):341–354

    Article  Google Scholar 

  43. Ghatee M, Hashemi SM (2007) Ranking function-based solutions of fully fuzzified minimal cost flow problem. Inf Sci 177(20):4271–4294

    Article  MathSciNet  MATH  Google Scholar 

  44. Ghatee M, Hashemi SM (2009) Application of fuzzy minimum cost flow problems to network design under uncertainty. Fuzzy Sets Syst 160(22):3263–3289

    Article  MathSciNet  MATH  Google Scholar 

  45. Eftekhari HR, Ghatee M (2017) The lower bound for dynamic parking prices to decrease congestion through CBD. Oper Res 17(3):761–787

    Google Scholar 

  46. Soudmand S, Ghatee M, Hashemi SM (2013) SA-IP method for congestion pricing based on level of service in urban network under fuzzy conditions. Int J Civ Eng 11(4A):281–291

    Google Scholar 

  47. Ceylan H, Bell MG (2005) Genetic algorithm solution for the stochastic equilibrium transportation networks under congestion. Transp Res Part B Methodol 39(2):169–185

    Article  Google Scholar 

  48. Kubrusly CS, Malebranche H (1985) Sensors and controllers location in distributed systems—a survey. Automatica 21(2):117–128

    Article  MathSciNet  MATH  Google Scholar 

  49. Iqbal M, Naeem M, Anpalagan A, Ahmed A, Azam M (2015) Wireless sensor network optimization: multi-objective paradigm. Sensors 15(7):17572–17620

    Article  Google Scholar 

  50. Addakiri K, Khallouki H, Bahaj M (2019) Intelligent urban transport decision analysis system based on mining in big data analytics and data warehouse. In: International conference on artificial intelligence and symbolic computation. Springer, Cham, pp 179–184

    Google Scholar 

  51. Ma X, Wang Y, Chen F, Liu J (2012) Transit smart card data mining for passenger origin information extraction. J Zhejiang Univ Sci C 13(10):750–760

    Article  Google Scholar 

  52. Sheffi Y (1985) Urban transportation networks, vol 6. Prentice-Hall, Englewood Cliffs, NJ

    Google Scholar 

  53. Bertsekas DP (1998) Network optimization: continuous and discrete models. Athena Scientific Belmont

    Google Scholar 

  54. Sładkowski A, Pamuła W (2016) Intelligent transportation systems-problems and perspectives, vol 303. Springer

    Google Scholar 

  55. Bianco L, Toth P (2012) Advanced methods in transportation analysis. Springer Science & Business Media

    Google Scholar 

  56. Ghatee M, Hashemi SM (2009) Traffic assignment model with fuzzy level of travel demand: An efficient algorithm based on quasi-Logit formulas. Eur J Oper Res 194(2):432–451

    Article  MathSciNet  MATH  Google Scholar 

  57. Ghatee M, Hashemi SM (2009) Some concepts of the fuzzy multicommodity flow problem and their application in fuzzy network design. Math Comput Model 49(5–6):1030–1043

    Article  MathSciNet  MATH  Google Scholar 

  58. Bui K-HN, Jung JJ (2019) ACO-based dynamic decision making for connected vehicles in IoT system. IEEE Trans Ind Inform 15:5648–5655

    Google Scholar 

  59. Tan Y (2019) A new shortest path algorithm generalized on dynamic graph for commercial intelligent navigation for transportation management. ArXiv Prepr. ArXiv190504469

    Google Scholar 

  60. Zeng J, Yao QG, Zhang YS, Lu JT, Wang M (2019) Optimal path selection for emergency relief supplies after mine disasters. Int J Simul Model IJSIMM 18(3):476–487

    Google Scholar 

  61. Alawadhi N, Shaikhli IA, Akandari A, Tahir M (2019) Optimal path planning for urban vehicles using internet of things: a new navigation perspective. J Comput Theor Nanosci 16(3):1074–1080

    Article  Google Scholar 

  62. Hamza-Lup GL, Hua KA, Peng R, Ho AH (2005) A maximum-flow approach to dynamic handling of multiple incidents in traffic evacuation management. In: Proceedings. 2005 IEEE intelligent transportation systems, 2005, IEEE, pp 1147–1152  

    Google Scholar 

  63. Ye P, Chen C, Zhu F (2011) Dynamic route guidance using maximum flow theory and its mapreduce implementation. In: 2011 14th International IEEE conference on intelligent transportation systems (ITSC), IEEE, pp 180–185

    Google Scholar 

  64. Tan Z (2019) How many passengers can we serve with ride-sharing? ArXiv Prepr. ArXiv 190107906

    Google Scholar 

  65. Ghatee M, Hashemi SM (2008) Generalized minimal cost flow problem in fuzzy nature: an application in bus network planning problem. Appl Math Model 32(12):2490–2508

    Article  MathSciNet  MATH  Google Scholar 

  66. Chiu Y-C, Huynh N, Mahmassani HS (2001) Determining optimal locations for variable message signs under stochastic incident scenarios. In: 80th Annual meeting transportation research board, Washington DC, United States, Paper 01-2927

    Google Scholar 

  67. Toro-DíAz H, Mayorga ME, Chanta S, Mclay LA (2013) Joint location and dispatching decisions for emergency medical services. Comput Ind Eng 64(4):917–928

    Article  Google Scholar 

  68. Ming L, Liang B, Zheng F, Chu F (2019) Stochastic airline fleet assignment with risk aversion

    Google Scholar 

  69. Boccia M, Crainic TG, Sforza A, Sterle C (2018) Multi-commodity location-routing: flow intercepting formulation and branch-and-cut algorithm. Comput Oper Res 89:94–112

    Article  MathSciNet  MATH  Google Scholar 

  70. Lee S, Lee J, Na B (2018) Practical routing algorithm using a congestion monitoring system in semiconductor manufacturing. IEEE Trans Semicond Manuf 31(4):475–485

    Article  Google Scholar 

  71. Li P, Mirchandani P, Zhou X (2015) Simulation-based traffic signal optimization to minimize fuel consumption and emission: a Lagrangian relaxation approach. In: 94th Annual meeting transportation research board, Washington DC, United States, Paper: 15-2358

    Google Scholar 

  72. Kachroo P, Özbay KM (2018) Traffic assignment: a survey of mathematical models and techniques. In: Feedback control theory for dynamic traffic assignment. Springer, pp 25–53

    Google Scholar 

  73. Patriksson M, Labbé M (2002) Transportation planning: state of the art, vol 64. Springer Science & Business Media

    Google Scholar 

  74. Zhang Y, Qin X, Dong S, Ran B (2010) Daily OD matrix estimation using cellular probe data. In: 89th Annual meeting transportation research board, vol 9. Washington DC, United States, Paper: 10-2472

    Google Scholar 

  75. Boyce D (2007) Forecasting travel on congested urban transportation networks: review and prospects for network equilibrium models. Netw Spat Econ 7(2):99–128

    Article  MathSciNet  MATH  Google Scholar 

  76. Kim H, Oh J-S, Jayakrishnan R (2009) Effects of user equilibrium assumptions on network traffic pattern. KSCE J Civ Eng 13(2):117–127

    Article  Google Scholar 

  77. Connors RD, Sumalee A (2009) A network equilibrium model with travellers’ perception of stochastic travel times. Transp Res Part B Methodol 43(6):614–624

    Article  Google Scholar 

  78. Chen Y-W, Tzeng G-H (2001) Using fuzzy integral for evaluating subjectively perceived travel costs in a traffic assignment model. Eur J Oper Res 130(3):653–664

    Article  MathSciNet  MATH  Google Scholar 

  79. Peeta S, Mahmassani HS (1995) System optimal and user equilibrium time-dependent traffic assignment in congested networks. Ann Oper Res 60(1):81–113

    Article  MATH  Google Scholar 

  80. Bell MG, Cassir C (2002) Risk-averse user equilibrium traffic assignment: an application of game theory. Transp Res Part B Methodol 36(8):671–681

    Article  Google Scholar 

  81. Lu C-C, Mahmassani HS, Zhou X (2008) A bi-criterion dynamic user equilibrium traffic assignment model and solution algorithm for evaluating dynamic road pricing strategies. Transp Res Part C Emerg Technol 16(4):371–389

    Article  Google Scholar 

  82. Xie J, Nie Y (2019) A new algorithm for achieving proportionality in user equilibrium traffic assignment. Transp Sci 53(2):566–584

    Article  Google Scholar 

  83. Li M, Di X, Liu HX, Huang H-J (2019) A restricted path-based ridesharing user equilibrium. J Intell Transp Syst 24:1–21

    Google Scholar 

  84. Yu H, Ma R, Zhang HM (2018) Optimal traffic signal control under dynamic user equilibrium and link constraints in a general network. Transp Res Part B Methodol 110:302–325

    Article  Google Scholar 

  85. Hazelton ML (1998) Some remarks on stochastic user equilibrium. Transp Res Part B Methodol 32(2):101–108

    Article  Google Scholar 

  86. Lam WH-K, Gao ZY, Chan KS, Yang H (1999) A stochastic user equilibrium assignment model for congested transit networks. Transp Res Part B Methodol 33(5):351–368

    Article  Google Scholar 

  87. Fisk C (1980) Some developments in equilibrium traffic assignment. Transp Res Part B Methodol 14(3):243–255

    Article  MathSciNet  Google Scholar 

  88. Maher M (1998) Algorithms for logit-based stochastic user equilibrium assignment. Transp Res Part B Methodol 32(8):539–549

    Article  Google Scholar 

  89. Huang H-J, Li Z-C (2007) A multiclass, multicriteria logit-based traffic equilibrium assignment model under ATIS. Eur J Oper Res 176(3):1464–1477

    Article  MATH  Google Scholar 

  90. Liu Z, Wang S, Meng Q (2014) Toll pricing framework under logit-based stochastic user equilibrium constraints. J Adv Transp 48(8):1121–1137

    Article  Google Scholar 

  91. Wen C-H, Koppelman FS (2001) The generalized nested logit model. Transp Res Part B Methodol 35(7):627–641

    Article  Google Scholar 

  92. Koppelman FS, Wen C-H (2000) The paired combinatorial logit model: properties, estimation and application. Transp Res Part B Methodol 34(2):75–89

    Article  Google Scholar 

  93. Yu Q, Fang D, Du W (2014) Solving the logit-based stochastic user equilibrium problem with elastic demand based on the extended traffic network model. Eur J Oper Res 239(1):112–118

    Article  MathSciNet  MATH  Google Scholar 

  94. Zhang K, Mahmassani HS, Lu C-C (2008) Probit-based time-dependent stochastic user equilibrium traffic assignment model. Transp Res Rec 2085(1):86–94

    Article  Google Scholar 

  95. Kitthamkesorn S, Chen A (2013) A path-size weibit stochastic user equilibrium model. Proc Soc Behav Sci 80:608–632

    Article  Google Scholar 

  96. Xuyan Q, Huapu L, Yuanyuan W (2005) A K-shortest-paths-based algorithm for stochastic traffic assignment model and comparison of computation precision with existing methods. Proc Eastern Asia Soc Transp Stud 5:1218–1232

    Google Scholar 

  97. Van der Zijpp NJ, Catalano SF (2005) Path enumeration by finding the constrained K-shortest paths. Transp Res Part B Methodol 39(6):545–563

    Article  Google Scholar 

  98. Raith A, Wang JY, Ehrgott M, Mitchell SA (2014) Solving multi-objective traffic assignment. Ann Oper Res 222(1):483–516

    Article  MathSciNet  MATH  Google Scholar 

  99. Chabini I (1998) Discrete dynamic shortest path problems in transportation applications: complexity and algorithms with optimal run time. Transp Res Rec 1645(1):170–175

    Article  Google Scholar 

  100. Chen BY, Lam WH, Sumalee A, Li Q, Tam ML (2014) Reliable shortest path problems in stochastic time-dependent networks. J Intell Transp Syst 18(2):177–189

    Article  Google Scholar 

  101. Liu HX, Ban X, Ran B, Mirchandani P (2003) Formulation and solution algorithm for fuzzy dynamic traffic assignment model. Transp Res Rec 1854(1):114–123

    Article  Google Scholar 

  102. Pi X, Qian ZS (2017) A stochastic optimal control approach for real-time traffic routing considering demand uncertainties and travelers’ choice heterogeneity. Transp Res Part B Methodol 104:710–732

    Article  Google Scholar 

  103. Chen W, Chen L, Chen Z, Tu S (2006) Wits: a wireless sensor network for intelligent transportation system. In: First international multi-symposiums on computer and computational sciences (IMSCCS’06), vol 2, pp 635–641

    Google Scholar 

  104. Chuan-zhi L, Ru-fu H, Ye H (2008) Method of freeway incident detection using wireless positioning. In: 2008 IEEE international conference on automation and logistics, Qingdao, China, IEEE, pp 2801–2804

    Google Scholar 

  105. Fadaei M, Ghatee M, Hashemi SM, (2012) Two new models for the traffic counters location problem to maximize the coverage of paths: tehran highways case-study. J Traffic Eng 13(48):19–23

    Google Scholar 

  106. Maher MJ, Zhang X, Van Vliet D (2001) A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows. Transp Res Part B Methodol 35(1):23–40

    Article  Google Scholar 

  107. Gartner NH, Al-Malik M (1996) Combined model for signal control and route choice in urban traffic networks. Transp Res Rec 1554(1):27–35

    Article  Google Scholar 

  108. Chiou S-W (2005) Bilevel programming for the continuous transport network design problem. Transp Res Part B Methodol 39(4):361–383

    Article  Google Scholar 

  109. Tornquist J, Persson JA (2005) Train traffic deviation handling using tabu search and simulated annealing. In: Proceedings of the 38th annual Hawaii international conference on system sciences, Big Island, HI, USA, IEEE, pp 1–10. https://doi.org/10.1109/HICSS.2005.641

  110. Ceylan H, Bell MG (2004) Traffic signal timing optimisation based on genetic algorithm approach, including drivers’ routing. Transp Res Part B Methodol 38(4):329–342

    Article  Google Scholar 

  111. Banks J, Carson II, Nelson BL, Nicol DM (2005) Discrete-event system simulation. Pearson

    Google Scholar 

  112. Badiru AB, Sieger DB (1998) Neural network as a simulation metamodel in economic analysis of risky projects. Eur J Oper Res 105(1):130–142

    Article  MATH  Google Scholar 

  113. Chambers M, Mount-Campbell CA (2002) Process optimization via neural network metamodeling. Int J Prod Econ 79(2):93–100

    Article  Google Scholar 

  114. Blanning RW (1975) The construction and implementation of metamodels. Simulation 24(6):177–184

    Google Scholar 

  115. Nasrabadi E, Hashemi SM, Ghatee M (2007) An LP-based approach to outliers detection in fuzzy regression analysis. Int J Uncertain Fuzziness Knowl Based Syst 15(04):441–456

    Google Scholar 

  116. Dehghan M, Ghatee M, Hashemi B (2008) Some computations on fuzzy matrices: an application in fuzzy analytical hierarchy process. Int J Uncertain Fuzziness Knowl-Based Syst 16(05):715–733

    Google Scholar 

  117. Hashemi SM, Ghatee M, Hashemi B (2006) Fuzzy goal programming: complementary slackness conditions and computational schemes. Appl Math Comput 179(2):506–522

    MathSciNet  MATH  Google Scholar 

  118. Rommelfanger H (1996) Fuzzy linear programming and applications. Eur J Oper Res 92(3):512–527

    Article  MathSciNet  MATH  Google Scholar 

  119. Hrušovskỳ M, Demir E, Jammernegg W, Van Woensel T (2018) Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty. Flex Serv Manuf J 30(3):486–516

    Article  Google Scholar 

  120. Sayyadi R, Awasthi A (2018) A simulation-based optimisation approach for identifying key determinants for sustainable transportation planning. Int J Syst Sci Oper Logist 5(2):161–174

    Google Scholar 

  121. Rosano M, Demartini CG, Lamberti F, Perboli G (2018) A mobile platform for collaborative urban freight transportation. Transp Res Proc 30:14–22

    Google Scholar 

  122. Zhao C, Li S, Wang W, Li X, Du Y (2018) Advanced parking space management strategy design: an agent-based simulation optimization approach. Transp Res Rec 2672(8):901–910

    Article  Google Scholar 

  123. Fikar C, Hirsch P, Nolz PC (2018) Agent-based simulation optimization for dynamic disaster relief distribution. Cent Eur J Oper Res 26(2):423–442

    Article  MathSciNet  Google Scholar 

  124. Yu B, Guo Z, Peng Z, Wang H, Ma X, Wang Y (2018) Agent-based simulation optimization model for road surface maintenance scheme. J Transp Eng Part B Pavement 145(1):04018065

    Article  Google Scholar 

  125. Shakibayifar M, Sheikholeslami A, Corman F (2018) A simulation-based optimization approach to reschedule train traffic in uncertain conditions during disruptions. Sci Iran 25(2):646–662

    Google Scholar 

  126. Zhao Y, Ioannou PA, Dessouky MM (2018) Dynamic multimodal freight routing using a co-simulation optimization approach. IEEE Trans Intell Transp Syst 20:2657–2667

    Google Scholar 

  127. Layeb SB, Jaoua A, Jbira A, Makhlouf Y (2018) A simulation-optimization approach for scheduling in stochastic freight transportation. Comput Ind Eng 126:99–110

    Article  Google Scholar 

  128. Niessner H, Rauner MS, Gutjahr WJ (2018) A dynamic simulation—optimization approach for managing mass casualty incidents. Oper Res Health Care 17:82–100

    Article  Google Scholar 

  129. Yao B, Yan Q, Chen Q, Tian Z, Zhu X (2018) Simulation-based optimization for urban transportation demand management strategy. Simulation 94(7):637–647

    Article  Google Scholar 

  130. Zadeh RB, Ghatee M, Eftekhari HR (2017) Three-phases smartphone-based warning system to protect vulnerable road users under fuzzy conditions. IEEE Trans Intell Transp Syst 19(7):2086–2098

    Article  Google Scholar 

  131. Merdrignac P, Shagdar O, Nashashibi F (2016) Fusion of perception and v2p communication systems for the safety of vulnerable road users. IEEE Trans Intell Transp Syst 18(7):1740–1751

    Article  Google Scholar 

  132. Regan MA, Oxley JA, Godley ST, Tingvall C (2001) Intelligent transport systems: safety and human factors issues, Royal Automobile Club of Victoria (RACV) Ltd – Report 01/01, Monash University, Australia

    Google Scholar 

  133. Anaya JJ, Talavera E, Giménez D, Gómez N, Felipe J, Naranjo JE (2015) Vulnerable road users detection using v2x communications. In: 2015 IEEE 18th international conference on intelligent transportation systems, pp 107–112

    Google Scholar 

  134. Guo L, Ge P-S, Zhang M-H, Li L-H, Zhao Y-B (2012) Pedestrian detection for intelligent transportation systems combining AdaBoost algorithm and support vector machine. Expert Syst Appl 39(4):4274–4286

    Article  Google Scholar 

  135. Themann P, Kotte J, Raudszus D, Eckstein L (2015) Impact of positioning uncertainty of vulnerable road users on risk minimization in collision avoidance systems. In: 2015 IEEE Intelligent Vehicles Symposium (IV), pp 1201–1206

    Google Scholar 

  136. Scholliers J, Bell D, Morris A, García AB (2014) Improving safety and mobility of vulnerable road users through ITS applications. Transp Res Arena 4:14–17

    Google Scholar 

  137. Tian W, Lauer M (2017) Tracking vulnerable road users with severe occlusion by adaptive part filter modeling. In: 2017 IEEE international conference on vehicular electronics and safety (ICVES), Vienna, Austria, IEEE, pp 139–144

    Google Scholar 

  138. Konforty M, Erez Y, Dor OBB (2010) Alerting a driver to the presence of a pedestrian on a road. U.S. Patent No. 7,777,646. Washington DC, U.S. Patent and Trademark Office

    Google Scholar 

  139. Muzammel M, Yusoff MZ, Meriaudeau F (2017) Rear-end vision-based collision detection system for motorcyclists. J Electron Imaging 26(3):033002

    Article  Google Scholar 

  140. Hub A, Diepstraten J, Ertl T (2005) Augmented indoor modeling for navigation support for the blind. In: International conference on computers for people with special needs (CPSN), Las Vegas, Nevada, USA, CSREA Press 2005, ISBN 1-932415-78-5, pp 54–62

    Google Scholar 

  141. Kafi MA, Challal Y, Djenouri D, Doudou M, Bouabdallah A, Badache N (2013) A study of wireless sensor networks for urban traffic monitoring: applications and architectures. Proc Comput Sci 19:617–626

    Article  Google Scholar 

  142. Abadi A, Rajabioun T, Ioannou PA (2014) Traffic flow prediction for road transportation networks with limited traffic data. IEEE Trans Intell Transp Syst 16(2):653–662

    Google Scholar 

  143. Karoonsoontawong A, Lin D-Y (2011) Time-varying lane-based capacity reversibility for traffic management. Comput Aided Civ Infrastruct Eng 26(8):632–646

    Google Scholar 

  144. Abdou W, Henriet A, Bloch C, Dhoutaut D, Charlet D, Spies F (2011) Using an evolutionary algorithm to optimize the broadcasting methods in mobile ad hoc networks. J Netw Comput Appl 34(6):1794–1804

    Article  Google Scholar 

  145. Huang B, Pan X (2007) GIS coupled with traffic simulation and optimization for incident response. Comput Environ Urban Syst 31(2):116–132

    Article  Google Scholar 

  146. Levinson D, Chang E (2003) A model for optimizing electronic toll collection systems. Transp Res Part Policy Pract 37(4):293–314

    Article  Google Scholar 

  147. Pan S, Ballot E, Fontane F (2013) The reduction of greenhouse gas emissions from freight transport by pooling supply chains. Int J Prod Econ 143(1):86–94

    Article  Google Scholar 

  148. Arnold P, Peeters D, Thomas I (2004) Modelling a rail/road intermodal transportation system. Transp Res Part E Logist Transp Rev 40(3):255–270

    Article  Google Scholar 

  149. Kuran MŞ, Viana AC, Iannone L, Kofman D, Mermoud G, Vasseur JP (2015) A smart parking lot management system for scheduling the recharging of electric vehicles. IEEE Trans Smart Grid 6(6):2942–2953

    Article  Google Scholar 

  150. Lu X-Y, Varaiya P, Horowitz R, Su D, Shladover SE (2011) Novel freeway traffic control with variable speed limit and coordinated ramp metering. Transp Res Rec 2229(1):55–65

    Article  Google Scholar 

  151. Joksimovic D, Bliemer M, Bovy P (2005) Dynamic road pricing optimization with heterogeneous users. In: 45th congress of the European regional science association: land use and water management in a sustainable network society, Amsterdam, The Netherlands, European Regional Science Association(ERSA), Louvain-la-Neuve, pp 1–18  

    Google Scholar 

  152. Liang Z, Jianmin XU, Lingxiang Z (2007) Application of genetic algorithm in dynamic route guidance system. J Transp Syst Eng Inf Technol 7(3):45–48

    Google Scholar 

  153. Shang S, Chen L, Wei Z, Jensen CS, Wen J-R, Kalnis P (2015) Collective travel planning in spatial networks. IEEE Trans Knowl Data Eng 28(5):1132–1146

    Article  Google Scholar 

  154. Gavalas D, Konstantopoulos C, Pantziou G (2016) Design and management of vehicle-sharing systems: a survey of algorithmic approaches. In: Smart cities and homes. Elsevier, pp 261–289

    Google Scholar 

  155. Bejani MM, Ghatee M (2019) Convolutional neural network with adaptive regularization to classify driving styles on smartphones. IEEE Trans Intell Transp Syst 21:543–552

    Google Scholar 

  156. Chen L, Englund C (2015) Cooperative intersection management: a survey. IEEE Trans Intell Transp Syst 17(2):570–586

    Article  Google Scholar 

  157. Karbowski D, Rousseau A, Pagerit S, Sharer P, Plug-in vehicle control strategy: from global optimization to real time application. In: 22th international electric vehicle symposium (EVS22). Yokohama

    Google Scholar 

  158. Onieva E, Milanés V, Villagra J, Pérez J, Godoy J (2012) Genetic optimization of a vehicle fuzzy decision system for intersections. Expert Syst Appl 39(18):13148–13157

    Article  Google Scholar 

  159. Han Y, Guan X, Shi L (2011) Optimization based method for supply location selection and routing in large-scale emergency material delivery. IEEE Trans Autom Sci Eng 8(4):683–693

    Article  Google Scholar 

  160. Li L, Jiang Z, Duan N, Dong W, Hu K, Sun W (2011) Police patrol service optimization based on the spatial pattern of hotspots. In: Proceedings of 2011 IEEE international conference on service operations, logistics and informatics, Beijing, China, IEEE, pp 45–50

    Google Scholar 

  161. Faturechi R, Miller-Hooks E (2014) A mathematical framework for quantifying and optimizing protective actions for civil infrastructure systems. Comput Aided Civ Infrastruct Eng 29(8):572–589

    Google Scholar 

  162. Orabi W, El-Rayes K, Senouci AB, Al-Derham H (2009) Optimizing postdisaster reconstruction planning for damaged transportation networks. J Constr Eng Manag 135(10):1039–1048

    Article  Google Scholar 

  163. He L, Trépanier M, Agard B (2019) Sampling method applied to the clustering of temporal patterns of public transit smart card users

    Google Scholar 

  164. Welch TF, Widita A (2019) Big data in public transportation: a review of sources and methods. Transp Rev 39:1–24

    Google Scholar 

  165. Kamel I, Hasnine S, Shalaby A, Habib KN, Abdulhai, B (2019) Evaluating time-based transit fares: integrated model of departure time and travel mode choice within a dynamic multimodal assignment framework. In: 98th Annual meeting transportation research board, Washington DC, United States, Paper: 19-01386

    Google Scholar 

  166. Zhang Y, Hu Q, Meng Z, Ralescu A (2019) Fuzzy dynamic timetable scheduling for public transit. Fuzzy Sets Syst 395:235–253

    Google Scholar 

  167. Salman A, El-Tawab S, Yorio Z (2019) Expanding coverage of an intelligent transit bus monitoring system via ZigBee radio network. In: Arai K, Bhatia R (eds) Advances in information and communication, FICC 2019, Lecture Notes in Networks and Systems, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-030-12388-8_15

  168. Kathuria A, Parida M, Sekhar CR (2019) A review of service reliability measures for public transportation systems. Int J Intell Transp Syst Res 18:1–13

    Google Scholar 

  169. Nikolić M, Teodorović D (2019) Mitigation of disruptions in public transit by Bee Colony optimization. Transp Plan Technol 42:1–14

    Google Scholar 

  170. Yan Y, Meng Q, Wang S, Guo X (2012) Robust optimization model of schedule design for a fixed bus route. Transp Res Part C Emerg Technol 25:113–121

    Article  Google Scholar 

  171. Guerriero F, Pezzella F, Pisacane O, Trollini L (2014) Multi-objective optimization in dial-a-ride public transportation. Transp Res Proc 3:299–308

    Google Scholar 

  172. Trépanier M, Tranchant N, Chapleau R (2007) Individual trip destination estimation in a transit smart card automated fare collection system. J Intell Transp Syst 11(1):1–14

    Article  Google Scholar 

  173. Bielli M, Bielli A, Rossi R (2011) Trends in models and algorithms for fleet management. Proc Soc Behav Sci 20:4–18

    Article  Google Scholar 

  174. Mesbah M, Sarvi M, Currie G (2011) Optimization of transit priority in the transportation network using a genetic algorithm. IEEE Trans Intell Transp Syst 12(3):908–919

    Article  Google Scholar 

  175. Skabardonis A (2000) Control strategies for transit priority. Transp Res Rec 1727(1):20–26

    Article  Google Scholar 

  176. Mahmassani HS, Kim Y, Jaillet P (2000) Local optimization approaches to solve dynamic commercial fleet management problems. Transp Res Rec 1733(1):71–79

    Article  Google Scholar 

  177. Tang M, Gong D, Liu S, Zhang H (2016) Applying multi-phase particle swarm optimization to solve bulk cargo port scheduling problem. Adv Prod Eng Manag, 11(4):299–310

    Google Scholar 

  178. Perrier N, Langevin A, Campbell JF (2006) A survey of models and algorithms for winter road maintenance. Part I: system design for spreading and plowing. Comput Oper Res 33(1):209–238

    Article  MATH  Google Scholar 

  179. Yang N, Schonfeld P, Kang MW (2009) A hybrid methodology for freeway work-zone optimization with time constraints. Public Works Manag Policy 13(3):253–264

    Article  Google Scholar 

  180. Deb T, Vishwas N, Saha A (2020) A comparative study on different approaches of road traffic optimization based on big data analytics. In: Performance management of integrated systems and its applications in software engineering. Springer, pp 119–126

    Google Scholar 

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Acknowledgments

It is my pleasure to thank my dear spouse and all my hard-working students for their support.

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  1. Department of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran

    Mehdi Ghatee

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  1. Tafresh University, Tafresh, Iran

    Navid Razmjooy

  2. Department of Electrical Engineering, Islamic Azad University (IAU), Majlesi Branch, Isfahan, Iran

    Mohsen Ashourian

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    Zahra Foroozandeh

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Ghatee, M. (2021). Optimization Techniques in Intelligent Transportation Systems. In: Razmjooy, N., Ashourian, M., Foroozandeh, Z. (eds) Metaheuristics and Optimization in Computer and Electrical Engineering. Lecture Notes in Electrical Engineering, vol 696. Springer, Cham. https://doi.org/10.1007/978-3-030-56689-0_4

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