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Erschienen in:
Motorway Traffic Flow Optimisation in Presence of Vehicle Automation and Communication Systems Kapitel lesen Erstes Kapitel lesen

2015 | OriginalPaper | Buchkapitel

Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities

verfasst von : Eleni I. Vlahogianni

Erschienen in: Engineering and Applied Sciences Optimization

Verlag: Springer International Publishing

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Abstract

With the overwhelming amount of transportation data being gathered worldwide, Intelligent Transportation Systems (ITS) are faced with several modeling challenges. New modeling paradigms based on Computational Intelligence (CI) that take advantage of the advent of big datasets have been systematically proposed in literature. Transportation optimization problems form a research field that has systematically benefited from CI. Nevertheless, when it comes to big data applications, research is still at an early stage. This work attempts to review the unique opportunities provided by ITS and big data and discuss the emerging approaches for transportation modeling. The literature dedicated to big data transportation applications related to CI and optimization is reviewed. Finally, the challenges and emerging opportunities for researchers working with such approaches are also acknowledged and discussed.

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Vorheriges Kapitel Simulation Optimization of Car-Following Models Using Flexible Techniques
Nächstes Kapitel Nature-Inspired Algorithms: Success and Challenges
Literatur
1.
Aldrich J (1995) Correlations genuine and spurious in Pearson and Yule. Stat Sci 10:364–376
2.
Arango C, Cortés P, Onieva L, Escudero A (2013) Simulation-optimization models for the dynamic berth allocation problem. Comput Aided Civil Infrastruct Eng 28(10):769–779
3.
Astarita V, Bertini L, d’Elia S, Guido G (2006) Motorway traffic parameter estimation from mobile phone counts. Eur J Oper Res 175:1435–1446MATHCrossRef
4.
Atzori L, Iera A, Morabito F (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805MATHCrossRef
5.
Aupetit S, Riff J, Buttelli O, Espié S (2013) Naturalistic study of rider’s behaviour in initial training in France: evidence of limitations in the educational content. Accid Anal Prev 58:206–217CrossRef
6.
Ayuso M, Guillén M, Pérez-Marín AM (2014) Time and distance to first accident and driving patterns of young drivers with pay-as-you-drive insurance. Accid Anal Prev 73:125–131CrossRef
7.
Bai Q, Ahmed A, Li Z, Labi S (2014) A hybrid pareto frontier generation method for trade-off analysis in transportation asset management. Comput Aided Civil Infrastruct Eng
8.
Balseiro SR, Loiseau I, Ramonet J (2011) An ant colony algorithm hybridized with insertion heuristics for the time dependent vehicle routing problem with time windows. Comput Oper Res 38(6):954–966MATHMathSciNetCrossRef
9.
Bar-Gera H (2007) Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: a case study from Israel. Transp Res Part C 15:380–391CrossRef
10.
Basyoni Y, Talaat H (2014) A bi-level traffic data extraction procedure via cellular phone network for inter-city travel. J Intell Transp Syst Technol Plann Oper (forthcoming)
11.
Bazzan AL (2009) Opportunities for multiagent systems and multiagent reinforcement learning in traffic control. Auton Agent Multi-Agent Syst 18(3):342–375CrossRef
12.
Bazzan AL, Klügl F (2014) A review on agent-based technology for traffic and transportation. Knowl Eng Rev 29(03):375–403CrossRef
13.
Bierlaire M, Chen J, Newman J (2013) A probabilistic map matching method for smartphone GPS data. Transp Res Part C: Emerg Technol 26:78–98CrossRef
14.
Boyd D, Crawford K (2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc 15(5):662–679CrossRef
15.
Breiman L (2001) Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci 16(3):199–231MATHMathSciNetCrossRef
16.
Caceres N, Wideberg JP, Benitez FG (2007) Deriving origin-destination data from a mobile phone network. Intell Transp Syst 1(1):15–26CrossRef
17.
Cai H, Jia X, Chiu AS, Hu X, Xu M (2014) Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet. Transp Res Part D: Transp Environ 33:39–46CrossRef
18.
Caragliu A, Del Bo C, Nijkamp P (2009) Smart cities in Europe. Serie Research Memoranda 0048 (VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics)
19.
Castro PS, Zhang D, Li S (2012) Urban traffic modelling and prediction using large scale taxi GPS traces. In: Pervasive computing. Springer, Berlin, Heidelberg, pp 57–72
20.
Ceylan H, Ceylan H (2012) A hybrid harmony search and TRANSYT hill climbing algorithm for signalized stochastic equilibrium transportation networks. Transp Res Part C: Emerg Technol 25:152–167CrossRef
21.
Ceylan H (2006) Developing combined genetic algorithm hill-climbing optimization method for area traffic control. J Transp Eng ASCE 132(8):663–671CrossRef
22.
Ceylan H, Bell MGH (2004) Traffic signal timing optimization based on genetic algorithm approach, including drivers’ routing. Transp Res Part B 38(4):329–342CrossRef
23.
Chan KY, Dillon T, Chang E, Singh J (2013) Prediction of short-term traffic variables using intelligent swarm-based neural networks. IEEE Trans Control Syst Technol 21(1):263–274CrossRef
24.
Chen B, Cheng HH (2010) A review of the applications of agent technology in traffic and transportation systems. IEEE Trans Intell Transp Syst 11(2):485–497CrossRef
25.
Chen C, Zhang D, Guo B, Ma X, Pan G, Wu Z (forthcoming) TripPlanner: personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Trans Intell Transp Syst
26.
Chen C, Zhang D, Li N, Zhou ZH (2014) B-Planner: planning bidirectional night bus routes using large-scale taxi GPS traces. IEEE Trans Intell Transp Syst 15(4):1451–1465CrossRef
27.
Cheng Z, Caverlee J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating twitter users. In: Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, Toronto, ON, Canada
28.
Cheng Z, Caverlee J, Lee K, Sui D (2011) Exploring millions of footprints in location sharing services. In: Proceedings of the fifth international conference on weblogs and social media. AAAI, Barcelona, Spain
29.
Cheng S, Shi Y, Qin Q, Bai R (2013) Swarm intelligence in big data analytics. In: Intelligent data engineering and automated learning-IDEAL 2013. Springer, Berlin, Heidelberg, pp 417–426
30.
Chevrier R, Pellegrini P, Rodriguez J (2013) Energy saving in railway timetabling: a bi-objective evolutionary approach for computing alternative running times. Transp Res Part C: Emerg Technol 37:20–41CrossRef
31.
Chira C, Sedano J, Villar JR, Cámara M, Corchado E (2014) Urban bicycles renting systems: modelling and optimization using nature-inspired search methods. Neurocomputing 135:98–106CrossRef
32.
Chowdhury M, Sadek AW (2012) Advantages and limitations of artificial intelligence. Artif Intell Appl Crit Transp Issues 6
33.
Collins C, Hasan S, Ukkusuri SV (2013) A novel transit rider satisfaction metric. J Public Transp 16(2):21–45CrossRef
34.
Cong Z, De Schutter B, Babuška R (2013) Ant colony routing algorithm for freeway networks. Transp Res Part C: Emerg Technol 37:1–19CrossRef
35.
D’Acierno L, Gallo M, Montella B (2012) An ant colony optimisation algorithm for solving the asymmetric traffic assignment problem. Eur J Oper Res 217(2):459–469MATHMathSciNetCrossRef
36.
Danalet A, Farooq B, Bierlaire M (2014) A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. Transp Res Part C: Emerg Technol 44:146–170CrossRef
37.
Doolan R, Muntean GM (2014) Time-ants: an innovative temporal and spatial ant-based vehicular routing mechanism. In: Intelligent vehicles symposium proceedings, 2014 IEEE. IEEE, pp 951–956
38.
Fagnant DJ, Kockelman KM (2014) The travel and environmental implications of shared autonomous vehicles, using agent-based model scenarios. Transp Res Part C: Emerg Technol 40:1–13CrossRef
39.
Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37
40.
Forcael E, González V, Orozco F, Vargas S, Pantoja A, Moscoso P (2014) Ant colony optimization model for tsunamis evacuation routes. Comput Aided Civil Infrastruct Eng 29(10):723–737CrossRef
41.
Forrester A, Sobester A, Keane A (2008) Engineering design via surrogate modelling: a practical guide. Wiley, Chichester
42.
Friedrich M, Immisch K, Jehlicka P, Otterstatter T, Schlaich J (2010) Generating origin-destination matrices from mobile phone trajectories. Transp Res Rec 2196:93–101CrossRef
43.
Galland S, Knapen L, Yasar AUH, Gaud N, Janssens D, Lamotte O, Wets G (2014) Multi-agent simulation of individual mobility behavior in carpooling. Transp Res Part C: Emerg Technol
44.
Geroliminis N, Kepaptsoglou K, Karlaftis MG (2011) A hybrid hypercube-genetic algorithm approach for deploying many emergency response mobile units in an urban network. Eur J Oper Res 210(2):287–300MATHMathSciNetCrossRef
45.
Glymour C, Madigan D, Pregibon D, Smyth P (1997) Statistical themes and lessons for data mining. Data Min Knowl Discov 1(1):11–28CrossRef
46.
Goksal FP, Karaoglan I, Altiparmak F (2013) A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery. Comput Ind Eng 65(1):39–53CrossRef
47.
Guido G, Gallelli V, Saccomanno F, Vitale A, Rogano D, Festa D (2014) Treating uncertainty in the estimation of speed from smartphone traffic probes. Transp Res Part C: Emerg Technol 47:100–112CrossRef
48.
Hand DJ (2000) Data mining. New challenges for statisticians. Soc Sci Comput Rev 18(4):442–449CrossRef
49.
Hasan S, Ukkusuri SV (2014) Urban activity pattern classification using topic models from online geo-location data. Transp Res Part C: Emerg Technol 44:363–381CrossRef
50.
Hashem IAT, Yaqoob I, Anuar NB, Mokhtar S, Gani A, Khan SU (2015) The rise of “big data” on cloud computing: review and open research issues. Inf Syst 47:98–115CrossRef
51.
Herrera JC, Work DB, Herring R, Ban X, Jacobson Q, Bayen A (2010) Evaluation of traffic data obtained via GPS-enabled mobile phones: the mobile century field experiment. Transp Res Part C 18:568–583CrossRef
52.
Hong WC (2012) Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput Appl 21(3):583–593CrossRef
53.
Hood J, Sall E, Charlton B (2011) A GPS-based bicycle route choice model for San Francisco. California. Transp Lett 3(1):63–75CrossRef
54.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRef
55.
Jha MK, Schonfeld P (2004) A highway alignment optimization model using geographic information systems. Transp Res Part A: Policy Pract 38(6):455–481
56.
Jia L, Feng X, Zou G (2013) Solving multiobjective bilevel transportation-distribution planning problem by modified NSGA II. In: 2013 9th international conference on computational intelligence and security (CIS). IEEE, pp 303–307
57.
Jin Y, Tang K, Yu X, Sendhoff B, Yao X (2013) A framework for finding robust optimal solutions over time. Memetic Comput 5(1):3–18CrossRef
58.
Jin Y, Sendhoff B (2009) A systems approach to evolutionary multiobjective structural optimization and beyond. IEEE Comput Intell Mag 4(3):62–76CrossRef
59.
Kallioras NA, Lagaros ND, Karlaftis MG (2013) An improved harmony search algorithm for emergency inspection scheduling. Eng Optim (ahead-of-print) 1–23
60.
Kammoun HM, Kallel I, Casillas J, Abraham A, Alimi AM (2014) Adapt-Traf: an adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model. Transp Res Part C: Emerg Technol 42:147–167CrossRef
61.
Kang C, Ma X, Tong D, Liu Y (2012) Intra-urban human mobility patterns: an urban morphology perspective. Phys A: Stat Mech Appl 391(4):1702–1717CrossRef
62.
Kang MW, Jha MK, Schonfeld P (2012) Applicability of highway alignment optimization models. Transp Res Part C: Emerg Technol 21(1):257–286CrossRef
63.
Kang MW, Schonfeld P, Yang N (2009) Prescreening and repairing in a genetic algorithm for highway alignment optimization. Comput Aided Civil Infrastruct Eng 24(2):109–119CrossRef
64.
Karlaftis MG, Vlahogianni EI (2009) Memory properties and fractional integration in transportation time-series. Transp Res Part C: Emerg Technol 17(4):444–453CrossRef
65.
Karlaftis MG, Kepaptsoglou K, Sambracos E (2009) Containership routing with time deadlines and simultaneous deliveries and pick-ups. Transp Res Part E: Logist Transp Rev 45(1):210–221CrossRef
66.
Karlaftis MG, Vlahogianni EI (2011) Statistics versus neural networks in transportation research: differences, similarities and some insights. Transp Res Part C: Emerg Technol 19(3):387–399CrossRef
67.
Kepaptsoglou K, Karlaftis MG (2009) The bus bridging problem in metro operations: conceptual framework, models and algorithms. Public Transp 1(4):275–297CrossRef
68.
Kepaptsoglou K, Karlaftis MG, Li Z (2010) Optimizing pricing policies in Park-and-Ride facilities: a model and decision support system with application. J Transp Syst Eng Inf Technol 10(5):53–65
69.
Kontou E, Kepaptsoglou K, Charalampakis AE, Karlaftis MG (2014) The bus to depot allocation problem revisited: a genetic algorithm. Public Transp 6(3):237–255CrossRef
70.
Lagaros ND, Kepaptsoglou K, Karlaftis MG (2012) Fund allocation for civil infrastructure security upgrade. J Manage Eng 29(2):172–182CrossRef
71.
Laney D (2001) 3D data management: controlling data volume, velocity and variety. Gartner. Accessed 6 Feb
72.
Lau HC, Chan TM, Tsui WT, Chan FT, Ho GT, Choy KL (2009) A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem. Expert Syst Appl 36(4):8255–8268CrossRef
73.
Levin R, Kanza Y (2014) TARS: traffic-aware route search. GeoInformatica 18(3):461–500CrossRef
74.
Liang X, Zheng X, Lv W, Zhu T, Xu K (2012) The scaling of human mobility by taxis is exponential. Phys A: Stat Mech Appl 391(5):2135–2144CrossRef
75.
Lin DY, Ku YH (2014) Using genetic algorithms to optimize stopping patterns for passenger rail transportation. Comput Aided Civil Infrastruct Eng 29(4):264–278CrossRef
76.
Liu Y, Kang C, Gao S, Xiao Y, Tian Y (2012) Understanding intra-urban trip patterns from taxi trajectory data. J Geogr Syst 14(4):463–483CrossRef
77.
Liu Y, Li P, Wehner K, Yu J (2013) A generalized integrated corridor diversion control model for freeway incident management. Comput Aided Civil Infrastruct Eng 28(8):604–620CrossRef
78.
Liu Y, Roshandeh AM, Li Z, Kepaptsoglou K, Patel H, Lu X (2014) Heuristic approach for optimizing emergency medical services in road safety within large urban networks. J Transp Eng 140(9):04014043CrossRef
79.
Liu Y, Wang F, Xiao Y, Gao S (2012) Urban land uses and traffic ‘source-sink areas’: evidence from GPS-enabled taxi data in Shanghai. Landscape Urban Plann 106(1):73–87CrossRef
80.
Lv Y, Duan Y, Kang W, Li Z, Wang FY (2014) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst (forthcoming)
81.
Meng Q, Khoo HL (2010) A Pareto-optimization approach for a fair ramp metering. Transp Res Part C: Emerg Technol 18(4):489–506CrossRef
82.
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):908–919CrossRef
83.
Musicant O, Bar-Gera H, Schechtman E (2010) Electronic records of undesirable driving events. Transp Res Part F: Traffic Psychol Behav 13(2):71–79CrossRef
84.
Musicant O, Bar-Gera H, Schechtman E (2014) Temporal perspective on individual driver behavior using electronic records of undesirable events. Accid Anal Prev 70:55–64CrossRef
85.
Paefgen J, Staake T, Fleisch E (2014) Multivariate exposure modeling of accident risk: insights from Pay-as-you-drive insurance data. Transp Res Part A: Policy Pract 61:27–40
86.
Paefgen J, Staake T, Thiesse F (2013) Evaluation and aggregation of pay-as-you-drive insurance rate factors: a classification analysis approach. Decis Support Syst 56:192–201CrossRef
87.
Pahlavani P, Delavar MR (2014) Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection. Transp Res Part C: Emerg Technol 40:14–35CrossRef
88.
Pan CX, Lu JG, Di S, Ran B (2006) Cellular-based data-extracting method for trip distribution. Traffic Urban Data 1945:33–39
89.
Papinski D, Scott DM, Doherty ST (2009) Exploring the route choice decision-making process: a comparison of planned and observed routes obtained using person-based GPS. Transp Res Part F: Traffic Psychol Behav 12(4):347–358CrossRef
90.
Pishvaee MS, Farahani RZ, Dullaert W (2010) A memetic algorithm for bi-objective integrated forward/reverse logistics network design. Comput Oper Res 37(6):1100–1112MATHCrossRef
91.
Putha R, Quadrifoglio L, Zechman E (2012) Comparing ant colony optimization and genetic algorithm approaches for solving traffic signal coordination under oversaturation conditions. Computer Aided Civil Infrastruct Eng 27(1):14–28CrossRef
92.
93.
Sarle WS (1994) Neural networks and statistical models. In: Proceedings of the nineteenth annual SAS users group international conference, 1–13 April
94.
Shafahi Y, Bagherian M (2013) A customized particle swarm method to solve highway alignment optimization problem. Computer Aided Civil Infrastruct Eng 28(1):52–67CrossRef
95.
Shichrur R, Sarid A, Ratzon NZ (2014) Determining the sampling time frame for in-vehicle data recorder measurement in assessing drivers. Transp Res Part C: Emerg Technol 42:99–106CrossRef
96.
Shimamoto H, Murayama N, Fujiwara A, Zhang J (2010) Evaluation of an existing bus network using a transit network optimisation model: a case study of the Hiroshima city bus network. Transportation 37(5):801–823CrossRef
97.
Shuldiner AT, Shuldiner PW (2013) The measure of all things: reflections on changing conceptions of the individual in travel demand modeling. Transportation 40(6):1117–1131CrossRef
98.
Stolfi DH, Alba E (2014) Red swarm: reducing travel times in smart cities by using bio-inspired algorithms. Appl Soft Comput 24:181–195CrossRef
99.
Sun D, Benekohal RF, Waller ST (2006) Bi-level programming formulation and heuristic solution approach for dynamic traffic signal optimization. Comput Aided Civil Infrastruct Eng 21(5):321–333CrossRef
100.
Takeda K, Miyajima C, Suzuki T, Angkititrakul P, Kurumida K, Kuroyanagi Y, Komada Y (2012) Self-coaching system based on recorded driving data: learning from one’s experiences. IEEE Trans Intell Transp Syst 13(4):1821–1831CrossRef
101.
Teklu F, Sumalee A, Watling D (2007) A genetic algorithm approach for optimizing traffic control signals considering routing. Comput Aided Civil Infrastruct Eng 22(1):31–43CrossRef
102.
Teodorović D (2008) Swarm intelligence systems for transportation engineering: principles and applications. Transp Res Part C: Emerg Technol 16(6):651–667CrossRef
103.
Terzi S, Serin S (2014) Planning maintenance works on pavements through ant colony optimization. Neural Comput Appl 25:1–11
104.
Toledo T, Musicant O, Lotan T (2008) In-vehicle data recorders for monitoring and feedback on drivers’ behavior. Transp Res Part C: Emerg Technol 16(3):320–331CrossRef
105.
Tselentis DI, Vlahogianni EI, Karlaftis MG (2014) Improving short-term traffic forecasts: to combine models or not to combine? IET Intell Transp Syst (forthcoming)
106.
Vlacheas P, Giaffreda R, Stavroulaki V, Kelaidonis D, Foteinos V, Poulios G, Demestichas P, Somov A, Biswas AR, Moessner K (2013) Enabling smart cities through a cognitive management framework for the internet of things. IEEE Commun Mag 51(6):102–111CrossRef
107.
Vlahogianni EI, Karlaftis MG (2012) Comparing freeway lane speed patterns under fine and adverse weather conditions. Nonlinear Dyn 69(4):1949–1963CrossRef
108.
Vlahogianni EI (2009) Enhancing predictions in signalized arterials with information on short-term traffic flow dynamics. J Intell Transp Syst: Technol Plann Oper 13(2):73–84CrossRef
109.
Vlahogianni EI, Karlaftis MG (2011) Aggregating temporal and spatial data: implication for statistical characteristics and model choice. Transp Lett: Int J Transp Res 3(1):37–49CrossRef
110.
Vlahogianni EI, Karlaftis MG (2013) Testing and comparing neural network and statistical approaches for predicting transportation time series. Transp Res Rec: J Transp Res Board 2399(1):9–22CrossRef
111.
Vlahogianni EI, Golias JC, Karlaftis MG (2004) Short-term traffic forecasting: overview of objectives and methods. Transp Rev 24(5):533–557CrossRef
112.
Vlahogianni EI, Karlaftis MG, Golias JC (2005) Optimized and meta-optimized neural networks for short-term traffic flow prediction: a genetic approach. Transp Res Part C: Emerg Technol 13(3):211–234CrossRef
113.
Vlahogianni EI, Karlaftis MG, Golias JC (2006) Statistical methods for detecting nonlinearity and non-stationarity in univariate short-term time-series of traffic volume. Transp Res Part C: Emerg Technol 14(5):351–367CrossRef
114.
Vlahogianni EI, Karlaftis MG, Golias JC (2008) Temporal evolution of short-term urban traffic flow: a nonlinear dynamics approach. Comput Aided Civil Infrastruct Eng 23(7):536–548CrossRef
115.
Vlahogianni EI, Karlaftis MG, Golias JC (2014) Short-term traffic forecasting: where we are and where we’re going. Transp Res Part C: Emerg Technol 43:3–19
116.
Vlahogianni EI, Karlaftis MG, Orfanou FP (2012) Modeling the effects of weather and traffic on the risk of secondary incidents. J Intell Transp Syst 16(3):109–117CrossRef
117.
Vlahogianni EI, Karlaftis MG, Golias JC, Kourbelis ND (2006) Pattern-based short-term urban traffic predictor. In: Intelligent transportation systems conference, 2006, ITSC’06. IEEE, pp 389–393
118.
Vlahogianni EI, Yannis G, Golias JC (2013) Critical power two wheeler riding patterns at the emergence of an incident. Accid Anal Prev 58:340–345CrossRef
119.
Vlahogianni EI, Yannis G, Golias JC (2014) Detecting powered-two-wheeler incidents from high resolution naturalistic data. Transp Res Part F: Traffic Psychol Behav 22:86–95CrossRef
120.
Wang FY (2010) Parallel control and management for intelligent transportation systems: concepts, architectures, and applications. IEEE Trans Intell Transp Syst 11(3):630–638CrossRef
121.
Wang H, Li M, Bu Y, Li J, Gao H, Zhang J (2014) Cleanix: a big data cleaning parfait. In: Proceedings of the 23rd ACM international conference on conference on information and knowledge management. ACM, pp 2024–2026
122.
Yang F, Jin PJ, Cheng Y, Ran B (2014) Origin-destination estimation for non-commuting trips using location-based social networking data. Int J Sustain Transp (just-accepted)
123.
Yang N, Kang MW, Schonfeld P, Jha MK (2014) Multi-objective highway alignment optimization incorporating preference information. Transp Res Part C: Emerg Technol 40:36–48CrossRef
124.
Yin W, Murray-Tuite P, Ukkusuri SV, Gladwin H (2014) An agent-based modeling system for travel demand simulation for hurricane evacuation. Transp Res Part C: Emerg Technol 42:44–59CrossRef
125.
Zhang G, Zhang H, Li L, Dai C (2014) Agent-based simulation and optimization of urban transit system. IEEE Trans Intell Transp Syst 15(2)
126.
Zhang L, Wu C, Li Z, Guo C, Chen M, Lau F (2013) Moving big data to the cloud: an online cost-minimizing approach. IEEE J Sel Areas Commun 31(12):2710–2721CrossRef
127.
Zhang S, Lee CKM, Chan HK, Choy KL, Wu Z (2015) Swarm intelligence applied in green logistics: a literature review. Eng Appl Artif Intell 37:154–169CrossRef
128.
Zhou Z, Chawla N, Jin Y, Williams G (2014) Big data opportunities and challenges: discussions from data analytics perspectives. IEEE Comput Intell Mag 9(4):62–74CrossRef
Metadaten
Titel
Computational Intelligence and Optimization for Transportation Big Data: Challenges and Opportunities
verfasst von
Eleni I. Vlahogianni
Copyright-Jahr
2015
Buch
Engineering and Applied Sciences Optimization

Print ISBN: 978-3-319-18319-0

Electronic ISBN: 978-3-319-18320-6

Copyright-Jahr: 2015

DOI
https://doi.org/10.1007/978-3-319-18320-6_7

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