LEADER 00997nam a2200265 i 4500 001 991000558899707536 008 100129s2007 it b 000 0 ita d 020 $a9788813279318 035 $ab13875589-39ule_inst 040 $aDip.to Studi Giuridici$bita 082 0 $a345.45$222 100 1 $aBartone, Nicola$0231951 245 10$aDiritto penale italiano :$bsistema e valori : giurisprudenza e ottica europea : attuale e nuova codificazione /$cNicola Bartone 250 $a2. ed 260 $aPadova :$bCEDAM,$c2007 300 $axxiv, 420 p. ;$c24 cm 504 $aBibliografia: p. 383-401 650 04$aDiritto penale$vManuali 650 04$aDiritto penale$xGiurisprudenza 907 $a.b13875589$b28-01-14$c29-01-10 912 $a991000558899707536 945 $aLE027 345.45 BAR04.01$g1$i2027000238873$lle027$o-$pE31.00$q-$rl$s- $t0$u5$v0$w5$x0$y.i15114041$z19-04-10 996 $aDiritto penale italiano$9230668 997 $aUNISALENTO 998 $ale027$b29-01-10$cm$da $e-$fita$git $h0$i0 LEADER 02620nam 2200397 450 001 9910583349703321 005 20230120002942.0 010 $a0-12-817027-1 035 $a(CKB)4100000007186394 035 $a(MiAaPQ)EBC5609141 035 $a(EXLCZ)994100000007186394 100 $a20181222d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aData-driven solutions to transportation problems /$fedited by Yinhai Wang, Ziqiang Zeng 210 1$aAmsterdam, Netherlands :$cElsevier,$d[2019] 210 4$dİ2019 215 $a1 online resource (302 pages) $cillustrations 311 $a0-12-817026-3 327 $a1. Overview of data-driven solutions -- 2. Data-driven energy efficient driving control in connected vehicle environment -- 3. Machine learning and computer vision-enabled traffic sensing data analysis and quality enhancement -- 4. Data-driven approaches for estimating travel time reliability -- 5. Urban travel behavior study based on data fusion model -- 6. Urban travel mobility exploring with large-scale trajectory data -- 7. Public transportation big data mining and analysis -- 8. Simulation-based optimization for network modeling with heterogeneous data -- 9. Network modelling and resilience analysis of air transportation : a data-driven, open-source approach -- 10. Health assessment of electric multiple units. 330 $aData-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. 606 $aTransportation$xMathematical models 615 0$aTransportation$xMathematical models. 676 $a388.015118 702 $aWang$b Yinhai 702 $aZeng$b Ziqiang$c(Assistant professor), 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910583349703321 996 $aData-driven solutions to transportation problems$92019420 997 $aUNINA