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index 50 Cent 84 Ancient Egyptians 175, Azerbaijan 232 205–7 Abbot, Russ 163 Angels & Demons 62–3 Babbitt, Natalie 53 ABC 199 angle of repose 109–10 Bacall, Lauren 163 absolute zero 112 ants 52 Bacon, Francis (1561– AC/DC 76–7 Ant and Dec 163 1626) 173, 192–4 Adams, Douglas 173 land 232 Bacon, Francis (1909–92) Adams, John 160 apartment blocks 229–30 192 adverbs 80–1 aphids Bad Boy Records 148 Afghanistan 222–3, 232 breeding habits 8, 35–7 Baden 211 African debt 168 milking 52 Bagnold, Ralph 109 agoraphobia 82–3 Apple Macs 60 Bagpuss 175–6 agoutis 30–1 apples, poisonous 28–9 Bahamas, undersea post air pressure 116–17 Argentina 231 office 223–6 al-Qaeda 222, 223 Armillaria ostoyae 42 Bailey, Martin 86 alarm clocks, you can’t set Armstrong, Neil 209 banknotes 210–11 Arundel, Earl of 193 cocaine traces 95–7 Ali G 57 Ascension Island 232 cotton 103–5 All in the Family 202 asexual reproduction 35 barometers 117 All Saints 90 Aubrey, John 173, 192–3 Baron Cohen, Sacha 57 Allen, Woody 67 Australia 227, 235 Barry, Gerald 194 alligators coat of arms 45–8 Barthes, Roland 184 tree climbing 31–3 driving 125, 127 Bartholdi, Frédéric weather forecasting 116 money 105 Auguste 226 ’Allo ’Allo! 57–9 police 143 baseball 37 ambigrams 63 snakebite 145–6 Bass, Saul 165 Amboise, Pierre 192 street names 76–7 Bassan, Harry 167 American Football 200–2 Sydney Opera House Batman films 66 American War 160 215–18 BBC 100, 131, 218 American Werewolf in Autry, Gene 157 Beaton, Cecil 162 London, An 144 Autumn, Kellar 23, 25 Beaumont, Laura 76 249 a INDEX a Beautiful Game, The 207 Carroll, James 234 collection tins, shaking Beck 87–8 Carroll, Lewis 189, 191–2 127–9 Berlin, Irving 135 Cash, Johnny 65–6 Collins, Phil 35, 76, 215 bestiality 63–5 Cassels, Richard 160 Colney Hatch Asylum, bicycle pedals 210–11 cat flaps 172–6 London 244–5 biggest organism, world’s Catherine de Valois 171 Combs, Sean John see 41–3 Cattrall, Kim 63 P. Diddy bin Laden, Osama 222 Caveman Lee 199 communist propaganda Birdseye, Clarence ‘Bugs’ Celsius 110–12 71–4 192 Cerf, Bennett 79–80 copyright 134, 135–7, 157 Björk 163 Changi Airport Skytrain corridors, world’s longest Blackburn, Tony 163 241 243–5 blue whales 41 charities 127–9 Costa awards 82, 83 Boch, Anna 85, 86 Charles I 191 Crane, Phil 145 Bolanios, Amelia 209 Chekhov, Anton 185 crime, and the moon Bon Jovi 87 Chemical Brothers 88 139–44 Bonaparte, Jerome- chewing gum 144–5 crocodiles 116 Napoleon Charles 170 chickens, snow-stuffed Croft, David 57 Bones, Jonathan 96 192–4 Croft, Lara 113 Borat 57–9 China 33, 100 Crosby, Bing 157 Bostrum, Arthur 58–9 chocolate bars, insect legs Crosland, John Francis Bosworth, battle of 190 in 106–8 166 Bowie, David 59 chocolate-covered bugs Cruise, Tom 74, 88 bowling 205–7 108 Cure, The 64 Bowman Flag 46 Choi Eun-hee 57 Customs & Excise 131–3 Brazil nuts 30–1 Christianity 71 Cutkosky, Mark 25 Brown, Dan 61–3, 79 chuggers (charity muggers) cyanide 28–9 Burton, Rod 75, 76 128, 129 Burton, Tim 66 churglars (charity burglars) Da Vinci Code, The 61–3 Bush, George W. 53–5 129 Daffy Duck 56 Bush, Laura 55 Clayton F. Summy Dahl, Roald 53 butterflies 19–21, 116 Company 135, 136 Dalmatians, spots 15–16 Clinton, Bill 147, 222 dancing girls, Kim cab drivers 138 clowns, fear of Jong-il’s 56 Cable & Wireless 232 (coulrophobia) 150 Dash, Mike 183–4 Campbell, David 87–8 Clusius, Carolus 182 David, Craig 163 Campbell, M.J. 40 CNN 56 de Burgh, Chris 137, Canada 143, 235 Coca-Cola 155, 176 138–9 Captain Pugwash 71 cocaine, on banknotes de Warden, William 172 capuchin monkeys 31 95–7 de Wendover, William 172 car prices 220–1 cochineal 106 Dearlove, Richard ‘Diddy’ Carle, Eric 53–5 Coleman, Robert 135, 137 149, 150 Carlson, Peter 43 Colgate 230–2 death 22 250 a INDEX a Death Star corridor 244 Elton, Ben 207 Friday the Thirteenth films dehydration, post-mortem Emerson, Ralph Waldo 56 22 52 Friedkin, William 97 Denmark 229 Emerson Tennent, James Fripp, Robert 60 density 120–2 52 frogs, weather forecasting Descartes, René 181 emus 45–8 114–17 Diana, Princess of Wales endangered species 26 frozen food 192–4 137 England fruit flies 17, 113 Dickens, Charles 81 furious driving 137–9 Dodd, Ken 149 Speaker of the House Garnier, Charles 185 dollars (US) 105 235–7 garnishee orders 153 dolphins, marriage to 131 water bills 151–3 gas 151 Donkey Kong 68–70 Eno, Brian 59–61 Gauguin, Paul 85, 86 Douglas, Kirk 67 explosives 97–9 Gaye, Marvin 88 Douglas, Michael 67, 89 eyeball growth 102–3 geckos 23–5 Douglas-Hamilton, Iain 14 eyes, artificial 103 Geisel, Theodor 80 Drais, Karl 211–12 genes 113–14 driving Fahrenheit 110 George II 171 bare foot 125–7 fairyflies 49 George, Jean Craighead 53 furious 137–9 Falkland Islands 243–5 Germany 114–17, 176–8, drug smugglers 119–20 Fanta 176–8 206–7, 211 Dry Bean Council (US) Ferdinand 214 Gervais, Ricky 188 118–19 Fesmire, Francis M. 167 goats, marriage to 130–1 duck-billed platypus 45 fingernails 21–2 Godzilla 56 Dumas, Alexandre 185 fingerprints, koala bear Goldthwait, Robert Dust Brothers 88 11–12 Francis ‘Bobcat’ 64–5 Dylan, Bob 88, 233 First World War 168 golf balls dynamite 97–9 Fish, Robert Lloyd 210 featherie 198 flies honey-containing 195–8 ear growth 8, 37–41 backwards take offs Goodies, The 75 Eastminster Abbey 171–2 16–18 Goschen, George 170 Edward the Confessor 161 genes 113 Gottlieb 191–2 Edward III 171 Florida 33, 116 Gounod, Charles 185 Eidos 113 flutterbies 19–21 Grahame, Kenneth 55 Eiffel, Gustav 226 food colourings 106 Grand Prix 203–4 Eiffel Tower 184–6 Food and Drug Grantham, tedious nature Ekström, Daniel 112 Administration (US) of 175, 179 El Salvador 207–10 106–7 gravity 93 electricity 151 football 207–10 gravity boots 62–3 elephants Ford Focus 220–1 Great Barrier Reef 42 and Brazil nuts 31 Formula One 203–4 Great Exhibition 1851 117 walking uphill 12–15 France 175 Greek national anthem Elizabeth II 188, 217–18 Franklin, Aretha 88 242–3 251 a INDEX a Greenland 227–30 Hobbes, Thomas 193 Iran 103 Grisham, John 79 Hodges, Ann Elizabeth Ireland 206, 196 Groves, Leslie R. 234 101 Italian language 237–9 Guinness 197 Holland 181–4 Gulick, Luther H. 164 Holloway, Bill 166 Jack the Ripper 92, 171, Guttenberg, Steve 7, 64 Honduras 207–10 189 honey-containing golf balls Jaffa Cakes 131–3 Hagen 197 195–8 James Bond franchise 57 Hagler, Marvin 198–9 Hooke, Robert 173 James, Henry 89 Hague, The 84 Hope, Bob 171 Japan 56 hair 21–2 horn 22 John, King 104 Hall, Diane 67 hot dogs 238, 239 Johnston, Edward 165 hamburgers, price of Hotlix 108 Jupiter 93 220–1 Houdini, Harry 68 Jurong Bird Park 241 Hamilton, ‘Diddy’ David House of Commons 236–7 149 House of Lords 237 kakapos 48 Hanks, Tom 61–2 House of Representatives kangaroos 45–8 Hanna and Barbera 73 235 Kapu´sci´nski, Ryszard 209 Hansen, Al 87 Hudson River 226–7 Kavenna, Joanna 15 Hansen, Bibbe 87 Humpty Dumpty 188–92 Keaton, Buster 67–8 Hanson 87–8 hurricanes 116 Keaton, Diane 67 Hanson Brothers 88 Hutchins, Levi 210–11 Keaton, Michael 66–8 ‘Happy Birthday to You’ Huygens, Christiaan 181 Keith, Max 177, 178 133–7 hyperopia 102 Kelly, Ivan W. 142 Heath, Edward 179 Kelvin scale 112 Heathcote, James 38–40 IBM 180 Kennedy, Bobby 77 Hellraiser films 21 ice 120, 122, 123 Kent, Rockwell 79 Henneberg, Maciej 12 ice cream 179–81 Kenya 222 Hennessy 56 Impressionists 85, 86 keratins 22 Henry V 171 Infinite Corridor, MIT 245 kidney beans, undigested Herbert, Johnny 203 insects 118–20 heroin 120 in chocolate 106–8 Kim Il-sung 57 hiccups, eternal 165–7 see also butterflies; flies; Kim Jong-il 55–7 Hill, Jessica 135 wasps King, Carole 88 Hill, Mildred 133–4, 135 International Organization King Crimson 60 Hill, Patty Smith 133–4, for Standardization King, Stephen 81 135 (ISO) 240 Kipling, Rudyard 55 Himalayas 218–19 International Security Kitchin, Mr Justice 150 Hitler, Adolf Assistance Force Klopfer, Donald 79 and Fanta 176, 177 (ISAF) 222–3 Knapik, Jane 53 Time’s Man of the Year International Space Station Knoy, Laura 62 158–9 225–6 koala bears 45 Hoban, James 160 Inuits 143 fingerprints 11–12 252 a INDEX a Laboulaye, Edouard de 226 Mars 110 Murphy, Eddie 183 Langdon, John 63 Martians 83 mushrooms 34–5, 41–3 Las Vegas 186 Martin Luther 206 Muslims 222–3 Latin Monetary Union 86 Martin, Michael 237 myopia 102 Laufmaschine 212 Marx, Karl 72 leeches, weather Mass Rapid Transit (MRT) Namco 113 forecasting 117 system 239–41, 242 Napoleon Bonaparte 170 Leinster House, Dublin Maupassant, Guy de Napoleonic Wars, British 160 184–6 debts from 167–70 Leonard, Elmore 80–1 May, Robert L. 155, 157 Narnia 71 Lewis, C.S. 71 Mayo, Simon 82 national anthems 242–3 Lewis, John S. 101 Meijerman, Lynn 39–40 National Portrait Gallery Liberty Island 226–7 Melbourne 76–7 161–3 Light Rapid Transit (LRT) Merryweather, George 117 nationalist songs 55–6 system 239–40 meta-analysis 142 nature 8–9 Linnaeus, Carolus 112 metathesis 21 Nazis 38, 58, 176–8 Lisle, Leconte de 185 meteorite fatalities 100–2 NBC 73 Lloyd, Jeremy 57 Mexico 207, 209 Neptune 93 Lloyd Webber, Andrew Michael, George 89–90 Nestlé 181 207 Microprose 204 nests, wasps’ 49–50 logos, London Microsoft 59, 60, 141 New Jersey 226–7 Underground 163–5 Millennium Corridor 244, New York 226–7 London Underground 245 New York Times 81 77–9, 163–5 Mingay, Frank Hedley New Zealand 48, 105, 235 Lyons Electronic Office 196–7 Newton, Sir Isaac 171, (LEO) 179–80 Minogue, Kylie 87 172–6 Mitsubishi Pajero 231–2 Newtown restaurant and M*A*S*H 202 Miyamoto, Shigeru 68, 69, club, Montreal 204 Maat, George J.R.
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    Delft University of Technology Adapting Automated People Mover capacity on airports to real time demand van Doorne, Mark; Lodewijks, Gabriel; Beelaerts van Blokland, Wouter Publication date 2019 Document Version Final published version Published in Proceedings 23rd World Conference Air Transport Research Society (ATRS 2019) Citation (APA) van Doorne, M., Lodewijks, G., & Beelaerts van Blokland, W. (2019). Adapting Automated People Mover capacity on airports to real time demand. In M. Dressner (Ed.), Proceedings 23rd World Conference Air Transport Research Society (ATRS 2019) Important note To cite this publication, please use the final published version (if applicable). Please check the document version above. Copyright Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons. Takedown policy Please contact us and provide details if you believe this document breaches copyrights. We will remove access to the work immediately and investigate your claim. This work is downloaded from Delft University of Technology. For technical reasons the number of authors shown on this cover page is limited to a maximum of 10. Adapting Automated People Mover Capacity on Airports to Real-Time Demand via Model-Based Predictive Control M.P. van Doorne1, G. Lodewijks2, W.W.A. Beelaerts van Blokland3 1 Airbiz Aviation Strategies Ltd., 92 Albert Embankment, SE1 7TY, London, United Kingdom 2 University of New South Wales, School of Aviation, NSW 2052, Sydney, Australia 3Delft University of Technology, Mekelweg 2, 2628 CD, Delft, The Netherlands Abstract The Automated People Mover (APM) is an important asset for many airports to transport passengers inside or between terminal and satellite buildings An APM system normally runs on fixed schedules throughout the day, which means that the capacity of the APM is pre-determined and not depending on the actual demand.
  • Rapid Transit

    Rapid Transit

    Rapid Transit UDLS 2018-05-04 http://transitoriented.design Jan Pilzer Tram, also streetcar or trolley: Rail based, on or along streets Urban Rail Light Rail: Not separated from other traffic, but right of way Rapid Transit, also heavy rail: Full grade-separation Transit Monorail: Track with a single rail Commuter Rail: Faster, less frequent, shared tracks with trains Public transport in urban areas Electric railways Rapid Transit Exclusive right-of-way, grade-separated in tunnels or elevation Definition Designated lines between stations Custom made trains adapted to the stations Typical capacity of 36 000 people per hour Metro: Most common with non-native English speakers Montreal Metro, Washington Metro, Los Angeles Metro Rail Subway, underground: Inspired by the use of tunnels Rapid Transit German: U-Bahn (Untergrundbahn), Swedish: T-bana (Tunnelbana) Terminology In Britain, subway is a pedestrian underpass L (elevated), Skytrain: Inspired by use of viaducts Chicago, Vancouver Third Rail Overhead Line Technology Island Platform Side Platform Single platform between Dual platforms on either side tracks Tracks are straight Facilities can be shared Can be extended or later Stations Tracks need to diverge added Length determined at Require a bridge across the construction tracks Safety issues due to Require duplicated entrances overcrowding and facilities First in Singapore 1987 Can be full or half height Prevents people falling on tracks Platform Reduces wind, better climate Screen Doors control Restricts