⮬ື㐠㌿⮬ື㌴ࢆ⪃៖ࡋࡓ࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢࣞࣅ࣮ࣗ࡜᳨ウ
AH16208 ᑠᯘ ኱ᆅᣦᑟᩍဨ ᒾ಴ ᡂᚿ㸬⫼ᬒ࡜┠ⓗ⮬ື㐠㌿㌴ࡢᬑཬࡣᑗ᮶㸪ேࠎࡢᬽࡽࡋ࡟኱ࡁ࡞ኚ໬ࢆࡶࡓࡽࡍ࡜ࡉࢀ࡚࠸ࡿ㸬౛࠼ࡤ㸪ேࡀ㐠㌿ࡍࡿᚲせࡀ࡞ࡃ࡞ࡿࡓࡵ㸪ே௳㈝๐ῶ࡟ࡼࡿࢱࢡࢩ࣮ࡢప㐠㈤໬ࡸಶேࡀ⛣ື୰࡟௙஦ࡸፗᴦάືࢆ⾜࠺࡜࠸ࡗࡓ⾜ືኚ໬ࡀᮇᚅࡉࢀࡿ㸬Correia ࡽ{Ϳࡣ࢜ࣇ࢕ࢫ࡛ࡢ௙஦ࡢ୍㒊ࢆ⮬ື㐠㌿㌴ෆ࡛⾜࠸⮬⏤࡞᫬㛫ࢆᚓࡿࡇ࡜࡟㨩ຊࡀ࠶ࡿࡇ࡜ࢆ♧ࡋ㸪⮬ື㐠㌿㌴ࡢᬑཬ࡟ࡼࡾάື
ࣃࢱ࣮ࣥࡀኚ໬ࡍࡿྍ⬟ᛶࢆ♧၀ࡋࡓ㸬௒ᚋ㸪ᑗ᮶ࡢ஺㏻ᨻ⟇ࡸ㒔ᕷᨻ⟇ࢆ⪃࠼ࡿ࠺࠼࡛㸪⮬ື㐠㌿࡟ࡼࡿάືࣃࢱ࣮ࣥࡢኚ໬ࢆ⪃៖ࡋࡓ஺㏻㟂せண ᢏ⾡ࢆ‽ഛࡋ࡚࠾ࡃᚲせࡀ࠶ࡿ㸬ࡋ࠿ࡋ㸪ᚑ᮶ࡢ㟂せண ᡭἲ࡛࠶ࡿᅄẁ㝵᥎ィἲ࡛ࡣ㸪⛣ື࡜άືࡢ┦஫㛵ಀࡀ⪃៖࡛ࡁࡎ㸪⮬ື㐠㌿㌴ࢆ⪃៖ࡋࡓ㟂せண ࡜ࡋ࡚㐺ษ࡜ࡣ࠸࠼࡞࠸㸬ᑐࡋ࡚㸪࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡣࠕ஺㏻㟂
せࡣὴ⏕㟂せ࡛࠶ࡿࠖ࡜࠸࠺ㄆ㆑࡟❧ࡕ㸪ಶேࡢάືࣃࢱ࣮ࣥࢆࣔࢹࣝ໬ࡍࡿࡇ࡜࡛ಶேࡢ⛣ື࡜άືࡢ㐃㛵ᛶࢆ⪃៖ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬ࡑࡇ࡛㸪ᮏ◊✲࡛ࡣ᪂ࡓ࡞
஺㏻㟂せண ᢏ⾡࡜ࡋ࡚⪃࠼ࡽࢀࡿ࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢᩥ⊩ࢆᩚ⌮ࡋ㸪⮬ື㐠㌿⮬ື㌴ࢆ⪃៖ࡋࡓࣔࢹࣝࡢ᳨ウࢆ⾜࠺㸬㸬࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢ᳨ウ ࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢᩚ⌮1㸧MDCEV(Multiple Discrete-Continuous Extreme Value)ࣔࢹࣝ
MDCEV ࣔࢹࣝࡣ Bhat ࡽ²Ϳࡀᥦ᱌ࡋࡓ」ᩘࡢάື࡟
᫬㛫ࢆ㓄ศࡍࡿ㞳ᩓ㐃⥆㑅ᢥࣔࢹ࡛ࣝ࠶ࡿ㸬ேࡣάືࢆ㑅ᢥࡍࡿ㸦㞳ᩓ㑅ᢥ㸧࡜ྠ᫬࡟㑅ࢇࡔάື࡟㈝ࡸࡍ᫬
㛫㸦㈨※㓄ศ㸧ࡶ㑅ᢥࡋ࡚࠸ࡿ࡜⪃࠼୧⪅ࢆ⪃៖ࡋ࡚ᐃᘧ໬ࡉࢀ࡚࠸ࡿ㸬ຠ⏝ᘧ࡜ไ⣙᮲௳ࡣᘧ(1)࡜ࡋ࡚⾲ࡉࢀࡿ㸬K ࢆಶேࡀ᫬㛫ࢆ㓄ศ࡛ࡁࡿ඲࡚ࡢάື✀㢮㸪᫬㛫‫ࢆܧ‬ไ⣙᮲௳࡜ࡋ㸪‫ݔ‬௞ ࢆಶேࡀάື✀㢮 k(=1,2,…,K)࡟㓄ศࡍࡿ᫬㛫࡜ࡍࡿ㸬ఊ௫ఈೖఊೖೖሾ݁‫݌ݔ‬ሺߚᇱ ‫ݖ‬௞ ൅ ߝ௞ ሻሿ ቄቀ ೖቁܷሺ‫ݔ‬ሻ ൌ σ௄௞ୀଵ

Subject toఈೖെ ͳቅ(1)σ௄௞ୀଵ ‫ݔ‬௞ ൌ ‫ܧ‬ǡ ‫ݔ׊‬௞ ൒ Ͳ  ߛ௞ ࡜ߙ௞ ࡣ㣬࿴ࣃ࣓࣮ࣛࢱ(ߙ௞ <1)ࢆ⾲ࡋ㸪್ࡀ኱ࡁ࠸࡯࡝άື✀㢮 k ࡬ࡢ㓄ศ᫬㛫ࡢቑຍ࡟క࠺ຠ⏝ࡢ㏴ῶࡀᑠࡉ࠸ࡇ࡜ࢆព࿡ࡍࡿ㸬ߚ ᇱ ࡣᮍ▱ࣃ࣓࣮ࣛࢱ㸪‫ݖ‬௞ ࡣㄝ᫂ኚᩘ㸪ߝ௞ ࡣ࢞ࣥ࣋ࣝศᕸ࡟ᚑ࠺ㄗᕪ㡯ࢆ⾲ࡍ㸬 ܷሺ‫ݔ‬ሻࡣάື k ࡟᫬㛫‫ݔ‬௞ ࢆ㓄ศࡍࡿࡇ࡜࡟ࡼࡗ࡚ᚓࡽࢀࡿຠ⏝࡛࠶ࡿ㸬ࡇࡢไ⣙᮲௳௜ࡁຠ⏝᭱኱໬ၥ㢟ࢆゎࡃࡇ࡜࡛」ᩘࡢάື࡟᫬㛫ࢆ㓄ศࡍࡿ㸬2㸧DAS(Dynamic Activity Scheduling) ࣔࢹࣝDAS ࣔࢹࣝࡣ Habib ࡽ³Ϳࡀᥦ᱌ࡋࡓ MDCEV ࣔࢹࣝࡢὴ⏕ᆺࡢ㞳ᩓ㐃⥆㑅ᢥࣔࢹ࡛ࣝ࠶ࡿ㸬ࡇࡢࣔࢹࣝࡣ

ᅗ '$6 ࣔࢹࣝᵓ㐀ᅗ SUHWULS ᆺࣔࢹࣝࢶ࣮ࣜᵓ㐀࡜ᴫᛕᅗ᫬㛫 T ࢆไ⣙᮲௳࡜ࡋ࡚㏲ḟⓗ࡟άືࢆ㑅ᢥࡋ㸪᫬㛫ࢆ㓄ศࡍࡿ ᅗ 㸬᫬㛫ࡣάື⏕ᡂ㐣⛬࡛ࡣ㓄ศࡉࢀ
ࡿ࡭ࡁ㈨※㸪ࢫࢣࢪ࣮ࣗࣜࣥࢢ㐣⛬࡛ࡣᾘ㈝ࡉࢀࡿࡶ
ࡢ࡜ࡋ࡚ാࡃ㸬ᘧ(2)ࡣಶேࡀάື j ࢆ㑅ᢥࡋࡓ᫬࡟ᚓࡽࢀࡿຠ⏝㸪ᘧ(3)ࡣᾘ㈝᫬㛫‫ݐ‬௞ ࢆ㑅ᢥࡋࡓ᫬࡟ᚓࡽࢀࡿຠ⏝ࢆ⾲ࡍ㸬݇ ൌ ͳࡣ㑅ᢥࡉࢀࡓάື㸪݇ ൌ ʹࡣ฼⏝ྍ⬟࡞᫬㛫ไ⣙ୗࡢṧࡾ඲యࡢάືࢆ⾲ࡍ㸬   (2)ܷ௝ ൌ ܸ௝ ൅ ߝ௝     ଵఈܷሺ‫ݐ‬௞ ሻ ൌ σଶ௞ୀଵ ݁‫݌ݔ‬ሺ߰௞ ‫ݖ‬௞ ൅ ߝ௞ᇱ ሻ ൫‫ݐ‬௞ ೖ െ ͳ൯ఈೖ

Subject to  ‫ݐ‬௝ ൅ ‫ݐ‬௖ ൌ ܶ

 (3)



 ߰௞ ࡣ☜ᐃ㡯㸪‫ݐ‬௝ ࡣ㑅ᢥࡉࢀࡓᾘ㈝᫬㛫㸪‫ݐ‬௖ ࡣṧࡾࡢ᫬㛫ࢆ⾲ࡍ㸬ࡑࢀࡒࢀࡢ㑅ᢥ☜⋡ࢆ⤫ྜࡋ㸪άື✀㢮࡜ᾘ㈝᫬㛫ࡢ౫Ꮡ㛵ಀࢆ⪃៖ࡋࡓྠ᫬☜⋡ศᕸࢆồࡵ㸪ຠ⏝᭱኱໬⌮ㄽ࠿ࡽάື࡜᫬㛫ࢆỴᐃࡍࡿ㸬ࡇࡢࣉࣟࢭ
ࢫࢆ㏲ḟⓗ࡟㐍ࡵࡿࡇ࡜࡛άືࣃࢱ࣮ࣥ࡜ࡋ࡚ฟຊࡉࢀࡿ㸬
3㸧pre-trip ᆺࣔࢹࣝpre-trip ᆺࣔࢹࣝࡣ Bowman ࡽ⁴Ϳࡀᥦ᱌ࡋࡓ 1 ᪥ࡢάືࡀ⮬ᏯࢆฟⓎࡍࡿ๓࡟Ỵᐃࡉࢀࡿ࡜௬ᐃࡋࡓ㞳ᩓ㑅ᢥࣔࢹ࡛ࣝ㸪άືࣃࢱ࣮ࣥ࡜᫬㛫ࡸሙᡤ㸪஺㏻ᡭẁࡢ
Nested Logit ࣔࢹ࡛ࣝ࠶ࡿ㸦ᅗ㸧㸬ࡇࡢࣔࢹࣝࡣࢶ࢔࣮࣮࣋ࢫࡢ࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝ࡜࠸ࢃࢀ㸪ᐙࢆฟ࡚㸪ᐙ࡟ᖐࡿࡲ࡛ࢆࢶ࢔࣮࡜⪃࠼㸪ඃඛ㡰఩࠿ࡽ୺せࢶ࢔࣮㸪஧ḟࢶ࢔࣮࡟ศ㢮ࡋ㸪ࢶ࢔࣮ࡢ⤌ࡳྜࢃࡏ࠿ࡽάືࣃࢱ࣮ࣥ࡜ࡍࡿ㸬 ⮬ື㐠㌿⮬ື㌴ࢆ⪃៖ࡋࡓࣔࢹࣝࡢ᳨ウ 1㸧࡜ 2㸧ࡢ㞳ᩓ㐃⥆㑅ᢥࣔࢹࣝࡣ᫬㛫ࢆ㐃⥆㔞࡜ࡋ࡚ᢅ࠺ࡇ࡜࡛⮬ື㐠㌿ࢆ⪃៖ࡋࡓ㝿ࡢάື᫬㛫ࡢኚ໬ࢆືⓗ࡟⾲ࡏࡿࡓࡵ 3㸧ࡢࣔࢹࣝࡼࡾ᫬㛫ࡢ⢭ᗘࡀ㧗࠸㸬ࡋ࠿ࡋ㸪1㸧࡛ࡣ㡰ᗎࡀ⪃៖࡛ࡁࡎࢫࢣࢪ࣮ࣗࣜࣥࢢࡀ⾜࠼࡞࠸࡜࠸ࡗࡓḞⅬࡀ࠶ࡿ㸬ࡲࡓ㸪2㸧ࡣຠ⏝ᵓ㐀ࡀ」㞧࡞Ⅼࡸ㐃⥆㔞ࡢ㑅ᢥࢆྵࡴࡓࡵࡍ࡭࡚ࡢ௦᭰㑅ᢥ⫥ࡢิᣲࡀᅔ㞴࡞Ⅼ࠿ࡽ㸪㑅ᢥ☜⋡ࡢᣲືࡢኚ໬ࢆ☜ㄆࡍࡿ࡟ࡣ㐺ࡋ࡚࠸࡞࠸㸬୍᪉࡛㸪3㸧ࡣ᫬㛫ࡀ㞳ᩓ㑅ᢥ࡜࡞ࡗ࡚ࡋࡲ࠺㸪㑅ᢥ⫥ࡀ⭾኱࡟࡞ࡿྍ⬟ᛶࡀ࠶ࡿ࡜࠸ࡗࡓḞⅬࡣ࠶ࡿࡀ㸪㝵ᒙࡈ࡜࡟௦᭰㑅ᢥ⫥ࡢ㑅ᢥ☜⋡ࡢᣲືࡢኚ໬ࡀ☜ㄆ࡛ࡁ㸪⮬ື㐠㌿⮬ື㌴ࡢᙳ㡪ࢆࡼࡾヲ⣽࡟⾲ࡍࡇ࡜ࡀ࡛ࡁࡿ㸬ࡉࡽ࡟㸪ࣔࢹࣝࡢ᧯సᛶࡶࡼࡃ㸪஺㏻ᡭẁࡸ┠ⓗᆅ㸪᫬้ࡢኚ໬ࡢᙳ㡪࠿ࡽ࡝ࢀࡔࡅάືࣃࢱ࣮ࣥࡀኚࢃࡿ࠿ࢆ☜ㄆ࡛ࡁࡿࡓࡵᮏ◊✲ࡢ┠ⓗ࡟㐺ษ࡛࠶ࡿ࡜⪃࠼ࡿ㸬 ࡋࡓࡀࡗ࡚㸪ᮏ◊✲࡛ࡣ pre-trip ᆺࣔࢹࣝࢆᇶ࡟ࣔࢹࣝࢆᵓ⠏ࡍࡿ㸬㸬࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢᵓ⠏ ࣔࢹࣝᴫせᮏ◊✲࡛ࡣࢶ࢔࣮࣮࣋ࢫࡢ࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢ
ᩥ⊩ 5㸧㹼12㸧࠿ࡽࣃ࣓࣮ࣛࢱࢆᩚ⌮ࡋ㸪H30 ࣃ࣮ࢯࣥࢺࣜࢵࣉㄪᰝࡢ㞟ィ⤖ᯝࢆཧ⪃࡟㒔ᚰ 5 ༊ࢆศᯒ⠊ᅖ࡜ࡋ㸪ࣃ࣓࣮ࣛࢱࢆእ⏕ࡋࡓ㸬௬ᐃࡋࡓពᛮỴᐃࣉࣟࢭࢫࢆᅗ ࡟♧ࡍ㸬άືࣃࢱ࣮ࣥࡣࢩࣥࢢࣝࢶ࢔࣮㸪ࢶ࢔࣮࡜ࢧࣈࢶ࢔࣮㸪࣐ࣝࢳࢶ࢔࣮㸪ࢶ࢔࣮࡞ࡋ࡟ࡑࢀࡒࢀ㞟⣙ࡋࡓ㸬᫬้㑅ᢥࣔࢹࣝ࡟㛵ࡋ࡚ࡣ 1 ᪥඲యࢆ኱ࡁࡃศ๭ࡋ
ࡓ᫬㛫ᖏࡢ⤌ࡳྜࢃࡏࡢᡭἲ࡛ࡣಶேࡢฟⓎ㸪฿╔᫬้ࡢኚ໬ࡀヲ⣽࡟ࡣศ࠿ࡽ࡞࠸ࡢ࡛㸪Vovsha ࡽ¹³Ϳࡀᥦ᱌ࡋࡓᡭἲࢆ⏝࠸ࡿ㸬ࡇࡢ᫬้㑅ᢥࣔࢹࣝࡣฟⓎ᫬้࡜฿╔᫬้㸪⥅⥆᫬㛫ࡢ⤌ࡳྜࢃࡏ࡛ືసࡍࡿ㞳ᩓ㑅ᢥࣔࢹ࡛ࣝ࠶ࡿ㸬Vovsha ࡽࡣ 1 ᪥ࢆ 1 ᫬㛫ࡈ࡜࡟ศゎࡋ㸪⤌ࡳྜࢃࡏ࠿ࡽ 190 ࡢ㑅ᢥ⫥࡛⾲ࡋࡓ㸬ᥦ᱌ࡉࢀࡓຠ⏝ᘧࡣࢩࣇࢺኚᩘ࡟ᇶ࡙࠸࡚࠾ࡾ㸪ࢩࣇࢺኚᩘ㸦ಶேᒓᛶ㸪᪑⾜᫬㛫➼㸧ࡣฟⓎ᫬้ࡸ⥅⥆᫬㛫࡟ࡑࢀࡒࢀ஌⟬ࡍࡿࡇ࡜࡛ࡑࡢ᫬้ࡢຠ⏝ࢆ⾲ࡍ㸬⮬ື㐠㌿㌴ᬑཬ
๓ᚋࡢኚ໬ࢆ☜ㄆࡍࡿ࡟࠶ࡓࡾ 1 ᫬㛫༢఩࡛ᣲືࡀ☜ㄆ࡛ࡁࡿⅬࡸ SAV(Shared Autonomous Vehicle)ࢆ᝿ᐃࡍ
ࡿ㝿࡟᪑⾜᫬㛫ࢆປാ᫬㛫ࡢ୍㒊࡜௬ᐃࡋ㸪⨨ࡁ᥮࠼ࡿࡇ࡜ࡀྍ⬟࡞Ⅼ࠿ࡽᮏ◊✲࡛⏝࠸ࡿ㸬 ௬ᐃࡍࡿࢩࢼࣜ࢜⮬ື㐠㌿⮬ື㌴ࡢᬑཬ࡟ࡼࡾ㸪⛣ື୰࡟௙஦ࡸፗᴦάືࢆ⾜࠺ࡇ࡜ࡀ࡛ࡁࡿ㸬ࡉࡽ࡟㸪ே௳㈝ࡀᚲせ࡜࡞ࡃ࡞ࡾࢱࢡࢩ࣮ࡢ㐠㈤ࡀᏳ౯࡟࡞ࡿࡓࡵάືࣃࢱ࣮ࣥࡀ
ኚ໬ࡍࡿ࡜࠸ࢃࢀ࡚࠸ࡿ㸬ᮏ◊✲࡛ࡣάືࣃࢱ࣮ࣥࡸ
ྛ㝵ᒙࡢ㑅ᢥ☜⋡ࡢኚ໬ࢆศᯒࡍࡿࡓࡵ 5 ࡘࡢࢩࢼࣜ
࢜ࢆ௬ᐃࡍࡿ㸬 Case1 ࡣ⌧ἣࡢሙྜ㸪Case2 ࡣ SAV㸦ࢱࢡࢩ࣮㸧ࡢ⛣ື᫬㛫࡟ປാࡢ୍㒊ࢆ๭ࡾᙜ࡚ࡿሙྜ㸪Case3 ࡣ Case2 ࡟ຍ࠼㸪ே௳㈝ࡢ๐ῶ࡟ࡼࡾ SAV ࡢ㈝⏝ 55 ෇km ࡜࡞ࡿሙྜ㸪Case4 ࡣ Case3 ࡟ຍ࠼㸪⛣ື୰࡟άື࡛ࡁࡿࡓࡵ㸪᫬㛫౯್ࡀ⌧ἣ࡜␗࡞ࡾపୗࡋ
ࡓ࡜௬ᐃࡋࡓሙྜ㸦ᩥ⊩ 14 ࡢ᫬㛫౯್ࢆ⏝࠸㸪Case3 ࡢ42.9%ࡢ᫬㛫౯್࡜ࡍࡿ㸧㸪 Case5 ࡣ Case4 ࡟ຍ࠼㸪እฟάືࡀቑ࠼ࡿ࡜௬ᐃࡋ࡚άືࣃࢱ࣮ࣥࡢࣃ࣓࣮ࣛࢱࢆㄪᩚࡋ㸪ᐃᩘ㡯ࡸࢲ࣑࣮ኚᩘࡢᙳ㡪ࢆᑠࡉࡃࡋࡓሙྜ࡜ࡍࡿ㸬 ศᯒ⤖ᯝศᯒ⤖ᯝࢆ⾲ 㹼 ࡟♧ࡍ㸬᫬้㑅ᢥࡣ A(7:00㹼9:59)㸪

ᅗ ពᛮỴᐃࣉࣟࢭࢫ⾲ άືࣃࢱ࣮ࣥ㑅ᢥ☜⋡  

ࢩࣥࢢࣝ
ࢶ࢔࣮
77.8(base)77.7(-0.1)
77.5(-0.3)
77.2(-0.6)
73.2(-4.6)

Case1
Case2
Case3
Case4
Case5

ࢶ࢔࣮࡜
ࢧࣈࢶ࢔࣮
8.3(base)
8.2(-0.1)
8.3(±0.0)
8.3(±0.0)
9.7(+1.4)

࣐ࣝࢳࢶ࢔࣮

ࢶ࢔࣮࡞ࡋ

13.7(base)
13.9(+0.2)
14.0(+0.3)
14.4(+0.7)
16.9(+3.2)

0.2(base)0.2(±0.0)
0.2(±0.0)
0.2(±0.0)
0.1(-0.1)

⾲ ┠ⓗᆅ㑅ᢥ☜⋡  
Case1
Case2
Case3
Case4
Case5

㏆㊥㞳
50.4(base)50.5(+0.1)
51.3(+0.9)
46.5(-3.9)
46.5(-3.9)

୰㊥㞳
38.9(base)38.9 (±0.0)38.4(-0.5)
40.9(+2.0)
40.9(+2.0)㛗㊥㞳
10.7(base)
10.6(-0.1)
10.3(-0.4)
12.6(+1.9)
12.6(+1.9)

⾲ ஺㏻ᡭẁ㑅ᢥ☜⋡  
PT
62.1

㏆㊥㞳
Car
32.2

SAV
5.6

PT
79.5

୰㊥㞳
Car
20.4

SAV
0.1

PT
90.7

㛗㊥㞳
Car
9.3

SAV
0.0

(base)(base)(base)(base)(base)(base)(base)(base)(base)

Case1Case2
Case3
Case4
Case5

60.9

31.6

7.5

79.5

20.4

0.1

90.7

9.3

0.0

(-1.2)(-0.6)(+1.9)(±0.0)(±0.0)(±0.0)(±0.0)(±0.0)(±0.0)

32.8

23.0

44.3

66.6

17.1

16.3

85.1

8.7

6.2

(-29.3)(-9.2)(+38.7)(-12.9)(-3.3)(16.2)(-5.6)(-0.6)(+6.2)

32.8

23.0

44.3

66.6

17.1

16.3

85.1

8.7

6.2

(-29.3)(-9.2)(+38.7)(-12.9)(-3.3)(16.2)(-5.6)(-0.6)(+6.2)

32.8

23.0

44.3

66.6

17.1

16.385.1

8.7

6.2(-29.3)(-9.2)(+38.7)(-12.9)(-3.3)(16.2)(-5.6)(-0.6)(+6.2)

⾲ ᫬้㑅ᢥ☜⋡  
Case1
Case2
Case3
Case4
Case5

A&B
7.7(base)
7.7(±0.0)
7.7(±0.0)
7.7(±0.0)
7.7(±0.0)

A&C
45.1(base)40.9(-4.2)
40.9(-4.2)
40.9(-4.2)
40.9(-4.2)

A&D
25.2(base)20.9(-4.3)
20.9(-4.3)
20.9(-4.3)
20.9(-4.3)

B&B
0.8(base)
1.5(+0.7)
1.5(+0.7)
1.5(+0.7)
1.5(+0.7)

B&C
3.7(base)
6.6(2.9)
6.6(2.9)
6.6(2.9)
6.6(2.9)

B&D
10.3(base)16.0(5.7)
16.0(5.7)
16.0(5.7)
16.0(5.7)

B(10:00㹼16㸸59)㸪C(17:00㹼19㸸59)㸪D(20:00㹼23㸸59)ࡢ⤌ࡳྜࢃࡏ࠿ࡽฟⓎ᫬้㸪฿╔᫬้ࢆ⾲ࡋ࡚࠸ࡿ㸬┠ⓗᆅ㑅ᢥࡢ Case1 ࡜ Case4 ࢆẚ㍑ࡍࡿ࡜୰㊥㞳ࡸ㛗㊥㞳ࢆ㑅ࡪ☜⋡ࡀቑຍࡋࡓ㸬ࡇࡢ⤖ᯝࡣ㸪⛣ື㈇ᢸࡢ㍍ῶ
࠿ࡽ୰㊥㞳ࡸ㛗㊥㞳ࡢ⛣ືࡀࡋࡸࡍࡃ࡞ࡾ㸪㐲࠸ሙᡤ࡬ࡢ࢔ࢡࢭࢫࡀቑ࠼ࡿྍ⬟ᛶࢆ♧ࡋࡓ㸬ࡲࡓ㸪᫬้㑅ᢥ࡛ࡣ Case1 ࡜ Case2 ࢆẚ㍑ࡍࡿ࡜㸪⥅⥆᫬㛫ࡀ▷ࡃ࡞ࡾ㸪᫨㡭ࡢฟⓎࡸᖐᏯࡀቑຍࡋࡓ㸬ࡇࡢ⤖ᯝࡣ࢜ࣇ࢕ࢫ
࡛ࡢປാ᫬㛫ࡀ▷ࡃ࡞ࡾ㸪⮬⏤࡞᫬㛫ࡀቑ࠼άືࡀά
Ⓨ࡟࡞ࡿྍ⬟ᛶࢆ♧ࡋࡓ㸬 ࡲ࡜ࡵ ᮏ◊✲࡛ࡣ㸪⮬ື㐠㌿⮬ື㌴ࢆ⪃៖ࡋࡓ࢔ࢡࢸ࢕ࣅࢸ࢕ࣔࢹࣝࡢᵓ⠏ࢆ⾜࠸㸪⮬ື㐠㌿⮬ື㌴ᬑཬ࡟ࡼࡿάືࣃࢱ࣮ࣥࡸ᫬㛫㸪ሙᡤ㸪஺㏻ᡭẁࡢ㑅ᢥ☜⋡ࡢኚ໬ࢆ☜ㄆࡋࡓ㸬⌧≧ࡢࣔࢹࣝࡢㄢ㢟࡜ࡋ࡚㸪άືࣃࢱ࣮ࣥࡢຠ⏝㛵ᩘࡀᐃᩘ㡯ࡸࢲ࣑࣮ኚᩘࡢࡳ࡛ࡋ࠿⾲ࡏ࡞࠸Ⅼ㸪ୗ఩ࡢࣟࢢࢧ࣒ຠ⏝ࡀάືࣃࢱ࣮ࣥࡢ㑅ᢥ࡟࠶ࡲࡾᐤ୚ࡋ࡞࠸Ⅼࡀᣲࡆࡽࢀࡿ㸬௒ᚋ㸪ࡼࡾ⮬ື㐠㌿⮬ື㌴ࢆ⪃៖ࡋࡓ㐺ษ࡞ࣔࢹࣝࢆ᳨ウࡍࡿᚲせࡀ࠶ࡿ㸬ཧ⪃ᩥ⊩1) Correia,G.H.D.A et al. : On the impact of vehicle automation on the Value of Travel Time while performing work andleisure activities in a car : theoretical insights and results from a stated preference survey . Transportation Research Part
A:Policy and Practice,119,pp359-382,2018
2) Chandra R .Bhat et al.: AN ECONOMETRIC MULTI -DIMENSIONAL CHOICE MODEL OF ACTIVITYTRAVELBEHAVIOR , Transportation Letters , pp 217-230 , 2010
3) Habib.K.M.N et al.: A Random Utility Maximization (RUM) Based Dynamic Activity Scheduling Model : Applicationin Weekend Activity Scheduling. Transportation 38(1):123̽151.20114) J.L.Bowman,M.E.Ben-Akiva : Activity-based disaggregate travel demand model system with activity schedules.
Transportation Research Part A 35, 1-28, 2001
5) Jasper Willigers et al. : UPDATING AND EXTENDING THE DISAGGREGATE CHOICE MODELS IN THE DUTCH
NATIONAL MODEL. The Hague, October 5th 20096) G.Ozonder et al. : Analysis of Activity Location and Trip Mode Choice̽A Study on Hierarchical Ordering. Procedia
Computer Science 151 , 739̽744 , 20197) Y.Shiftan et al. : Activity-Based Modeling as a Tool for Better Understanding Travel Behaviour.,10th International
Conference on Travel Behaviour Research Lucerne, 10-15 August 2003
8) Sacramento Activity-Based Travel Simulation Model (SACSIM07) : MODEL REFERENCE REPORT㸪Sacramento
Area Council of Governments,November 2008
9) Model Specification Report,Atlanta Activity-Based Travel Model Coordinated Travel–Regional Activity Based
Modeling Platform,October 2009,Last Revised December 201510) Inbal Glickman et al. : Integrating activity-based travel-demand models with land-use and other long-term lifestyledecisions㸪The Journal of Transport and Land Use Vol . 8 No. 3㸪pp. 71̽93㸪201511) Gennaro Nicola Bifulco et al. : An activity-based approach for complex travel behavior modelling , Eur. Transp. Res.
Rev. 2:209̽221,201012) Sadayuki Yagi et al. : Modeling Daily Activity̽Travel Tour Patterns Incorporating Activity Scheduling Decision Rules ,Transportation Research 2076(2076):123-131,December 200813) Peter Vovsha , Mark Bradley : Hybrid Discrete Choice Departure-Time and Duration Model for Scheduling Travel
Tours. Transportation Research Board,No.1894,Washington,D.C.2004
14) Kolarova.V, Steck.F et al. : Estimation of the value of time for automated driving using revealed and stated preference
methods. Transportation Research Procedia 31,pp35-46,2017