Ko'proq

Trafik ma'lumotlarini (nuqtasini) yo'l tarmog'iga (chiziqqa) ulang - Ikki yo'l orasidagi qism - ArcGIS

Trafik ma'lumotlarini (nuqtasini) yo'l tarmog'iga (chiziqqa) ulang - Ikki yo'l orasidagi qism - ArcGIS


Men Melburnning (AUS) yo'l tarmog'idagi transport vositalarining harakatlanishini tahlil qilishga harakat qilaman.

Menda ikkita fayl bor: transport ma'lumotlari (yo'llar orasidagi o'rtacha soatlik trafik bilan berilgan kenglik va uzunlik bo'yicha nuqta ma'lumotlari) va yo'l tarmog'i:

Trafik ma'lumotlari (binafsha nuqta) va yo'l tarmog'i (yashil)

Yo'l qatlami quyidagi ko'rinishda bo'laklarga bo'linadi:

Trafik ma'lumotlarini quyidagi ikkita bo'limga belgilashim kerak:

Menda 1616 tirbandlik bor, ularni yo'l tarmog'ining uchastkalariga belgilashim kerak. Buni avtomatik ravishda amalga oshirishning biron bir usuli bormi yoki qo'lda bajarishim kerakmi?


Near yordamida eng yaqin yo'l segmentining FID / OBJECTID-ni oling.

Ballar va yo'l bo'laklari o'rtasida atribut birikmasidan foydalaning; ushbu bosqichda mos keladigan yo'l nomlari va nuqtalardagi ismlarni tekshirib, to'g'ri kelganingizga ishonch hosil qiling ... ba'zi bir qo'l ishi talab qilinishi mumkin.

Id identifikatorlarini tasdiqlaganingizdan so'ng, (1: 1) nuqtalarga chiziqlarni birlashtiring, so'ngra maydonni hisoblang yoki qo'shilish joyida eksport qiling.


Yo'l ma'lumotlari


Yo'llarning bir miliga, qatorlar sonidan qat'i nazar, markaziy milya deyiladi. O'rtacha masofa yurish qatorlar sonini hisobga olmasa-da, qatorli yurishlar. Yurish masofasini doimiy haydash yo'llari va markaziy masofani ko'paytirish orqali topish mumkin. Burilish yo'laklari kabi vaqtinchalik yo'llar hisobga olinmaydi, shuningdek, panduslarda yoki dam olish joylari kabi yordamchi joylarda ham bo'lmaydi.

Bir yo'lni uch mildan iborat bo'lgan bir milya uzunlikdagi misolni ko'rib chiqaylik - milya soni uchta.

Miles va Lane Mileage hisobotlarini yaratish uchun LRS-dan yozuvlar olinadi. Doimiy raqamlarni taqdim etish uchun hisobotlar har dekabrda olingan ma'lumotlar bazasining HPMS suratiga qarshi ishlaydi. Ushbu oniy rasm yangi rasm olinmaguncha hisobot uchun ishlatiladi. Quyida LRS yozuvlarini tanlash mezonlari keltirilgan:

  • Geografik hudud (shtat bo'yicha tuman, munitsipalitet, MnDOT qurilish okrugi yoki hudud transporti bo'yicha sheriklik bo'yicha)
  • Yo'nalish turi (ya'ni avtomobil yo'llari yoki mahalliy yo'llar)
  • Zarur bo'lganda boshqa mezonlar (yo'lning sirt turlari, funktsional sinflar va boshqalar).

Agar hisobot uchun yo'lning yurishi so'ralsa, doimiy haydash yo'llarining soni olinadi va har bir guruhlash kombinatsiyasidagi ma'lumotlar elementlari qiymatiga qarab hisoblab chiqiladi. Keyinchalik, bu qiymatlar markaziy masofa ko'rsatkichlari bilan ko'paytiriladi.

Sayohat qilingan transport vositasi (VMT)


Avtotransport vositalarining bosib o'tgan masofalari (VMT) har bir o'rtacha yillik kunlik trafikni (AADT) ko'rib chiqilayotgan har bir yo'l segmentining markaziy masofasiga ko'paytirish orqali hisoblanadi. Og'ir tijorat VMT (HCVMT), shuningdek, tijorat yuk mashinalari hajmlarini baholash yordamida hisoblanishi mumkin. HCVMT faqat Minnesota shtatining magistral magistral tizimi uchun ishlab chiqariladi, shuning uchun og'ir reklama roliklarining tarixiy tendentsiyalari faqat Interstate, AQSh va State Highways-da mavjud. VMT ko'rsatkichlari MnDOT tomonidan transport tarmog'iga bo'lgan talabni o'lchash uchun ishlatiladi. Hisobotni shakllantirish uchun VMT va markaziy masofa statistikasi guruhlanganida, uchta o'zaro bog'liq guruhlar aniqlanishi mumkin. Shuningdek, barcha magistral magistral yo'llar uchun qiymatlarni birlashtirish kabi birlashtirilgan qiymatlarni ta'minlash uchun individual qiymatlarni guruhlash mumkin. Doimiy raqamlarni taqdim etish uchun VMT hisobotlari har dekabrda olingan ma'lumotlar bazasining & quotsnapshot & quot-ga qarshi ishlaydi. Statistika yangi suratga olinmaguncha doimiy bo'lib qoladi. VMT bo'yicha so'nggi hisobotlarni bizning Data Products sahifamizda ko'ring.

Magistral magistrallarni qayd etish punktlari, mos yozuvlar punktlari va haqiqiy millar


Magistral avtomagistralni hisobga olish punkti bundan keyin CHIMES-ga kiradigan loyihalar uchun ishlatilmaydi (2018 yil yoki undan keyin boshlanadigan avtomobil yo'llari loyihalari)

Shtat bo'ylab magistral avtomagistralni hisobga olish bo'yicha hisobotda 2014 yil yanvarida muzlatilgan va 2012 yil qurilish mavsumiga qadar eskirgan transport ma'lumotlari tizimining (TIS) ma'lumotlari ishlatilgan. Tez orada yangi Lineer Reference System (LRS) ma'lumotlaridan foydalangan holda almashtirish hisoboti ishlab chiqiladi.

Trunk Highway Log Point hisobotida har xil davlatlararo, AQSh avtomagistrali va Minnesota shtatidagi avtomagistral yo'nalishlarini kesib o'tadigan diqqatga sazovor joylar, ularning haqiqiy yurish masofalariga qarab ketma-ketlikda keltirilgan. Ushbu xususiyatlarga boshqa yo'llar, ko'priklar, temir yo'l o'tish joylari, shahar va tuman chegaralari va Qurilish okrugi chegaralari kiradi. Ro'yxatlar quyidagi tavsiflovchi ustunlarni o'z ichiga oladi:

  • Yo'nalish tizimi
  • Marshrut raqami
  • Malumot ballari
  • Kesishuvchi xususiyatlarning tavsifi
  • Kesishadigan xususiyatlarga haqiqiy markaziy masofa (ACCUM Miles ustuni)
  • Qurilish tumani
  • Trafikning eng so'nggi hajmi (AADT)

Log Point hisoboti markaziy masofadan tashqari, mos yozuvlar nuqtalari deb nomlangan o'lchovlarning ikkinchi turini ham o'z ichiga oladi. Yo'naltiruvchi punktlar taxminan bir milya oralig'ida yo'lning chetida joylashgan raqamli belgilar bo'lgan mos yozuvlar postlariga ("milya postlari" deb ham ataladi) asoslangan. Har bir mos yozuvlar o'z navbatida marshrutning haqiqiy kilometri bilan bog'liq. Ushbu o'lchovlar o'rtasidagi munosabatlar va tarjimalar kompyuter tomonidan hisoblanadi. Yo'naltiruvchi punktlar joylashuv barqarorligining turini ta'minlash uchun ishlatiladi. Haqiqiy yurish marshruti har bir alohida marshrut uchun noldan boshlanadigan marshrut bo'ylab oddiygina odometr yurishdir. Yo'nalish bo'ylab haqiqiy masofalar vaqt o'tishi bilan o'zgarishi mumkin (masalan, qayta yo'naltirishlar tufayli), mos yozuvlar postiga / nuqtasiga bog'langan voqealar barqaror bo'lib qoladi. Shtat bo'ylab True Mileage hisobotida shuningdek, muzlatilgan TIS ma'lumotlari ishlatiladi va Minnesota shtatining magistral magistral yo'llari bo'ylab har bir yo'naltiruvchi post uchun haqiqiy markaziy masofa haqida ma'lumot mavjud.

Qurilish loyihasi jurnali


MnDOT-ning qurilish loyihalari jurnali - bu magistral magistral yo'llarni qurish va ta'mirlashning tezkor vizual tarixi - bu yo'lning dastlabki qurilishidan beri amalga oshirilgan dala ishlariga qachon, qaerda va qaerda & rdquo ko'rsatma. Har bir loyiha jurnali sahifaning yuqori qismida joylashgan to'g'ri chiziq xaritasi bilan tasvirlangan boshlanish va tugatish chegaralariga ega. Shaxsiy loyiha chegaralari ushbu xaritaning bir qismida joylashgan gorizontal chiziqlar bilan tavsiflanadi. Har bir loyiha sahifaning chap tomonida sanab o'tilgan ishlarning qisqacha tavsifi, SP raqami, qurilgan yili, yuzasi kengligi va chuqurligi, material turi va boshqalar bilan belgilanadi. Loyiha jurnallari tuman va tumanlar bo'yicha tuzilgan, keyin esa nazorat bo'limi bo'yicha.

Odatda u faqat magistral ishlarni o'z ichiga olganligi sababli, panduslar, yo'l bo'yidagi ishlar, burilish yo'llari, vaqtinchalik va aylanib o'tish qurilishi, o'rtacha to'siqlar va ko'priklarni ta'mirlash ishlari to'g'risidagi ma'lumotlar kiritilmaydi. Shuningdek, loyiha jurnali ko'prik tafsilotlari uchun ma'lumot manbai bo'lishi uchun mo'ljallanmagan. Ko'prikni qurish va almashtirish odatda magistral yo'llarni baholash va sirtini qoplash loyihalariga kiritilgan yoki ularga bog'langan. Va nihoyat, barcha avtomobil yo'llari uchastkalarida loyiha jurnallari mavjud emas. Agar dastlab yo'l boshqa yo'l idorasi tomonidan qurilgan bo'lsa va keyinchalik MnDOTga topshirilgan bo'lsa, jurnal odatda mavjud emas. Agar yo'l qismi dastlab MnDOT tomonidan qurilgan bo'lsa, loyiha jurnali mavjud bo'lishi kerak.

Qurilish loyihasi jurnalining sahifasini yaxshilashni ko'rib chiqmoqdamiz. So'rovnomamizni olib borish orqali qurilish loyihalari jurnalini yaxshilashga yordam bering.

Qo'shni shaharlar metro zonasida Urban Local yo'llar uchun eng keng tarqalgan funktsional tasnifdir. Yuqoridagi Minneapolisdagi ko'chalar singari shahar ko'chalari markaziy masofaning yarmidan ko'pini tashkil qiladi.

Funktsional tasnif


Funktsional tasniflash - bu ko'chalar va avtomobil yo'llarini ular taqdim etadigan xizmat xususiyatlariga ko'ra sinflarga yoki tizimlarga birlashtirish. Tasnifga o'zgartirishlar kiritilganda, yo'llar davlat ko'magi tizimiga kiritilishi mumkinligi tekshiriladi. O'zgarishlar yuz berganda, obodoniyaning jismoniy o'zgarishini aks ettirish uchun avtomagistralni qayta ko'rib chiqish va asosiy xarita yo'nalishi va onlayn xaritalar yangilanadi. MnDOT-ning funktsional tasnifi sahifasi o'zgarish jarayoni, FHWA talablari va ko'rsatmalari, xaritalar va boshqa manbalarni har tomonlama ko'rib chiqadi.

Nazorat bo'limlari va qonuniy (konstitutsiyaviy / qonunchilik) yo'nalishlari


Davlat magistral yo'llari Boshqaruv bo'limlari deb nomlangan segmentlarga bo'linadi. Ushbu bo'limlarning maqsadi - ish yuritishning barqarorligini ta'minlash. Magistral yo'llar ko'chirilganda / qayta tayinlanganda, Boshqarish bo'limi va unga tegishli barcha ma'lumotlar marshrutga emas, balki yo'lakka bog'langan holda qoladi. Bu avtomobil yo'lini, shu jumladan texnik xizmat ko'rsatish, qurilish va ma'muriy maqsadlarni yaxshiroq boshqarish imkonini beradi.

To'rt xonali raqam sifatida nazorat bo'limi aniqlanadi. Dastlabki ikkita raqam ketma-ket tuman kodini aniqlaydi va oxirgi ikki raqam ushbu okrug ichidagi identifikatsiya raqamidir. Boshqaruv bo'limlari termini deb nomlangan boshlanish va tugash joylariga ega. Termini odatda tuman chegarasi yoki boshqa avtomagistralning kesishishi bo'ladi. Boshqaruv uchastkalari o'zgarishi mumkin va avtomagistralni qayta qurish, yangi davlat magistral yo'lini qurish yoki ushbu marshrutga egalik huquqini o'tkazish sababli (odatda shtatdan okrugga) o'tish sababli qayta ko'rib chiqilishi mumkin.

Maqsadli marshrutlar Minnesota shtati qonun chiqaruvchisi tomonidan belgilanadi va MnDOT-ga avtomobil yo'llarini qurish huquqini beradi. Ular konstitutsiyaviy va qonunchilik yo'llaridan iborat. 1 dan 70 gacha raqamli marshrutlar 1920 yilda Minnesota shtati Konstitutsiyasiga Babkok tuzatishining bir qismi sifatida belgilangan konstitutsiyaviy marshrutlar deb ataladi va ularni shtat konstitutsiyasiga o'zgartirish kiritmasdan o'zgartirish yoki olib tashlash mumkin emas. 70 dan katta bo'lgan marshrutlar qonun chiqaruvchi marshrutlar deb nomlanadi, ular qonun chiqaruvchi tomonidan qo'shilishi yoki olib tashlanishi mumkin. Shuni ta'kidlash kerakki, qonuniy marshrutning soni, albatta, siz avtoulovning bir xil soniga teng emas, chunki siz uni belgida ko'rishingiz mumkin. Ushbu marshrutlar bitta yoki bir nechta magistrallarga berilishi mumkin va ularning maqsadi jamoalar o'rtasida & ldquoconnectivity & rdquo ni ta'minlashdir.

Ma'lumotlar Mahsulotlari sahifasida nazorat bo'limlari to'g'risidagi so'nggi hisobotni va boshqaruv bo'limlari, qonuniy marshrutlarni va yodgorlik marshrutlarini aks ettiruvchi xaritalarni ko'ring.


Kooperativ trafik ma'lumot tizimlari uchun peer-to-peer ma'lumotlar tuzilmalari

Yo'l harakati kooperativ axborot tizimlari boshqa haydovchilar tomonidan to'plangan transport ma'lumotlariga asoslanib, marshrutni tanlashda avtomobil haydovchisini qo'llab-quvvatlaydi. Bunday tizim ishtirokchilari o'rtasida trafik ma'lumotlarini tarqatish uchun uyali aloqa tarmoqlari orqali Internetga ulanishga asoslangan peer-to-peer tarmog'idan foydalanishni taklif etamiz. Ushbu yondashuv taniqli VANET-ga asoslangan cheklovlardan qochadi. Trafik ma'lumotlarining kooperativ tizimida saqlanadigan ma'lumotlar juda aniq tuzilishga ega bo'lgani uchun, "peer-to-peer" ga umumiy yondashuvlarni qayta ishlatish o'rniga tizimni ushbu aniq dastur domeniga moslashtirish, ayniqsa, tarmoqli kengligi iste'moli va kechikish nuqtai nazaridan foydalidir. . Ushbu tushuncha bizni trafik ma'lumotlarini boshqarish uchun maxsus ishlab chiqilgan "peer-to-peer" tarmog'ining rivojlanishiga olib keldi. Ushbu maqolada biz bosqichma-bosqich GraphTIS-ning asosiy mexanizmlarini, peerTIS deb nomlangan birinchi echimni belgilaydigan standart peer-to-peer tizimiga asoslanib, ushbu standart DHT modifikatsiyasiga asoslangan va trafik bo'yicha axborot tizimlarini qo'llab-quvvatlash uchun maxsus ishlab chiqilgan yangi peer-to-peer tizimi GraphTISni taqdim etadi.

Asosiy voqealar

► Biz yo'l harakati ma'lumotlarini almashish uchun uyali tarmoqqa asoslangan P2P tizimini ko'rib chiqamiz. ► Ushbu yondashuv VANET aloqa imkoniyatlarini cheklashdan qochadi. ► Biz ushbu dastur uchun ma'lumotlar tuzilishini saqlab qolish muhimligini ko'rsatamiz. ► Geografik yaqinlik va grafik asosidagi munosabatlarga asoslangan yondashuvlarni taqqoslaymiz. ► Ikkala yondashuv ham past kechikishlarga erishadi, ammo yuk taqsimoti jihatidan farq qiladi.


Maqsad

Ushbu tadqiqot Intellektual transport tizimini, ya'ni Presto ITS mobil dasturini ishlab chiqish va ishlatish metodologiyasini ishlab chiqishga mo'ljallangan. Android ilovasi Presto ITS, shahar trafik ma'lumotlarini boshqarish markazi (UTDMC) bilan birgalikda foydalanuvchilarga marshrutni tanlash to'g'risida qaror qabul qilishga imkon berish orqali yo'l tarmoqlarida tirbandlikni kamaytirishga yordam beradigan ishlarni ishlab chiqdi. Bu ishlab chiqishda ishlatilgan turli xil texnologiyalar haqida umumiy ma'lumot beradi va amalga oshirilgan algoritmlarni batafsil bayon qiladi. Va nihoyat, ushbu tadqiqotda ishlab chiqilgan mobil ilova Android platformasida ishlab chiqilgan bo'lib, marshrutdagi tirbandlik stsenariysini hal qilish uchun audio va matn formatida xabarlarni taqdim etadi. Tavsiya etilgan tizim UPDMC saytini yaratish uchun PhP, HTML5, CSS3, ma'lumotlar bazasi uchun MySQL, Android Studio va JAVA mobil dastur va Google Maps-dan foydalanib, dasturning qulayligini oshirish uchun foydalangan, chunki foydalanuvchilarning foydalanuvchi interfeysi bo'yicha talablari juda katta. yuqori. PRESTO ITS yo'l tarmog'ining tasodifiy foydalanuvchilari va shaharga tashrif buyuruvchilarning axborot ehtiyojlarini qo'llab-quvvatlash uchun foydali bo'ladi. Bundan tashqari, taklif qilingan tizim, shuningdek ma'lumotlar bazasini mos ravishda o'zgartirish orqali Hindistonning boshqa shaharlarida ham qo'llanilishi mumkin.


II bilan bog'liq ish

II-a trafik oqimini prognoz qilish

Tarixiy o'rtacha ko'rsatkichlar, vaqt qatorlari va Kalman filtrlashni o'z ichiga olgan an'anaviy prognoz modellari ko'pincha transport sharoitlarini taxmin qilish uchun statistik tahlillardan foydalanadilar. Tarixiy o'rtacha model bashorat natijasi sifatida tarixiy ma'lumotlarning o'rtacha qiymatidan bevosita foydalanadi. Vaqt seriyali modeli joriy ma'lumotlar va tarixiy ma'lumotlar o'rtasidagi bog'liqlikdan foydalanadi va davriylik va ma'lumotlarning bashorat qilish tendentsiyasini hisobga oladi. ARMA modeli

1979 yilda taklif qilingan [1] vaqt qatorlarini o'rganish uchun muhim usuldir. U avtoregressiv (AR) model va harakatlanuvchi o'rtacha (MA) modeldan iborat. AR modeli avtokorrelyatsiya funktsiyasidan foydalanib, model parametrlarini topish va vaqt tarixini asl tarixiy ma'lumotlar yordamida bashorat qilish, MA modeli esa avtokorrelyatsiya funktsiyasining xato muddatini to'playdi. ARIMA modeli [8] bu ARMA ning umumlashtirilgan versiyasi bo'lib, avtomatik ravishda differentsiyalashning qo'shimcha komponentiga ega va har ikkala ARIMA va ARMA modellari ham vaqt seriyasining turg'unligini boshlang'ich nuqtasi sifatida qabul qiladi. Kalman filtrlash modeli bashorat qilish uchun shovqinni filtrlash uchun holat tenglamasi va kuzatuv bilan aniqlangan holat oralig'idan foydalanadi.

Mashinada o'qitish va chuqur o'rganishning uzluksiz rivojlanishi bilan intellektual prognozlash modellarining afzalliklari tobora oshib bormoqda. Ushbu modellar ko'p miqdordagi yig'ilgan tarixiy trafik ma'lumotlarini kirish sifatida qabul qiladi va keyin trafik holatini bashorat qilish uchun ma'lumotlardagi potentsial naqsh va xususiyatlarni avtomatik ravishda o'rganadi. Intellektual prognozlash modellarini asosan ikkita toifaga bo'lish mumkin: an'anaviy mashina o'rganish yondashuvlari va chuqur o'rganish yondashuvlari. Neyron tarmoqlari eng ko'p ishlatiladigan yondashuvlardan biri sifatida bashorat qilish uchun kirish ma'lumotidagi chiziqli bo'lmagan munosabatlarni o'rganadi. Sun'iy neyron tarmoqlari (ANN) va qo'llab-quvvatlash vektorining regressiyasi (SVR) amaliy bashorat qilish vazifalari uchun ikkita keng tarqalgan modeldir. SVR tarixiy ma'lumotlarning etarli xususiyatlaridan foydalanib, chiziqli bo'lmagan statistik naqshlarni o'rganadi. K ga yaqin qo'shnilar va loyqa mantiqiy modellar chiziqli bo'lmagan parametrik modellarning ikkita qo'shimcha namunasidir. Shu bilan bir qatorda, ANN

orqaga tortish yoki radial asos funktsiyasi (RBF) orqali uning og'irliklari va yon tomonlarini sozlaydi

va chiziqli bo'lmagan faollashtirish funktsiyasini qo'llaganidan keyin chiziqli bashorat natijalarini oladi.

Yuqorida keltirilgan modellar kelajakni bashorat qilish uchun tarixiy trafik holati ma'lumotlaridan foydalanadilar. Ko'pgina yo'l qismlarining tugunlaridan tashkil topgan yo'l tarmog'i uchun yo'l uchastkalari orasidagi qo'shnilik to'g'ridan-to'g'ri yoki bilvosita yakuniy bashoratga ta'sir qiladi. Bayes tarmog'i (BN) yo'l sharoitlarini prognoz qilish uchun yo'l tarmoqlaridagi qo'shni munosabatlarni tahlil qiladi. Yo'l tarmoqlaridan topologik ma'lumotlardan foydalanadigan yana bir model - bu grafik qo'shma tarmoq (GCN), uning kiritilishi qo'shni matritsadan va xususiyat matritsasidan iborat. Qo'shni matritsa yo'l tarmog'ining topologik xususiyatlarini, xususiyati matritsasi esa transport ma'lumotlarini o'z ichiga oladi. GCN kelajakdagi transport sharoitlarini prognoz qilish uchun yo'l uchastkalari tugunlari orasidagi aloqa munosabatlarini yozib oladi. Biroq, ushbu modellar faqat yo'l tarmoqlaridagi fazoviy munosabatlar haqida ma'lumotni saqlaydi va xususiyatlar matritsasida vaqtinchalik munosabatlarni ushlab turish qobiliyatiga ega emas. Shunga mos ravishda, masalan, besleme oldinga NN

[23], DBN [9], RNN [37] va RNN variantlari GRU [5] va LSTM [28] trafik xususiyatlarining tendentsiyalari va davriyligini aks ettiradi, ammo ular shahar transport tarmog'ining ichki topologik xususiyatlarini inobatga olmaydi. Ko'pgina tadqiqotchilar ushbu muammoni payqashdi va tarmoqlarning topologik tuzilmalaridan va trafik ma'lumotlariga vaqtinchalik bog'liqlikdan to'liq foydalanadigan ko'p sonli makonga oid prognozlash modellari taklif qilindi. Bunday modellarga ST-ResNet [34], SAE [18], FCL-Net [31], DCRNN [32] va T-GCN [36] va boshqalar kiradi.

Yo'l harakati holatlariga tarixiy trafik ma'lumotlaridan tashqari, turli xil tashqi omillar ta'sir ko'rsatishi mumkin, masalan, ob-havo sharoiti, metro stantsiyasi va avtobus bekatlari to'g'risidagi ma'lumotlar, POI va boshqa omillar. Trafikni bashorat qilishning dolzarb vazifasining asosiy vazifasi tashqi omil ma'lumotlarini bashorat qilish modellariga qo'shilishdir. Ko'p manbali ma'lumotlarni hisobga oladigan ba'zi usullar avvalgi tadqiqotlarda taklif qilingan. Liao B va boshq. [14] LSTM [28] asosida kodlovchi yordamida tashqi ma'lumotlarni kodladi va integral multimodal ma'lumotlarini bashorat qilish modelining kirish ketma-ketligi sifatida ko'rib chiqdi. GRU asosida Da Z va boshqalar taklif qilgan model. [33] kirish trafik xususiyatlari va ob-havo ma'lumotlarini birlashtiradi.

Ko'p manbali ma'lumotlarda Ii-B aloqalarini qazib olish

Ko'p manbali ma'lumotlardagi aloqalar asosan tarmoqlar ko'rinishida taqdim etiladi va vakillik vektorlari orqali tarmoqlarda mavjud bo'lgan tarkibiy va relyatsion ma'lumotlarni qazib olish tarmoq ma'lumotlarini olishning asosiy usuli hisoblanadi. Umuman olganda, tugun turlariga ko'ra tarmoqlarni bir hil tarmoqlarga va heterojen tarmoqlarga bo'lish mumkin. Bir hil tarmoqlar faqat bitta ma'lumot turini ko'rib chiqadi, ya'ni tugunlarning turlari bir xil bo'lishi kerak, ammo haqiqiy tarmoqlarning aksariyati turli xil tugunlarga ega. Bir hil tugunlarning cheklanishini bartaraf etish uchun har xil turdagi tugunlarning ma'lumotlarini va tugunlar o'rtasidagi munosabatlarni ifodalash uchun heterojen tarmoqlar taklif etiladi. PTE [26] matnlarni, so'zlarni va yorliqlarni tasniflaydi va heterojen tarmoqlarni qurish uchun ularning juft munosabatlarini ifodalaydi. [6] va [7] heterojen voqealar tarmog'ini qurish uchun bir-biri bilan kuchli korrelyatsiyaga ega voqealarni modellashtiradigan HEBE-ning ichki tizimini taklif qiladi. Heterojen tarmoqlarning muhim kamchiligi shundaki, tugunlar orasidagi munosabatlarni ifodalashda aniq metapatalar tuzilishi kerak va aniq metapatalar geterogen tarmoqlarni ma'lum bir tarmoq doirasida cheklashiga olib kelishi mumkin. So'nggi yillarda bilimlar grafikalarining paydo bo'lishi yuqoridagi muammo uchun yanada kengroq g'oyalarni taqdim etdi. Zamonaviy bilim tushunchasi dastlab Google tomonidan taklif qilingan va keyinchalik turli sohalarda qo'llanila boshlangan. Grafik tuzilmalar va axborotlarni qayta ishlashda bilimlar grafikalari kuchliligi tufayli tobora ko'proq tadqiqotchilar turli xil sohalarda, masalan, ijtimoiy tarmoqlar [22], qidiruv tizimlari [11], aqlli savollar va javoblar tizimlari va aqlli tavsiyalar bo'yicha bilim grafikalarini tushunishga va qo'llashga kirishdilar [ 24]. Bilim grafikalari elektron tijorat kabi sohalarda ham qo'llaniladi [29]. Ular transportda, masalan, sayt tanlashda [25] va yo'l-transport hodisalarida [30, 20] rollarni o'ynaydilar.


Bangalor shahridagi GIS yordamida shahar yo'llarining ish faoliyatini baholash va yo'laklarni boshqarish

Geografik axborot tizimi (GIS), masofadan turib zondlash va global joylashishni aniqlash tizimi (GPS) avtomobillarga texnik xizmat ko'rsatish va boshqarish ishlarini olib borish uchun juda mos keladi. Hindiston hukumati yo'l aloqasi uchun katta mablag 'ajratmoqda. Moslashuvchan yo'lakchalar Hindistondagi avtomobil yo'llarining katta qismini tashkil etadi, chunki qurilish qiymati past. Yo'llarga texnik xizmat ko'rsatish va boshqarish tizimi (PMMS) parvarishlash modellarini kelajakdagi ma'lumotlarga asoslanib, parvarishlash strategiyasini shakllantirish mumkin bo'lgan kelajakdagi ma'lumotlarni taxmin qilish uchun ishlatadi.

Ushbu tadqiqot shahar yo'llarining ishlashini Geografik Axborot Tizimi (GIS) yordamida modellashtirishga qaratilgan harakatdir. Rutting, chuqurliklar va yoriqlar kabi batafsil ma'lumotlar to'plangan. Ma'lumotlar bazasi GIS dasturiy ta'minotiga kiritilgan bo'lib, u yo'l bilan bog'liq barcha atributlarning ma'lumotlarini aks ettiradi va u qarorlar qabul qilish va muammolarni hal qilish uchun ishlatiladi.

Avtomobil transporti Hindistonning umumiy transport tizimida juda ustun mavqega ega. Mustaqillikdan keyingi davrda avtomobil transportining o'sishi tovar va yo'lovchi tashish hajmi bo'yicha misli ko'rilmagan darajada bo'ldi. Afsuski, yo'llar tarmog'idagi o'sish transport vositalarining ko'payishi bilan mutanosib emas. Trafik yuklanishi belgilangan 10,2 tonna chegarasidan ancha og'irroq bo'ldi. Tegishli va o'z vaqtida ta'mirlanmagan holda, yo'llar haddan tashqari yomonlashadi, bu esa transport vositalarining ekspluatatsiya xarajatlari oshishiga, avtohalokatlar sonining ko'payishiga va transport xizmatlarining ishonchliligining pasayishiga olib keladi.

Yo'l qoplamalarini boshqarish tizimi, ular yo'l qoplamasi bilan bog'liq barcha tadbirlarni (rejalashtirish, loyihalash, qurish, qurish) organik ravishda birlashtirib qurilgan taqdirdagina samarali ishlashi mumkin.

texnik xizmat ko'rsatish, reabilitatsiya, baholash, iqtisodiy tahlil va tadqiqotlar) va ma'lumotlar banki. Keyinchalik, eng muhim narsalar - bu yulka sifatini ifodalovchi xizmatga yaroqlilik indeksini yaratish va vaqt (va / yoki trafik) va indeks o'rtasidagi bog'liqlik bilan ifodalanadigan ishlashni bashorat qilish. Yo'l qoplamasining sifati ikkita asosiy omildan iborat: haydash sifati va siljishga qarshilik. Yugurish sifatiga ta'sir etuvchi omillar yo'lakning buzilishi va / yoki pürüzlülüğüdür. Yulka bezovtalanishining uchta asosiy omili bu yorilish, yorilish va bo'ylama profil. Tavsiyalar odatda yo'lni yanada rekonstruksiya qilishni talab qilguncha buzilishiga yo'l qo'ymaslik o'rniga, profilaktika ishlariga asoslangan.

Yo'l qoplamalarini boshqarish tizimlari tomonidan amalga oshiriladigan odatiy vazifalarga quyidagilar kiradi.

Yo'l qoplamalarining sharoitlarini inventarizatsiya qilish, yaxshi, adolatli va yomon qoplamalarni aniqlash.

Trafik hajmi, yo'lning funktsional klassi va jamoatchilik talabidan kelib chiqib, yo'l segmentlari uchun ahamiyatlilik reytingini belgilang.

Yaxshi yo'llarni yaxshi holatda saqlash uchun ularni saqlash jadvalini tuzing.

Qolgan mablag 'imkoni borligi sababli yomon va adolatli qoplamalarni ta'mirlash jadvalini tuzing.

Kompyuterlashtirilgan yo'l qoplamalarini boshqarish tizimlari va texnik xizmat ko'rsatish va reabilitatsiya strategiyalarini rejalashtirish, loyihalash va baholashda muhandislarga yordam berish uchun bilim tizimiga asoslangan ekspert tizimining qarorlarini qabul qilish vositalarini yaratish Biroq, bunday strategiyalar va tegishli investitsiya qarorlari ekspert tizimining yondashuvlari yoki inson mutaxassislari tomonidan ishlab chiqilganligidan qat'i nazar, asosiy ma'lumotlar sifatida bir xil yo'l qoplamasi va sirt holati ma'lumotlari talab qilinadi. Bitumli qoplamalarga nisbatan, bu charchoq (yoki alligator) yorilishi, uzunlamasına yorilish, ko'ndalang yorilish, shag'al va yamoq kabi qiyinchiliklarning darajasi va og'irligini o'z ichiga oladi. Ushbu ma'lumotlar tarkibiy va funktsional ko'rsatkichlarning ko'rsatkichlari.

Yo'l qoplamalarini saqlash va tiklash talablarini aniqlash bo'yicha ilmiy yondashuvni ishlab chiqish zarur. Mavjud yo'llar tarmog'ini takomillashtirish uchun yo'llarni boshqarish va rejalashtirish vositalarini rivojlantirish bo'yicha harakatlar ham zarur. Ushbu vositalar moliyaviy ehtiyojlarni baholash, muqobil texnik xizmat ko'rsatish strategiyasini baholash va ish dasturlarini birinchi o'ringa qo'yish uchun juda muhimdir. Bunday vaziyatda yo'laklarni boshqarishning samarali tizimini (PMS) ishlab chiqish va amaliyoti yo'l tarmoqlarini saqlab qolish bilan bog'liq izchil va tejamkor qarorlarni ta'minlash uchun ob'ektiv ma'lumot va foydali tahlilni taqdim etadi.

Yo'llarni boshqarish tizimi (PMS) qimmatbaho vosita va avtomobil transporti infratuzilmasining muhim elementlaridan biridir. Dastlabki PMS kontseptsiyasi 1960 yillarga borib taqaladi

(Norlela Ismoil va boshqalar (6) va Amir Tavakoli va boshqalar (7) tomonidan berilgan). Ilg'orlarning tez o'sishi bilan

axborot texnologiyalari, ko'plab tergovchilar yulka bilan bog'liq qarorlarni qabul qilishni qo'llab-quvvatlash uchun zarur bo'lgan ma'lumotlarni saqlash, olish, tahlil qilish va hisobot berish uchun Geografik Axborot tizimini (GIS) PMS-ga muvaffaqiyatli kiritdilar. GIS tizimining asosiy xarakteristikasi shundaki, u transportda an'anaviy ravishda foydalaniladigan milepost yoki mos yozuvlar-nuqta tizimi o'rniga ma'lumotlar / ma'lumotlarni geografik joylashuvi (masalan, kenglik / uzunlik yoki davlat tekisligi koordinatalari) bilan bog'laydi. Bundan tashqari, GIS topologik ma'lumotlar tuzilishi va modeli yordamida real dunyoning topologik munosabatlarini tavsiflashi va tahlil qilishi mumkin. GIS texnologiyasi, shuningdek, ma'lumotlar bazasidan ma'lumotlarni tezda olish qobiliyatiga ega va texnik xizmat ko'rsatish joylarini aniqlash kabi muayyan ehtiyojlarni qondirish uchun avtomatik ravishda moslashtirilgan xaritalarni yaratishi mumkin.

Geografik axborot tizimlari (GIS) geografik ma'lumotlarni to'plash, saqlash, tahrirlash, namoyish qilish va tahlil qilish uchun ishlatiladigan apparat, dasturiy ta'minot va ma'lumotlardan tashkil topgan axborot texnologiyasini aks ettiradi. Ma'lumotlar bazasini boshqarish va amaliy dasturlar muhiti bilan bog'liq bo'lgan rivojlanish faoliyatining asosiy oqimiga GIS kiritiladigan aniq joylashuv ma'lumotlarini yig'ish qobiliyatining so'nggi yutuqlari.

Ushbu tadqiqot uchun Bangalor shahridagi Arterial halqa yo'li hisoblanib, yo'l tadqiqotlari olib borilishi kerak bo'lgan yaqin kelajakda yo'l harakati va xulq-atvorini bashorat qilish uchun Geografik Axborot Tizimi (GIS) dan foydalangan holda shahar yo'llarining ish faoliyatini baholash va asfalt qoplamasini boshqarish nazarda tutilgan. qoplamaning strukturaviy va funktsional holatini o'rganish. So'ngra kerakli ma'lumotlar yig'iladi va GIS dasturiga yuklanadi, bu erda xaritani o'rganish uchun olingan yo'llar o'z ichiga olgan raqamlashtiriladi.

Hozirgi o'rganish maqsadi

Ushbu tadqiqotning asosiy maqsadi Bangalor shahar yo'llarining tanlangan uchastkalarining qoplamalarini ishlashini baholashdir.

Ikkala tarkibiy va funktsional holat ma'lumotlarini hisobga olgan holda shahar yo'llari uchun GIS asosidagi ma'lumotlarni yaratish.

Har xil parvarishlash strategiyalari bo'yicha kelajakdagi yo'l qoplamasining holatini bashorat qilish.

Bump Integrator tomonidan yulka tengsizligi / pürüzlülüğü tadqiqotlari bilan bir qatorda yorilish, yamoq, rutting, ravelling va chuqurliklar singari yuzaki muammolarni qoplash inventarizatsiyasining funktsional holatini baholash.

Benkelman Beam Deflection tadqiqotini olib borish orqali qoplamani strukturaviy baholash.

Qatlamning ishlashi to'g'risidagi ma'lumotlarni GIS dasturiy ta'minotiga DBMS (Data Bse Management System) shaklida yuklash.

Toshihiko Fukuhara va boshqalar (1): Bu lazer, video va tasvirni qayta ishlash usullaridan foydalangan holda tizim yaratildi. Ushbu tizim tadqiqot vositasi va ma'lumotlarni qayta ishlash tizimidan iborat. The

tadqiqot mashinasi yorilish, tirqish va bo'ylama profilni bir vaqtning o'zida, aloqa qilmasdan, tez va aniq o'lchashi mumkin. Ma'lumotlarni qayta ishlash tizimi o'lchov qilingan ma'lumotlarni avtomatik ravishda yulka ma'lumotlari bankida ishlatilishi mumkin bo'lgan formatlarga o'zgartirishi mumkin. Tizim yoriqlarni avtomatik ravishda aniqlashga imkon beradi, bu odatdagidek, faqat odamlar tomonidan amalga oshiriladi.

Yoriq, qirqish va uzunlamasına profilni o'lchash va ma'lumotlarni qayta ishlash to'liq avtomatlashtirildi va ish samaradorligi sezilarli darajada yaxshilandi, shuningdek noyob chiziqlarni topish algoritmiga ega. Avtomatik ravishda yoriqlarni aniqlashga imkon beradigan va ma'lumotlarni tahlil qilishdagi muammolarni hal qiladigan maxsus ko'p mikroprotsessorli tizim ishlab chiqilgan joyda.

Turki I va boshq. (2): Sirt pürüzlülüğünün o'zgarishi, asfalt sirtining buzilish o'lchovi sifatida qabul qilindi. Regressiya modellari muntazam parvarishlash xarajatlari darajasi, yulka yoshi va sirt pürüzlülüğünde transport vositalarining yuklanishini ta'sirini o'rganish uchun ishlab chiqilgan.

Dastlab ushbu tadqiqotda oltita muntazam parvarishlash ishlari ko'rib chiqildi: sayoz yamoq, chuqur yamoq, remiksni tekislash, muhrni qoplash, uzunlamasına yoriqlar va bo'g'inlarni yopish. Amaliyot qo'llanmasida (AASHTO 1981), yo'lakning yomonlashishi xizmatga yaroqsizligi yoki PSI yo'qolishi bilan ifodalangan. Ushbu tadqiqotda, turli darajadagi muntazam parvarishlashdan oldin va keyin qoplamaning sirt pürüzlülüğünü bilish orqali

magistral yo'lning ma'lum bir qismi, qoplama yuzasining buzilishi sirt pürüzlülüğünün o'zgarishi sifatida o'lchandi. Ushbu kontseptsiya ushbu maqolada yulka sirtining yomonlashishini kamaytirishdagi muntazam samaradorligini aks ettirish uchun foydalanilgan.

Ularning fikriga ko'ra, yo'lning yoshi va transportni yuklash o'zgaruvchilari muhim ahamiyatga ega. Shunday qilib, ushbu modellar yo'lning yoshi va transport vositalarining yuklanishining sirt pürüzlülüğünün o'zgarishiga va natijada parvarishlash samaradorligiga ta'sirini baholash uchun ishlatilgan. Oddiy yoki yaxshi holatdagi yo'laklarni muntazam parvarishlash samaradorligi juda yaxshi holatdagi yo'laklarga qaraganda yuqori ekanligi aniqlandi. Premiksni tekislash va plomba qoplamasini o'z ichiga olgan parvarishlash ishlari bo'g'inlar va yoriqlarni yopish va yamoqlarga nisbatan ancha yuqori samaradorlikni ta'minladi.

Mohd Zulkifli B va boshq. (3): Ushbu tadqiqotda muallif ArcView GIS amaliy dasturini qabul qildi va yo'llar ma'lumotlar bazasini boshqarish samaradorligini ko'rib chiqdi va tahlil qildi. Ushbu ma'lumotlar keyinchalik yo'lni samarali va tizimli ta'mirlashni ta'minlashda menejmentga yordam berish uchun ishlatiladi. Keys-tadqiqot sifatida Malayziyaning Penang shahridagi yo'llarning odatiy modeli qo'llaniladi.

GISni qabul qilish raqamli ma'lumotlarni, ayniqsa yo'l ma'lumotlari bilan bog'liq ma'lumotlarni yanada uyushgan boshqarishga olib keladi. Xususan, ushbu tizim ilovasi yo'llarni ta'mirlashni boshqarishda ish unumdorligini oshiradi. U foydalanishni nisbatan qulayligi bilan ma'lumotlarni tezkor ravishda chaqirib olish imkoniyatiga ega edi, shuningdek, bu makon ma'lumotlarini to'plashda behuda takrorlanishni minimallashtiradi va ma'lumotlar almashinuvini yaxshilaydi, saqlanadigan ma'lumotlarning aniqligi va izchilligini ta'minlaydi.

Stiven G. Ritchi (4): Ushbu maqolada yo'laklarni boshqarishda raqamli tasvirni qayta ishlashga oid tushunchalar va ilovalar keltirilgan bo'lib, ular yo'lning yuzasida yuzaga keladigan muammolar, asosiy mashina ko'rish va raqamli tasvirlarni qayta ishlash kontseptsiyalari, avtomatlashtirilgan uchun video tizim xususiyatlarini o'z ichiga oladi. muammo - ma'lumotlar yig'ish.

Nisbatan qisqa vaqt ichida distress-ma'lumotlarni yig'ish va talqin qilish uchun avtomatlashtirilgan tizimlarni ishlab chiqishda sezilarli yutuqlarga erishildi va yaqin kelajakda kutilayotgan imkoniyatlarning kengayishi, bu harakatlarda raqamli tasvirlash texnologiyasi muhim rol o'ynaydi degan xulosaga keldi. .

Yo'l qoplamasining funktsional muammolari va uning tushunchalari

Yo'l qoplamasining xizmatga yaroqliligi-kontseptsiyasini tahlil qilish to'g'risidagi ma'lumotlar, yo'llar qismining haydash sifati tarixi va shu vaqt oralig'idagi tirbandligi to'g'risida ma'lumotga muhtoj. Buni davriy kuzatuvlar yoki yozuvlar bilan haydash sifatini o'lchash orqali aniqlash kerak

trafik tarixi va vaqti. Yo'l qoplamasi qoniqarli yoki qoniqarsiz deb topildi. The type and extent of maintenance for a road also depends on the serviceability standard laid down, the maintenance needs, funds available and the priorities for the maintenance operations. The current engineering practice for design and construction of pavement overlays and selection of maintenance and rehabilitation alternatives is based on subjective judgment and engineering experience. An efficient pavement maintenance program is a program that identifies what maintenance action is to be taken and where and when is to be applied, so that most cost effective results are obtained.

Causes and Consequence Effects of Pavement Distress

The causes for structural and functional distresses may be of three criteria:

Overload including excessive gross loads, high repetition of loads and high tyre pressures can cause either structural or functional failure.

The climatic and the environmental conditions may cause surface irregularities and structural weakness develops. Example: Frost heaving, change of volume of soil due to wet and dry process, the breakup of surface resulting from freezing and thawing action or improper drainage may be the prime cause of pavement distress.

The cause may be disintegration of the paving materials, due to freezing and thawing and/or wetting and drying process. Example: Use of nondurable aggregates, the base-course materials may breakdown, thus generating fines which may cause unstable mix. Sub grades are also susceptible to climatic conditions.

At times construction practices may induce some effect as well the inadequate inspection during construction are certain factors that causes pavement deterioration. Design procedures must be strictly applied and field control to provide adequate pavement structure.

Asphalt Pavement Distress

Distress surveys are required for the periodic evaluation of pavements. The surveys are directed towards assessing the maintenance measures needed to prevent accelerated distress and to determine the type of rehabilitation measures needed. These surveys provide the information required to define the distress types, severity and density of identified distresses. In addition, the surveys provide the data needed to develop the deduct values associated with each distress and severity levels. The following section describes some of the pavement distress parameters viz., cracking, patching, raveling, rutting and potholes along with their probable causes. There are four

major categories of common asphalt pavement surface distress:

Ravelling, Flushing, Polishing.

Rutting, Distortion – Rippling and Shoving, Settling, Frost heave.

Transverse, Reflection, Slippage, Longitudinal, Block, and Alligator Cracks.

In this study, from Hosur Road Silkboard junction to Nayandahally is taken, as shown in the table. Data has been collected for the following survey carried out: Volume Count Survey (VCS), Benkelman Beam Deflection Studies (BBD), Pavement Condition Survey (PCS) (By using Hawkeye 2000), Roughness (Bump Integrator) and digitising the stretches of the Bangalore map using GIS software.


Traffic Volume Count

Traffic Data Collection is basic requirements for transport planning. Traffic Data forms an integral part of national economics and such knowledge is essential in drawing up a rational transport policy for movement of passengers and goods by both government and the private sectors. Traffic Volume Count is counting of number of vehicles passing through a road over a period of time. It is usually expressed in terms of Passenger Car Unit (PCU) and measured to calculate Level of Service of the road and related attributes like congestion, carrying capacity, V/C Ratio, identification of peak hour or extended peak hour etc. Traffic volume count or TVC is usually done as a part of transportation surveys, TVC can be classified or unclassified.

Need of Traffic Volume Count Survey

Traffic Volume Survey is an essential part of Town Planning, especially for a town planner. It includes counting the number of vehicles passing through a survey station. The study of Classified Traffic Volume Count is to understand factors that form the basis of:

a) Checking the efficiency/saturation of the road network by comparing current traffic volume with the calculated capacity or by identifying level of service
b) Establishing the use of the road network by vehicles of different categories, traffic distribution, PCU/vehicle value
c) Need of median shifting or road widening

Purpose of Traffic Volume Count

The purpose classified traffic volume count is to draw inferences on the basis of data collected. To provide possible solutions and improvement suggestion for the problem identified. The objectives covered in it includes identifying the hourly distribution of vehicles and peak hour, identify level of service and compare modal composition on different hierarchy of roads.

Methods of doing Traffic Volume Count

Traffic Volume Count can be done by various methods depending upon various factors like manpower available, budget, technology/instrument available, magnitude of traffic data required or to be collected which will then determine quality and type of vehicle classification to be adopted. Traffic counting falls in two main categories, namely: manual count and automatic count.Traffic data collection forms the integral part of traffic volume study as it provides the raw data and includes primary survey. The various types and methods used to collect traffic data not only provide a good and valuable coverage of the required traffic information. Different methods of traffic volume count are as mentioned below –

Duration and Interval of Traffic Counts
In order to predict traffic flow volumes that can be expected on the road network during specific periods, knowledge of the fact is required that traffic volumes changes considerably at each point in time. There are three important cyclical variations:

  • Hourly pattern: the way traffic flow characteristic varies throughout the day and night
  • Daily Pattern: The day-to-day variation throughout the week
  • Monthly and yearly Pattern: The season-to-season variation throughout the year.
  • Hourly patterns –Typical hourly patterns of traffic flow, particularly in urban areas, generally show a number of distinguishable peaks. Peak in the morning followed by a lean flow until another peak in the middle of the afternoon, after which there may be a new peak in the late evening. The peak in the morning is often more sharp by reaching the peak over a short duration and immediately dropping to its lowest point. The afternoon peak on the other hand is characterised by a generally wider peak. The peak is reached and dispersed over a longer period than the morning peak.
  • Daily patterns –The traffic volume generally varies throughout the week. The traffic during the working days (Monday to Friday) may not vary substantially, but the traffic volume during the weekend is likely to differ from the working days on different type of roads and in different directions
  1. Manual Count: The most common method of collecting traffic volume data is the manual method of traffic volume count, which involves a group of people recording number of vehicles passing, on a predetermined location, using tally marks in inventories. Raw data from those inventories is then organized for compilation and analysis. This method of data collection can be expensive in terms of manpower, but it is nonetheless necessary in most cases where vehicles are to be classified with a number of movements recorded separately, such as at intersections also in case where automatic methods cannot be used due to lack of infrastructure, necessary authorization etc.

2. Automatic Count: This method is employed in cases where manual count method is not feasible. Various instruments are available for automatic count, which have their own merits and demerits. Some of the widely used instruments are pneumatic tubes, inductive loops, weigh-in-motion Sensor, micro-millimeter wave Radar detectors and video camera. Both types of count can be classified or unclassified. Classified traffic volume count gives a better understanding of the types of vehicles which uses the road and can be used for number of other purposes apart from the transportation surveys. It can also be used for calculating the modal split of vehicles on the road. Unclassified traffic volume count is done where sufficient manpower is not available or the budget for the survey is low. This type of volume count does not give a good information about the road.

Some of the widely used instruments are –

i) Pneumatic tubes – These are tubes placed on the top of road surfaces at locations where traffic counting is required. As vehicles pass over the tube, the resulting compression sends a burst of air to an air switch.

ii) Inductive loops – Inductive loop detector consists of embedded turned wire. It includes an oscillator, and a cable, which allows signals to pass from the loop to the traffic counting device. Inductive loops are cheap, almost maintenance-free and are currently the most widely used equipment for vehicle counting and detection

  • Bending Plates which contains strain gauges that weigh the axles of passing vehicles
  • Capacitive Strip is a thin and long extruded metal used to detect passing axles. Capacitive strips can be used for both statistical data and axle configuration.
  • Capacitive Mat functions in a similar manner as the capacitive strip but it is designed to be mobile and used on a temporary basis only.
  • Piezo-electric Cable is a sensing strip of a metallic cable that responds to vertical loading from vehicle wheels passing over it by producing a corresponding voltage. The cable is very good for speed measurement and axle-space registration, and is relatively cheap and maintenance

iv) Micro-millimetre wave Radar detectors – Radar detectors actively emits radioactive signals at frequencies ranging from the ultra-high frequencies (UHF) of 100 MHz, to 100 GHz, and can register vehicular presence and speed and can be used determine vehicular volumes and classifications in both traffic directions..

v) Video Camera – Video image processing system utilize machine vision technology to detect vehicles and capture details about individual vehicles when necessary. The system is useful for traffic counting and give a +/- 3% tolerance, and is not appropriate for vehicular speed and their classification.

Factors to be considered while doing a traffic volume survey on mid block –

  1. Surveyor should not affect the flow of traffic.
  2. Survey station should be located at position where queuing do not take place.
  3. Vehicles should be classified if possible as it saves time for Classified Traffic Volume Survey. Also classified results have many other application.
  4. Safety of surveyor should be kept in mind and safe location should be selected. This becomes more important in rural area where carriageway is not well-defined.
  5. Equipment used while automatic count should be placed such that they do not draw attention of driver.

Traffic Volume Survey can be done manually or by use of automatic methods depending upon various factors like manpower available, budget, technology/instrument available, magnitude of traffic data required


Join traffic data (point) to a road network (line) - Section between two roads - ArcGIS - Geographic Information Systems

Arkansas Scenic Byways Program was established shortly after Congress passed the Intermodal Surface Transportation Efficiency Act of 1991. This federal legislation created the framework to develop a network of National Scenic Byways.

The Arkansas Highway Commission adopted criteria by which routes in Arkansas could be designated as Arkansas Scenic Byways. The purpose of this program is to facilitate Arkansans’ recognition of special routes within the state.

Scenic Byways:

The Crowley’s Ridge Parkway, a 198-mile long route consisting of segments of 17 highways, two county roads, and several city streets, was designated as an Arkansas Scenic Byway in 1997. In 1998, it was designated as Arkansas’ first National Scenic Byway by the U.S. Department of Transportation, Federal Highway Administration.

This byway follows the geologic formation known as Crowley’s Ridge through northeast and east-central Arkansas. Approximately two million years ago, wind blown soils collected in an area between the meandering channels of the Mississippi and Ohio Rivers. This wind blown soil, known as loess, formed a ridge rising up to 200 feet in places above the surrounding flat delta region.

This high ground quickly became a magnet for human settlement. Today it is characterized by upland hardwood forest, farmland, orchards, and a wide variety of recreational and historical resources.

The Great River Road was established in 1938 as the national parkway of the Mississippi River. It extends through ten states along the river and offers glimpses into how the heartland of America developed. The 362-mile route in Arkansas consists of segments of 13 highways, several forest service and county roads and city streets. The Great River Road was designated as an Arkansas Scenic Byway in 2001. In 2002, it was designated as Arkansas’ second National Scenic Byway by the U.S. Department of Transportation, Federal Highway Administration.

The Great River Road traverses the ten Arkansas Counties that border the Mississippi River. This region, known as the Delta, is part of the nation’s largest alluvial plain. Travelers on the route experience both the mighty river and its legacy of shaping landscapes and lives along its path.

At the time of pioneer settlement, most Delta terrain was lowlands and swamps, rich in virgin timber and wildlife. Some two centuries later, it is largely agricultural, producing voluminous crops of soybeans, rice, cotton and wheat.

For much of its length, the Great River Road traverses agricultural lands, passing remnants of the original wetlands and traveling through towns whose histories and economies were influenced by the river. From Marianna to Helena, however, the route penetrates the woodlands of the St. Francis National Forest on Crowley’s Ridge.

Highway 88 from Highway 71 in Mena to the Oklahoma State Line was designated as an Arkansas Scenic Byway in 1998. This route climbs to the ridge of Rich Mountain, elevation 2,681 feet, and passes through Queen Wilhelmina State Park on its way to the Oklahoma State Line 18 miles to the west. This route is also part of the Talimena Scenic Drive which extends from Mena west to Talihina, Oklahoma. In 2005, Talimena Scenic Drive was designated the State’s third National Scenic Byway. Noted for colorful fall foliage, the route is considered one of the premier motorcycle routes in the state.

Highway 7 from Arkadelphia to Harrison became Arkansas’ first scenic byway in 1993. The scenic byway was extended in 1999 to include the highway from the Louisiana state line to Arkadelphia. Scenic Highway 7 is approximately 290 miles in length.

Scenic 7 starts in the coastal plain region of southern Arkansas. This area consists of lowland rolling hills covered with dense pine forests and numerous river valleys covered with bottomland hardwood forests. The region is rich in wildlife, outdoor recreation, and historical resources.

South of Hot Springs travelers will enter the Ouachita Mountains which are noted for wide valleys, rich agricultural lands, timbered mountains and abundant wildlife. The Ouachita Mountains are unique in that they are the only mountain range in the U.S. whose ridges and valleys are oriented west to east.

Next is the Arkansas River valley between the Ouachita and Ozark Mountains. Since the earliest days of European settlement in Arkansas, this valley has been an important transportation corridor and has been served by flatboats, keelboats, and steamboats, and the overland stage coach.

In the northwest portion of Arkansas, Scenic 7 traverses the Ozark Mountains. This region is famous for its colors, particularly in the fall when the oak-hickory forest turns to yellows, oranges, and reds contrasted by bright green pines and the spring when the dogwoods, redbuds, wild plums, and wildflowers bloom. The Ozark Mountains are also noted for their clear mountain streams.

Two separately designated routes make up the Boston Mountains Scenic Loop.

Highway 71 from Dean’s Market to Fayetteville was designated as an Arkansas Scenic Byway in 1998. Scenic Highway 71 begins at the edge of the Arkansas River Valley and extends 42 miles across the Ozark Mountains to Fayetteville. These rugged mountains are home to quaint craft shops, mountaintop lodging, and spectacular vistas.

Interstate 540 from Dean’s Market to Fayetteville was designated as an Arkansas Scenic Byway in 1999. This byway roughly parallels Highway 71 and also begins at the edge of the Arkansas River Valley, winding 38 miles north to Fayetteville. Constructed through very rugged terrain in the Ozark Mountains, this route includes several high-span bridges and the State’s only highway tunnel, the Bobby Hopper Tunnel.

This byway is known for its spectacular views of the oak-hickory forested mountains and small farms nestled in picturesque valleys.

For 35 miles, Highway 21 from Highway 64 to the Buffalo National River traverses the Boston Mountains region of the Ozark Mountains. The Byway, designated in 2005, offers a serene drive through the Ozark National Forest as well as many recreational opportunities and striking vistas before arriving at the nation’s first National River. Watch for elk near the Buffalo National River, deer, black bear and eagles.

Designated as a State Scenic Byway in 2001, this Scenic Byway extends 15 miles from Highway 256 in White Hall to Highway 65 southeast of Pine Bluff. I-530 traverses part of the 300-mile long Bayou Bartholomew, the world’s longest bayou. These wetlands are populated by bald cypress trees, an assortment of birds and waterfowl and more than a hundred species of fish, as well as otters and alligators.

This 45-mile portion of Highway 309 was designated as an Arkansas Scenic Byway in 1994. This byway starts at highway 10 in Havana and proceeds north over Mount Magazine, the highest point in Arkansas at 2,753 feet. This mountain was named by French explorers who recognized a resemblance to magazines used by the French military to store ammunition.

North of Mount Magazine, Highway 309 descends into the rich farmland of the Arkansas River Valley where it ends at Highway 23 in Webb City.

The Pig Trail Scenic Byway is a Forest Service Scenic Byway. Beginning in the southeast corner of the Ozark National Forest near I-40, The Byway extends north along Highway 23 for 19 miles to Highway 16 at Brashears. With its many steep inclines and sharp curves, driving the Pig Trail may be likened to riding a roller coaster. Trees crowd the roadside, creating a shady corridor during the summer in some areas and display stunning fall color. Very little development has occurred along this route, offering travelers a glimpse of the Ozarks that would have been familiar to the early settlers.

The Sylamore Scenic Byway is a Forest Service Scenic Byway located in the southeast corner of the Ozark National Forest. The byway begins at Calico Rock and proceeds along Highways 5 and 14 and Forest Service Road 110 to Blanchard Springs Caverns. The white oak-hickory forests provide an early spring view of dogwoods and redbuds and majestic fall color. Hiking trails abound for a closer view of the many hillsides and waterfalls.

Designated as a State Scenic Byway in 2005, the West-Northwest Scenic Byway is comprised of 261 miles of interconnected highways including Highway 71 from Mena to I-540 and portions of Highway 10 from Ola to the Oklahoma State Line and of Highways 23 and 96. Much of the Byway lies within the Ouachita National Forest. Crossing the only mountains in North America that are oriented east-west, the terrain is less rugged than the Ozark Mountains, so was more hospitable to early settlers. Many museums, historical sites and small towns characterized the area today.

Designation Criteria

Upon receipt of a written request to designate a route or a portion of a route as an Arkansas Scenic Byway, the following steps will be taken.

  1. The route must be designated as a “scenic highway” by the State General Assembly.
  2. An active organization composed of various private and governmental groups, businesses, and agencies who are interested in preservation, enhancement, marketing, and development of the route’s scenic, cultural, recreational, and historic qualities must be established.


Identification of Accident Blackspots on Rural Roads Using Grid Clustering and Principal Component Clustering

Identifying road accident blackspots is an effective strategy for reducing accidents. The application of this method in rural areas is different from highway and urban roads as the latter two have complete geographic information. This paper presents (1) a novel segmentation method using grid clustering and K-MEDOIDS to study the spatial patterns of road accidents in rural roads, (2) a clustering methodology using principal component analysis (PCA) and improved K-means to create recognition of road accident blackspots based on segmented results, and (3) using accidents causes in police report to analyze recognition results. The proposed methodology will be illustrated by accident data in Chinese rural area in 2017. A grid-based partition was carried on by using intersection as a basic spatial unit. Appended hazard scores were then added to the segments and using K-means clustering, a result of similar hotspots was completed. The accuracy of the results is verified by the analysis of the cause extracted by Fuzzy C-means algorithm (FCM).

1. Introduction

Traffic accidents are contingent events and are defined by a series of variables—the accident index, hidden danger index, and risk index—that explain them. When data is difficult to obtain in detail or changes greatly (such as in rapidly developing rural areas), latent variable models will be more suitable for safety evaluation. With the increase of car ownership and accidents in rural areas, developing countries like China are increasingly aware of the importance of rural road safety. By the end of the “Twelfth Five-Year Plan” (2015), the total mileage of rural roads in China reached more than 3.95 million kilometers. By the end of 2016, the number of household cars per 100 rural households was 17.4 (2016 Social Development Statistics Bulletin, 2017). At the same time, about two-thirds of all traffic accident deaths occurred on rural roads in 2016 (China Ministry of Transport, 2017). The Chinese government has put forward the slogan of “Four Good Rural Roads” and regards it as the main task of the Thirteenth Five-Year Plan.

One of the major difficulties in traffic safety evaluation is the heterogeneity of the data [1]. The threshold of selected variables is only used for accident black spots recognition, not considering the relationship between similar accidents, thus isolating the specific relationship between variables. In the establishment of the model for black spot recognition of accidents, the creation of multiple variables will have a certain degree of multicollinearity. Therefore, the model based on this contains vast amounts of redundant information [2]. Cluster analysis was used to identify black spots with the advantage of taking historical statistics and theoretical calculations into account [3]. It not only enables better clustering of similar segments, but also embodies the characteristics of different segments. It solves the problem of historical statistics.

Discretization of continuous attributes is an important preprocessing step in data mining. In the process of identifying the black spots of the accident, it is necessary to divide the intricate road network into continuous road segment for the road black spots identification. In the identification process of accident black spots on highways, the road segments are divided according to fixed length, and data processing only selects the appropriate pile spacing. When identifying black spots of urban roads, GIS (Geo-Information system) [4] and Kernel density estimation [5] are well used because of the complete geographic information of urban roads and accident points. However, when identifying black spots in rural roads, especially for developing countries, the geographical location of rural roads is incomplete, and the description of accident locations is vague. This makes the segmentation process of rural roads different and more complicated than highways and urban roads. de Ona [6] uses Latent Class Cluster (LCC) as a preliminary tool for segmentation of accidents on rural highways in Granada. de Ona divides accident data into multiple hidden clusters according to the condition and severity, while geographic information has not been taken into account. Based on the basic idea of gridding-based cluster, this paper quantifies the analysis object into limited road segments. Being different from the CLIQUE algorithm [7] setting the grid of the established step size, this paper uses the intersection as the unit and clusters the rural road accident points according to the threshold of density. This is the preparatory work for the following principal component clustering. To the best of our knowledge, this is the first time that both approaches have been used together.

Among the methods of identifying black spots, data mining technologies are approved for the reliability and prospects. Many previous studies have focused on compressing and identifying key factors that have an impact on the severity of road accidents. Neural network [8], rough set [9], fuzzy logit, and decision tree learning [10, 11] have been applied for recognition. The establishment of the recognition model requires multiple variables, while the existing relationships between the variables are easily ignored, so the establishment of the dimensions and weights of the variables can be important [12]. In order to avoid the multicollinearity problem between multivariables, this paper uses PCA to quantify the information of each road segment and extracts the principal components. On this basis, the improved K-means clustering is used to identify the black spots of the accident. In order to verify the reliability of identification results, the causes of the accidents are chosen for analysis. Fuzzy c-means algorithm (FCM) is widely used to identify the causes of accident [13]. This paper further improves its accuracy and noise immunity.

The foci of this paper are as follows: (1) to present a methodology for the segment division of rural highway, (2) based on PCA’s hazard scoring on the segment, to use K-means clustering to identify the black spots of the accident, and (3) to connect the cause of the accident to analyze and test the identified black spots of the accident.

2. Methodology

This chapter firstly introduces the accident segment division method based on gridding-based clustering. On this basis, the principal component cluster is introduced, which includes using the principal component to score the segment and using K-means to cluster the scoring results. Finally, fuzzy cluster is introduced to test the aforesaid results.

2.1. Gridding-Based Clustering

When analyzing conventional clustering problems, the Euclidean distance formula is generally used to measure the distance between two points. However, for the road traffic accidents, it is necessary to consider the spatial distribution difference between them and other general events that is, traffic accidents are strictly restricted to road traffic networks. Being different from the one-dimensional linearity of the expressway, when analyzing the accident points in rural roads, the vehicles are strictly bound in the road network. If the Euclidean distance is used to describe the distance between the accidents, many actual errors will be generated in the road network which is easy to amplify the danger.

The gridding-based clustering algorithm refers to quantify an object space into a finite road segment to form a grid-like structure. This approach will increase processing speed and constrain the disorganized points in the space to the grid for analysis [14, 15], which brings the possibility of simplification to the rural road black spots featured by linear complexity and inaccurate road network. Classic grid clustering ideas, such as the CLIQUE algorithm, segment each dimension into nonoverlapping communities, so that the entire embedded space of the data object is segmented into units, and presupposed density thresholds can identify dense units. Gridding-based clustering requires two presupposed parameters: one is the step size of the grid and the other is the threshold of the density. This paper, when analyzing rural roads, replaces the units segmented by the established steps with intersections. The critical distance between dense intersections has no explicit provision. Referring to the “General Principles for the Design of Chinese Civil Buildings”, Anderson [5], Benedek [16], and Ulak [17] which define the window width of the accident intersection, scholars generally believe that it is reasonable to set up 100-200 meters in a city. For expressways, the distance is considered to be longer than 500 meters. The rural road network studied in this paper is relatively sparse compared with the urban road network. Hence, the critical distance between adjacent segments is set to 200 meters in this paper. The specific process is as follows:

(1) Scanning all grids. When the first dense grid is found, the grid begins to expand. Divided segment


Synthetic time series technique for predicting network-wide road traffic

Affiliations: [ a ] Department of Civil and Environmental Engineering, University of Maryland, MD, USA | [ b ] Department of Mathematics, University of Maryland, MD, USA | [ c ] Joint Program in Survey Methodology, University of Maryland, MD, USA

Correspondence: [*] Corresponding author: Kartik Kaushik, Department of Civil and Environmental Engineering, University of Maryland, MD, USA. E-mail: [email protected]

Abstract: Short term traffic forecasting cannot be more important in the current world of cash strapped economies, placing ever increasing importance on managing existing facilities as opposed to building new infrastructure. The advent of autonomous vehicles further stresses the need for robust and fast fram eworks to forecast traffic over the horizon of a typical trip length so that the best routing decision might be made. There is an extensive amount of research on this topic already. However, most of the techniques in literature do not scale well with data or the size of the network in terms of model complexity, computational power and time. Proposed in this paper is a flexible synthetic time series framework that aims to solve the complexity and scalability problems with most models in literature. The synthetic time series framework takes advantage of the repeatability of the traffic patterns such that real-time predictions can be quickly made. It is flexible enough to work with most models in literature, and extendable quite easily with additional parameters to make predictions more robust. Presented in this work are the Autoregressive Integrated Moving Average (ARIMA) models within the synthetic time series framework. It is shown that reasonably accurate predictions can be made by using just the basic structure of ARIMA without any auxiliary variables accounting for the upstream/downstream conditions, incidents or weather. With a robust model fitted within the synthetic framework, prediction errors can be further reduced, while ensuring scalable computation power. Predictions are performed online, where incoming data is fed to the fitted model as the independent variable, and predictions are obtained as the dependent variable.

Keywords: Traffic forecasting, ARIMA, network, probe data

Journal: Statistical Journal of the IAOS, vol. 34, no. 3, pp. 425-437, 2018


Videoni tomosha qiling: Color choices in ArcMap