Yıl: 2020 Cilt: 0 Sayı: Ejosat Özel Sayı 2020 (ICCEES) Sayfa Aralığı: 522 - 529 Metin Dili: İngilizce İndeks Tarihi: 02-11-2022

Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS)

Öz:
Travel time plays a major role in handling the traffic rate. Bluetooth technology is one of the approaches this time observable. Traffic tracking, vehicle determination on a certain route, and travel time information can be obtaine dusing the bluetooth data gathered using this tool. The Bluetooth technology will be used to analyze certain features affecting travel time results. Highway travel time can be used as a new and efficient data collection tool through the bluetooth sensors which are widely used today. The central control software system consists of a comprehensive system for storing and organizing data at a central location, processing data in vehicles and displaying it to drivers. The central system architecture can be used to display congested road data to the driver, including scenarios, text messages and visuals, identified by traffic information message signs (VMS), which are also linked to the system on the particular highway via a data fusion process in line with data from a variety of sources, for example sensors. Providing information about travel time distribution, both average and variance, will play a more effective role in drivers' high likelihood of arriving on time and in selecting efficient routes. In order to determine the travel time flow, an inhomogeneous data fusion tracking is performed by combining the scattered collected data with distance detectors. With this method preferred in the research, road travel time flows are determined with the help of sensors. The travel time of the roads without sensors is obtained from the data of GPS- based service providers. In addition to the travel time flow, the Dempster-Shafer theory is combined with the travel time results from the distance sensors. Based on the travel time results obtained, the method of improvement in travel time flow has been developed.
Anahtar Kelime: Travel time Public transport Traffic management Bluetoth sensor Traffic time estimation

Akıllı Trafik Sistemlerinde (ITS), Bluetooth Sensor Verileri Yardımıyla Seyahat Süresi Tahmini Gerçekleştirme

Öz:
Trafik yoğunluğu yönetimi için seyahat süresi önemli bir rol oynar. Bu süreyi saptayabilecek yöntemlerden biri de bluetooth teknolojisidir. Bu yöntemle toplanan bluetooth verileri ile; trafik izleme, belirli bir rotadaki araçları belirleyebilme ve seyahat süresi gibi bilgiler elde edilebilmektedir. Bluetooth teknolojisi ile seyahat süresi verilerini etkileyen belirli özellikler analiz edilmiştir. Günümüzde aktif olarak kullanılan bluetooth sensörleri aracılığıyla, otoyol seyahat süresi yeni ve etkili bir veri toplama aracı olarak kullanılabilmektedir. Merkezi kontrol yazılım sistemi, merkezi konumda verileri toplamak, biçimlendirmek, araçlardaki verileri işlemek ve sürücülere sunmak amacıyla bütünsel bir sistem içermektedir. Merkezi sistem tasarımı, bir veri kaynaştırma işlemi yoluyla, bir dizi kaynaktan örneğin sensorlerden gelen veriler doğrultusunda ilgili otoyol üzerinde yine sisteme bağlı olan trafik bilgilendirme mesaj işaretlerine (VMS) tanımlanan senaryolar, metin mesaj ve görseller olmak üzere sürücüye ilgili tıkanık yol verilerini sunmak için kullanılabilmektedir. Hem ortalama hem de varyans olmak üzere seyahat süresi dağılım bilgilerinin sağlanması, sürücülerin zamanında ulaşma olasılığının yüksek olması ve güvenilir yol seçimlerinde daha etkili bir rol oynayabilmektedir. Seyahat süresi akışını belirleyebilmek için dağınık toplanan verileri, mesafe detektörleriyle birleştirerek homojen olmayan bir veri füzyon takibi yapılmaktadır. Yapılan çalışmada tercih edilen bu yöntemle, yol seyahat süresi akışları sensörler yardımıyla tespit edilmektedir. Sensör bulundurmayan yolların ise seyahat süresi tespiti, GPS tabanlı servis sunucularının verilerinden elde edilmektedir. Seyahat süresi akışında ek olarak Dempster-Shafer teorisi, mesafe sensörlerinden elde edilen seyahat süresi sonuçları ile birleştirilmiştir. Elde edilen seyahat süresi sonucuna bakarak, yol seyahat süresi dağılımlarını iyileştirme yöntemi geliştirilmiştir.
Anahtar Kelime: Trafik yönetimi Bluetooth sensörü Trafik süresi tahmini Seyahat süresi

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
0
0
0
  • [1] A. Das, A. Ghose, A. Razdan, H. Saran, R. Shorey (2002) Enhancing performance of asynchronous data traffic over the Bluetooth wireless ad-hoc network. Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213)
  • [2] Chris Bachmann,Matthew J. Roorda,Baher Abdulhai &Behzad Moshiri (2013)Fusing a Bluetooth Traffic Monitoring System With Loop Detector Data for Improved Freeway Traffic Speed Estimation. Journal of Intelligent Transportation Systems Volume 17, 2013 - Issue 2
  • [3] Ashish Bhaskar, Edward Chung André, Gilles Dumont (2010) Fusing Loop Detector and Probe Vehicle Data to Estimate Travel Time Statistics on Signalized Urban Networks. https://doi.org/10.1111/j.1467-8667.2010.00697.x
  • [4] Coifman B, Krishnamurthy S (2007) Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure. Transp Res Part C Emerg Technol 15(3):135–153
  • [5] Bhaskar A, Qu M, Nantes A, Miska M, Chung E (2015) Is bus overrepresented in Bluetooth MAC scanner data? Is MAC-ID really unique? Int J Intell Transp Syst Res 13(2):119–130
  • [6] Cortes C, Lavanya R, Oh JS, Jayakrishnan R (2002) Generalpurpose methodology for estimating link travel time with multiple- point detection of traffic. Transp Res Rec J Transp Res Board 1802:181–189
  • [7] Dailey DJ (1999) A statistical algorithm for estimating speed from single loop volume and occupancy measurements. Transp Res Part B Methodol 33(5):313–322
  • [8] Wang Y, Nihan NL (2003) Can single-loop detectors do the work of dual-loop detectors? J Transp Eng 129(2):169–176
  • [9] Qian QQ, Lin S, He ZY, Li XP (2012) Travelling wave timefrequency characteristic-based fault location method for transmission lines. Gener Transm Distrib IET 6(8):764–772
  • [10] Sharma A, Bullock D, Bonneson J (2007) Input-output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections. Transp Res Rec J Transp Res Board 2035:69–80
  • [11] Vigos G, Papageorgiou M, Wang Y (2008) Real-time estimation of vehicle-count within signalized links. Transp Res Part C Emerg Technol 16(1):18–35
  • [12] Stevanovic AZ, Martin PT (2008) Assessment of the suitability of microsimulation as a tool for the evaluation of macroscopically optimized traffic signal timings. J Transp Eng 134(2):59–67
  • [13] Wang Y, Malinovskiy Y, Lee UK, Wu YJ (2011) Investigation of bluetooth-based travel time estimation error on a short corridor. In: Transportation research board 90th annual meeting (No. 11–3056)
  • [14] Welsh E, Murphy P, Frantz JP (2002) Improving connection times for Bluetooth devices in mobile environment. In: Proceedings of the 2002 international conference, on fundamentals of electronics communications and computer science of IEICE (ICFS 2002), 27–28 Mar 2002, Tokyo, Japan, pp 1–5
  • [15] Ahmed H, El-Darieby L, Abdulhai B, Morgan Y (2008) Bluetooth- and Wi-Fi-based mesh network platform for traffic monitoring. In: TRB 87th annual meeting compendium of papers DVD, 13–17 Jan 2008, Washington, DC, pp 1–11
  • [16] Sharifi E, Hamedi M, Haghani A, Sadrsadat H (2011) Analysis of vehicle detection rate for Bluetooth traffic sensors: a case study in Maryland and Delaware. In: Proceedings of the 18th world congress on intelligent transport systems, 16–20 Oct 2011, Orlando, FL, pp 1–12
  • [17] Quayle SM, Koonce P, DePencier D, Bullock DM (2010) Arterial performance measures with media access control readers: Portland, Oregon, pilot study. Transp Res Rec J Transp Res Board 2192:185–193. doi:10.3141/2192-18
  • [18] Quayle SM, Koonce P, DePencier D, Bullock D (2010) Arterial performance measures using MAC readers: Portland pilot study. In: Transportation research board annual meeting proceedings CD-ROM
  • [19] Wasson JS, Sturdevant JR, Bullock DM (2008) Real-time travel time estimates using media access control address matching. ITE J 78(6):20–23
  • [20] Sadabadi KG, Hamedi M, Haghani A (2010) Evaluating moving average techniques in short-term travel time prediction using an AVI dataset. In: Transportation research board, annual meeting proceedings CD-ROM
  • [21] Huston TranStar and Bluetooth Traffic Monitoring (2012). http://traffic.houstontranstar.org/bluetooth/transtar_bluetooth.html. Accesse 22 July 2013
  • [22] Jaume B, Lı ́dia M, Laura M, Carlos C(2010) A Kalman-filter approach for dynamic OD estimation in corridors based on Bluetooth and WiFi data collection. 12th WCTR, Lisbon, Portugal
  • [23] Sawant H, Tan J, Yang Q, Wang Q (2004) Using Bluetooth and sensor networks for intelligent transportation systems. In: IEE intelligent transportation systems conference, Washington, D.C., USA
  • [24] Bullock D, Haseman R, Wasson J, Spitler R (2010) Automated measurement of wait times at airport security: deployment at Indianapolis international airport, Indiana. Transp Res Rec J Transp Res Board 2177:60–68
  • [25] Van Boxel D, Schneider W IV, Bakula C (2011) Innovative realtime methodology for detecting travel time outliers on interstate highways and urban arterials. Transp Res Rec J Transp Res Board 2256:60–67
  • [26] Malinovskiy Y, Lee UK, Wu YJ, Wang Y (2011) Investigation of Bluetooth-based travel time estimation error on a short corridor. In: Transportation research board 90th annual meeting (No. 11-3056)
  • [27] Puckett DD, Vickich MJ (2010) Bluetooth-based travel time/ speed measuring systems development (No. UTCM 09-00-17)
  • [28] Horn C, Klampfl S, Cik M, Reiter T (2014) Detecting outliers in cell phone data: correcting trajectories to improve traffic modeling. Transp Res Rec J Transp Res Board 2405:49–56
  • [29] Khoei AM, Bhaskar A, Chung E (2013) Travel time prediction on signalised urban arterials by applying SARIMA modelling on Bluetooth data. In: 36th Australasian Transport Research Forum (ATRF) 2013
  • [30] Qiao W, Haghani A, Hamedi M (2013) A nonparametric model for short-term travel time prediction using bluetooth data. J Intell Transp Syst 17(2):165–175
  • [31] Nantes A, Ngoduy D, Miska M, Chung E (2015) Probabilistic travel time progression and its application to automatic vehicle identification data. Transp Res Part B Methodol 81:131–145
  • [32] Steven CHIEN, Kitae KIM (2012) Evaluation of floating car technologies for travel time estimation. Journal of Modern Transportation.
APA CIVCIK L, KOÇAK S (2020). Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). , 522 - 529.
Chicago CIVCIK LEVENT,KOÇAK SEMİH Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). (2020): 522 - 529.
MLA CIVCIK LEVENT,KOÇAK SEMİH Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). , 2020, ss.522 - 529.
AMA CIVCIK L,KOÇAK S Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). . 2020; 522 - 529.
Vancouver CIVCIK L,KOÇAK S Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). . 2020; 522 - 529.
IEEE CIVCIK L,KOÇAK S "Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS)." , ss.522 - 529, 2020.
ISNAD CIVCIK, LEVENT - KOÇAK, SEMİH. "Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS)". (2020), 522-529.
APA CIVCIK L, KOÇAK S (2020). Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). Avrupa Bilim ve Teknoloji Dergisi, 0(Ejosat Özel Sayı 2020 (ICCEES)), 522 - 529.
Chicago CIVCIK LEVENT,KOÇAK SEMİH Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). Avrupa Bilim ve Teknoloji Dergisi 0, no.Ejosat Özel Sayı 2020 (ICCEES) (2020): 522 - 529.
MLA CIVCIK LEVENT,KOÇAK SEMİH Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). Avrupa Bilim ve Teknoloji Dergisi, vol.0, no.Ejosat Özel Sayı 2020 (ICCEES), 2020, ss.522 - 529.
AMA CIVCIK L,KOÇAK S Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). Avrupa Bilim ve Teknoloji Dergisi. 2020; 0(Ejosat Özel Sayı 2020 (ICCEES)): 522 - 529.
Vancouver CIVCIK L,KOÇAK S Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS). Avrupa Bilim ve Teknoloji Dergisi. 2020; 0(Ejosat Özel Sayı 2020 (ICCEES)): 522 - 529.
IEEE CIVCIK L,KOÇAK S "Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS)." Avrupa Bilim ve Teknoloji Dergisi, 0, ss.522 - 529, 2020.
ISNAD CIVCIK, LEVENT - KOÇAK, SEMİH. "Travel Time Prediction with Bluetooth Sensor Data in Intelligent Traffic System (ITS)". Avrupa Bilim ve Teknoloji Dergisi Ejosat Özel Sayı 2020 (ICCEES) (2020), 522-529.