Bluetooth Verisi Kullanarak Kentsel Arterlerde Seyahat Süresinin Değerlendirilmesi

Yıl 2022, Cilt: 25 Sayı: 2, 767 - 773, 01.06.2022

Oruç Altıntaşı Mehmet Eray Balcı Murat Özen

https://doi.org/10.2339/politeknik.784652

Öz

Bir kentsel arterde hızın veya seyahat süresinin güvenilir bir şekilde tahmin edilmesi, trafiğin daha iyi yönetilmesi için gereklidir. Geleneksel olarak, bu tür veriler döngü detektörleri, endüktif döngüler, video kameralar aracılığıyla toplanır. Fakat kurulum maliyetlerinin yüksek olması nedeniyle bu cihazları trafikteki her bir noktaya yerleştirmek mümkün değildir. Son zamanlarda, Bluetooth (BT) teknolojisi, i) düşük kurulum maliyeti ve ii) 24 saatlik periyotta bile sürekli veri sağlama avantajı nedeniyle bir trafik veri kaynağı olarak yaygın bir şekilde kullanılmaktadır. BT tabanlı trafik verileri, Bluetooth cihazlarının tekil Ortam Erişim Kontrollerinin (MAC) zaman bazlı olarak kaydedilmesi prensibine dayanır. Birden fazla farklı konumda aynı MAC adreslerinin algılanması, yolculukların Başlangıç ve Varış noktalarının yanı sıra seyahat süresi bilgilerinin de tahmin edilmesini sağlar. Bu çalışmada, Mersin ilinde bulunan ana sinyalize kentsel arterlerden elde edilen BT verileri kullanarak hesaplanan seyahat sürelerinin dağılımı incelenmiştir. Veriler, hafta içi iki gün 07:30-09:30 (sabah zirve saatleri) arasındaki ana arterler üzerindeki 5 ardışık sinyalize kavşaktan toplanmıştır. Sonuç olarak, yeterli örneklem düzeyi sağlandığı için, veriler seyahat sürelerini tahmininde ve kentsel trafiğin izlenmesinde başarılı olmuştur. Bununla birlikte, motorlu hareketleri motorsuz olanlardan ayırmak için filtreleme işlemi dikkatlice yapılması gerektiği sonucuna varılmıştır.

Anahtar Kelimeler

Bluetooth teknolojisi, seyahat süresi tahmini, seyahat süresi dağılımı, trafik verisi

Urban Arterial Travel Time Evaluation using Bluetooth Data

Öz

Reliable estimation of speed or travel time (TT) of an urban arterial is the fundamental task for better management of the traffic. Traditionally, such data are collected via loop detectors, inductive loops, video cameras, but their installation cost were not always allowed to locate every specific point. Recently, Bluetooth (BT) technology has been widely used as a traffic data source due to the i) low installation cost, and ii) providing continuous data even 24-h period. The principle of BT-based traffic data is simply capturing the timestamped of the unique Media Access Control (MAC) of Bluetooth devices. Detecting the same MAC addresses from multiple different locations enabled to estimate the Origin and Destination (O-D) of the trips and travel time information. This study evaluates the distribution of TT information from different perspectives in which BT-based traffic data were obtained from five consecutive signalized intersections located in Mersin, Turkey during morning peak hours of 07:30-09:30 for the two weekdays. The results indicated that the data had considerable success in estimating travel times and urban traffic monitoring with adequate sampling rates. However, the filtering process must be carefully handled to distinguish the motorized movements from non-motorized ones.

Anahtar Kelimeler

Bluetooth technology, travel time estimation, travel time distribution, traffic data

Kaynakça

• [1] Altintasi, O., Tuydes-Yaman, H., Tuncay, K., “Quality of floating car data (FCD) as a surrogate measure for urban arterial speed”,Canadian Journal of Civil Engineering, 46(12): 1187-1198, (2019).
• [2] Yucel, S., Tuydes-Yaman, H., Altintasi, O., Ozen, M., “Determination of vehicular travel patterns in an urban location using Bluetooth technology”, ITS America Annual Meeting and Expo, Nashville, 1-11, (2013).
• [3] Díaz, J. J. V., González, A. B. R., Wilby, M. R., “Bluetooth traffic monitoring systems for travel time estimation on freeways”, IEEE Transactions on Intelligent Transportation Systems, 17(1): 123-132, (2015).
• [4] Araghi, B. N., Hammershøj Olesen, J., Krishnan, R., Tørholm Christensen, L., & Lahrmann, H., “Reliability of bluetooth technology for travel time estimation”, Journal of Intelligent Transportation Systems, 19(3): 240-255, (2015).
• [5] Haghani, A., Hamedi, M., Sadabadi, K. F., Young, S., Tarnoff, P., “Data collection of freeway travel time ground truth with bluetooth sensors”, Transportation Research Record, 2160(1): 60-68, (2010).
• [6] Martchouk, M., Mannering, F., Bullock, D., “Analysis of Freeway Travel Time Variability Using Bluetooth Detection”, Journal of Transportation Engineering, 137(10): 697–704, (2011).
• [7] Liu, Y., Xia, J. C., & Phatak, A., “Evaluating the Accuracy of Bluetooth-Based Travel Time on Arterial Roads: A Case Study of Perth, Western Australia”, Journal of Advanced Transportation, (2020).
• [8] Tufuor, E.O.A, and Laurence R. R., “Validation of the Highway Capacity Manual urban street travel time reliability methodology using empirical data”, Transportation Research Record, 2673(4): 415-426, (2019).
• [9] Saeedi, A., Park, S., Kim, D. S., Porter, J. D., “Improving accuracy and precision of travel time samples collected at signalized arterial roads with bluetooth sensors”, Transportation Research Record, 2380(1): 90-98, (2013).
• [10] Tsubota, T., Bhaskar, A., Chung, E., Billot R., “Arterial Traffic Congestion Analysis Using Bluetooth Duration Data”. Proc., Australasian Transport Research Forum, Adelaide, Australia, 1-14, (2011).
• [11] Transportation Research Board, “Highway Capacity Manual”, National Research Council, Washington D.C., (2010).
• [12] Yang, S., Wu, Y. J., “Travel mode identification using Bluetooth technology”, Journal of Intelligent Transportation Systems, 22(5): 407-421, (2018).
• [13] Laharotte, P. A., Billot, R., Come, E., Oukhellou, L., Nantes, A., El Faouzi, N. E., “Spatiotemporal analysis of Bluetooth data: Application to a large urban network”, IEEE Transactions on Intelligent Transportation Systems, 16(3): 1439-1448, (2014).
• [14] Barcelö, J., Montero, L., Marqués, L., Carmona, C., “Travel time forecasting and dynamic origin-destination estimation for freeways based on bluetooth traffic monitoring”, Transportation Research Record, 2175(1): 19-27, (2010).
• [15] Michau, G., Nantes, A., Chung, E., Abry, P., Borgnat, P., “Retrieving dynamic Origin-Destination matrices from Bluetooth data”, Transportation Research Board (TRB) 93rd Annual Meeting, Washington D.C., 1-11, (2014).
• [16] Yu, W., Park, S., Kim, D. S., Ko, S. S., An arterial incident detection procedure utilizing real-time vehicle reidentification travel time data, Journal of Intelligent Transportation Systems, 19(4): 370-384, (2015).
• [17] Quayle, S. M., Koonce, P., DePencier, D., Bullock, D. M., “Arterial performance measures with media access control readers: Portland, Oregon, pilot study”, Transportation Research Record, 2192(1): 185-193, (2010).
• [18] Yuan, J., Abdel-Aty, M., Gong, Y., Cai, Q., “Real-time crash risk prediction using long short-term memory recurrent neural network”, Transportation Research Record, 2673(4): 314-326, (2019).
• [19] Saito, M., and T. Forbush. Automated Delay Estimation at Signalized Intersections: Phase I Concept and Algorithm Development. UT-11.05. 2011. Utah Department of Transportation Research Division, Salt Lake City, (2011).
• [20] Park, S., Saeedi, A., Kim, D. S., Porter, J. D., “Measuring intersection performance from Bluetooth-based data utilized for travel time data collection”, Journal of Transportation Engineering, 142(5): 1-9, (2016).
• [21] Abbas, M., Rajasekhar, L., Gharat, A., Dunning, J. P., “Microscopic modeling of control delay at signalized intersections based on Bluetooth data”, Journal of Intelligent Transportation Systems, 17(2): 110-122, (2013).
• [22] Moghaddam, S. S., & Hellinga, B., “Evaluating the Performance of Algorithms for the Detection of Travel Time Outliers”, Transportation Research Record: Journal of the Transportation Research Board, 2338: 67–77, (2013).
• [23] Van Boxel, D., Schneider IV, W. H., Bakula, C., “Innovative real-time methodology for detecting travel time outliers on interstate highways and urban arterials”, Transportation Research Record, 2256(1): 60-67, (2011).
• [24] Bachmann, C., M. J. Roorda, B. Abdulhai, and B. Moshiri, Fusing a Bluetooth Traffic Monitoring System with Loop Detector Data for Improved Freeway Traffic Speed Estimation, Journal of Intelligent Transportation Systems, 17(2): 152–164, (2013).
• [25] Porter, J. D., Kim, D. S., Magaña, M. E., Poocharoen, P., & Arriaga, C. A. G., “Antenna characterization for Bluetooth-based travel time data collection”. Journal of Intelligent Transportation Systems, 17(2): 142-151, (2013).
• [26] Gong, Y., Abdel-Aty, M., & Park, J., “Evaluation and augmentation of traffic data including Bluetooth detection system on arterials”, Journal of Intelligent Transportation Systems, 1-13, (2019).
• [27] Mitsakis, E., Salanova Grau, J. M., Chrysohoou, E., Aifadopoulou, G., “A robust method for real time estimation of travel times for dense urban road networks using point-to-point detectors”, Transport, 30(3): 264-272, (2015).