تباين زمن الرحلة والتحليل المكاني الزمني للشوارع الحضرية بإستخدام نظام المواقع العالمي: مراجعة
محتوى المقالة الرئيسي
الملخص
يرجع تقدير زمن الرحلة الى حد كبير الى العملية العشوائية لوصول ومغادرة المركبات وقياسات موثوقيتها مع الاخذ بالإعتبار قضايا مهمة لتحسين كفاءة التشغيل وسلامة شبكات الطرق المرورية وخلص إستكشاف تغاير زمن الرحلة والتحليل المكاني الزمني للشوارع الحضرية بإستخدام نظام المواقع العالمي الى إن الإستخدامات المختلفة للأراضي وزحام النقل يؤدي الى زيادة زمن الرحلة والتأخير، وإن مؤشرات إمكانية الوصول تزداد بزيادة نقاط الوصول وتقليل الحجم المروري، ويمكن لشبكات نظام المعلومات الجغرافي انتاج نموذج يتغلب على بعض القيود المفروضة على مؤشرات إمكانية الوصول. تم تطوير نماذج تنبؤ مختلفة لتحديد المعاملات الرئيسية المتعلقة بزمن الرحلة. خلصت النتائج الى أن التأخير في التقاطعات المسيطر عليها بالإشارات الضوئية كان من العوامل الرئيسية المؤثرة على إجمالي زمن الرحلة و إجمالي زمن التأخر في الشوارع الحضرية الرئيسية. إن خوارزميات تقدير زمن الرحلة المستندة على الكاشفات الحلقية لبيانات السرعة تتسبب في حدوث إختلافات طفيفة عندما يكون مسار الدراسة قصيرﴽ نسبياً والإنتقال البطيء من الحالة الحرة الى حالة الزحام، وتتأثر نتائج زمن الرحلة بموقع المستشعرات وقلة جودتها وبالتالي تزداد أخطاء التقدير بزيادة التباعد بين الكاشفات
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Ahmed, R.M., Alkaissi, Z.A., and Hussain, R.Y., 2021. Travel time analysis of selected urban streets in Baghdad city. Journal of Engineering and Sustainable Development, 2nd Online Scientific Conference for Graduate Engineering Students 2021 June, Mustansiriyah University, Baghdad, Iraq. (pp. 3-157-3-164). https://doi.org/10.31272/jeasd.conf.2.3.15.
Alattar, E.F., Alkaissi, Z.A., and Kadem A.J., 2021. Travel time reliability indices for urban routes in Baghdad city. Journal of Engineering and Sustainable Development, 25(5), pp. 1–14. https://doi.org/10.31272/jeasd.25.5.1.
Alhamadani, O.Y.M., Saeed, M.Q., 2018. Producing coordinate time series for Iraqi's CORS site for detection geophysical phenomena. Journal of Engineering, 24(1), pp. 41–52. https://doi.org/10.31026/j.eng.2018.01.03.
Ali, H.K.M., Majid, H.M., 2023. Comparative Evaluation of roundabout capacities methods for single-lane and multi-lane roundabout. Journal of Engineering, 29(3), pp. 76–97. https://doi.org/10.31026/j.eng.2023.03.06.
Alkaissi, Z.A., 2017. Travel time prediction models and reliability indices for Palestine urban road in Baghdad city. Al-Khwarizmi Engineering Journal, 13(3), pp. 120–130. https://doi.org/10.22153/kej.2017.01.007.
Alkaissi, Z.A., 2022. Traffic simulation of urban street to estimate capacity. Journal of Engineering, 28(4), pp. 51–63. https://doi.org/10.31026/j.eng.2022.04.04.
Alkaissi, Z.A., Ahmed, R.M., and Hussain, R.Y., 2022. Application of GIS to assess the accessibility of urban streets in Baghdad city. IOP Conference Series: Earth and Environmental Science 2022 (Vol. 961, pp. 1–12). https://doi.org/10.1088/1755-1315/961/1/012036.
Alkaissi, Z.A., Hussain, R.Y., 2020. Delay time analysis and modelling of signalised intersections using Global Positioning System (GPS) receivers. IOP Conference Series: Materials Science and Engineering 2020 (Vol. 671, pp. 1–15). https://doi.org/doi:10.1088/1757-899X/671/1/012110.
Alkaissi, Z.A., Kadem, A.J., and Alattar, E.F., 2021. Travel time prediction models for major arterial road in Baghdad city using manufactured GPS device. IOP Conference Series: Materials Science and Engineering 2021 (Vol. 1090, pp. 1–14). https://doi.org/10.1088/1757-899X/1090/1/012110.
Alomari, A.H., Al-Omari, A.A., and Aljizawi, W.K., 2022. Evaluation of travel time reliability in urban areas using mobile navigation applications in Jordan. Journal of Applied Engineering Science, 20(3), pp. 644-656. https://doi.org/10.5937/jaes0-35118.
Alomari, A.H., Al-Omari, B.H., and Al-Hamdan, A.B., 2020. Validating trip travel time provided by smartphone navigation applications in Jordan, Jordan Journal of Civil Engineering, 14(4), pp. 500-510.
Antwi, T., Quaye-Ballard, J.A., Arko-Adjei, A., Osei-wusu, W., and Quaye-Ballard, N.L., 2020. Comparing spatial accessibility and travel time prediction to commercial centres by private and public transport: a case study of
Oforikrom district. Journal of Advanced Transportation 2020, pp. 1-8. https://doi.org/10.1155/2020/8319089.
Ayob, S.T., Alkaissi, Z.A., 2021. Assessment of travel speed for urban streets using global positioning system. Journal of Engineering and Sustainable Development, 2nd Online Scientific Conference for Graduate Engineering Students 2021 June (pp. 3-174-3-185). https://doi.org/10.31272/jeasd.conf.2.3.17.
Banik, S., Kumar, B.A., and Vanajakshi, L., 2022. Stream travel time reliability using GPS-equipped probe vehicles. Current Science, 123(9), pp. 1107–1116.
Bauer, D., Tulic, M., and Scherrer, W., 2019. Modelling travel time uncertainty in urban networks based on floating taxi data. European Transport Research Review 2019, 11:46. https://doi.org/10.1186/s12544-019-0381-5.
Belliss, G., 2004. Detailed speed and travel time surveys using low-cost GPS equipment. Technical Conference, IPENZ Transportation Group.
Bock, J., Krajewski, R., Moers, T., Runde, S., Vater, L. and Eckstein, L., 2020. The inD dataset: A drone dataset of naturalistic road user trajectories at German intersections. In Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV) 2020, 19 October–13 November, Las Vegas, NV, USA. (pp. 1929–1934). https://doi.org/10.48550/arXiv.1911.07602.
Carrese, S., Cipriani, E., Crisalli, U., Gemma, A., and Mannini, L., 2020. Bluetooth traffic data for urban travel time forecast. Transportation Research Procedia 52, 23rd EURO Working Group on Transportation Meeting, EWGT 2020, September 16-18; Paphos, Cyprus (pp. 236-243). https://doi.org/10.1016/j.trpro.2021.01.027.
Carrion, C., Levinson, D., 2013. Valuation of travel time reliability from a GPS-based experimental design. Transportation Research Part C 2013, 35, pp. 305–323. http://dx.doi.org/10.1016/j.trc.2012.10.010.
Chawuthai, R., Ainthong, N., Intarawart, S., Boonyanaet, N., and Sumalee, A., 2022. Travel time prediction on long-distance road segments in Thailand. Applied Sciences, 12(11), pp. 1-18. https://doi.org/10.3390/app12115681.
Choi, K., Chung, Y., 2002. A data fusion algorithm for estimating link travel time. Journal of Intelligent Transportation Systems, 7, pp. 235–260. https://doi.org/10.1080/714040818.
Civcik, L., Kocak, S., 2020. Travel time prediction with Bluetooth sensor data in Intelligent Traffic System (ITS). European Journal of Science and Technology, 1st International Conference on Computer, Electrical and Electronic Sciences ICCEES (Special Issue, pp. 522-529).
Ding, F., Chen, X., He, S., Shou, G., Zhang, Z., and Zhou, Y., 2019. Evaluation of a Wi-Fi signal based system for freeway traffic states monitoring: an exploratory field test. Sensors. 19(2), pp. 1-15. https://doi.org/10.3390/s19020409.
Erkan, I., Hastemoglu, H., 2016. Bluetooth as a traffic sensor for stream travel time estimation under Bogazici Bosporus conditions in Turkey. Journal of Modern Transportation, 24(3), pp. 207-214.
Faghri, A., Hamad, K., 2002. Travel time, speed, and delay analysis using an integrated GIS/GPS system. Can. J. Civ. Eng., NRC Canada, 29, pp. 325–328. https://doi.org/10.1139/l02-014.
Faghri, A., Hamad, K., and Duross, M., 2003. Application of GIS and GPS for collecting and analyzing travel time, speed and delay. Scientia Iranica, 10(2), pp. 153–163.
Faghri, A., Li, M., and Russell, S., 2015. Application of Global Positioning System (GPS) to travel time and delay measurements. Delaware Center for Transportation, University of Delaware, Newark, DE 19716(302), pp. 831-1446.
Fosgerau, M., 2016. The valuation of travel time variability. International Transport Forum, Quantifying the Socio-Economic Benefits of Transport, Paris, France, 4.
Gallotti, R., Bazzani, A., and Rambaldi, S., 2015. Understanding the variability of daily travel-time expenditures using GPS trajectory data. EPJ Data Science, 4, pp. 1-14. https://doi.org/10.1140/epjds/s13688-015-0055-z.
Genser, A., Hautle, N., Makridis, M. and Kouvelas, A., 2022. An experimental urban case study with various data sources and a model for traffic estimation. Sensors, 22(1), pp. 1-19. https://doi.org/10.3390/s22010144.
Hadachi, A., 2013. Travel time estimation using sparsely sampled probe GPS data in urban road networks context, PhD thesis. Normandie University, INSA of Rouen, XNT: 2013ISAM0003, tel-00800203.
Haghani, A., Hamedi, M., Sadabadi, K.F., Young, S., and Tarnoff, P., 2010. Data collection of freeway travel time ground truth with Bluetooth sensors. Transp. Res. Rec. 2160(1), pp. 60-68. https://doi.org/10.3141/2160-07.
Jedwanna, K., Boonsiripant, S., 2022. Evaluation of Bluetooth detectors in travel time estimation. Sustainability, 14(8), pp. 1-23. https://doi.org/10.3390/su14084591.
Jiang, D., Zhao, W., Wang, Y., and Wan, B., 2024. A spatiotemporal hierarchical analysis method for urban traffic congestion optimization based on calculation of road carrying capacity in spatial grids. International Journal of Geo-Information, 13(2), pp. 1-23. https://doi.org/10.3390/ijgi13020059.
Jie, L., Zuylen, H., Chunhua L., and Shoufeng L., 2011. Monitoring travel times in an urban network using video, GPS and Bluetooth. Procedia Social and Behavioral Sciences 2011 (pp. 630-637). https://doi.org/10.1016/j.sbspro.2011.08.070.
Jiménez-Meza, A., Arámburo-Lizárraga, J. and de la Fuente, E., 2013. Framework for estimating travel time, distance, speed, and street segment Level Of Service (LOS), based on GPS data. Procedia Technology 7 (pp. 61-70). https://doi.org/10.1016/j.protcy.2013.04.008.
Kajalić, J., Čelar, N., and Stanković, S., 2018. Travel time estimation on urban street segment, Traffic Engineering, Promet - Traffic & Transportation 2018, 30(1), pp. 115–120. https://doi.org/10.7307/ptt.v30i1.2473.
Li, A., Lam, William H.K., Ma, W., Wong, S.C., Chow, Andy H.F., and Tam, Mei Lam, 2024. Real-time estimation of multi-class path travel times using multi-source traffic data. Expert Systems with Applications, 237(Part C). http://doi.org/10.1016/j.eswa.2023.121613.
Li, R., Bradley, M., Jones, M. and Moloney, S., 2015. Quality investigation and variability analysis of GPS travel time data in Sydney. Australasian Transport Research Forum, Proceedings 2015, 30 Septemper-2 October; Sydney, Australia (pp. 1-16).
Li, Y., Gunopulos, D., 2017. Urban travel time prediction using a small number of GPS floating cars. Proceedings of SIGSPATIAL’17, Los Angeles Area, CA, USA. https://doi.org/10.1145/3139958.3139971.
Liao, Y., Gil J., Pereira, R.H.M., Yeh, S., and Verendel, V., 2020. Disparities in travel times between car and transit: spatiotemporal patterns in cities. Scientific Reports, Natureresearch 10:4056. https://doi.org/10.1038/s41598-020-61077-0.
Macababbad, R.M., Regidor, J.F., 2011. A study on travel time and delay survey and traffic data analysis and visualization methodology. Proceedings of the Eastern Asia Society for Transportation Studies, 8. https://doi.org/10.11175/EASTPRO.2011.0.318.0.
Mahdi, H.J., Al-Bakri, M., and Ubaidy, A.L., 2023. Evaluating roads network connectivity for two municipalities in Baghdad-Iraq. Journal of Engineering, 29(6), pp. 60–71. https://doi.org/10.31026/j.eng.2023.06.05.
Mallem, S., Faghri, A., Taromi, R., and Deliberty, T., 2009. Utilization of GPS travel time and delay data for optimal routing. Proceedings of the 12th IFAC Symposium on Transportation Systems 2009 September 2-4, Redondo Beach, CA, USA. (pp. 562-568). https://doi.org/10.3182/20090902-3-US-2007.0044.
Martchouk, M., Mannering, F., and Bullock, D., 2011. Analysis of freeway travel time variability using Bluetooth detection. Journal of Transportation Engineering, 137(10), pp. 697-704. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000253.
Nguyen, M.H., Armoogum, J., Madre J., and Garcia C., 2020. Reviewing trip purpose imputation in GPS-based travel surveys. Journal of Traffic and Transportation Engineering (English edition), 7(4), pp. 395-412. https://doi.org/10.1016/j.jtte.2020.05.004.
Osei, K.K., Adams, C.A., Sivanandan, R., and Ackaah, W., 2022. Modelling of segment level travel time on urban roadway arterials using floating vehicle and GPS probe data, Scientific African, 15. https://doi.org/10.1016/j.sciaf.2022.e01105.
Puangprakhon, P., Narupiti, S., 2017. Allocating travel times recorded from sparse GPS probe vehicles into individual road segments. Transportation Research Procedia 25, World Conference on Transport Research - WCTR 2016 July 10-15, Shanghai. (pp. 2208-2221). https://doi.org/10.1016/j.trpro.2017.05.423.
Quiroga, C.A., 1997. An integrated GPS-GIS methodology for performing travel time studies. LSU Historical Dissertations and Theses. Louisiana State University LSU Digital Commons, 6514.
Rahmani, M., 2015. Urban travel time estimation from sparse GPS Data: an efficient and scalable approach. PhD thesis. KTH School of Architecture and Built Environment; Stockholm, Sweden.
Sihag, G., Parida, M., and Kumar, P., 2022. Travel time prediction for traveler information system in heterogeneous disordered traffic conditions using GPS trajectories, Sustainability, 14(16), pp. 1-20. https://doi.org/10.3390/su141610070.
Singh, V., Gore, N., Chepuri, A., Arkatkar, S., Joshi, G., and Pulugurtha, S., 2019. Examining travel time variability and reliability on an urban arterial road using Wi-Fi detections-a case study, Journal of the Eastern Asia Society for Transportation Studies, 13, pp. 2390-2411. https://doi.org/10.11175/easts.13.2390.
Susilawati, Ramli, M.I., and Yatmar, H., 2020. Delay distribution estimation at a signalized intersection. IOP Conference Series: Earth and Environmental Science 2020 (Vol. 419, pp. 1-11). https://doi.org/10.1088/1755-1315/419/1/012090.
Taher, S.H., Alkaissi, Z.A., 2024. Analysis the reliability of travel time in urban corridors in Baghdad City. Journal of Engineering, 30(7), pp. 202–217. https://doi.org/10.31026/j.eng.2024.07.12.
Tamin, O., Ikram, B., Ramli, A.L.A., Moung, E.G., and Yee C.C.P., 2022. Travel-time estimation by cubic hermite curve. Information, 13(7), pp. 1-25. https://doi.org/10.3390/info13070307.
Torrisi, V., Ignaccolo, M. and Inturri, G., 2017. Estimating travel time reliability in urban areas through a dynamic simulation model. Transportation Research Procedia 27, 20th EURO Working Group on Transportation Meeting, EWGT 2017, September 4-6, Budapest, Hungary. (pp. 857-864). https://doi.org/10.1016/j.trpro.2017.12.134.
Traffic Detector Handbook, 2006. 3rd ed., U.S. Department of Transportation, Federal Highway Administration.
Vo, T., 2011. An investigation of Bluetooth technology for measuring travel times on arterial roads: a case study on
Spring street. MSc thesis. School of Civil & Environmental Engineering, Academic Faculty; Georgia Institute of
Technology.
Wang, Z., Goodchild A.V., and McCormack E., 2016. Freeway truck travel time prediction for freight planning using truck probe GPS data, EJTIR, 16(1), pp. 76-94. https://doi.org/10.18757/ejtir.2016.16.1.3114.
Woodard, D., Nogin, G., Koch, P., Racz, D., Goldszmidt, M., and Horvitz, E., 2017. Predicting travel time reliability using mobile phone GPS data, Transportation Research Part C, 75, pp. 30-44. https://doi.org/10.1016/j.trc.2016.10.011.
Wu, L., Yang, B., and Jing, P., 2016, Travel mode detection based on GPS raw data collected by smartphones: a systematic review of the existing methodologies, Information, 7(4), pp. 1-19. https://doi.org/10.3390/info7040067.
Xuegang, J., Yuwei, L., Alexander, S. and Margulici, J., 2010. Performance evaluation of travel-time estimation methods for real-time traffic applications. Journal of Intelligent Transportation Systems 2010, 24(2), 54–67.
Yazici, M.A., Kocatepe, A. and Ozguven, E.E., 2017. Urban travel time variability in New York city: a spatio-temporal analysis within congestion pricing context. Final Report, University Transportation Research Center-Region 2: New York.
Yildirimoglu, M., Geroliminis, N., 2013. Experienced travel time prediction for congested freeways, Transportation Research Part B, 53, pp. 45-63. https://doi.org/10.1016/j.trb.2013.03.006.
Zhan, X., Hasan, S., Ukkusuri, S.V. and Kamga, C. 2013, Urban link travel time estimation using large-scale taxi data with partial information. Transportation Research Part C 2013, 33, pp. 37-49. http://dx.doi.org/10.1016/j.trc.2013.04.001.
Zhao, L., Li, Y., 2022. Identifying origin-destination trips from GPS data – application in travel time reliability of dedicated trucks, Intelligent Transport Systems, Promet–Traffic & Transportation 2022, 34(1), pp. 25-38. https://doi.org/10.7307/ptt.v34i1.3799.
Zheng, F., Van Zuylen, H., and Liu, X., 2017. A methodological framework of travel time distribution estimation for urban signalized arterial roads. Transportation Science 2017, 51(3), pp. 893–917. https://doi.org/10.1287/trsc.2016.0718.