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پیشبینی وضعیت ترافیک با الگوریتمهای یادگیری ماشین برای افقهای زمانی کوتاه مدت و میان مدت | ||
| نشریه مهندسی عمران امیرکبیر | ||
| دوره 54، شماره 4، تیر 1401، صفحه 1503-1520 اصل مقاله (1.7 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/ceej.2021.19650.7219 | ||
| نویسندگان | ||
| آرش رساءایزدی1؛ سید احسان ابریشمی* 2 | ||
| 1برنامه ریزی حمل و نقل، دانشکده مهندسی عمران، دانشگاه تربیت مدرس، تهران، ایران | ||
| 2استادیار، دانشکده مهندسی عمران و محیط زیست، دانشگاه تربیت مدرس | ||
| چکیده | ||
| پیشبینی متغیرهای ترافیکی و اطلاعرسانی آن به مسافرین و گردانندگان شبکه حملونقل یکی از راهکارهای مدیریت تقاضای سفر است. با اطلاعرسانی وضعیت آینده ترافیک از طریق سیستمهای حملونقل هوشمند، آمادگی بیشتری جهت اجتناب از وقوع تراکم ترافیک به وجود میآید. در این مطالعه به منظور پیشبینی وضعیت ترافیک ساعتی، شامل سه وضعیت روان، نیمهسنگین و سنگین، در جاده برونشهری کرج به چالوس در شمال ایران، سه مدل یادگیری ماشین، شامل ماشین بردار پشتیبان، جنگل تصادفی و حافظه طولانی کوتاه مدت به دو صورت کوتاه مدت و میان مدت آموزش داده شدهاند. متغیرهای پیشبینی کننده در مدلهای میان مدت اطلاعات تقویمی، آب و هوا و محدودیتهای ترافیکی هستند در صورتی که در مدلهای کوتاه مدت علاوه بر متغیرهای نام برده، وضعیت ترافیک مشاهده شده در سه تا هشت ساعت گذشته نیز استفاده شده است و این مدلها تنها قادر به پیشبینی وضعیت ترافیک یک و دو ساعت آینده هستند. نتایج نشان میدهد مدل حافظه طولانی کوتاه مدت با دقتی معادل با 90/11 درصد دقیقترین مدل پیشبینی کننده وضعیت ترافیک با افق کوتاه مدت است. این مدل برای افق بلند مدت نیز متغیر وضعیت ترافیک را با 82/07 درصد دقت، دقیقتر از دو مدل دیگر پیشبینی کرده است و بیشترین مقادیر شاخص F (F1) برای پیشبینی سه وضعیت ترافیک سبک، نیمهسنگین و سنگین را به همراه داشته که به ترتیب برابر با 0/86، 0/93 و 0/81 به دست آمدهاند. همچنین متغیرهای ساعت و تعطیلی همان روز و نوع تعطیلی و متغیرهای مشاهدات سه تا هشت ساعت پیش وضعیت ترافیک به ترتیب بیشترین تأثیر را بر افزایش دقت مدلهای میان مدت و کوتاه مدت دارند. | ||
| کلیدواژهها | ||
| پیشبینی وضعیت ترافیک؛ ماشین بردار پشتیبان؛ جنگل تصادفی؛ حافظه طولانی کوتاهمدت؛ سیستمهای حملونقل هوشمند | ||
| موضوعات | ||
| یادگیری ماشین؛ ترافیک شهری و بیرون شهری؛ راه و ترابری و حمل و نقل - ترابری هوشمند؛ راه و ترابری و حمل و نقل - مدل سازی؛ مدیریت ترافیک | ||
| عنوان مقاله [English] | ||
| Traffic state prediction with machine learning algorithms for short-term and mid-term prediction time horizons | ||
| نویسندگان [English] | ||
| Arash Rasaizadi1؛ Seyed Ehsan Seyedabrishami2 | ||
| 1Transportation Planning, Civil engineering department, Tarbiat Modares University, Tehran, Iran | ||
| 2Assistant Professor at the Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran | ||
| چکیده [English] | ||
| Predicting traffic variables and informing the passengers and the transportation network operators is one way to manage the travel demand. By informing the future state of traffic through intelligent transportation systems, there is more readiness to avoid congestion. In this study, three machine learning algorithms, including support vector machine (SVM), random forest (RF), and long short-term memory (LSTM), were used to predict the hourly traffic state, consist of light, semi-heavy and heavy states, for Karaj to Chaloos rural road in the north of Iran. Predictor variables of mid-term models are calendar information, weather, and road blockage policies. In contrast, in short-term models, in addition to the mentioned variables, the observed traffic states in the past three to eight hours have been used, and these models can only predict the future of one and two hours. The results show that short-term LSTM is the most accurate traffic state predictive model, with an accuracy equal to 90.11%. Among the mid-term models, the LSTM model has predicted traffic state more accurately than SVM and RF, and its accuracy is equal to 82.07%. Also, LSTM has the highest values of f1 measure to predict light, semi-heavy, and heavy, which are equivalent to 0.86, 0.93, and 0.81, respectively. Also, the hour, holiday, and type of holiday variables and traffic state observed in 3 to 8 hours later variables have the greatest effect on increasing the accuracy of mid-term and short-term models, respectively. | ||
| کلیدواژهها [English] | ||
| Traffic state prediction, Support vector machine, Random forest, Long short-term memory, Intelligent transportation systems | ||
| مراجع | ||
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