Abstract
Short-term prediction of traffic variables aims at providing information for travelers before commencing their trips. In this paper, machine learning methods consisting of long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN) are employed to predict traffic state, categorized into A to C for segments of a rural road network. Since the temporal variation of rural road traffic is irregular, the performance of applied algorithms varies among different time intervals. To find the most precise prediction for each time interval for segments, several ensemble methods, including voting methods and ordinal logit (OL) model, are utilized to ensemble predictions of four machine learning algorithms. The Karaj-Chalus rural road traffic data was used as a case study to show how to implement it. As there are many influential features on traffic state, the genetic algorithm (GA) has been used to identify 25 of 32 features, which are the most influential on models’ fitness. Results show that the OL model as an ensemble learning model outperforms machine learning models, and its accuracy is equal to 80.03 percent. The highest balanced accuracy achieved by OL for predicting traffic states A, B, and C is 89, 73.4, and 58.5 percent, respectively.
Highlights
Sustainable transportation networks need to use data obtained from intelligent transportation systems (ITS) to relieve traffic congestion and its consequences, such as air and noise pollution and wasting energy and time
To tune the parameters of models, different values are set for them. e final parameters are selected in terms of the accuracy of predictions on the rest of the dataset. ese parameters include K in K-nearest neighbors (KNN), the number of trees to grow (NT), and the number of variables randomly sampled as candidates at each split (NV) in the random forest (RF) model and cost (C) in the support vector machine (SVM) model
Short-term traffic state prediction is a tool in the advanced traveler information system that aims to bring a more sustainable and more reliable transportation network
Summary
Sustainable transportation networks need to use data obtained from intelligent transportation systems (ITS) to relieve traffic congestion and its consequences, such as air and noise pollution and wasting energy and time. AITSs provide useful information about the current or future traffic conditions to travelers and transportation agencies [2]. Ese systems’ effectiveness is more when predicting the future state of the transportation network and letting users have better plans for their trips [3]. Traffic volume and average speed are well-known continuous traffic variables that can be predicted [4, 5]. E traffic volume to capacity ratio and average speed to free-flow speed ratio are more informative and meaningful for users [6]. Is variable is determined regarding the volume to capacity ratio and the speed to free-flow speed ratio Instead of predicting traffic volume and speed, we can predict the traffic state as a nominal traffic variable. is variable is determined regarding the volume to capacity ratio and the speed to free-flow speed ratio
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