Intelligent traffic control for autonomous vehicle systems based on machine learning

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Intelligent Traffic Control for Autonomous Vehicle Systems Based on Machine Learning Sangmin Lee , Younghoon Kim , Hyungu Kahng , Soon-Kyo Lee , Seokhyun Chung , Taesu Cheong , Keeyong Shin , Jeehyuk Park , Seoung Bum Kim PII: DOI: Reference:

S0957-4174(19)30791-2 https://doi.org/10.1016/j.eswa.2019.113074 ESWA 113074

To appear in:

Expert Systems With Applications

Received date: Revised date: Accepted date:

24 February 2019 3 October 2019 3 November 2019

Please cite this article as: Sangmin Lee , Younghoon Kim , Hyungu Kahng , Soon-Kyo Lee , Seokhyun Chung , Taesu Cheong , Keeyong Shin , Jeehyuk Park , Seoung Bum Kim , Intelligent Traffic Control for Autonomous Vehicle Systems Based on Machine Learning, Expert Systems With Applications (2019), doi: https://doi.org/10.1016/j.eswa.2019.113074

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Highlights  We propose a predictive traffic control system for the autonomous vehicle routing problem.  We develop an applicable control system to complement the existing routing algorithm.  Proposed method can predict and prevent traffic congestion.  A real-time simulation presents the utility and superiority of the proposed method.

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Intelligent Traffic Control for Autonomous Vehicle Systems Based on Machine Learning

Sangmin Lee School of Industrial Management Engineering, Korea University 145 Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea [email protected],Tel. 82–2–3290–3769

Younghoon Kim School of Industrial Management Engineering, Korea University 145 Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea [email protected], Tel. 82–2–3290–3769

Hyungu Kahng School of Industrial Management Engineering, Korea University 145 Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea [email protected], Tel. 82–2–3290–3769

Soon-Kyo Lee School of Industrial Management Engineering, Korea University 145 Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea [email protected], Tel. 82–2–3290–3484

Seokhyun Chung Industrial and Operations Engineering, University of Michigan, 1221 Beal Avenue, Ann Arbor, MI 48109, United States [email protected], Tel. 1-734-274-0044

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Taesu Cheong School of Industrial Management Engineering, Korea University 145 Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea [email protected], Tel. 82-2-3290-4056

Keeyong Shin Material Handling Automation Group, Samsung Electronics SamsungJeonJa-ro 1, Hwaseong-si, Gyeonggi-do, 18448, Republic of Korea [email protected]

Jeehyuk Park Material Handling Automation Group, Samsung Electronics SamsungJeonJa-ro 1, Hwaseong-si, Gyeonggi-do, 18448, Republic of Korea [email protected]

Seoung Bum Kim* School of Industrial Management Engineering, Korea University 145 Anam-dong, Seongbuk-gu, Seoul 02841, Republic of Korea [email protected], Tel. 82–2–3290–3397

* Corresponding author.

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Intelligent Traffic Control for Autonomous Vehicle Systems Based on Machine Learning Abstract This study aimed to resolve a real-world traffic problem in a large-scale plant. Autonomous vehicle systems (AVSs), which are designed to use multiple vehicles to transfer materials, are widely used to transfer wafers in semiconductor manufacturing. Traffic control is a significant challenge with AVSs because all vehicles must be monitored and controlled in real time, to cope with uncertainties such as congestion. However, existing traffic control systems, which are primarily designed and controlled by human experts, are insufficient to prevent heavy congestion that impedes production. In this study, we developed a traffic control system based on machine learning predictions, and a routing method that dynamically determines AVS routes with reduced congestion rates. We predicted congestion for critical bottleneck areas, and utilized the predictions for adaptive routing control of all vehicles to avoid congestion. We conducted an experimental evaluation to compare the predictive performance of four popular algorithms. We performed a simulation study based on data from semiconductor fabrication to demonstrate the utility and superiority of the proposed method. The experimental results showed that AVSs with the proposed approach outperformed the existing approach in terms of delivery time, transfer time, and queuing time. We found that adopting machine learning-based traffic control can enhance the performance of existing AVSs and reduce the burden on the human experts who monitor and control AVSs.

Keywords: Intelligent traffic control, machine learning, autonomous vehicle systems, material handling, vehicle routing 4

1. Introduction Semiconductor fabrication is a complex process in which wafers are transported through thousands of steps. Automated material handling systems (AHMSs) are widely used to ensure efficient, safe wafer transfers between facilities (Pillai, et al., 1999). Autonomous vehicle systems (AVSs), the most recent version of AMHSs, are designed to transfer materials using several thousand vehicles from one facility to another within a production plant. Typically, overhead hoist transports (OHTs) are the main vehicles in AVSs. These transports are designed to move wafers by traveling along monorail networks installed on the ceiling of a plant as shown in Figure 1(a). Thousands of vehicles should be continuously assigned to transfer requests between facilities. Figure 1(b) shows an example of a single transfer request on a sample railway. A railway network typically contains lanes surrounding all of the equipment (outer loop) and the circular lanes in the middle (center loop).

(a) Overhead hoist transports (OHTs)

(b) Transfer request on railway network

Figure 1. OHTs and a single transfer request on a sample railway network layout in a fabrication

Recently, the role of AVSs has become more important because the transportation and storage of materials within plants typically represents 70% of their operating costs (Leno et al., 2012). Insufficient capacity of AVSs causes slow wafer transfer, impedes production schedules, and results in low utilization of machines, leading to production losses (Wakabayashi et al., 2004; Siebert et al., 2017). Traffic control is a significant challenge in 5

AVSs. There are two main issues: (1) railway structure designs that are vulnerable to congestion; and (2) vehicle overcrowding on the railways exceeding transport capacity. Railways in AVSs allow only unidirectional movement, and accommodations for lane changes are scarce. Increasing the traffic therefore renders the railways more vulnerable to congestion. Existing routing methods in AVSs use a shortest distance heuristic for the route selection criterion. This approach typically leads to heavy congestion in the center loops, as depicted in Figure 1(b) (Wang and Lin, 2004). Optimization of the layout design of AVSs is fundamental for the solution of traffic problems (Thiesse and Fleisch, 2008). Several studies have produced guidelines for effective railway network design to prevent congestion and blocking problems (Hsieh et al., 2012; Kortus et al., 2018; Reith et al., 2019; Lee et al., 2019). However, once a layout has been put in place, it is difficult to revise. Adding, deleting, or reversing a single path causes delays and blocks traffic flow. Because these studies are primarily concerned with network layout, they are not suited to operating plants that need software solutions for their installed systems. Most studies on operational issues in AVSs have concentrated on dispatching rules (Kim et al., 2009; Wang and Chen, 2012; Tao and Qui, 2015), idle vehicle policies (Kim and Park, 2009), and vehicle route planning (Qui et al., 2002; Lin et al., 2014; Morais et al., 2014). However, these studies, although valuable, have only addressed vehicle operational issues under steady-state production conditions. Their approaches, which rely on the assumption of deterministic production conditions, cannot accurately reflect the dynamic production conditions frequently encountered in real manufacturing systems (Lin and Chang, 2013). Several studies have addressed the alleviation of traffic control problems by optimizing the scheduling and routing of vehicles. Most congestion studies have used either exact or heuristic optimization methods, metaheuristic algorithms, and simulation studies (Corréa et al., 2007; Shirazi et al., 2010). Other methods have focused on the detection of deadlock and 6

livelock problems caused by interference among vehicles and have addressed ways to systematically resolve these problems (Im et al., 2010; Chen et al., 2017; D'Andrea and Marcelloni, 2017; Zhou and Zhou, 2018; Bagloee et al., 2019). Although these authors have presented solutions to practical issues, the solutions do not cover all of the variables in AVSs. Some studies have presented traffic-balancing methods to reduce congestion (Zhang et al., 2009; Hong et al., 2012). However, these studies have only considered small problems involving a few vehicles. They have therefore not been verified sufficiently for application to large production issues. One of the studies on congestion has dealt with the uncertainty inherent in designing vehicle routes online. Lau and Woo (2008) used a node-based updating method involving multiple cooperating agents. Bartlett et al. (2014) relied on exponential smoothing when proposing an arc-based updating algorithm. These approaches can re-route vehicles because routes are redefined by updating baseline information at the time of a route decision. However, both approaches are impractical in terms of resolving congestion in AVSs, for two reasons. First, short-cycle route recalculations for all vehicles generate an excessive load for system resources such as centralized AVS servers and networks. Second, the effectiveness of real-time rerouting is trivial because of the short travel times and distances in AVSs. Few opportunities are available to select alternative routes within a limited time. As such, a realtime version of the routing method is inappropriate despite its capture of traffic conditions. For more recent studies, we identified two research streams: heuristics and machine learning approaches. With respect to heuristic approaches, Lee et al. (2018) proposed a congestion-monitoring system to detect and alleviate congestion. Kabir and Suzuki (2019) conducted comparative analysis of variant routing heuristics both for charging the batteries of vehicles and improving productivity. These studies focused on routing with real-time updates of the penalty parameters. However, these must be analyzed and devised by human experts. 7

Devising heuristics that reflect complex traffic patterns with congestion require a great deal of effort by experts. With respect to the machine learning approach, Rothe et al. (2015) described a predictive logistics model to optimize wafer transfer and leverage machine utilization. Several approaches have used reinforcement learning (RL) for effective dynamic routing for vehicle routing problems (Hwang et al., 2017; Hwang and Jang, 2019). However, these approaches have had limited opportunities for application to large-scale problems, because their algorithms are independent of the number of vehicles and traffic patterns on railway networks. Moreover, human intervention is still essential to building well-trained models, because of the difficulty of RL convergence and application. In addition, this study is closely associated with multi-robot path planning (MPP) problems that optimize trajectory of the path for multi-robots (Das et al., 2016). A centralized approach is generally used to resolve goal assignment, path planning, and local reciprocal collision avoidance at once (Alonso-Mora, 2014). An MPP is considered as a nondeterministic polynomial time (NP) hard problem (Canny, 1988). Thus, metaheuristic algorithms are typically used to search for approximate solutions, including genetic algorithms (GA) (Tuncer and Yildirim, 2012), particle swarm optimization (PSO) (ContrerasCruz et al., 2015; Thabit and Mohades, 2018), and ant colony algorithms (ACO) (Zeng et al., 2016). Recently, several studies used stochastic dynamic programming methods to model the future states and uncertainties of the robots (Du Toit et al., 2012; Luna et al., 2014). Although the abovementioned studies have investigated similar vehicle routing problems to ours, our work differs from MPP in that we account for the characteristics of unidirectional railway networks in AVSs, which are particularly vulnerable to congestion. Further, traffic load balancing should be discussed to disperse traffic in critical congestion bottleneck areas of existing complex networks.

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We focused on how to systematically resolve a traffic problem in large-scale semiconductor fabrication. In this paper, as motivated by the traffic control problem without human intervention in practice, we present a traffic control system based on machine learning-based predictions which is robust to more diverse AVS conditions. Our approach has four strengths in terms of the methodology and industrial viewpoints. First, we propose to use a predictive model to enhance traffic control while minimizing human intervention. We used a random forest algorithm, which provides predictions with low bias and variance. The random forest approach also provides accurate predictions and generates a model which is interpretable. The details of the random forest algorithm are provided in Section 3.1. We also provide a sequential hypothesis test-based method for the problem of detecting the state of change of vehicle speed. The optimum change points of the traffic state for overall railway networks are addressed in Section 2.4.1. With respect to the practical implications, the proposed system is guaranteed to be applied to real plants with regard to system scalability and robustness, because the variables used in the proposed model are almost identical to actual fabrication specifications and layouts. Finally, we tested our approach using simulation software that delivers a test bed identical to an actual plant. No prior research has presented thorough validations on scalability. The main contributions of this study are as follows: (1) We propose a predictive traffic control system for vehicle routing based on a machine learning-based predictive algorithm that forecasts traffic congestion in predefined critical areas. By learning the spatially and temporally congested traffic patterns of AVSs, the proposed system can predict and prevent congestion. This leads to the provision of sufficient transport capacity of AVSs, and the achievement of stable production under dynamic conditions. To the best of our knowledge, our study is the first to develop a machine learningbased traffic control system for effective vehicle routing of AVSs in manufacturing. 9

(2) We developed a traffic control system to complement the existing shortest-path routing algorithm in actual plants. The full replacement of a routing algorithm in AVSs is impractical because the large-scale manufacturing industry tends to resist changes that involve full renovation. We addressed this problem using a plugin-based method loosely coupled with message-oriented middleware. The proposed system can easily be applied to an existing algorithm to introduce congestion awareness. (3) To demonstrate the applicability and usefulness of the proposed system, we compared our predictive approach with the existing approach under overloaded manufacturing conditions. To obtain practical solutions for the resolution of congestion, we staged a real-time simulation to assess the effectiveness of the proposed system. The simulation results demonstrated the superior performance of the proposed method. (4) The experimental results confirm that the traditional AVS system, which is monitored and operated by experts, can be improved by the incorporation of machine learning approaches. In the existing system, experts controlled the system, evaluating the congestion or resolving the congestion directly. The proposed method, however, would reduce the burden on experts, by incorporating an intelligent expert system that automatically predicts congestion and bypasses autonomous vehicles. (5) In a theoretical setting, we verified that an optimization model that reflects future dynamics outperformed optimization models assuming a static environment. Existing AVS systems are based on the static shortest path problem, which determines the best route give the current state of an AVS. The method works appropriately when the environment of an AVS is static. However, in real world situations, the environment changes dynamically. Our method reflects the dynamics to optimize the model using machine learning techniques, and shows better performance than a model assuming a static environment.

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The rest of the paper is organized as follows. Section 2 presents the motivation and the proposed predictive traffic control system. Section 3 presents the comparative results of machine learning-based predictive models and simulation studies based on real data from semiconductor fabrication. Section 4 includes our concluding remarks.

2. Proposed predictive traffic control system 2.1 Overview Figure 2 presents an overview of a predictive traffic control system. The data sets were collected from vehicles, and represent traffic conditions in a railway network. Predictive algorithms were used to predict congestion in critical bottleneck areas under specific conditions. The traffic control system adjusted the routing configuration parameters for critical areas according to these predictions. Finally, a real-time simulation was performed to evaluate the effectiveness of the proposed system as it responded to changes in traffic conditions in overloaded manufacturing.

Figure 2. Overview of the proposed approach with a predictive traffic system.

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2.2 Data description We used a simplified version of a real-world railway network to produce a graph-based representation containing nodes and arcs, as shown in Figure 3(a). Nodes are loading and unloading ports that are connected to production facilities and rail intersection points. Arcs are unidirectional rails. To reduce the complexity of the network connectivity and achieve data consistency, we converted the network to an intersection point network as shown in Figure 3(b). We then calculated traffic indices for each section, as identified by the branching and merging points on the network structure.

(a) Real-world railway network

(b) Intersection point railway network

Figure 3. Simplified version of vehicle railway network for experimental study

Because of the company’s confidentiality policy on railway networks, we have provided only the connectivity information for the network. We estimated the network shape to be as shown in Figure 4. The estimated networks represent the overall connections and the directions between sections. We used these networks to obtain the relative positions of significant independent sections of a predictive model.

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(a) Estimated Network Structure I

(b) Estimated Network Structure II

(c) Detailed connection information and direction between sections Figure 4. Estimation of railway network structure. We used Gephi, which is an open source graph visualization platform, to estimate the network shape (Bastian et al., 2009).

For the driving logs of vehicles, we obtained three days of real data from a semiconductor fab to estimate traffic conditions. Driving logs include timestamps, driving states, locations, speed, assigned transfer jobs, and designated vehicle routes. We aggregated the logs to trace the overall driving records of vehicles before calculating traffic indices for each section.

(a) Raw data

(b) Section-based traffic data set

Figure 5. Data preparation procedure 13

(c) Data structure for building prediction models

Figure 5 shows the overall procedure for data preparation. Estimating traffic conditions on the railway network required three additional preprocessing steps. We first converted the driving logs of vehicles into section-based traffic data sets. Second, for each section, we generated two traffic measures that will be briefly introduced in Section 3. We then verified the accuracy of the given messages to ensure consistency of the data. Traffic data were collected for 626 sections, including two sections of special interest (section IDs: 545, 652), in which frequent traffic congestion was known to occur. First, as shown in Figure 5(a), we combined information about the railway network structure and driving logs of vehicles to produce traffic speed and volume data sets. The average values of the three indices measured in intervals of 5, 10, and 15 minutes were calculated for all sections to construct data sets (Figure 5b). Three data sets were constructed for each index with rows and columns representing measured times and sections. The column values of a section of interest were set as the dependent (output) variable, and the remaining column values of the 625 sections were set as independent (input) variables. To predict congestion after one unit of time in a section of interest, we adjusted the data so that we mapped the independent variables and the dependent variable after one unit of time (Figure 5c).

2.3 Predictive model In this section, we present a machine learning-based predictive model to predict two traffic indexes. To select the final model, we compared the predictive performance of four widelyused models: random forests (Breiman, 2001), support vector machines (Basak et al., 2007), gradient boosting machines (Friedman, 2001), and deep neural networks (Lecun et al., 2015). The random forest is an ensemble learning algorithm that constructs multiple decision trees to avoid overfitting. For regression, the random forest algorithm makes predictions by averaging the predicted values of its individual decision trees (Breiman, 2001). Its main 14

advantage over other machine learning algorithms is that it is robust to noise and overfitting. The support vector machines is a supervised learning algorithm which efficiently handles high-dimensional data. In a support vector regression, the independent variables are first mapped onto a high-dimensional feature space to construct a linear model (Basak et al., 2007). The gradient boosting machine produces a predictive model combining weak learners such as decision trees in an iterative fashion (Friedman, 2001). Deep neural networks have recently gained attention because of their impressive predictive performance. These networks use a cascade of multiple layers of nonlinear processing units to perform highly abstract feature extraction and transformation (Lecun et al., 2015). We used 10-fold cross validation (CV) to obtain a reliable estimate of performance. We used a grid search along with CV to select hyperparameters to minimize both the mean absolute error for each algorithm and for each data set (for example, the maximum depth and the minimum impurity split and the bootstrap option in the random forest; the kernel function, the epsilon-tube rate, and the shrinking option in the support vector machine; and the learning rate, the subsample size, and the number of boosting stages in the gradient boosting machine). For deep neural networks, the model consisted of one input layer (625 units), four hidden layers (two to four layers with 300, 100, 50, and 10 hidden units, respectively), and one output layer (one unit). The number of units in the input layer was the number of features representing the traffic conditions in other sections. We used a dropout with probabilities of 0.05 to prevent overfitting, and used a rectified linear unit as an activation function. Furthermore, we used a root mean square propagation (RMSprop) optimizer that uses the magnitude of recent gradients to normalize the gradients. We empirically set the parameters of RMSprop as follows: learning rate = 0.001; decay rate = 0.9; and where

=

. We used the mean absolute percentage error for a loss function is a loss function between the original output 15

and predicted output ̂.

̂)

We used Python 2.7.15 and the scikit-learn library (an open-source machine learning library in Python) and Keras with the TensorFlow backend (an open-source library to provide building blocks for neural networks) to train the deep neural network models.

2.4 Traffic control model In this section, we present a traffic control method using predictive results to prevent congestion. To identify the level of congestion in a section, we constructed two different predictive models for traffic volume and speed. The predictive model for traffic volume was used to verify whether traffic volume fluctuations caused congestion, and the predictive model for speed was used to identify the level of congestion.

2.4.1 Traffic density discretization For sophisticated traffic control, we analyzed the relationship between traffic volume and speed. Figure 6(a) shows a speed-volume scatter plot, which shows that the averages and variances of speed decreases when the volume increases in a section.

(a) Scatter plot of traffic volume and speed

(b) Change in distribution with increasing traffic volume

Figure 6. Congestion levels of traffic volume with change points

We determined that the degree of congestion could be divided into three levels: free, busy, and congested. In the free traffic level, a section has low traffic volumes with large 16

variations in average speed. This is because the average speed calculation involves about 20 seconds of wafer hoisting time. Meanwhile, in busy and congested traffic, the effect of hoisting time on average speed is trivial because there are enough samples of driving vehicles. The speed deceleration starts with increasing vehicle volume in busy traffic, while speed drastically decreases because of extreme traffic volume in congested traffic. The criteria for distinguishing these three groups differ for each section because the acceptable traffic volume differs for each section. To overcome the problem of detecting the state of changes in speed, we used changepoint detection (CPD) algorithms (Pollak and Siegmund, 1991; Siegmund and Venkatraman, 1995; Huang et al., 2018). In CPD, a change point is determined as the moment when sequential hypothesis tests detect the probability distribution of data changes. In this study, we used the Brown-Forsythe test (Brown and Forsythe, 1974), which is useful in indicating a statistically significant difference in variances across different groups. The Brown-Forsythe test statistic uses the

statistic to compare absolute deviations from the median between

different groups. The

statistic is determined as: ̅

∑ ∑

where

,

(1)

and N denote groups and the total sample size of all groups, respectively. ̃

̅ =∑

̅

∑

⁄

where and

̅ =∑

are dependent values and ̃ ∑

and

, are detected sequentially by inspecting

-value of the hypothesis test between the null hypothesis

occurs‖ and the alternative hypothesis of the moving window. In general,

.

⁄ .

In Figure 6(b), times of state change, the

is

: ―no change in variance

: ―a change in variance occurs.‖ Here,

is the size

can be determined empirically. For nonparametric tests,

we conducted a bootstrap resampling of 1,000 runs. 17

Figure 7. Experiments in change-point detection to identify an optimal change-point

Figure 7 presents experiments conducted to search for an optimal change-point, , with the smallest

-value. The figure on the left shows the detected optimal point, , and the

figure on the right shows the smallest

-value at that point in the moving time window.

Experiments on hundreds of sections showed that the Brown-Forsythe test outperformed the other tests in terms of

-value. Based on this proposed CPD approach, we determined free,

busy, and congested traffic for all sections.

2.4.2 Predictive traffic control system In this section, we present a practical approach to the provision of a congestion-avoidance algorithm for the existing system. Figure 8 shows a procedure for the proposed predictive traffic control system that contains two models: traffic control and predictive models. Internally, the proposed system performs prediction and control once every five minutes. Two interfaces — message and database — exist between the proposed traffic control model 18

and the existing routing system. To determine a vehicle's route, we use Dijkstra’s algorithm based on the shortest-path problem. For message communication, we used the commercial message-oriented middleware solution, Tibco Rendezvous, for scalability and robustness (TIBCO, 2018). We used message communication to broadcast information about the size of traffic from the existing routing system to the traffic control model. While also predicting traffic condition changes, the traffic control model updates the routing configuration parameters in the database. The proposed approach requires minor changes to the existing routing system.

Figure 8. Predictive traffic control system

The routing configuration parameters correspond to the penalty costs of the sections, which were used as the distance criteria for route calculation. If a penalty cost increases, the section is more likely to be excluded from the route selection. The updated penalties are only applied to newly-assigned vehicles. Based on the congestion level with

and

, the

traffic control model increases the penalty costs of sections where congestion is predicted. The existing cost

for section k is maintained in a free state. When busy or congested

traffic is expected, the traffic control model will update the cost to where

<

<

or

, respectively,

. These cost values for a section were experimentally determined.

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3. Experiments 3.1 Predictions 3.1.1 Traffic prediction results We measured two traffic indices — volume and speed — in 626 sections of vehicles over the actual running time. Traffic volume represents the number of vehicles that pass through a section in a specific period. Speed is the average speed of vehicles that pass through a section in a period. Volume and speed in a certain section are two of the most intuitive indices indicating traffic conditions because traffic congestion slows vehicles. Predictive performance is measured by the predictive accuracy of third-day traffic indices: training predictive models with first- and second-day data, predicting third-day traffic indices, and then calculating the predictive accuracy. We used the following mean absolute percentage accuracy (MAPA) as the accuracy measure:

1

1 N yi  yˆ i ,  N i 1 yi

(2)

where yi is the actual value and yˆ i is the predicted value. The predictive results can be compared regardless of the scale difference of the actual value because accuracy is calculated as a relative ratio. Based on the domain knowledge of semiconductor engineers, we set 0.8 as the threshold for determining an appropriate predictive model for traffic control. Table 1 shows the predicted results for 545 and 652 sections in terms of volume and speed. We used three data sets measured at intervals of 5, 10, and 15 minutes, which are sufficiently short for an effective traffic control system. In practice, short-term predictive models are required to prevent congestion because the vehicle system is vulnerable to a few minutes of congestion.

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Table 1. Prediction results of random forest (RF), support vector machine (SVM), gradient boosting machine (GBM), and deep neural networks (DNN) for 545 and 652 sections in terms of volume and speed. The best accuracy is indicated in bold face.

Section Index

545 Minute

Volume

Speed

Average

RF

SVM

652 GBM

DNN

RF

SVM

GBM

DNN

5

0.833

0.833

0.817

0.801

0.833

0.726

0.703

0.668

10

0.897

0.894

0.890

0.855

0.897

0.855

0.843

0.804

15

0.906

0.911

0.906

0.904

0.888

0.872

0.892

0.866

5

0.919

0.913

0.919

0.889

0.838

0.830

0.832

0.767

10

0.944

0.943

0.943

0.943

0.890

0.868

0.894

0.821

15

0.957

0.923

0.951

0.941

0.888

0.872

0.892

0.866

0.909

0.903

0.904

0.889

0.872

0.837

0.843

0.799

The random forest model was most accurate in most situations. Specifically, in the cases of five-minute intervals, the random forest models always yielded the most accurate predictions in terms of volume and speed in both the 545 and 652 sections. In practice, controllability for short intervals such as five minutes is the most significant. Moreover, the random forest model allows identification of significant independent variables which are critical in predicting traffic in a section. Therefore, we identified the random forest model as being the most suitable predictive model for a traffic control system because of the need for speed in predictions and decisions.

3.1.2 Selection of important sections To determine which sections to use as significant independent variables to predict traffic for a section, we calculated the variable importance scores for the independent variables. The random forest algorithm ranks the independent variables by randomly permuting the values for every out-of-bag (OOB) sample and measuring the mean decrease in accuracy. Figures

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9(a) and 9(b) show cases of selection of important variables. We found that there is a great influence from adjacent sections.

(a) Important variables selection for section 171005

(b) Important variables selection for section 167005

Figure 9. Important section selection in traffic prediction. The blue-colored section represents the dependent variable and the other sections represent the independent variables. In independent variables, the darker the color, the greater the effect.

3.2 Simulations 3.2.1 Simulation setup We conducted a simulation study to examine the performance of the proposed approach. We used high-fidelity simulation software that delivers a test bed identical to the actual semiconductor fabrication environment. The simulation software contains multiple vehicle emulators and replicates the three core operations of actual AVSs running in the factory; system communication, transfer job assignment, and autonomous driving. Hence, the simulation software, which is responsible for the final pilot test in the actual plant, replicates actual real-time behaviors and traffic patterns. We ran simulations on a PC with Windows 7, an i5-4310M 2.70GHz processor, and 16GB of RAM. To obtain statistically meaningful comparisons, we conducted 10 replications with different random seeds and measured the average of the performance measures. We used a Java SE Development Kit 7u2 to implement the proposed system.

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Figure 10. Test bed platform for experimental simulations

As shown in Figure 10, we developed a test bed platform to build a real-time simulation. The platform has five components: simulator, vehicle emulators, control system mock-up, traffic control agent, and data storage. The real-time simulation that we ran between components is as follows. Thousands of vehicle emulators were instantiated with a base simulator. Based on driving information broadcast from vehicle emulators, the control system mock-up determined a route for a vehicle. The traffic control agent gathered all information on the vehicles and periodically created traffic information for all sections. The system executed the model once every five minutes to predict traffic volume and speed for critical sections. Then, the system stored actual and predicted traffic information in the evaluation database and updated the control parameters in the configuration database. The control system mock-up refers to the updated routing configuration.

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Figure 11. Specifications of the predictive traffic control system

Figure 11 shows the specifications of the predictive traffic control system containing two modules: the real-time message and prediction handlers. In the first module, we used the reactor design pattern, one of the widely used event-handling patterns to dispatch multiple service requests because several thousand vehicle messages should be handled in a nonblocking manner. The prediction handler then executes prediction models to predict traffic for critical sections and rebuilt prediction models if the prediction accuracy drops below the predefined threshold value

for section

.

Traffic workload was gradually increased by 5%, up to a 140% workload, with a base condition of 100%. The higher the traffic load, the more severe the expected congestion. Under the higher load, we expected our method to be more effective than the existing one. Eighteen hours of real data from a semiconductor fab were used to configure the number of transfer requests. Simulations were performed in real time to ensure real-world conditions for test bed platforms.

3.2.2 Simulation results To evaluate the performance of the proposed system under real conditions, we conducted experiments of the three evaluation criteria, delivery time, transfer time, and queued time. 24

Delivery time includes the queued time before assigning a wafer lot to a vehicle, and the transfer time. The transfer time is the elapsed time between the unloading of a wafer lot at the departure machine and when it reaches its destination. The queued time is the time from the delivery request to the assignment of a vehicle. Averages and standard deviations were calculated based on measures collected once per hour in simulation replications.

(a) Results of the existing system

(b) Results of the proposed system

Figure 12. Simulation results for transfer time

(a) Results of the existing system

(b) Results of the proposed system

Figure 13. Simulation results for queued time

Figure 12 shows that the proposed predictive system clearly outperformed the existing system in terms of transfer time, reducing it by five seconds on average, and reducing 25

variance by eight seconds. Figure 13 shows that the average and the standard deviations of queued time under the proposed system also decreased significantly. Across all replications, while delivery delay was continuously increasing in the existing system, the average queued time in the proposed system declined from 5m 18s to 2m 16s. The deviation was reduced by up to 50%, and there was no significant increase until the simulation ended. These results demonstrate the usefulness and superiority of the proposed system, which makes AVSs robust even under overloaded manufacturing conditions.

Table 2. Prediction results of the existing and proposed systems in terms of delivery time for 17 bottleneck sections. Sections are sorted in the descending order of the mean delivery time.

The existing system (based on Dijkstra algorithm) Section ID Mean

The proposed system (based on machine learning)

Delivery time (seconds) Standard Mean deviation 1,620.78 665.67

Standard deviation 1,276.36

P-values for one-sided KolmogorovSmirnov (KS) tests ( =0.05)

99072

942.584

< 0.001*

166005

689.474

1,099.58

437.511

575.945

< 0.001*

166016

543.385

915.227

429.044

676.037

< 0.001*

99035

542.538

940.605

287.479

359.141

< 0.001*

171005

415.899

651.266

390.38

600.437

< 0.001*

2201

397.319

836.903

229.394

163.268

< 0.001*

99016

387.514

432.564

321.005

362.588

< 0.001*

5047

363.705

834.39

470.974

950.579

0.457

2710

337.456

854.4

370.256

863.212

0.562

4320

319.004

775.837

373.711

776.28

0.845

152073

292.405

574.419

173.462

237.405

< 0.001*

6343

261.664

665.693

229.498

472.85

0.002*

169044

240.968

161.932

239.282

163.084

0.535

166051

218.145

87.121

225.831

61.051

0.485

151040

217.006

313.087

230.251

310.235

0.897

2641

196.587

332.7

227.324

423.554

0.926

3352

193.767

200.986

200.721

208.916

0.315

Table 3. Prediction results of the existing and proposed systems in terms of transfer time for 17 bottleneck sections. Sections are sorted in the descending order of the mean delivery time.

26

The existing system (based on Dijkstra algorithm) Section ID Mean

The proposed system (based on machine learning)

Transfer time (seconds) Standard Mean deviation 82.326 203.652

Standard deviation 59.428

P-values for one-sided KolmogorovSmirnov (KS) tests ( =0.05)

99072

250.030

< 0.001*

166005

274.618

101.416

240.503

88.104

0.001*

166016

249.250

95.823

241.434

76.437

< 0.001*

99035

257.092

89.731

192.711

62.310

< 0.001*

171005

240.839

92.641

238.471

79.239

< 0.001*

2201

193.783

62.761

191.061

48.089

< 0.001*

99016

214.983

75.378

189.502

64.897

< 0.001*

5047

175.296

53.386

186.630

59.968

0.995

2710

159.297

57.973

168.829

61.470

0.995

4320

162.018

79.453

185.903

78.937

0.999

152073

160.078

59.244

139.725

53.170

< 0.001*

6343

156.698

56.836

161.683

60.343

0.613

169044

209.776

67.731

206.797

58.561

0.001*

166051

188.691

68.254

185.624

59.920

0.132

151040

168.192

92.981

178.622

68.777

0.996

2641

150.119

56.617

158.093

57.949

0.967

3352

159.849

62.980

163.762

57.923

0.306

Tables 2 and 3 demonstrate the effectiveness of the proposed system in terms of delivery and transfer times for 17 bottleneck sections. Delivery and transfer times were calculated only for the vehicles that pass the bottleneck sections on their routes. Note that the proposed system dramatically reduced delivery time in the sections with large mean and standard deviation of delivery time (Table 2). However, the proposed system has little or no effect in the sections with relatively small delivery time The reasons why the proposed system showed outperformance in the sections with high delivery time was that the proposed system had more chances to prevent heavy congestion and balance traffic load in more significant bottleneck sections. The higher the delivery time, the higher the risk that having heavy traffic congestion among bottleneck sections. Overall, delivery time was reduced by an average of

27

19% and standard deviation of 53%. Except for queued time, transfer time was reduced by an average of 4% and standard deviation by 15% (Table 3). We conducted Kolmogorov-Smirnov (KS) tests to statistically assess whether the rank of the population mean differed between the existing Dijkstra-based shortest path planning system and the proposed predictive traffic control system. The p-values for more significant bottleneck sections were less than 0.001, indicating that the performance difference between the existing algorithm and the proposed algorithm is statistically significant.

4. Conclusions In this study, we developed a machine learning-based predictive traffic control method for AVSs in manufacturing, especially for high-workload situations. The proposed approach demonstrated improvement in steady-state performance in delivery and transfer times, and elimination of congestion in significant bottleneck sections. Experimental results based on actual data with high-fidelity simulations demonstrated that the proposed method can be of practical use. We plan to apply the predictive traffic control system to medium- and largesized 300mm wafer facilities in a Korean semiconductor manufacturing company. The practical implication of this work is that the proposed approach controls the traffic flow of AVSs by preventing heavy traffic congestion and balancing traffic load among significant bottleneck sections. In plants, many human experts monitor traffic and congestion in real time. Manual monitoring is labor-intensive and may not be sufficient to resolve congestion. Thus, the proposed approach could be valuable to prevent traffic congestion in several significant bottleneck areas without human intervention. Further, the proposed approach can be a practical solution to achieve stable production. Our predictive approach leads to the alleviation of congestion, allowing fast wafer transfer, and maintaining the production schedule to prevent idle machines. 28

The advantages of the proposed approach can be summarized as follows. We verified that the optimization approach that reflects future dynamics outperforms existing optimization approaches, assuming a static environment. Second, the proposed system enhances traffic control and minimizes the need for human intervention. Third, we ensured that the proposed system is appropriate for large-scale semiconductor fabrication. Fourth, the proposed system can complement existing AVSs in actual plants without the full replacement of a routing algorithm. Because of the conservative nature of the manufacturing industry, a plugin approach to the existing system, involving minimal modifications, is essential. There are some limitations to our study. We assumed stationary traffic conditions for AVSs. However, traffic pattern changes are inevitable in real-world problems. Thus, longterm traffic control plans require the incorporation of non-stationary traffic conditions. Achieving accurate performance for reliable predictive control should involve the phenomenon of concept drift: changes in the conditional distribution between the dependent and independent variables. Specifically, no explicit learning framework has been used to resolve the following problems: 1) how to detect concept drift in a regression task using accuracy performance and 2) how to update a predictive model from pattern changes at unspecified intervals. We plan to extend our study to reflect non-stationary traffic conditions using incremental learning techniques. With respect to the training phase, we can consider multitask learning techniques to address the similarity among section-based traffic prediction tasks. Our study built independent predictive models for each bottleneck section. However, significant bottleneck sections are expected to be correlated, because the sections are connected directly or indirectly in railway networks. To resolve this issue, multitask learning techniques can be used to leverage accurate performance of a predictive model. Third, the proposed method is only designed to alleviate short-term congestion, on the order of five minutes. We could extend this approach to resolving the shorter and longer-term congestion 29

to ensure overall coverage of traffic control for AVSs. Fourth, regarding the experimental results in the Section 3.2.2, our approach was ineffective in the sections with less congestion. To overcome this, we need to facilitate more precise traffic control according to the expected level of congestion. For instance, we should identify optimal routing penalty costs for busy and congested traffic in bottleneck sections. To do so, parameter optimization of the routing penalty parameters should be considered. Finally, our approach could be generalized to other control problems. Thus, future studies will extend our predictive approach to model other control applications in manufacturing to achieve stable production.

Conflict of Interest We have NO conflicts of interest to declare.

Acknowledgements The authors would like to thank the editor and reviewers for their useful comments and suggestions, which were greatly help in improving the quality of the paper. This research was supported by Samsung Electronics, Co., Ltd., Brain Korea PLUS, Korea Institute for Advancement of Technology (KIAT) grand funded by the Korea Government (MOTIE) (P0008691, The Competency Development Program for Industry Specialist), the National Research Foundation of Korea grant funded by the Korea government (MSIT) (No. NRF2019R1A4A1024732), the Ministry of Trade, Industry & Energy under Industrial Technology Innovation

Program

(R1623371)

and

the

Institute

for

Information

& Communications Technology Promotion grant funded by the Korea government (No. 2018-000440, ICT-based Crime Risk Prediction and Response Platform Development for Early

30

Awareness of Risk Situation), and the Ministry of Culture, Sports and Tourism and Korea Creative Content Agency in the Culture Technology Research & Development Program 2019.

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Credit Author Statement Sangmin Lee  Formulation of overarching research goals 

Development of methodology, creation of models



Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection



Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms



Verification, whether as a part of the activity or separate, of the overall replication/ reproducibility of results/experiments and other research outputs



Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation)

37

Younghoon Kim  Development of methodology, creation of models 

Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection



Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms



Preparation, creation and/or presentation of the published work, specifically writing the initial draft (including substantive translation)

Hyungu Kahng  Development of methodology, creation of models 

Conducting a research and investigation process, specifically performing the experiments, or data/evidence collection



Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms

Soon-Kyo Lee  Development of methodology, creation of models 

Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms

Seokhyun Chung  Development of methodology, creation of models 

Programming, software development; designing computer programs; implementation of the computer code and supporting algorithms

Teasu Cheong  Formulation of overarching research goals 

Verification, whether as a part of the activity or separate, of the overall replication/ reproducibility of results/experiments and other research outputs



Acquisition of the financial support for the project leading to this publication

Keeyoung Shin  Formulation of overarching research goals 

Verification, whether as a part of the activity or separate, of the overall replication/ reproducibility of results/experiments and other research outputs 38

Jeehyuk Park  Formulation of overarching research goals 

Verification, whether as a part of the activity or separate, of the overall replication/ reproducibility of results/experiments and other research outputs

Seoung Bum Kim  Formulation of overarching research goals 

Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision – including pre-or postpublication stages



Oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team



Acquisition of the financial support for the project leading to this publication

39