Canadian Journal of Civil Engineering A new method for time-of-day breakpoints determination based on clustering and image processing Journal: Canadian Journal of Civil Engineering Manuscript ID cjce-2019-0153.R2 Manuscript Type: Article Date Submitted by the 28-Sep-2019 Author: Complete List of Authors: Shen, Hui; Southwest University of Science and Technology; Mianyang Polytechnic Yan, Jing; Southwest University of Science and Technology Liu, Daoguang; Southwest University of Science and Technology Liu, Zhigui;Draft Southwest University of Science and Technology; Southwest University of Science and Technology, School of computer science and technology Keyword: signal control, clustering, image segment, TOD breakpoints Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? : https://mc06.manuscriptcentral.com/cjce-pubs Page 1 of 33 Canadian Journal of Civil Engineering A new method for time-of-day breakpoints determination based on clustering and image segmentation Hui Shen1,3, Jing Yan2, Daoguang Liu2, Zhigui Liu*1,2 1. School of Information Engineering, Southwest University of Science and Technology, 59, Qinglong Road, Fucheng District, Mianyang, 621000, China. 2. School of Computer Science and Technology, Southwest University of Science and Technology, 59, Qinglong Road, Fucheng District, Mianyang, 621000, China. 3. Corresponding author. Mianyang Polytechnic, 1, Xianren Road, Youxian District, Mianyang, 621000, China. Draft Abstract: Signal control is an important part of the transportation system and it plays an important role in improving the capacity of intersections. This paper proposes a new traffic time division method for multi-period fixed-time control strategy. Firstly, we put forward a new concept-transportation overlap rate, in order to complete the clustering of daily traffic flow patterns. Then, all the daily traffic flow data belonging to the same category are composed into a matrix, which is converted into the corresponding image later with the aim of using the Fast and Robust Fuzzy C-Means Clustering (FRFCM) method to segment it. Finally, the traffic time division and breakpoints location are obtained through further analysis and processing of the segmentation results. For each period, the optimal signal cycle and green split are separately calculated by Webster’s signal timing method, in order to satisfy different traffic demands of each period and effectively improve the operation efficiency of the intersection. The simulation results at a certain intersection in Mianyang city demonstrate the effectiveness and practicability of the proposed method. Keywords: signal control; clustering; image segment; TOD breakpoints 1. Introduction In 1868, the first colored traffic signal light appeared in Westminster, England (Webster https://mc06.manuscriptcentral.com/cjce-pubs Canadian Journal of Civil Engineering Page 2 of 33 and Cobbe, 1966). Since then, signal lights at intersections have played an indispensable role in the modern traffic system, directly affecting the operation of vehicles. According to the National Transportation Operations Coalition (NTOC, 2012), 5-10% of all traffic delays on main roads are caused by delays at signal-controlled intersections (Zhuofei Li; Lily Elefteriadou; Sanjay Ranka 2014). A reasonable signal control scheme can greatly benefit the entire transportation system by reducing or even eliminating road bottlenecks, improving transportation capacity, and relieving traffic pressure in congested areas. During a long process of development, a lot of work has been done and many achievements have been obtained in the theoretical and experimental studies. Webster (1958) proposed an optimal method of signal timing for fixed-cycle signal system with the goal of minimizing the average vehicle delay. His method and the calculation of vehicle delay at unsaturated intersections are still in use today. However, his method is not applicable for over-saturated traffic conditions. Akcelik (1981), the pioneer of the multi-objective optimal control, improved Webster’s algorithm by combining the vehicle delay and shutdown times, and took them as the new optimization Draftobjects of signal timing. But the process of solving multi-objective optimization problem is so complex that there is still no perfect solution. With the rise of vehicular networking technology, some researchers began to study new methods of traffic signal control for connected vehicles. Priemer and Friedrich (2009) proposed an adaptive traffic signal control algorithm by using V2I communication data to reduce the queue length, and employed complete enumeration and dynamic programming to solve the problem; Goodall et al. (2013) developed a predictive microscopic simulation algorithm (PMSA) for traffic signal control, which can predict the future traffic volume by utilizing connected vehicles data, and optimize various traffic indicators. However, the execution time of the program is too long to be suitable for real-time applications. A lot of facts demonstrate that the execution time of system program will affect the efficiency of problem solving. Therefore, scientists are considering efficient ways to improve the speed of optimization. Zhuofei Li et al. (2014) suggested a signal control optimization method that can effectively improve the capacity of the intersection and reduce vehicle delays; but this method is only applicable to connected vehicles. The connected vehicles have not been applied widely, and it will take about twenty-five years to reach a high penetration rate (Volpe National Transportation Systems Center 2008). In the meantime, the key technologies are in the research and development stage, and the existing https://mc06.manuscriptcentral.com/cjce-pubs Page 3 of 33 Canadian Journal of Civil Engineering research results still need validation and support. Therefore, the current study of traffic signal timing mainly focuses on traditional vehicles, rather than intelligent vehicles. At present, time-of-day (TOD) multi-period fixed-time control and actuated signal control at intersections are considered to be wildly used solutions. The realization of actuated signal control needs various types of sensor information, and it is necessary to take not only the cost but also the fusion of various types of data into account, all of which will result in the high initial investment and operation costs, as well as the complex control processes. By contrast, multi-period fixed-time control is simple and easy to implement. Furthermore, the sensor type used in this scheme is single and the detected data can be corrected by field investigation, so it is more suitable for traffic control at signalized intersections. In practice, traffic engineers usually collect the counting traffic data in 1-2 days manually to plot the aggregated volumes, then divide the period of traffic time and get the TOD breakpoints according to engineering judgment (Byungkyu Park; Pinaki Santra; IlsooYun; Do-Hoon Lee 2018). However, this method is not always reliable and efficient, because it cannot automatically adjust to changes in Draftdaily traffic flow, and experts' personal judgments are often different from each other. Therefore, we propose a new time division method based on image segmentation, which can generate an intuitive and visible TOD scheme by utilizing a large amount of archived traffic data without manual counting and experts’ subjective judgments. Normally, the amount of traffic data is normally huge and complex, so it is impractical to apply them directly. Cluster analysis is an effective approach to solve such problems, because it is good at data feature recognition and dimensionality reduction, and maintains the functional integrity of the data at the same time (Chung and Rosalion 2001). It has also been claimed that classification and clustering of the traffic data can really lead to the insight into the effectiveness of traffic strategies (Wang et al. 2006). So in this paper, the cluster analysis of traffic flow data is carried out first. In recent years, some excellent achievements have been made in the research of image segmentation, such as superpixel-based fast fuzzy C-means clustering method (Tao Lei, Xiaohong Jia; Yanning Zhang et al. 2018), automatic fuzzy clustering framework (Tao Lei; Peng Liu; Xiaohong Jia et al. 2018), etc. These researches bring new ideas and innovations to the field of image processing. We draw support from the image segmentation algorithm to complete the division of traffic time in this paper. The next section of this paper describes the processing of traffic flow data. In section 3, we https://mc06.manuscriptcentral.com/cjce-pubs Canadian Journal of Civil Engineering Page 4 of 33 propose both the clustering algorithm based on overlapping rate of hourly traffic volume distribution and the traffic time division method based on image segmentation. The simulation results are described in section 4. Finally, we present our discussions and conclusions in section 5. 2. Data processing Traffic data information should be fully utilized in the study of intersection signal control. The data used in this paper were collected at a signalized intersection in Mianyang City. The satellite image of the intersection geometry and its schematic depiction of the lane configuration are shown in Fig. 1 and Fig. 2. The traffic flow data of every lane were measured and collected per hour by the real-time monitoring devices, which are installed on the poles above the stop lines of