Time-of-Day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections

Abstract

This paper proposes a novel two-order optimization model of the division of time-of-day control segmented points of road intersection to address the limitations of the randomness of artificial experience, avoid the complex multi-factor division calculation, and optimize the traditional model over traffic safety and data-driven methods. For the first-order optimization—that is, deep optimization of the model input data—we first increase the dimension of traditional traffic flow data by data-driven and traffic safety methods, and develop a vector quantity to represent the size, direction, and time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. Then, a time-series segmentation algorithm is used to recurse the distance amongst adjacent vectors to obtain the initial scheme of segmented points, and the segmentation points are finally divided by the combination of the preliminary scheme. For the second-order optimization—that is, model adaptability analysis—the traffic flow data at intersections are subjected to standardised processing by five-number summary. The different traffic flow characteristics of the intersection are categorised by the K central point clustering algorithm of big data, and an applicability analysis of each type of intersection is conducted by using an innovated piecewise point division model. The actual traffic flow data of 155 intersections in Yuecheng District, Shaoxing, China, in 2016 are tested. Four types of intersections in the tested range are evaluated separately by the innovated piecewise point division model and the traditional total flow segmentation model on the basis of Synchro 7 simulation software. It is shown that when the innovated double-order optimization model is used in the intersection according to the ‘hump-type’ traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 5.6%. In particular, the delay time in the near-peak-flow buffer period is significantly reduced by approximately 17%. At the same time, the traffic accident rate has also dropped significantly, effectively improving traffic safety at intersections.

A Field Study of Work Type Influence on Air Traffic Controllers’ Fatigue Based on Data-Driven PERCLOS Detection

“…In fact, fatigue has recently become a research hotspot because of its threat to safety and hazards to occupational health [31,32]. This study provides data-driven evidence that work type influences an ATCO's fatigue.…”

Space-Time Resource Integrated Optimization Method for Time-of-Day Division at Intersection Based on Multidimensional Traffic Flows

“…In order to solve the above problems, some studies began to use multidimensional trafc-fow data to carry out the TOD division research. For example, Xu et al [22] developed a doubleorder optimization model of TOD control segmented point division, the dimensions of the traditional trafc-fow data were increased, and the classic model was reconstructed in the frst-order optimization. Dai et al [23] calculated the cycle time by considering the diference of trafc fow direction at the intersection, studied the TOD division method at the intersection, and provided a basis for a multiperiods timing scheme.…”

“…In the process of TOD division, most studies have not fully considered signal phase optimization. For example, [21,22,25] all adopted Synchro software to optimize the timing scheme but did not mention the phase optimization. In addition, hardly has literature considered the lane-use assignment optimization in the process of TOD division, and the fnal TOD division result has not been further tested according to lane-use assignment, signal phase, and timing scheme.…”

Signal Control Period Division Method Based on Locally Linear Embedding and Particle Swarm Optimization Combined With K-Means Clustering

“…Then a new control period division method was proposed which took the difference of diverging amount into account in the case of similar total flow. Xu et al [19] constructed a three-dimensional vector based on the total traffic flow, total flow direction, and the time frequency of the conflict point with the downstream at the intersection. And the distances between adjacent vectors were recursively combined to determine control period division points.…”