Stop-and-Go Decision-making Mechanism Analysis of E-bikes During Signal Change Interval

2021 ◽  
Vol 22 (7) ◽  
pp. 1661-1672
Author(s):  
Sheng Dong Sheng Dong ◽  
Jibiao Zhou Sheng Dong ◽  
Shuichao Zhang Jibiao Zhou ◽  
Lin Guo Shuichao Zhang ◽  
Zhijian Wang Lin Guo

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Sheng Dong ◽  
Jibiao Zhou

The stop/go decisions at signalized intersections are closely related to driving speed during signal change intervals. The speed during stop/go decision-making has a significant influence on the dilemma area, resulting in changes of stop/go decisions and high complexity of the decision-making process. Considering that traffic delays and vehicle exhaust pollution are mainly caused by queuing at intersections, the stop-line passing speed during the signal change interval will affect both vehicle operation safety and the atmospheric environment. This paper presents a comparative study on drivers’ stop/go behaviors when facing a transition signal period consisting of 3 s green flashing light (FG) and 3 s yellow light (Y) at rural high-speed intersections and urban intersections. For this study, 1,459 high-quality vehicle trajectories of five intersections in Shanghai during the transition signal period were collected. Of these five intersections, three are high-speed intersections with a speed limit of 80 km/h, and the other two are urban intersections with a speed limit of 50 km/h. Trajectory data of these vehicle samples were statistically analyzed to investigate the general characteristics of potential influencing factors, including the instantaneous speed and the distance to the intersection at the start of FG, the vehicle type, and so on. Decision Tree Classification (DTC) models are developed to reveal the relationship between the drivers’ stop/go decisions and these possible influencing factors. The results indicate that the instantaneous speed of FG onset, the distance to the intersection at the start of FG, and the vehicle type are the most important predictors for both types of intersections. Besides, a DTC model can offer a simple way of modeling drivers’ stopping decision behavior and produce good results for urban intersections.


Author(s):  
Hu Feifei ◽  
Zeng Shibo ◽  
Hong Danke ◽  
Zhang Situo ◽  
Song yongwei ◽  
...  

As the decision-making brain for power system operation, grid regulation and operation is a comprehensive decision-making control that combines a large amount of data, mechanism analysis, operating procedures and professional experience, and a new generation of artificial intelligence development ideas and evolution characterized by data-driven and knowledge-guided. The directions are very close. However, the current scheduling control is still based on experience and manual analysis. The massive and diverse data of the control center and the lack of logical models between the plans require a large amount of experience and knowledge associations by the control personnel. There are more repetitive human brain labor and relatively low intelligence. Therefore, deep learning is applied to the learning of power control knowledge, and a semantic understanding network based on deep Long Short Term Memory is proposed. It uses sequence labeling to extract in-depth semantic related information of different keywords and query questions, and finds key information about language problems in order to achieve fine-grained and precise query. Experiments show that the proposed network model is superior to the previous methods, and it achieves better performance in the joint extraction of fine-grained evaluation words and evaluation objects, extracts the key information and deep semantic information of query problems and corresponding cases, and realizes power scheduling based on voice interaction The model can be effectively applied in the field of power dispatching and solve a large number of problems in power dispatching and control.


2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sheng Dong ◽  
Minjie Zhang ◽  
Zhenjiang Li

Aiming at solving a typical problem of past research using accident experience statistics of being unable to adapt to changing traffic flows, this paper provides an evaluation method of the risk of vehicle rear-end collisions at red-light-camera (RLC) intersections based on theoretical probabilities. Taking advantage of trajectory data of vehicles at the two similar intersections, which are Cao’an Road and Lvyuan Road with RLCs and Cao’an Road and Anhong Road without RLCs in Shanghai, a binary logit (BL) model of stop-and-go decision-making is established. Using the model and adjusting the headway and potential travel time, we can perform simulation and analysis of rear-end collisions. The result shows that this method is feasible to analyse the influence of RLCs on rear-end collisions. The analysis indicates that RLCs can cause higher speeds for vehicles passing the RLC intersection and more abnormal driving behaviors, which increase the difficulty of stop-and-go decision-making. RLCs do not always lead to an increase of rear-end collisions. For vehicles close to or far from intersection at the decision-making time, RLCs will significantly reduce the possibility of rear-end collisions; however, for vehicles in the potential travel time of 2 s∼3 s, RLCs will increase the probability of rear-end collisions.


2018 ◽  
Vol 41 ◽  
Author(s):  
Patrick Simen ◽  
Fuat Balcı

AbstractRahnev & Denison (R&D) argue against normative theories and in favor of a more descriptive “standard observer model” of perceptual decision making. We agree with the authors in many respects, but we argue that optimality (specifically, reward-rate maximization) has proved demonstrably useful as a hypothesis, contrary to the authors’ claims.


2018 ◽  
Vol 41 ◽  
Author(s):  
David Danks

AbstractThe target article uses a mathematical framework derived from Bayesian decision making to demonstrate suboptimal decision making but then attributes psychological reality to the framework components. Rahnev & Denison's (R&D) positive proposal thus risks ignoring plausible psychological theories that could implement complex perceptual decision making. We must be careful not to slide from success with an analytical tool to the reality of the tool components.


2018 ◽  
Vol 41 ◽  
Author(s):  
Kevin Arceneaux

AbstractIntuitions guide decision-making, and looking to the evolutionary history of humans illuminates why some behavioral responses are more intuitive than others. Yet a place remains for cognitive processes to second-guess intuitive responses – that is, to be reflective – and individual differences abound in automatic, intuitive processing as well.


2014 ◽  
Vol 38 (01) ◽  
pp. 46
Author(s):  
David R. Shanks ◽  
Ben R. Newell

2014 ◽  
Vol 38 (01) ◽  
pp. 48
Author(s):  
David R. Shanks ◽  
Ben R. Newell

2020 ◽  
Vol 43 ◽  
Author(s):  
Valerie F. Reyna ◽  
David A. Broniatowski

Abstract Gilead et al. offer a thoughtful and much-needed treatment of abstraction. However, it fails to build on an extensive literature on abstraction, representational diversity, neurocognition, and psychopathology that provides important constraints and alternative evidence-based conceptions. We draw on conceptions in software engineering, socio-technical systems engineering, and a neurocognitive theory with abstract representations of gist at its core, fuzzy-trace theory.


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