A two-stage safety evaluation model for the red light running behaviour of pedestrians using the game theory

When interacting with others, it is often important for you to know what they have done in similar situations in the past: to know their reputation. One reason is that their past behavior may be a guide to their future behavior. A second reason is that their past behavior may have qualified them for reward and cooperation, or for punishment and revenge. The fact that you respond positively or negatively to the reputation of others then generates incentives for them to maintain good reputations. This article surveys the game theory literature which analyses the mechanisms and incentives involved in reputation. It also discusses how experiments have shed light on strategic behavior involved in maintaining reputations, and the adequacy of unreliable and third party information (gossip) for maintaining incentives for cooperation.

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The Superiority of Quantum Strategy in 3-Player Prisoner’s Dilemma

Mathematics ◽

10.3390/math9121443 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1443

Author(s):

Zhiyuan Dong ◽

Ai-Guo Wu

Keyword(s):

Game Theory ◽

Prisoner's Dilemma ◽

Prisoner’S Dilemma ◽

Quantum Game ◽

Final State ◽

The Game Theory ◽

Initial States ◽

Quantum Strategy ◽

Selection Of

In this paper, we extend the quantum game theory of Prisoner’s Dilemma to the N-player case. The final state of quantum game theory of N-player Prisoner’s Dilemma is derived, which can be used to investigate the payoff of each player. As demonstration, two cases (2-player and 3-player) are studied to illustrate the superiority of quantum strategy in the game theory. Specifically, the non-unique entanglement parameter is found to maximize the total payoff, which oscillates periodically. Finally, the optimal strategic set is proved to depend on the selection of initial states.

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Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Behavioral Sciences ◽

10.3390/bs11070101 ◽

2021 ◽

Vol 11 (7) ◽

pp. 101

Author(s):

Andrew Paul Morris ◽

Narelle Haworth ◽

Ashleigh Filtness ◽

Daryl-Palma Asongu Nguatem ◽

Laurie Brown ◽

...

Keyword(s):

Autonomous Vehicles ◽

Red Light ◽

Road User ◽

Red Light Running ◽

Crash Data ◽

Passenger Vehicles ◽

Road Users ◽

Vulnerable Road User ◽

Vulnerable Road Users ◽

Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

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Town Traffic Safety Evaluation Model Based on PSR – SPA

ICCTP 2011 ◽

10.1061/41186(421)218 ◽

2011 ◽

Author(s):

Jianxiao Ma ◽

Taowei Yan ◽

Li Lin ◽

Jingwen Jiang

Keyword(s):

Traffic Safety ◽

Evaluation Model ◽

Safety Evaluation ◽

Model Based

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Multi-Robot Searching using Game-Theory Based Approach

International Journal of Advanced Robotic Systems ◽

10.5772/6232 ◽

2008 ◽

Vol 5 (4) ◽

pp. 44 ◽

Cited By ~ 17

Author(s):

Yan Meng

Keyword(s):

Game Theory ◽

Dynamic Programming ◽

Utility Function ◽

Real Time ◽

Dynamic Environment ◽

Equilibrium Solutions ◽

Strategy Equilibrium ◽

The Game Theory ◽

Target Searching ◽

Multi Robot

This paper proposes a game-theory based approach in a multi–target searching using a multi-robot system in a dynamic environment. It is assumed that a rough priori probability map of the targets' distribution within the environment is given. To consider the interaction between the robots, a dynamic-programming equation is proposed to estimate the utility function for each robot. Based on this utility function, a cooperative nonzero-sum game is generated, where both pure Nash Equilibrium and mixed-strategy Equilibrium solutions are presented to achieve an optimal overall robot behaviors. A special consideration has been taken to improve the real-time performance of the game-theory based approach. Several mechanisms, such as event-driven discretization, one-step dynamic programming, and decision buffer, have been proposed to reduce the computational complexity. The main advantage of the algorithm lies in its real-time capabilities whilst being efficient and robust to dynamic environments.

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Real-Time Prediction of Vehicle Trajectories for Proactively Identifying Risky Driving Behaviors at High-Speed Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118797211 ◽

2018 ◽

Vol 2672 (38) ◽

pp. 233-244 ◽

Cited By ~ 1

Author(s):

Chaopeng Tan ◽

Nan Zhou ◽

Fen Wang ◽

Keshuang Tang ◽

Yangbeibei Ji

Keyword(s):

Phase Transition ◽

High Speed ◽

Red Light ◽

Risky Behaviors ◽

Risky Driving ◽

Red Light Running ◽

Driving Behaviors ◽

Vehicle Trajectories ◽

Single Vehicle ◽

Risky Driving Behaviors

At high-speed intersections in many Chinese cities, a traffic-light warning sequence at the end of the green phase—three seconds of flashing green followed by three seconds of yellow—is commonly implemented. Such a long phase transition time leads to heterogeneous decision-making by approaching drivers as to whether to pass the signal or stop. Therefore, risky driving behaviors such as red-light running, abrupt stop, and aggressive pass are more likely to occur at these intersections. Proactive identification of risky behaviors can facilitate mitigation of the dilemma zone and development of on-board safety altering strategies. In this study, a real-time vehicle trajectory prediction method is proposed to help identify risky behaviors during the signal phase transition. Two cases are considered and treated differently in the proposed method: a single vehicle case and a following vehicle case. The adaptive Kalman filter (KF) model and the K-nearest neighbor model are integrated to predict vehicle trajectories. The adaptive KF model and intelligent driver model are fused to predict the following vehicles’ trajectories. The proposed models are calibrated and validated using 1,281 vehicle trajectories collected at three high-speed intersections in Shanghai. Results indicate that the root mean square error between the predicted trajectories and the actual trajectories is 5.02 m for single vehicles and 2.33 m for following vehicles. The proposed method is further applied to predict risky behaviors, including red-light running, abrupt stop, aggressive pass, speeding pass, and aggressive following. The overall prediction accuracy is 95.1% for the single vehicle case and 96.2% for the following vehicle case.

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Prediction of Red Light Running Based on Statistics of Discrete Point Sensors

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2128-14 ◽

2009 ◽

Vol 2128 (1) ◽

pp. 132-142 ◽

Cited By ~ 23

Author(s):

Liping Zhang ◽

Kun Zhou ◽

Wei-bin Zhang ◽

James A. Misener

Keyword(s):

Red Light ◽

Discrete Point ◽

Red Light Running

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Evaluation of Automated Enforcement Program in Amman

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.10939 ◽

2018 ◽

Author(s):

Hana Naghawi ◽

Bushra Al Qatawneh ◽

Rabab Al Louzi

Keyword(s):

Traffic Safety ◽

Questionnaire Survey ◽

Positive Impact ◽

Red Light ◽

Crash Frequency ◽

Red Light Running ◽

Red Light Cameras ◽

Percent Confidence Level ◽

Before And After ◽

Traffic Violations

This study aims, in a first attempt, to evaluate the effectiveness of using the Automated Enforcement Program (AEP) to improve traffic safety in Amman, Jordan. The evaluation of the program on crashes and violations was examined based on a “before-and-after” study using the paired t-test at 95 percent confidence level. Twenty one locations including signalized intersections monitored by red light cameras and arterial roads monitored by excessive speed cameras were selected. Nine locations were used to study the effectiveness of the program on violations, and twelve locations were used to determine the effectiveness of the program on frequency and severity of crashes. Data on number and severity of crashes were taken from Jordan Traffic Institution. Among the general findings, it was found that the AEP was generally associated with positive impact on crashes. Crash frequency was significantly reduced by up to 63%. Crash severities were reduced by up to 62.5%. Also, traffic violations were significantly reduced by up to 66%. Finally, drivers’ opinion and attitude on the program was also analyzed using a questionnaire survey. The questionnaire survey revealed that 35.5% of drivers are unaware of AEP in Amman, 63.9% of drivers don’t know the camera locations, most drivers knew about excessive speed and red light running penalties, most drivers reduce their speed at camera locations, 44.4% of drivers think that the program satisfies its objective in improving traffic safety and 52% of drivers encourage increasing the number of camera devices in Amman.

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