The trade-off between safety and time in the red light running behaviors of pedestrians: A random regret minimization approach

In this study, discrete choice models that combine different behavioural rules are estimated to study the visitors’ preferences in relation to their travel mode choices to access a national park. Using a revealed preference survey conducted on visitors of Teide National Park (Tenerife, Spain), we present a hybrid model specification—with random parameters—in which we assume that some attributes are evaluated by the individuals under conventional random utility maximization (RUM) rules, whereas others are evaluated under random regret minimization (RRM) rules. We then compare the results obtained using exclusively a conventional RUM approach to those obtained using both RUM and RRM approaches, derive monetary valuations of the different components of travel time and calculate direct elasticity measures. Our results provide useful instruments to evaluate policies that promote the use of more sustainable modes of transport in natural sites. Such policies should be considered as priorities in many national parks, where negative transport externalities such as traffic congestion, pollution, noise and accidents are causing problems that jeopardize not only the sustainability of the sites, but also the quality of the visit.

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A regret-based route choice model with asymmetric preference in a stochastic network

Advances in Mechanical Engineering ◽

10.1177/1687814018793238 ◽

2018 ◽

Vol 10 (8) ◽

pp. 168781401879323

Author(s):

Lei Zhao ◽

Hongzhi Guan ◽

Xinjie Zhang ◽

Xiongbin Wu

Keyword(s):

Loss Aversion ◽

Equilibrium Model ◽

Choice Model ◽

User Equilibrium ◽

Stochastic User Equilibrium ◽

Regret Minimization ◽

Route Network ◽

Travel Decision ◽

Gains And Losses ◽

Random Regret Minimization

In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.

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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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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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Random Regret-Minimization Model for Emergency Resource Preallocation at Freeway Accident Black Spots

Journal of Advanced Transportation ◽

10.1155/2018/3513058 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Siliang Luan ◽

Qingfang Yang ◽

Wei Wang ◽

Zhongtai Jiang ◽

Ruru Xing ◽

...

Keyword(s):

Large Scale ◽

Traffic Accidents ◽

Weather Conditions ◽

Time Cost ◽

Frequency Method ◽

Regret Minimization ◽

Accident Probability ◽

Black Spots ◽

Random Regret Minimization

The preallocation of emergency resources is a mechanism increasing preparedness for uncertain traffic accidents under different weather conditions. This paper introduces the concept of accident probability of black spots and an improved accident frequency method to identify accident black spots and obtain the accident probability. At the same time, we propose a three-stage random regret-minimization (RRM) model to minimize the regret value of the attribute of overall response time, cost, and demand, which allocates limited emergency resources to more likely to happen accident spots. Due to the computational complexity of our model, a genetic algorithm is developed to solve a large-scale instance of the problem. A case study focuses on three-year rainy accidents’ data in Weifang, Linyi, and Rizhao of China to test the correctness and validity of the application of the model.

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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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