Red Light Running at Heterogeneous Saturated Intersections in Mumbai, India: On the Existence of Two Regimes and Causal Factors

Author(s):  
Mihir Bhosale ◽  
B. K. Bhavathrathan ◽  
Gopal R. Patil

This paper presents an analysis of red light running (RLR) conducted at saturated intersections in the city of Mumbai, India, where the traffic is highly heterogeneous with respect to vehicle classes and driver behavior. When all vehicles are considered, almost one in 17 drivers is seen to be jumping red signals there. Unlike the RLR behavior that has been previously reported from intersections elsewhere, a peculiarity observed here is that, within a single red phase, two distinguishable segments of RLR behavior exist. The authors classified them into two regimes: Regime 1, just after the onset of red, and Regime 2, just before the onset of the next green. About one-third of RLR events occur in Regime 1 and the rest in Regime 2. The authors fit different distributions on the time distribution of RLR events. The Kolmogorov–Smirnov test suggests that, at all intersections, exponential distribution fits best for RLR behaviors in Regime 1, and extreme value distribution fits for Regime 2. In addition to those two regimes, RLR at a lower rate is observed in the period between those regimes, and normal distribution fits there. To analyze the causal factors of RLR behavior in the two regimes, the authors developed models at a mesoscopic level specific to vehicle class and regime. Although the red-to-green ratio and the presence of policing prove to be relevant factors affecting RLR in both the regimes, the relative time for which the conflict area is free affects RLR in Regime 2 but not in Regime 1.

Transport ◽  
2016 ◽  
Vol 33 (1) ◽  
pp. 268-279
Author(s):  
Milan Vujanić ◽  
Dalibor Pešić ◽  
Boris Antić ◽  
Nenad Marković

Although traffic light controlled intersections separate, the traffic flows by time and space, road traffic accidents still occur, usually due to Red-Light Running (RLR). In order to define countermeasures to solve this problem, it is necessary to collect and analyze certain data that will indicate type of measures, which should be applied. In this paper, it was done on the example of one 3-leg and one 4-leg intersection where citizens provided information about frequent RLR to the City Administration of Belgrade (Serbia). The statistical significance of differences between the collected data was tested by ANOVA analysis and by PostHoc Tukey test, which showed that forecasting of second of RLR after red-light onset could effectively be conducted by Cubic distribution. In order to define the so-called RLR risk indicator for the intersection, the use of the Danger Degree (DD) indicator, that presents the rate between the number of dangerous situations caused by RLR and the total number of RLR, was proposed.


2021 ◽  
Vol 72 (7) ◽  
pp. 800-810
Author(s):  
Dung Chu Tien

Red-light running (RLR) is the most significant factor involved in traffic crashes and injuries at signalized intersections. In Vietnam, little knowledge of factors affecting RLR has been found. This paper applied an ordered probit model to investigate factors associated with RLR using questionnaire data collected in Hanoi. Generally, this paper found that males and motorcyclists have a higher likelihood of RLR than females and car drivers. In addition, the younger and lower-income road users and the ones who are businessmen and who have a commuting trip in off-peak hours are more likely to run the red light. By contrast, the road users who go to school and the people who understand traffic law are less likely to violate the red light. In the future, it is necessary to collect data in different cities to generalize the results. In addition, may need to apply a more powerful method such as the latent class model, which can discover hidden facts among respondents. In the new model, other factors such as weather, waiting time, and countdown signal will be considered to investigate their effects on RLR.


2016 ◽  
Vol 96 ◽  
pp. 71-78 ◽  
Author(s):  
Weihua Zhang ◽  
Kun Wang ◽  
Lei Wang ◽  
Zhongxiang Feng ◽  
Yingjie Du

2020 ◽  
Vol 99 (4) ◽  
pp. 405-411
Author(s):  
Elena Ju. Gorbatkova

Introduction. The important factors affecting health and performance of young people are the conditions of education, in particular, a comfortable microclimate in the classrooms of higher educational institutions. Materials and methods. In view of the urgency of this problem, an analysis was made of the microclimate parameters of educational organizations of different profiles (Ufa city, the Republic of Bashkortostan). 294 classrooms were studied in 22 buildings of 4 leading universities in Ufa. A total of 3,822 measurements were taken to determine the parameters of the microclimate. The analysis of ionizing radiation in the aerial environment of classrooms. There was performed determination of radon and its affiliated products content. In order to assess the conditions and lifestyle of students of 4 higher educational institutions of the city of Ufa, we conducted an anonymous survey of 1,820 students of I and IV years of education. Results. The average temperature in the classrooms of all universities studied was 23.9±0.09 C. The average relative humidity in all classrooms was 34.2 ± 0.42%. Analysis of ionizing radiation (radon and its daughter products decay) in the aerial environment of the classrooms and sports halls located in the basement determined that the average annual equivalent equilibrium volumetric activity of the radon daughter products (EROA ± Δ222Rn) ranged from 28 ± 14 to 69 ± 34.5 meter, which meets the requirements established by SanPiN. Conclusion. The hygienic assessment of the microclimate parameters of educational institutions of various profile revealed a number of deviations from the regulated norms. The results indicate the need to control the parameters of the microclimate, both from the administration of universities, and from the professors. According to the results of the study, recommendations were prepared for the management of higher educational institutions in Ufa.


2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Usha Arora ◽  
Deepti Dabas Hazarika

Economies all over the world are moving towards a focus on services. Tourism has emerged as a major contributor to economies all over the world. This is why specific focus is being placed on tourism, as Destination Management Organizations (DMOs) concentrate their efforts on tourism. India has been one of the countries where the share of tourism in national income has steadily been increasing. As the national capital, the city of Delhi has a major role to play in the tourist inflow to the country, as well as within the country. Successful tourism marketing requires that the concepts of tourist destination and underlying factors are comprehended in detail. An analysis of the available, pertinent literature on the area shows the manner in which numerous factors come together to form the image of a tourist destination. In fact, it needs to be understood that image formation may be done differently for different consumers. This further necessitates a detailed study of the factors influencing tourist destination image.


2021 ◽  
Vol 11 (7) ◽  
pp. 101
Author(s):  
Andrew Paul Morris ◽  
Narelle Haworth ◽  
Ashleigh Filtness ◽  
Daryl-Palma Asongu Nguatem ◽  
Laurie Brown ◽  
...  

(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.


2019 ◽  
Vol 11 (17) ◽  
pp. 2014 ◽  
Author(s):  
Bahaa Mohamadi ◽  
Timo Balz ◽  
Ali Younes

Urban areas are subject to subsidence due to varying natural and anthropogenic causes. Often, subsidence is interpreted and correlated to a single causal factor; however, subsidence is usually more complex. In this study, we adopt a new model to distinguish different causes of subsidence in urban areas based on complexity. Ascending and descending Sentinel-1 data were analyzed using permanent scatterer interferometry (PS-InSAR) and decomposed to estimate vertical velocity. The estimated velocity is correlated to potential causes of subsidence, and modeled using different weights, to extract the model with the highest correlations among subsidence. The model was tested in Alexandria City, Egypt, based on three potential causes of subsidence: rock type, former lakes and lagoons dewatering (FLLD), and built-up load (BL). Results of experiments on the tested area reveal singular patterns of causal factors of subsidence distributed across the northeast, northwest, central south, and parts of the city center, reflecting the rock type of those areas. Dual causes of subsidence are found in the southwest and some parts of the southeast as a contribution of rock type and FLLD, whereas the most complex causes of subsidence are found in the southeast of the city, as the newly built-up areas interact with the rock type and FLLD to form a complex subsidence regime. Those areas also show the highest subsidence values among all other parts of the city. The accuracy of the final model was confirmed using linear regression analysis, with an R2 value of 0.88.


Author(s):  
Chaopeng Tan ◽  
Nan Zhou ◽  
Fen Wang ◽  
Keshuang Tang ◽  
Yangbeibei Ji

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.


Sign in / Sign up

Export Citation Format

Share Document