Traffic Levels Research Articles

Exposure to road traffic noise and behavioral problems in Chinese schoolchildren: A cross-sectional study.

Transportation noise is second only to air pollution as an environmental contributor to adverse health outcomes in Western countries. However, no studies investigated the association between road traffic noise and behavioral problems among schoolchildren in China. To investigate the association between exposure to road traffic noise at home and behavioral problems in Chinese schoolchildren. From January to June 2017, we screened 3236 children aged 7-13 years in Guangzhou (Guangdong, China) from the first investigation of an ongoing school-based cohort study with complete information on behavioral problems and residential geolocation. Residential road traffic noise exposure levels were assessed using a validated modeling method in different periods of the day, including daytime (Lday), nighttime (Lnight), and weighted 24-hr (Ldn). The annual mean concentration of nitrogen dioxide was also modeled in our study. Behavioral problems were assessed by the parent-rated Strengths and Difficulties Questionnaire (SDQ). Generalized linear mixed models were utilized to estimate the associations between road traffic noise and behavioral problems. In the main adjusted model, a 10-dB increase in Ldn was associated with 0.33 (95% CI: 0.08, 0.59), 0.08 (95% CI: -0.01, 0.17), and 0.09 (95% CI: 0.01, 0.16) increases in three subscales score for total difficulties score, emotional problems, and conduct problems, respectively. The same increase in Ldn was also associated with the abnormal total difficulties score (OR = 1.25, 95%CI: 1.01, 1.55), emotional problems (OR = 1.19, 95%CI: 0.98, 1.44), and conduct problems (OR = 1.28, 95%CI:1.02, 1.63). Externalising behavior appears more susceptible to traffic noise than internalising behavior. NO2 had a small impact on the associations between Ldn and behavioral problems. Our estimates were generally robust in several sensitivity analyses. Residential road traffic noise exposure might be related to increased behavioral problems in Chinese schoolchildren, such as emotional symptoms and conduct problems.

Structural health monitoring of railway bridges using innovative sensing technologies and machine learning algorithms: a concise review

Abstract Railway bridges are a vital element of railway infrastructures, and their safety can directly affect the regional economy and commuter transportation. However, railway bridges are often subjected to severe loading and working conditions, caused by rising traffic levels and heavier vehicles, and increases in train running speeds makes the bridges extremely susceptible to degradation and failure. One of the promising tools for evaluating the overall safety and reliability of railway bridges is the bridge structural health monitoring (SHM) system, which not only monitors the structural conditions of bridges and maintains the safety of train operations, but also helps to expand the lifespan of bridges by enhancing their durability and reliability. While a multitude of review papers on SHM and vibration-based structural damage detection methods have been published in the past two decades, there is a paucity of literature that provides a review or overview on the SHM of railway bridges. Some of the review papers have become obsolete and do not reflect the state-of-the-art research. Therefore, the main goal of this article is to summarize state-of-the-art SHM techniques and methods that have been widely used and popular in recent years. First, two state-of-the-art SHM sensing technologies (i.e. fiber optic sensing (FOS) technology and computer vision-based (CV) technology) are reviewed, including the working principles of various sensors and their practical applications for railway bridge monitoring. Second, two state-of-the-art machine learning algorithms (i.e. convolutional neural networks (CNN) and transfer learning (TL)) and their applications for railway bridge structural condition assessment are exemplified. Third, the principle of digital twin (DT) and its applications for railway bridge monitoring are presented. Finally, issues related to the future direction and challenges of the monitoring technologies and condition assessment methods of railway bridges are highlighted.

Statistical Validation of a New Tensile Creep Test for Asphalt Mastics

Abstract Currently, the increasing traffic level and climatic change are exacerbating the premature distress of pavement infrastructures. In order to assess this problem, the proposed research activity aims to statistically evaluate the parameters of a new mastic tensile creep test, which is based on a uniaxial tensile configuration. This allows the material deformation to be kept in the small-strain domain and the mechanical behavior to be considered as linear viscoelastic (LVE). Particularly, the main tasks of the study herein were the statistical analysis of the proper testing temperature and load. Moreover, the storage period of the samples was also analyzed, comparing four storage durations (1, 2, 4, and 12 h). The proposed analysis was performed using three fillers (one pure limestone and two limestone-based fillers) and two asphalt binders, neat and 3.5 % Styrene-butadiene-styrene (SBS) modified, respectively. The validation of the testing protocol also involved the analysis of the operator sensitivity using the one-way ANOVA statistical test. The results showed that the testing protocol is not operator-sensitive and allows the LVE behavior to be kept in the small-strain domain. The results also indicated that the m-value seems to interpret the accumulation of the permanent deformation for the mastic materials, which may be used as the property of materials, well.

Investigating traffic safety reckoning hyperbolic driving following behavior using trajectory data

Using vehicle trajectory datasets developed over a study section for three traffic flow levels, a rectangular hyperbolic relation between time-to-collision and relative speeds in vehicle-following behavior were observed. A new methodology for estimating the probable rear-end collisions in the given traffic stream is developed based on this relation. The vehicle-following behavior is examined in terms of the hysteresis phenomenon concerning the distance gap (DG) and relative speed (RS). Further, based on the follower’s attention towards the leader vehicle, a novel surrogate safety measure, called Instantaneous Heeding Time (IHT), was conceptualized. This measure represents the time gap available based on the positions of the leader and follower vehicles. After exploring vehicle-following behavior, IHT, DG, and RS were used to estimate the rear-end collision probability. The applicability of the proposed methodology is tested using different thresholds (IHT, DG, and RS) and applied to the study section at the three traffic flow levels.

Traffic Risk Assessment Based on Warning Data

To address the issues of insufficient danger excavation and long data collection period in traditional traffic risk assessment methods, this paper proposes a risk assessment method based on driver’s improper driving behavior and abnormal vehicle state warning data. Meanwhile, this paper analyses the built environment’s impact on traffic risk using the spatial econometric model. Firstly, a risk assessment system with the relative incidence of driver’s improper driving behavior (eye closure, yawn, and looking away) and abnormal vehicle state (rapid acceleration, rapid deceleration, and lane departure) warnings as assessment indicators is constructed. Then, the risk responsibility weights of each warning type were determined using the entropy weight method. The risk classification thresholds were determined based on the Gaussian Mixture Model algorithm. Finally, a spatial econometric model was used to quantify the impact of built environment factors characterized by Point of Interest (POI) data on regional traffic risk, with the results of risk class classification as the dependent variable. The data of bus vehicle warnings in Zhenjiang, Jiangsu Province, are employed as an example for validation. The geographic cell of 1 km × 1 km scale is applied as the basic risk assessment unit. The results show that the optimal risk classification threshold for road traffic risk levels I and II is 1.92, the accuracy rate of class classification is 79.3%; the optimal risk classification threshold for levels II and III is 0.75, and the accuracy rate of class classification is 83.4%. The number of residential areas, Point of Interest (POI) mixing degree, and bus stops were significantly and positively correlated with transit traffic risk. The study results provide references for developing customized accident prevention measures and the appropriate setting of urban supporting facilities.

Robust and energy-efficient user association and traffic routing in B5G HetNets

Next-generation cellular networks, i.e., beyond fifth generation (B5G) and sixth generation (6G) networks, aim at significantly increasing capacity by deploying a massive number of small cells (SCs) and leveraging millimeter wave (mmWave) links that have large bandwidth availability. However, as optical connections to a large number of base stations (BSs) are not cost effective, wireless backhaul (BH) links may be used to connect SCs to the core network using mmWave frequencies and forming multihop mesh BH paths. While very flexible in deployment, the uncertainty in user demand, which varies over time and place, makes network planning and management challenging. In this work, we propose novel algorithms to solve the joint problem of energy-efficient user association, BH traffic routing and BS/BH link on/off switching. We first formulate an exact model assuming user traffic demand uncertainty leveraging Γ-robustness theory. As the model is intractable for large-scale instances, we develop a novel greedy robust heuristic (P-HEUR), which includes a robustifying step to effectively cope with demand uncertainty. Our evaluation demonstrates that P-HEUR provides robust and energy-efficient solutions quickly for different scenarios and traffic demand uncertainty levels, and it significantly outperforms state-of-the-art approaches while achieving up to 83% of the optimal capacity, even in the most demanding scenarios in terms of traffic load and demand uncertainty, with up to 200k times lower complexity.

The Spatial Network Structure of Tourism Efficiency and Its Influencing Factors in China: A Social Network Analysis

Although tourism has gradually become a popular form of leisure and entertainment in China, the quality of China’s tourism development remains unclear. Through the panel data of 30 provinces in China, an SBM-DEA model and a social network analysis are used to explore the quality of tourism development, and a spatial econometric regression is used to identify the relevant factors affecting tourism efficiency. The study found that the level of tourism efficiency in Southwest China is high and stable. The northwest region has a low level of tourism efficiency, but a slow growth trend. The rest of the regions show fluctuating trends of tourism efficiencies. The spatial correlation network of provincial tourism efficiency is gradually complicated. Regarding influencing factors, the number of patents granted, traffic levels, financial development, and government macro-control all have positive effects on tourism efficiency. The study uncovered some useful management insights and implications for the travel industry.

Regional public transportation safety risk grading assessment under time dimension: A case study of Chinese mainland

Traffic risk hinders the development of human society. Due to the influence of weather and climate, the traffic risks vary in different time periods. This paper innovatively reconstructs the criterion system for evaluating regional traffic safety risk from a time series perspective. On the basis of the novel criterion system, this paper proposes a monthly dimension regional public traffic safety risk grading model with Analytic Hierarchy Process Sort II (AHPSort II) and Criteria Importance Though Intercriteria Correlation (CRITIC) (i.e., AHPSort II-CRITIC method), in which, AHPSort II is used to obtain the traffic safety risk levels of different regions, and the CRITIC is used to determine the weight vector of the criteria system. The proposed method is applied to evaluate the regional traffic safety risk levels of 31 provinces, municipalities, and autonomous regions in the Chinese mainland and some cities in Hubei province in the whole year of 2019. The results of this paper show that the traffic safety risk levels are high in June, July, and August, and the performances of other months are relatively more optimistic. Finally, this paper puts forward some transport policy implications, which provide a suitable basis for policy formulation to reduce regional traffic safety risk.

Location-specific demand forecasting for park and ride lots: The use of models in practice

The management of hundreds of Virginia park and ride lots requires substantial public investment, and forecasting their demand helps agencies use scarce resources to improve multimodal mobility. Unfortunately, direct application of existing demand estimation approaches yielded errors typically 14 to 141 times the mean occupancy of a lot. Accordingly, this paper reports on the development of 19 regional models that yielded an average demand forecasting error of 0.56 times the mean occupancy.Because of Virginia’s geographic diversity, the most appropriate forecasting technique varies by region: in some cases, the key step is to group lots by commuting boundary or population density; in other cases, diverse socioeconomic variables (e.g., population, commuting distance, rent relative to income); facility-specific variables (e.g., transit availability, lighting); or traffic characteristics (e.g., volume on the road serving the lot, highest roadway volume within 2.5 miles) were needed to improve model performance. Ultimately, such variables explained on average 76% of the variation in lot demand. In practice, the results demonstrated the necessity of regional calibration: in rural southwest Virginia, for instance, the only key determinants of demand were the number of commuters living near a lot and having a job more than 50 miles away. In the more urban area that encompasses the state capital (Richmond), the two determinants were traffic volume and congestion levels. Although the models themselves are not transferrable, the methodology underlying their development can be used by other agencies as the data and techniques are publicly available.

Forecasting of Total Column Ozone using Regression Analysis and LSTM-RNN Machine Learning Approach

Objectives: Atmospheric Ozone plays an important role in global climate change, human health and environmental conditions. Accurate and timely prediction of variation in Total Column Ozone (TCO) concentration is very important for both climatology and environment. In this context, the present study aims to utilize the advancement of Artificial Intelligence in Machine learning and a conventional method to develop models for the prediction of TCO concentration using historical time series data over a tropical region, India. Methods: Long Short-Term Memory (LSTM) deep learning networks are very useful in classifying, processing and making predictions based on time series data. In this work, Multiple Linear Regression (MLR) model, Long Short-Term Memory (LSTM) models are developed for forecasting TCO over a tropical region. The Statistical Parameters such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to analyze and evaluate the performance of the proposed models. Findings: In the present study the TCO concentration varied between 217.4 DU and 288.6 DU. The performances of MLR and LSTM model were compared. From the results, it is evident that the values of RMSE, MAE and MAPE of the MLR model are 3.807924, 2.93766, 1.15 respectively whereas the values for LSTM model are 3.074492, 2.574, 1.01 respectively. LSTM was found to be the accurate model for the study data set and the predicted TCO concentration has a very good correlation with the actual observations and it does not rely on the meteorological parameters. Novelty: The LSTM models are usually employed in the field of machine translation, speech recognition and for the prediction of traffic levels, stock levels and air pollutants. This work utilizes the fast numerical machine learning computing library Tensorflow and a high level neural network library Keras that runs on top of Tensorflow to develop a LSTM network model for the prediction of the concentration levels of TCO. Keywords: Total Column Ozone; Regression; Forecasting; Climate Change; Long Short-Term Memory

Field Performance and Cost-Effectiveness of Chip Seals and Asphalt Concrete Overlays with and without Asphalt Concrete Stripping Damage

The objective of this study was to evaluate the effects of asphalt concrete (AC) stripping damage on the field performance and cost-effectiveness (CE) of chip seals and AC overlays in pavement maintenance and rehabilitation. To achieve this objective, in-service pavement sections were selected from the Louisiana pavement management system (LaPMS) database and the presence of moisture damage was confirmed through the visual inspection of extracted cores. Sections were categorized according to traffic volume and their pavement condition index before treatment (PCI-). Road sections in each group were then divided into two groups as stripped and non-stripped sections. The average deterioration rate (ADR), extension in pavement service life (ΔPSL), average condition improvement over the treatment service life (PI), and CE were compared for stripped and non-stripped sections. Results showed that for chip seal sections, moisture damage negatively affected the performance of the sections with PCI- < 80 and low traffic volumes. For sections with PCI- > 80, similar performance was observed for stripped and non-stripped sections. For AC overlays, moisture-induced damage significantly affected the long-term pavement performance at all traffic levels. On average, moisture-induced damage decreased ΔPSL, PI, and CE of AC overlays by 5 years, 24%, and 0.5%, respectively. Overall, results of the study demonstrated that moisture damage has a significant effect on the performance of chip seal and AC overlay. Therefore, it is critical to identify and repair stripped sections before the maintenance and rehabilitation of in-service pavements to ensure adequate performance and optimum CE.

Improving the Performance Grade and Traffic Loading of Egyptian Asphalt Binders by Recycled Polyethylene Modification

Recent SUPERPAVE technology allows assessment and performance grading (PG) of modified asphalts according to AASHTO T 350–14 and ASTM D7405-15 standards. These testing protocols use modified - multiple stress creep recovery (MSCR) test, to yield the high temperature PG and the traffic level of the asphalt binder. Latest AASHTO T 350–14 test protocol is used to assess two major Egyptian bitumen products, Alex and Suez (pen 60/70) before and after polymer modification. These standard products have different fractional composition since they are obtained from different local origins. Low density polyethylene modification (LDPE) of such products leads to a major enhancement in their PGs and decreases their high temperature rutting potential. A characteristic comparison between virgin a and LDPE modified bitumen samples is provided to achieve the desired PG and traffic level for high reliability projects deployed in Egyptian hot climate regions. it was found that a 2% wt recycled LDPE modification, enhances the high temperature performance grade and the traffic grade from PG 58S-XX to PG 64S-XX for the bitumen produced by Alexandria Oil Company, and from PG 58S-XX to PG 70S-XX for the bitumen produced by Suez Oil Company.

An Enhanced Cell Transmission Model for Multi-Class Signal Control

Existing multi-class cell transmission model (CTM) based methodologies for signal timing or traffic assignment may transfer prioritized transit vehicles from one cell to the next one before processing their preceding passenger cars. In addition, existing CTM-based methodologies process a proportion of a slow-moving transit vehicle in each time step. As such a portion of each transit vehicle remains in each cell and it never clears them. This paper presents constraints to project the position of transit vehicles based on the speed and cell occupancy variations between different classes of vehicles and incorporates them into the CTM. The resulting optimization program is a mixed-integer nonlinear problem. We used a distributed receding horizon control framework to solve it in real-time. The proposed formulation is executed in a simulated arterial street with four signalized intersections in Springfield, IL with different traffic volume levels and transit vehicle frequencies. The results showed that the proposed algorithm addressed the mentioned issues of the existing multi-class CTM, and yielded more efficient network performance than the conventional transit signal priority-based (CTSP) systems. The proposed formulation reduced average bus delay by 1% to 70% and car delay by 52% to 76% compared to CTSP.

Optimizing protection resource allocation for traffic-driven epidemic spreading.

Optimizing the allocation of protection resources to control the spreading process in networks is a central problem in public health and network security. In this paper, we propose a comprehensive adjustable resource allocation mechanism in which the over allocation of resources can be also numerically reflected and study the effects of this mechanism on traffic-driven epidemic spreading. We observe that an inappropriate resource allocation scheme can induce epidemic spreading, while an optimized heterogeneous resource allocation scheme can significantly suppress the outbreak of the epidemic. The phenomenon can be explained by the role of nodes induced by the heterogeneous network structure and traffic flow distribution. Theoretical analysis also gives an exact solution to the epidemic threshold and reveals the optimal allocation scheme. Compared to the uniform allocation scheme, the increase in traffic flow will aggravate the decline of the epidemic threshold for the heterogeneous resource allocation scheme. This indicates that the uneven resource allocation makes the network performance of suppressing epidemic degrade with the traffic load level. Finally, it is demonstrated that real-world network topology also confirms the results.

Modelling the Free Flow Speed and 85th Percentile Speed Using Artificial Neural Network (ANN) and Genetic Algorithm

One of the main and crucial purposes of highway system is to provide a safe, comfortable, and efficient mode of transportation. Vehicular speed is considered as one of the high-priority issue to transportation engineers, planners and researchers, where it is a significant measure of effectiveness of traffic performance particularly under mixed traffic condition, which affects the traffic movement and level of service at different transportation facilities. In order to propose a new analytical method for vehicular speed, speed prediction models based on Artificial Neural Network (ANN) models and Genetic Algorithm (GA) have been developed for major urban arterial roads in Jordan. Prediction models have been developed with several input parameters and included within the ANN and GA. Traffic and geometric variables such as left lateral clearance, right lateral clearance, speed limit, Present Condition Index (PCI), shoulder width, lane width, access point density, International Roughness Index (IRI) and Present Serviceability Rating (PSR) have been included in both models. Meanwhile, the outputs of prediction models have been 85th percentile speed and Mean Free Flow Speed (MFFS) km/hr. The computational results show that the speeds predicted by the developed models are highly correlated with the measured ones. ANN modeling has a better performance than GA in estimation of 85th percentile speed. This study provides useful information to predict the FFS and 85th percentile speed on roads depending on variables, which are available on any constructed or to be constructed roads.

On-road trust and perceived risk in Level 2 automation

To encourage appropriate use of driving automation, we need to understand and monitor driver’s trust and risk perception. We examined (1) how trust and perceived risk are affected by automation, driving conditions and experience and (2) how well perceived risk can be inferred from behaviour and physiology at three levels: over traffic conditions, aggregated risk events, and individual risk events.30 users with and without automation experience drove a Toyota Corolla with driving support. Safety attitude, subjective ratings, behaviour and physiology were examined.Driving support encouraged a positive safety attitude and active driver involvement. It reduced latent hazards while maintaining saliently perceived risks. Drivers frequently overruled lane centring (3.1 times/minute) and kept their feet on or above the pedals using ACC (65.8% of time). They comfortably used support on curvy motorways and monotonic and congested highways but less in unstable traffic and on roundabouts. They trusted the automation 65.4%, perceived 36.0% risk, acknowledged the need to monitor and would not engage in more secondary tasks than during manual driving.Trust-in situation reduced 2.0% when using automation. It was 8.2% higher than trust-in-automation, presumably due to driver self-confidence. Driving conditions or conflicts between driver and automation did not affect trust-in-automation.At the traffic condition level, physiology showed weak and partially counter-intuitive effects. For aggregated risk events, skin conductance had the clearest response but was discernible from baseline in < 50%. Pupil dilation and heart rate only increased with strong braking and active lane departure assist. For individual risk events, a CNN classifier could not identify risk events from physiology. We conclude that GSR, heart rate and pupil dilation respond to perceived risk, but lack specificity to monitor it on individual events.

Measuring, Mapping, and Evaluating Daytime Traffic Noise Levels at Urban Road Intersections in Doha, Qatar

In this study, equivalent hourly traffic noise levels at different intersections in the city of Doha, Qatar were measured and compared to the local and World Health Organization (WHO) thresholds. As part of the study, equivalent sound pressure levels, ambient temperature, humidity, and wind speed were recorded during the morning, afternoon, and evening hours on weekdays and weekends. The results showed that regardless of the day (weekday or weekend), the mean 16-h daytime traffic noise levels at all sites exceeded the local and the WHO’s recommended thresholds. The values of the mean weekday noise levels at the sites ranged between 67.6 dB(A) and 77.5 dB(A), whereas the weekend values ranged between 68.8 dB(A) and 76.9 dB(A). The measured noise levels were also compared with traffic noise levels reported in other countries. Finally, some recommendations to reduce excessive traffic noise levels were suggested. The results of the study could be used as a benchmark of traffic noise levels in the country after the implementation of any countermeasures in the future.

Measurement and Influencing Factors of Low Carbon Urban Land Use Efficiency—Based on Non-Radial Directional Distance Function

The development and use of urban land is a major source of carbon emissions. How to reduce carbon emissions in the process of urban land use without harming the economy has become an extremely important issue. This paper integrating carbon emissions into the urban land use efficiency evaluation index system, measures low-carbon urban land use efficiency using a non-radial directional distance function and analyses its spatial and temporal evolution and its influencing factors using a combination of a kernel density estimation method and a Tobit model. The study found that: (1) China’s low-carbon urban land use efficiency shows a fluctuating development and tends to converge; (2) there is much room for reducing land input and carbon emissions in China, and in 2016 alone, land input and carbon emissions in the sample could be reduced by 10.38% and 5.31%, respectively; (3) at the national level, land finance, economic level and population density have a positive impact on low-carbon urban land use efficiency, while the traffic level has negative effects, and these effects show regional heterogeneity. Accordingly, the paper proposes corresponding policy recommendations.

A comprehensive experimental study of the influence of temperature on urban road traffic noise under real-world conditions

The effect of road traffic noise in urban environments is an issue of social and scientific interest, due to its public health and economic impacts. Scientific literature showed a decrease in the level of tyre/road noise generated as temperature increases, but usually under standardised traffic conditions in non-urban environments. Based on a wide network for the hourly monitoring of road traffic flow, air temperature and noise levels across the city of Madrid (Spain), this work proposes and applies a new experimental methodology for studying the dependence of urban road traffic noise on temperature. This study was conducted under real-world traffic conditions involving a wide variability in urban configurations and in the type and state of preservation of vehicles, tires and pavements. From the analysis of data for a whole year, a time interval was identified (from Tuesday to Thursday and between 8 a.m. and 8 p.m.) in which the variability in road traffic flow for the whole city of Madrid was stable enough to allow for a linear regression study between temperature and noise levels from urban road traffic. The relationships found were highly significant (p ≤ 0.001) for data from all the noise monitoring stations, with values of higher than 20% and up to 42% for the explanation of the variability in the measured noise levels by temperature at most of the measurement points. The values of the slope coefficients at the noise monitoring stations ranged from −0.036 to −0.125 dB/°C, with an average value of −0.090 ± 0.011 dB/°C. These results are within the range of values reported in the scientific literature for experimental tests conducted under conditions of controlled or free-flowing traffic.

Modelling traffic safety at uncontrolled median openings: A case study in India

Assessment of traffic safety is an essential study in transportation engineering. In a developing country like India, around 150,000 people die in road crashes every year. Furthermore, at uncontrolled median openings, the severity of road crashes is higher due to the presence of impatient U-turning road users who don't obey the rule of priority. Traditionally, road crash data have been used since long to analyze traffic safety. However, in developing countries, the main drawback of this conventional method is limited availability of accident data as very few accidents get reported. Moreover, the accuracy of these reported data is questionable. Therefore, now-a-days, various surrogate traffic safety measures like Post Encroachment Time (PET), and Time to Collision (TTC) are being used to examine the safety of road users. Among them, PET is regarded as the most consistent, and most widely used safety indicator. Therefore, in the present study, PET across different traffic volume levels has been determined. Videography data has been collected from selected median openings located on six-lane divided urban roads. PET values for different traffic volumes, and different category of vehicles have been analysed in detail. Further, the distribution of PET values across the full width of road has also been studied. Concept of critical speed is introduced which is compared with conflicting speed to assess unsafe conflicts and determine a critical PET. Finally, regression models have also been proposed with good levels of accuracy to determine the PET values for various category of vehicles travelling at different conflicting speeds.