The rapid urbanization of Nepal has resulted in increased population and vehicle registration, causing traffic congestion in urban areas. To implement effective reduction measures, it is necessary to identify congestion characteristics. As Free Flow Speed (FFS) is a key input parameter, this study aimed to develop an FFS model incorporating site-specific parameters. Six road sections of Koteshwor-Suryabinayak road were selected, and field and videographic surveys were conducted. FFS distribution of vehicle classes was found normally distributed, and vehicle class-wise speed limits were established. A multiple linear regression analysis was employed to develop a vehicle class wise and overall (2-W,4-W and Bus) FFS models. The overall model was found to be the most accurate. Therefore, the proposed model would be helpful in analysis of free flow speed in heterogenous traffic condition and determination of level of service of the road section of four lane divided urban sub-arterial roads.

Pervious Concrete (PC) has an open-graded framework, as it contains no or minimal fine aggregate and can help to reduce night-time glare and vehicular noise. This study investigated PC samples with varying densities and compaction techniques for sound absorption and reflectance. The samples were tested for sound reflectance using an acoustic enclosure to analyse the reflected audio signals and were then photographed to be analysed using Python. The findings revealed a positive relationship between density and sound reflectance, indicating that higher-density PC exhibited increased reflectivity. Light reflectivity analysis demonstrated that density and the compaction method during casting influenced the reflectance characteristics. Also, increased surface porosity led to a reduction in glare and pavement noise. Understanding the relationship between PC density, porosity, and acoustic and reflective properties is essential for optimizing PC design and application. This knowledge can be used to design PC pavements that effectively reduce noise and glare.

The aim of this study is offering a new framework for evaluating functional classification of the roads and exploring a new expressway using micro-scaled mobility data, T-Map data in South Korea. The data includes the origins and destinations, links, speed, and vehicle type for each individual traffic, so link traffic, which is the goal of the study, can be viewed in the individual traffic unit. Evaluation indicators are calculated through two-step processing using T-map navigation data. In step 1, traffic attributes are analyzed by individual vehicle units and traffic attributes are aggregated by road link units. In step 2, evaluation indicators are calculated and analyzed by road link unit. Clustering analysis and GIS analysis by five variables: total traffic volume (TTV), heavy vehicle ratio (HVR), average vehicle travel length (AVTL), inter-regional ratio (ITR_Ratio), and long-distance travel ratio (LTR_Ratio) reveal the area need for a new road construction project.

This study focuses on enhancing the Area Traffic Control (ATC) system in Bangkok area by leveraging Floating Car Data (FCD) obtained from mobile GPS probe data. The aim is to supplement the existing stationary roadside detectors used to detect real-time traffic congestion and optimize traffic signal timings. By comparing the congestion lengths derived from processed FCD with those obtained through field surveys, the study demonstrates a high level of accuracy, with an RMSE of 99.3, surpassing the accuracy of the stationary detectors installed at approximately 200-meter intervals. Furthermore, by incorporating geometric conditions such as number of traffic lanes and intersection types into the regression model, the study proposes a slight adjustment to the estimated queue lengths, bringing them closer to the actual values. This approach overcomes the cost limitations of installing and maintaining additional detectors, thereby improving efficiency and effectiveness of the ATC system in managing traffic congestion in Bangkok.

Reckless driving can be the major factor for accidents in Malaysia while many studies reported that speeding is one of the most important factors causing the accidents. In order to reduce the speed of vehicles, traffic calming measures are used with the intention of slowing down the vehicles. Despite the significant contributions of traffic calming devices, present understanding of the devices is limited as there has been very little attention emphasizing the relationship between its effect on rural roads. Therefore, we design this study to to evaluate the effect of the rumble strip in terms of vehicle speed, pollutant emission and fuel consumption using Vissim. The results show that the average speed was reduced by 15.74 km/h or 28%. Besides, the implementation of rumble strip cannot be considered as environmentally friendly as it increased 2% to 3% level of CO, NOx, and VOC emissions and 2.5% of fuel consumption.

Access management is vital to maintain a safe flow of traffic within a development without compromising the access needs of the public. Good access management can be achieved by limiting and separating conflict points to obtain a balance between access and mobility. This study was set to assess the effect of different layouts of access management at public transport station on the traffic flow within Klang Valley. Attributes such as traffic volume, traffic speed, traffic conflict and merging time were considered within this study. Data was collected at 8 public transport stations during weekdays during peak and off-peak hours. By categorizing the existing access management available at the 8 public transport stations into 5 types, the findings revealed that there is no general significant relationship between traffic speed, traffic composition and road layout though the general fact is that no access management leads to more vehicular interactions and reduction in traffic speeds. It is also found that no specific pattern between merging time, traffic composition and road layout. The results suggest that the access management is site specific (as there is no one-size-fits-all in implementing access management) and merely considering the traffic operation parameters in design is inadequate to promise efficient and safe operational of traffic system. Instead, other elements such as the local and regional mobility, the behaviour of drivers within the area, traffic composition, interaction between vehicles and pedestrians, distance to the nearest traffic signal and crossing facilities should be considered in the course of design.

In recent years, there has been increased focus on the reconstruction, maintenance, and repair of bridges in Japan to ensure a safe and comfortable expressway space. These road works have often caused traffic congestion owing to lane closures. Setting an appropriate traffic capacity is essential to provide highly accurate prediction of traffic congestion and understand the factors that affect traffic. This study focuses on the breakdown flow rate (BDF) and queue discharge flow rate (QDF) as indicators of traffic capacity. Multiple regression analyses are performed to better understand the factors affecting traffic capacity of the construction lane closures implemented at the Tomei Expressway and Chuo Expressway, Japan, which are inter-urban expressways. A model is developed that includes not only known factors, such as nighttime and large-sized vehicle inclusion rate, but also new factors, such as traffic congestion involvement time and average spacing.

This paper presents an adaptive traffic signal system based on the convolution neural network (CNN) technique. This work differs from others by evaluating the adaptive controller in a simulated stochastic traffic environment and liberating the signal operations to an acyclic assignment. In addition, a practical communication protocol is integrated to capture state conditions. The agent optimises signal operation using a management policy for waiting time gaps between competing travel directions. The testing models included morning and evening peak hours, and the performance of the CNN system is compared against a fixed time controller representing the status quo of the studied isolated junction. The findings indicated that the CNN control significantly improved the travel time between 25% and 35% compared to the existing control method. The work also includes sensitivity analysis to determine the impact of the time plan and saturation flow level on the performance of the proposed control system.

The paper presented an investigation result of the effect on collision prevention of a field trial of the V2X Motorcycle Safety Warning System (MSWS) which was conducted for reducing motorcycle accidents in Taiwan. The analysis was conducted by comparing the Post Encroachment Time (PET) and Time to Collision (TTC) of before and after implemented the MSWS using statistic method and Survival Theory. The results showed that, on average, the PET increased from 2.15 to 2.34 seconds for left turn crossing conflicts; the PET increased from 2.56. to 2.96 seconds for right angle turns; and TTC increased from 2.20 to 2.74 seconds for right turn merge conflicts. The accident occurrence also decreased afterwards in the field trial sites of 30%. Furthermore, according to the Kaplan-Meier survival curve, those who utilized the MSWS had a higher survival rate overall than without the MSWS system based on the value of the survivor curve using PET or TTC. Overall, the system had the positive effect and expected to help drivers to avoid accident.

Traffic congestion not only reduces the quality of life of daily commuters but also contributes huge economic losses. According to the JICA, the economic cost of transportation in Metro Manila has risen to Php3.5 billion a day and that the situation could get worse if interventions will not be made. This study aims to improve traffic flow through a signalized intersection using an agent-based modeling approach. Using the concept of critical utilization in queuing theory, a linear regression was used to calculate the traffic light green times given the varying occupancy levels of the four legs of the intersection. We observed that the throughput increased without significantly penalizing the average speed and waiting time of vehicles in the system. Moreover, by having a permissive right turn traffic, we also showed that adding superstreets could significantly improve traffic flow, increasing the number of vehicles that can pass through the intersection, avoiding unnecessary waiting at red light.

With the expansion of the freeway network, traffic congestion at freeway merging points is expected to increase. While ordinary drivers focus only on safe merging for their own vehicles, automated vehicles can apply a variety of merging behaviors as long as safety and comfort are ensured. In such cases, it would be possible to strategically implement merging behaviors that contribute to the overall optimization of traffic flow. Therefore, in this study, we model merging behavior by reinforcement learning, assuming an automated vehicle. In addition, four reward settings (safety, comfort, efficiency, and overall efficiency) are proposed and analyzed for differences in vehicle behavior and their effects on surrounding vehicles. As a result, we were able to improve the safety, comfort, and efficiency of merging vehicles and reduce their average trip time.

Gap acceptance behavior highly influences the performance and safety of roundabouts. This study investigated the gap acceptance behavior of different vehicle classes at the roundabout through two parameters, critical gap and follow-up time. Vehicles were categorized into five classes. Class 2 has the lowest mean accepted and rejected gaps, 12.24 s and 1.87 s, respectively. Passenger cars following passenger cars had the longest follow-up time, with 2.75 s (morning) and 2.77 s (evening). At the same time, motorcycles following motorcycles had the smallest follow-up time values, with 0.85 s (morning) and 1.02 s (evening). Raff's method was used to calculate the vehicle critical gaps. Class 5 has the smallest overall critical gap, with 3.67 s., followed by Class 1 (4.82 s), 2 (6.00 s), and 4 (6.34 s). The findings can help in designing new roundabouts and developing capacity models.

The present study revealed how time-gap settings of Adaptive Cruise Control (ACC) affect a driver's risk feeling (RF) closing with a lead vehicle on snowy roads. A field experiment on the test truck was conducted to measure changes of the RF according to the time-gap settings on snowy roads. We proposed a model to estimate value of the RF when the lead vehicle decelerated on snowy roads. Also, parameters for the car-following model with ACC were estimated using results of the field experiment. This study indicated driver's acceptable time-gap settings on dry and snowy roads in terms of value of the RF. These results give quantitatively support following two facts, the time-gap should be long and deceleration should be low on snowy roads in order to over driver's acceptable risk feeling.

Motorcyclists comprise a significant proportion of the mixed traffic in Southeast Asia. However, few studies have examined the effect of the motorcycle proportion on accident risk. This study utilized a microscopic traffic simulator and applied a zero-inflated negative binomial regression model to analyze the relationship between traffic composition and accident risk. The results demonstrated an inverse relationship between motorcycle proportions and the normalized average frequency of critical TTC (FCT) in two-lane two-way undivided roads, whereas a positive relationship was observed in four-lane and six-lane divided roads. Moreover, this study identified the significant influences of signalized intersections and access density on the normalized average FCT. These findings emphasize the need to address the critical situations that lead to rear-end crashes for specific road types. By bridging this research gap, this study provides valuable insights for developing effective road-safety strategies in Southeast Asian countries, where mixed traffic prevails.

To accurately control traffic at intersections in India, it is necessary to understand traffic performance influenced by different types of vehicles and driving behaviors under heterogeneous traffic. This study aims to estimate the saturation flow rate as a performance index by analyzing the impacts of autorickshaws and motorbikes. The trajectory of all vehicles was observed as an image from the sky using UAVs (Unmanned Aerial Vehicles) at the Paldi junction in Ahmedabad, India. The saturation flow rate under heterogeneous traffic is calculated. It was also found that the proportion of autorickshaws has an impact that is directly proportional to the saturation flow rate. However, there was lower impact of the percentage of motorbikes rather than it of autorickshaws.

The Passenger Car Unit (PCU) is commonly utilized to convert heterogeneous traffic volume into a homogeneous equivalent, ensuring precise measurement of homogeneous traffic flow and desired service levels in mixed traffic situations in developing countries in general and specifically in Vietnam. Therefore, this present study constructs a new multiple regression equation to estimate the Passenger Car Unit (PCU) for urban roads based on heterogeneous traffic flow components, considering equivalent space mean speed and travel time conditions. From this, evaluate the correlation between two PCU estimation methods: speed-based and multiple linear regression. The new findings show that both methods yield identical PCU estimates at a 2.1-meter car distance. Simultaneously, the study also improved the YOLOv5 model for detecting and counting two-wheelers with high precision, recall, and f1 score. The combination of the regression equation and enhanced YOLOv5 model effectively estimates PCU, expediting the analysis of traffic data for prominent vehicles. A field data survey was conducted on three-lane urban roads in Hanoi, Vietnam. The study results provide specific PCU for urban roads in Vietnam, particularly for two-wheelers, and assess the interrelationship between estimation methods. Recommendations for PCU estimation in heterogeneous traffic flows and potential object detection applications are also discussed to enhance future estimation capabilities.

The provision of information on potential traffic accident occurrence risks is aimed at enhancing the safety of drivers and decreasing the accidents by redirecting traffic to safer routes. In addition to accident occurrence risk information, other indicators that can provide road users with incentives to choose safer routes include "accident encounter risk", which indicates the likelihood of encountering accident scenes on an expressway, and "accident loss risk", which indicates the potential financial losses that could occur due to accidents. This study aims to assess the potential impact of these different accident risk indicators on accident reduction by analyzing the effect of providing various accident risk indicators on road users' route choices. Using a route choice model that incorporates accident risk information, we specifically focus on an expressway network in western part of Japan and quantitatively evaluate the accident reduction that could result from providing accident risk information to road users.

The objective of this study is to determine which shape and length of speed humps have the fewest side effects at low speed and which hump is uncomfortable at high speed, using computer simulation and field survey data from a vibration meter and accelerometer. The MATLAB Simulink model developed by Japan Automotive Model-Based Engineering (JAMBE) was used to determine the vibration of a vehicle on a rough road surface. One-dimensional numerical information representing road surface height was prepared for sinusoidal, trapezoidal, and semicircular profiles, which were used in the Simulink model. Sequential data of the vertical acceleration of the car were obtained from the simulation and compared with field data for verification. After the verification, the acceleration and jerk of each shape were compared at different speeds. The results show that the shape of the slope and length of the hump appear to have a significant effect on car vibration while crossing speed humps.

Unreasonable speed limits caused by the lack of coordination between operating speed and posted speed limit can be a major contributor to traffic accidents. A number of vulnerable road users have increased in the urban traffic stream than before. Therefore, on some roads speed limit imposing authority may have to revise the speed limits in such a way so that it will be safe for every road user. Considering general tendency of driver to drive above the speed limit, an approach was given to revise the speed limit so that even if driver drove above the speed limit but still it will be within safe limit. Relationships between speed limits and operating speeds, operating speeds and flow rates were developed. Finally based on regression models a simplified approach is proposed to revise the speed limit value based on existing traffic parameters from the safety point of view.

This study compares Japanese and Indonesian drivers' perceptions of traffic information and route-choice behavior. The data were obtained from a web survey and roadside interviews with toll road users in both countries. Hypothetical choices showing the range of expected travel times and tolls were obtained for behavior modeling through a stated preference survey. The respondents were grouped, and a binary logit model of route choice was created for each. Evidently, the Japanese drivers did not depend on the displayed traffic information and preferred their own route. Among the Indonesian drivers, those with more driving experience, middle-to-high monthly expenses, or a high level of education were unlikely to be aware of traffic information. The Japanese group that was aware of and dependent on traffic information had the highest value of time; however, the Indonesian group with the same characteristics had the lowest value of time.

This paper investigates the vehicle interaction characteristics at the weaving section of a multi-lane roundabout. On-site observation and video recording were used to collect traffic data at a multi-lane roundabout in Kuching during weekday's peak hours (morning and evening). The results are categorized into: (i) types of the lane-changing strategy adopted by the drivers; (ii) location of lane-changing prior to and within the roundabout; and (iii) the factors that trigger the lane-changing phenomenon. Within both approach and circulating lane, the result shows that most drivers preferred to adopt the Direct Single Lane-changing strategy. Furthermore, the frequency with which a vehicle initiates lane-changing within the approach lane increases when the distance from the yield line increases. In contrast, lane changes within the circulating lane are typical between the start and end distance between the entry and the next exit leg. Lastly, both lane's congestion levels and drivers' destination would trigger the drivers to change lanes within the roundabout.

Variable Speed Limit (VSL) is widely used to mitigate congestion on freeways. However, the current variable speed limit control is not always optimal and does not respond well to real traffic flows. Therefore, this study proposes a speed limit control method on freeways to reduce congestion and travel time on freeways by applying microscopic-simulation-based reinforcement learning model. The proposed speed control system consists of a reinforcement learning model and a traffic situation prediction model based on microscopic traffic flow simulation. By applying reinforcement learning, the system learns the optimal speed limit values under different traffic conditions and calculates the optimal speed limit for each road section. The validity of the constructed speed control system was verified and improvements in traffic conditions, such as a reduction in average travel time, were observed with and without the control.

Buffer-type wire rope barriers are useful for installation on existing roads and narrow median strips as they have thin posts and require only a narrow width for installation. However, due to their structural property of absorbing the impact of collisions with vehicles by warping, it was difficult to improve their deformation performance in case of vehicle collisions. In this study, a rope interlocking member that reduces the amount of protrusion into the opposite lane in case of vehicle collisions was developed to improve the performance of wire rope barriers, and the optimal specifications were determined by conducting a large vehicle collision test. As a result, the amount of protrusion into the opposite lane in case of collisions with large vehicles was reduced by approximately 60% and the deformation performance was improved 2.5 times.

The sight distances play an important role in the design of various highway facilities. Among different types of sight distances, Stopping Sight Distance (SSD) plays a major role in the design of horizontal and vertical curves. The Stopping Sight Distance value is considered same for all types of vehicles. To understand this issue, the present study mainly focuses on effect of vehicle type on stopping sight distance at vertical summit curves. The main aim of the study is to measure the Stopping Sight Distances of the selected vehicle types for their respective speeds. Video graphic method and Open Street Map (OSM) tracker are used for measuring the Stopping Sight Distance and the obtained distances are modeled and validated. The results of the present study give a better understanding of Stopping Sight Distances of vehicles and can be used in carrying out the safety analysis of vehicles at vertical summit curves.

This study was conducted to newly establish related response guidelines to minimize damage to electric vehicle fire accidents in tunnels. Data related to electric vehicle fires in the tunnel were investigated to derive major matters that differ from general vehicle fires, and trends and major opinions related to electric vehicle fires were synthesized through expert interviews. Based on this, appropriate guidelines were established for each accident stakeholder divided into four groups.

Motorized two-wheeler riders are the most involved in traffic crashes. This study is aimed at understanding the risky riding behaviour and also the riders' perception and positive affect towards risky riding. A questionnaire was designed based on the identified factors that influence the motorized two-wheeler users risky riding. Structural equation modeling was carried out to find out the relationship between the riders' perception, positive affect and their risky riding behaviour. The model result shows that there is a significant relationship between risk perception, positive affect and risky riding behaviour. It was also found that positive affect is having more significant influence on risky riding behaviour when compared to risk perception. The present study about the riders risky riding behaviour will be helpful in formulating new measures to reduce the road traffic accidents involving motorized two-wheeler riders.

Pedestrians are one of the most vulnerable groups in road traffic. Pedestrian-motor vehicle crashes often occur in urban areas at midblock locations. This paper presents the effectiveness of midblock pedestrian crossing improvements, comparing (1) a typical zebra crossing (C1) and (2) an intelligent signalized pedestrian crossing (C2). A controlled field study was conducted at four different sites. Field observations included speed measurements, video recordings, and questionnaire surveys. The before-after data was used to determine the effectiveness. The study found that the installations of C2 led to a decrease in the estimated maximum speeds (reduced by 6-34 km/h), and driver's behavior of failure to obey traffic signs and signals at crossings significantly decreased by 41.3%. Moreover, the legal crossings behavior of pedestrians greatly increased by 52-78%. Therefore, the intelligent signalized pedestrian crossing can be promoted as a pedestrian safety measure at midblock locations on urban arterial roads.

To determine the initial speed in automotive accident reconstruction, a variety of data sources including in-vehicle event data recorders (EDRs), traffic cameras, GPS data, computer simulations, photogrammetry, radar detectors, and LIDAR are utilized. Although it is recommended to use accurate methods, traditional methods such as skid marks analysis, crush analysis, witness statements, speedometer analysis, speedometer cable examination, photogrammetry, and speed and distance calculations are still commonly used in Mongolia. This is due to the insufficient development of the intelligent transport system, the age of the vehicles involved in accidents, and a lack of support for modern technology. This research aims to determine the energy spent in transition during the collision of vehicles and to propose a method for determining the initial speed during the collision by calculating the energy spent in the elastic elements of the vehicle when it crosses an obstacle.

Crashes at urban intersections account for more than 50% of the total traffic accidents in Taiwan. The most common type of crashes is side collision, with left-turn failure being the second most common cause. In this study, Post-Encroachment Time (PET) is used as a crash surrogate to quantify the proximity of left-turning vehicles to their oncoming counterparts at urban signalized intersections. The PET thresholds under different vehicle pairs are evaluated using the Peak over Threshold (POT) method in Extreme Value Theory (EVT). The correlation between traffic conflicts and crashes is first formulated. Second, count data and logistic regression models are developed to explore factors that might result in left-turn traffic conflicts both at the intersection and vehicle-pair levels, respectively. Using the vehicle trajectory data collected by Unmanned Aerial Vehicle (UAV), the empirical study results indicate that traffic conflict thresholds differ for different vehicle-pairing samples under a mixed traffic flow condition, and engineering-based countermeasures help reduce potential left-turn traffic conflicts at signalized intersections.

Road crashes have been a significant contributory in fatal and serious injuries. Moreover, motorcycle injuries are one of the top causes of road crash death. The main objective of this study is to improve safety of motorcycle riders at intersections using indirect approach. With this, intersection designs can be improved to achieve minimal casualties and safer roads by conducting studies on factors affecting intersection safety. For this study, traffic signal change and clearance interval, stopping and intersection sight distance were evaluated. Each of these was conducted to find relationship with one another and the collisions that occurred on the selected intersections. Using traffic conflict analysis, the study revealed significant relationships between hourly traffic volume and traffic conflicts such as slow vehicle-same direction, lane change, and rear-end, and total number of road crashes.

Before the era of smart mobility there will be a transitional period in which both autonomous vehicles (AVs) and human-driving vehicles (HDVs) share the same road networks in mixed traffic. The main objective of this research is to evaluate and rank the safety performance of the different AV models. The evaluation is based on studying 190 accident records of level 3 AVs operated in the United States of America. In the methodology, a risk index is developed for classifying the different models of AVs by accident exposure. Then, a clustering and an ordinal logistic model have been developed to investigate the main factor affecting the risk of different AV groups. In the results, the AV models have been ranked based on the developed risk index. Moreover, the results showed that the accident risk of the AVs increases with speed, especially at intersections.

Traffic accidents caused by elderly drivers is a serious social problem in Japan facing super aging society. The traffic accidents are mainly caused by decline in physical and cognitive abilities due to aging. Today, the new technologies such as automated driving is developing toward the realization of a safe automotive society. However, it will take a considerable amount of time before such new technologies are widely used in the society. Therefore, safe driving awareness remains an important means of reducing the occurrence of traffic accidents. In this study, safe driving management was conducted for elderly drivers. Driving data of the elderly drivers were collected and analyzed, and a driving evaluation sheet was created. It was verified whether drivers who received the driving evaluation sheet would practice safer driving by recognizing their own driving. As a conclusion, it became clear that driving behavior recognition by the feedback effects on safer driving.

Globally 1.35 million individuals have lost their lives in road crashes, with 500 children perishing daily due to such incidents. 2883 individuals lost their lives in Nepal roads in fiscal year 2021/22. Kathmandu shares more than half of the national road crashes and 471 children were involved in such crashes. Eight schools of Kathmandu valley were selected for the study. Star Rating for School (SR4S) tool is utilized to evaluate, handle, and communicate the road crash risk that children face in school and vicinity. This is the first pilot project for assessing road safety of schools within the country. The proposed road safety interventions were implemented, and the safety scenario increased to 3-star, 4-star, and 5-star at two, four and two schools respectively. Through implementation of speed management techniques, the operating speed decreased by 20% to 40% at each school.

Vehicle speed and driver behavior are critical for road safety. The high vehicular density at the junctions makes the drivers prone to road crashes. This study focuses on factors like the geometry of the island and the gender of the driver to study the behavior patterns at corner directional islands. Seven intersections with ten islands were studied in Raipur and Naya Raipur, Chhattisgarh, India. The shape, size, and turning radius varied across the multiple islands. Real-time speed and durations for the test vehicle were recorded by V-Box automotive tool. Z, Mann-Whiteny, ANOVA and Krusakl-wallis test were performed on the obtained data to analyze the behavioral patterns of the drivers. It was observed that various geometrical factors of the island such as size and turning radius have a greater impact on drivers' speed choice. The female drivers were slower as compared to their male counterparts.

Category-wise distribution of accidents in India reveals that more than 31% of accidents and more than 36% of fatalities took place on National Highways (NHs), which is about 2% of total roads. This is a major concern for road safety on NHs and needs to be explored for improvement. The present study looks to develop a macroscopic crash frequency model for NHs in India. Towards this, state-wise crash frequency data are utilized for three years. It is likely to have a high probability of unobserved heterogeneity in modeling crash frequency utilizing macro-level variables – the study proposes to use the Random Parameter Negative Binomial model instead of a fixed-effect model. The study also identifies that the share of female driving license holders plays a key role in the variability of crash frequency. Additionally, a lower elasticity is observed for the proportion of four-lane (and above) roads compared to other categories.

Walking is a mode of transportation that allows people to move from one location to another. When there is a need to walk, there is also a need to cross the road at pedestrian crossings. The purpose of this paper is to investigate pedestrian perceptions of various pedestrian crossings in Kuala Lumpur with the goals of identifying types of pedestrian crossings found, pedestrian behavior, and validating preferred pedestrian crossings that suit pedestrian preferences at different urban areas. For that, a series of questionnaires and observations were conducted, with observations for pedestrian behavior when crossing the road and questionnaires allowing for a more in-depth understanding of opinions about pedestrian crossings. The results revealed that pedestrian crossings that were frequently encountered were signalized and zebra crossings, implying that pedestrian preferred crossings should be recommended. Personal and situational factors were found in three different case studies in Kuala Lumpur in regard to different demographic profile and land use activities of pedestrians crossing the road.

In recent years, traffic accident risk estimation models have been developed, which evaluate the likelihood of the accident occurrence quantitatively. These models evaluate the probability of accident occurrence, or “accident risk", on time-space plane with 30 minutes in time and 1km in length. It is expected that traffic safety measures based on the accident risk can improve traffic safety effectively. This study aims to reveal the types of safety measures to be implemented depending on traffic conditions. Using a questionnaire survey, a comparison analysis was conducted to examine the differences in the ability of traffic administrators at traffic control center and general drivers to recognize the accident risks. Then, appropriate safety measures are investigated based on the opinion of traffic administrators. As a results, it was shown that the recognition ability of the traffic administrator is superior to general drivers, and effective safety measures were identified depending on traffic conditions.

Road traffic accidents can have a considerable negative impact on health and economics in many countries, and Malaysia is no exception. According to reports, these accidents were greatly impacted by risky driving behaviour. This study examined the factorial validity and reliability of the Driver Behaviour Questionnaire (DBQ), one of the most used driving measurement instruments, among Malaysian drivers in order to measure risky driving behaviour. Violations, errors, and lapses were the three factors identified through principal component analysis (PCA). The three factors in this structure explained 59.75 per cent of the total variance. The analysis revealed that five of the most frequent behaviour (between occasionally ‘2’ and quite often ‘3’) resulted from ‘violations’, one from ‘errors’, and the remaining five were caused by‘lapses’. Although violations have been linked to collision involvement, both violations and errors are potentially dangerous and have been shown to predict involvement in ongoing accidents.

Active transport is an important form to enhance physical activity among school-going children. However, these active transport modes have been facing traffic safety problems among school-going children. Insights of active transport mode among school-going children could contribute to a decrease in traffic accidents. Children’s traffic behavior depends on their knowledge, attitude, and risk perception. The present study aims to discover the key elements impacting school-aged children’s walking and cycling by surveying 320 targeted students from 7th to 12th grades in Kerala and Maharashtra. The results indicated that students using active transport modes gained road safety knowledge, positive attitudes towards traffic safety, and risk perception; however, they behaved in an unsafe way on roads such as no use of zebra crossing. Traffic safety education which uses local situations and concentrates on skill development could enhance both safe behaviours and knowledge, attitude, and risk perception among school-going children using active transport modes.

The study is aimed to validate the reliability and validity of the positive driving behaviors scale (PDBS) and its association with traffic accidents. A total of 623 participants in Pakistan willfully and confidentially completed a questionnaire containing items related to positive driving behaviors (PDBS), aberrant driving behaviors (DBQ), the dangerous driving behaviors (DDDI), and the self-reported accidents. The Pakistani PDBS was found to be valid and reliable using criterion and convergent validity measures. The PDBS was negatively correlated with DDDI dimensions and positively associated with age. More importantly, the PDBS negatively predicted the traffic accidents. In contrast to previous research, gender and driving experience had no significant association in our sample, which could be attributed to a lack of female representation and experienced drivers. The traffic management division is encouraged to give careful consideration to the promotion of safe driving habits by including it into driver training programs.

The present work aims to assess the riding behaviour of motorcyclists in a mid-sized Indian city, and estimates their influence on crash risks. For this purpose, a suitable questionnaire was developed and data were collected using a paper-pencil based face-to-face interviews among the riders. Exploratory factor analysis reveals four latent factors of riding behaviour which could explain 57% of the total variance. These factors are coined as ‘traffic offences and errors’, ‘control errors’, ‘safety perception’ and ‘speed violations’. Binary Probit model shows ‘control errors’ and ‘safety perception’ of riders along with their gender, income level, academic background, engine capacity of the vehicle and preference on helmet usage strongly influence crash risk of motorcyclists. The analysis helps to identify the target group of riders who are most vulnerable, and justifies the need for giving due attention to deter them from performing unsafe acts while riding.

Predominance of jaywalking, heterogeneous traffic condition and mobile phone-induced distractions make pedestrians highly vulnerable to road crashes. Using the Bayesian Belief Network (BBN), this study investigated different predictors associated with jaywalking and mobile phone-induced pedestrian safety hazards at highway intersections in Bangladesh. An interactive framework comprised of a questionnaire and observation study was employed at 32 intersections on national and regional highways. Furthermore, the Chi-square test was used to investigate the relationship between jaywalkers' socioeconomic attributes and risk perception. The study's findings show that gender, jaywalker activities, types of jaywalking, waiting time, and frequency of phone use while jaywalking had the most impact on any jaywalker's risk assessment. Less waiting time at each intersection discourages pedestrians from using their phones and maintains a high-risk perception. Additionally, the driving experience of any age group can increase risk perception. Moreover, women feel more confident crossing the street when they are accompanied.

Incidents of violation of helmet and seat belt rules are predominant in developing countries. To identify the determinants of helmet and seat belt use, interviews with 898 motorcycle riders and 627 vehicle drivers were conducted across 32 highway intersections in Bangladesh. The collected data was fitted into Bayesian Network models and probabilistic inferences were made. Results showed that riders not wearing helmets have lower educational qualifications and higher previous accident records. Education level, motorbike ownership, safety awareness, and helmet avoidance tendency highly influenced helmet-wearing decisions. Similarly, drivers not wearing seat belt had a higher probability of owning a non-professional driving license, driving a personal car, being less educated, and believed that wearing seat belt can’t reduce accidents. Seat belt wearing was significantly influenced by license type, car ownership, education level, faith in the effectiveness of seat belt, and perception of injury for not wearing seat belt.

Mental health issue has been one of the many significant health and welfare issues in Malaysia especially since the recent global pandemic. In the context of road driving or riding however, mental health and the impact to safety are still not properly addressed. Recognizing that there are a good amount of knowledge and efforts around the world targeted to curb the mental health issues among drivers, this paper explores the execution and the applicability of some interventions that are piloted and studied. The findings suggest that intervention programs on driver assessment, mindfulness, cognitive therapy and chanting practice can be implemented in Malaysia with some adaptations and adjustments into existing programs or policies.

To maintain freeway traffic, it is critical to model and attain better understanding of the non-recurrent congestion caused by freeway incidents. This research develops prediction models to estimate the impact of incidents on freeway traffic in terms of impact duration. The model can enable the provision of more accurate traffic information to drivers and management agencies according to the estimation of queue lengths and associated delays. Based on the vehicle detector data derived from the Freeway Bureau and the records of emergency freeway incident responses during 2015 and 2020, this study seeks to explore the impact patterns of incidents on freeway traffic. The Tobit model, machine learning, and a deep-learning framework are adopted to estimate the delay caused by freeway incidents. The prediction of impact duration can contribute to several aspects of traffic management, such as improving the dissemination of traffic information to mitigate traffic congestion.

The Commonwealth Avenue in Quezon City is known as the “killer highway” in Metro Manila with an average of 17 fatal road crash per year from 2017-2021. The high occurrence of vehicular road crashes in Commonwealth Avenue has led to several research interests in identifying and analyzing road crash hot spots using the Geographical Information Systems (GIS). This study used data from the Metro Manila Accident Reporting and Analysis System (MMARAS) data of Metropolitan Manila Development Authority (MMDA) and employed kernel density estimation using free open-source software to identify public utility vehicle (PUV) road crash hotspots along Commonwealth Avenue. The study revealed five fatal crash locations along Commonwealth Avenue. High density road crashes occurred during the time period of 7:00 to 10 am and 5:00 to 8:00 pm. specifically on Fridays at the Regalado Avenue The study's results have significant implications that can be used to identify black spots, develop targeted interventions, and take measures to reduce the severity of PUV crashes.

Successful integration of automated vehicle (AV) technology requires safety evaluation during their large scale deployment. This paper focus on a literature review on safety impact of AVs by examining the behavioral models and parameter settings. For the later research component, traffic and crash data from Route 4 of Tokyo Metropolitan Expressway Company Limited was used to develop a RTCPM using Dynamic Bayesian Network (DBN) and a microscopic traffic simulation environment was reproduced using PTV VISSIM tool to create the environment for AV introduction. The findings from literature review identifies several challenging issues including appropriate parameter setting, lower marker shares of AV, and lower automation level on the existing network. The simulation model outcome suggests that crash risk can be reduced by 11.1% and 13.1% under normal driving and cautious driving behavior respectively with mixed scenarios.

The study aims to develop simplified rational criteria for identification of accident blackspots along the National Highways, so that the practitioners can quickly identify the blackspots and intervene. A statistical approach has been used to identify the rational criteria, i.e., the threshold value of each of the different crash parameters were identified, above which a location will be treated as a blackspot. The analysis is based on past 3-years’ crash data of NH of West Bengal, India. The identified blackspots have been compared with the Ministry of Road Transport and Highways (MoRTH), Govt. of India guidelines and it is found that the number of blackspots by MoRTH guidelines is grossly over-estimated. Finally, the blackspots have been prioritized based on its severity as observed from the analysis. This will help the practitioners prioritize the blackspot locations while planning the countermeasures. Although the work has been demonstrated based on crash data of West Bengal, the technique can be adopted by other researchers or practitioners to determine the simplified rational criteria for other regions as well.

This study examines the factors affecting electric vehicle (EV) crash severity in Metro Manila by utilizing spatio-temporal analysis tools and machine learning classifiers, such as Random Forest (RF), K-Nearest Neighbors (KNN), Naïve-Bayes (NB), Artificial Neural Network (ANN), and an experimental Random Forest Classifier with Genetic Algorithm tuning (RF-GA). The spatio-temporal analysis reveals that most EV crashes occur during off-peak hours and are concentrated in areas with high population density. Crash hotspots were identified in locations without dedicated infrastructure. The results indicate that the NB and RF-GA models outperform the other classifiers in predicting crash severity, with car involvement as the most important variable. These findings hold significant implications for developing targeted interventions, identifying high-risk areas, and implementing measures to reduce the severity of EV crashes in Metro Manila, Philippines.

Fatal crash comes with a huge cost of person loss besides vehicle, goods, and property damage which impacts the overall economy of the country. Therefore, there is a need to identify factors responsible for the high severity of crashes, especially for low and middle countries. Numerous studies have already proved that Machine Learning techniques are more effective than traditional statistical modelling and have recently been extensively used in road safety research. The present study is motivated to study the effect of various parameters on road crash severity and derive measures to enhance safety using exploratory analysis, neural network, and unsupervised agglomerative clustering. The insights obtained from the study indicated that the time of the crash, speed limit signs, and road type as the top three features. The insights obtained from the study were utilized to propose recommendations for safety improvement on the Indian road network.