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Pedestrian crossing speed and waiting time are critical parameters for designing traffic signals and ensuring pedestrian safety. This study aimed to carry out microscopic level research on pedestrian crossing speed and waiting time at intersections in Dhaka. To fulfill this aim, crossing-related data of 560 pedestrians were collected from three intersections in Dhaka using a videography survey method. Descriptive and statistical analyses were carried out, and then two multiple linear regression (MLR) models were developed for these two parameters by using the collected data. From the results, 1.15 m/s was found to be the design pedestrian crossing speed. Results also show that the crossing speed of pedestrians was associated with intersection control type, gender, age, crossing type, crossing group size, compliance behavior with control direction, and crossing location. In case of waiting time, findings show that pedestrians did not want to wait more than 20–30 s to cross the road. Furthermore, the waiting time of the pedestrians varied with intersection control type, gender, age, minimum gap, waiting location, and vehicle flow. Findings of this study will help to alleviate traffic safety problems by designing an effective intersection control system.

In developing countries, heavy-duty trucks play an important role in transportation for infrastructure construction. However, frequent truck accidents cause great losses. Previous studies have mainly focused on passenger drivers; to date, little has been done to assess the driving behavior of heavy truck drivers. The overall objective of this study is to classify driving styles at intersections, analyze the impacts of differing types of traffic control at intersections on driving styles, and identify potentially risky intersections. We selected 11 heavy-duty truck drivers and collected kinematic driving parameters (including driving speed and both lateral and longitudinal acceleration) from field experiments in Nanjing for our study. Our study on driving styles followed the following steps. First, we reduced data size and extracted data features on the basis of time windows in Python. Second, driving styles were classified into three driving styles: cautious, normal, and aggressive, based on the K-means clustering method, and the corresponding thresholds for each category were obtained. Kinematic driving parameters were used as driving style measurements. Third, according to classifications of driving style, the impacts of four different intersection traffic control types: two-phase signalized, multiphase signalized, stop, and yield intersections, on driving styles have been analyzed using the multinomial logit model. Moreover, based on the above analysis, potentially risky intersections were identified. The results suggest that different types of traffic control at intersections lead to variations in driving styles and have different influences on driving styles. In terms of accuracy, our method, which uses driving speed, both lateral and longitudinal acceleration, and jerk as features, performs better than traditional methods which only use speed and acceleration. The results of the study allow us to analyze the driving data of heavy-duty trucks and identify drivers who drive more aggressively during a trip. In addition, the results show that aggressive driving styles mostly occur at stop intersections and in the dilemma zones of signalized intersections. Therefore, early-warning interventions can be provided during a driver’s trip by analyzing the different types of traffic control at intersections on the route in advance. Finally, the cumulative analysis of driving styles at intersections over multiple trips can be used to identify potentially high-risk intersections. It is possible to eliminate potential risks in these areas through measures such as early warnings and by improving traffic management control methods.

Cycling safety at intersections is a growing concern as both cycling activity and motor vehicle traffic continue to rise. Intersections pose heightened risks for cyclists due to complex traffic patterns, ambiguous right-of-way rules, and insufficient signaling, often leading to collisions. This study investigates how intersection control types and operational characteristics influence driver failure-to-yield behavior toward cyclists. Using ten years of Michigan crash data involving single motor vehicle–cyclist collisions, we apply a Mixed Logit Model to account for unobserved heterogeneity in driver behavior. The analysis focuses on three types of intersection control: traffic signals, stop/yield signs, and uncontrolled intersections, examining their impact on various driver-cyclist interaction scenarios. Key findings indicate that driver age, day of the week, vehicle type, and speed limit consistently affect yielding behavior across all control types. Impairment due to alcohol or drugs significantly increases the likelihood of hazardous driver actions. Drivers are more prone to fail to yield in straight-ahead scenarios, though they are less likely to be deemed at fault in non-yield crashes. Intersection control effectiveness also varies by maneuver type; signalized intersections reduce failure rates in straight-travel scenarios, while stop/yield signs are more effective during left turns. This research addresses a critical gap by linking infrastructure features with driver yielding performance, offering evidence-based insights for improving intersection safety. The findings support targeted interventions in roadway design, driver education, and the integration of advanced technologies such as cyclist detection systems and vehicle-to-vehicle communication to enhance cyclist protection.

The Union-Intersection and Intersection-Union hypotheses testing problems are considered for all possible combinations of united and intersected sub-sets of hypotheses. Constrained Bayesian Method is developed for solving these problems. Optimal decision rules are derived for all stated combinations of hypotheses. Theorems on the optimality of the derived decision rules in the sense of the restrictions on Type-I and Type-II error rates to the desired levels are proved. The proposed theoretical methods are enhanced for practical examples. Extensive simulation results are presented to confirm the theoretical results and to illustrate the properties of the proposed procedures for a finite sample.

Red-light running (RLR) has been identified as one of the prominent contributing factors involved in signalized intersection crashes. In order to reduce RLR crashes, primarily, a better understanding of RLR behavior and crashes is needed. In this study, three RLR crash types were extracted from the general estimates system (GES), including go-straight (GS) RLR vehicle colliding with go-straight non-RLR vehicle, go-straight RLR vehicle colliding with left-turn (LT) non-RLR vehicle, and left-turn RLR vehicle colliding with go-straight non-RLR vehicle. Then, crash features within each crash type scenario were compared, and risk analyses of GS RLR and LT RLR were also conducted. The results indicated that for the GS RLR driver, the speed limit displayed the highest effects on the percentages of GS RLR collision scenarios. For the LT RLR driver, the number of lanes displayed the highest effects on the percentages of LT RLR collision scenarios. Additionally, the drivers who were older than 50 years, distracted, and had a limited view were more likely to be involved in LT RLR accidents. Furthermore, the speeding drivers were more likely to be involved in GS RLR accidents. These findings could give a comprehensive understanding of RLR crash features and propensities for each RLR crash type.

Road safety is one of the predominant public health responsibilities among traffic engineers, and it requires high attention. The sight distances play a crucial role in the highway geometric design. Among all the sight distances to be provided, the stopping sight distance is highly important in the geometric design of roads. The traffic volume in heterogeneous traffic conditions is broadly classified into five categories: two wheelers, three wheelers, car, LCV, and HCV. The highway capacity manual (HCM Transportation Research Board: National Research Council, Washington, DC 2010) introduced the concept of passenger car units, to convert different vehicle types into single vehicle category. Even after the introduction of PCU, the stopping sight distance for different vehicle types is considered as same value, because there is no other factor accommodating for vehicle type. In real traffic scenario, the physical characteristics of vehicles such as length and width are different for different vehicle categories. So, in order to understand this problem, the study focusses on impact of vehicle type on stopping sight distance of vehicles. For this purpose, the data were collected at intersections using both video graphic technique and OSM (Open Street Map) tracker. The field measured stopping sight distances are modeled considering the vehicle type, and the developed models are validated. The study results showed that two wheelers require higher Stopping Sight Distance compared to other vehicle categories and Light Commercial Vehicles (LCV) require least sight distance. Also, the results of the present study are helpful in assessing the safety analysis of vehicles.

The performance of roundabouts can affect urban transport systems in terms of environmental and operational impacts, safety and efficiency. The development of roundabout traffic management and control systems can be carried out through road traffic micro-simulation models which are computer models where the movements of individual vehicles travelling around road networks are determined by using simple car following, lane changing and gap acceptance rules. Unfortunately, despite the great diffusion of these tools, appropriate methods are still needed in order to validate and calibrate these models. In general, the calibration process can be defined in this way: the process of comparing model parameters with real-world data to ensure that the model realistically represents the traffic environment. The objective is to minimize the discrepancy between model results and measurements or observations. The aim of this paper is the presentation of a first comparative approach between observed performances and performances obtained by the use of popular microsimulation software, in particular urban intersections such as roundabouts. In particular, an experimental investigation is designed and carried out in order to acquire some vehicular parameters for a roundabout placed in an urban contest of southern Italy. The calibration process is carried out by an analysis of variance of the kinematic parameters of an n-tuple of roundabout scenarios. This calibration procedure has permitted to derive some important conclusions about the choice of the most significant input parameters for the output results of each simulation scenario. Outcomes of this study are expected to benefit both practitioners and researchers.

Accidents at intersections in Queensland accounted for 45% of total accidents and 19% of fatal accidents on the State roads in 2002. The number of accidents at intersections is purported to be primarily correlated to the number of conflict points but geometric characteristics also influence intersection safety. Queensland Transport database of reported accidents over the past ten years has been used in this study to investigate the contribution of intersection design and characteristics on the relative safety of intersections. Queensland Transport’s road crash database, WebCrash2, has been used to analyse the accident records at intersections in the Townsville region. The detailed analysis has included road user, vehicle type, collision type, BAC, use of seat belt and intersection type. Considering the common variations in the number of approaches, entry and exit lanes, type of control, phase design for signalisation, the road environment and other features like flaring, channelisation and signage, each intersection is peculiar and unique in some way. However, research has been focussed on three main types of intersections. These include T junctions, cross intersections and roundabouts. Three levels of control are also considered. The objective is to relate the intersection safety to the number of conflict points, conflict types, and intersection geometry. The results show that roundabouts are the safest types of intersection while uncontrolled cross intersections are least safe. The level of safety decreases with the increase in the number of approaches and the number of conflict points.

According to Cafiso, Lamm and La Cava, in the millions of crashes occurring worldwide each year, more than 500,000 people are killed and more than 15 million are injured. More specifically, 9.6 crashes occur at signalized intersections per year where STOP or YIELD signs control traffic. The rationale behind conducting this research is that vehicle crashes are common occurrences at signalized intersections. This study explores the hypothesis that different types of collisions are affected by different independent factors. Tree regression was used as a simplistic approach to relate the expected number of crashes reported on both long and short forms for each type of crash with the characteristics of the intersections. The results of this study show that when attempting to forecast the number of expected crashes, it is imperative that the analyses are performed for each type of collision instead of aggregating crash types to predict the total number of crashes.