Multi Vehicle-Type Right Turning Gap-Acceptance and Capacity Analysis at Uncontrolled Urban Intersections

Intersections are the critical zones where conflicting, merging and diverging movements influence the intersection capacity. Uncontrolled intersections in particular pose dangerous situations to vehicular traffic. During peak vehicular flow, the unpredictable crossing behavior of minor stream vehicles induces delay and reduces the capacity of the intersection. Capacity at uncontrolled intersections is typically measured either by gap acceptance method, empirical regression approaches and conflict technique. Gap acceptance is an important characteristic for analyzing uncontrolled intersections. The behavior of different vehicle types and gap of subject vehicle type from minor street taking right turn to merge with major traffic stream is analyzed using gap acceptance method. The objective of the current study is to analyze the effect of major stream vehicle type combinations on the minor stream vehicle gap-acceptance behavior and to determine the capacity of the minor stream taking into account the influence of the right turning vehicles. The capacity of minor stream calculated using Highway Capacity Manual (HCM) 2010, Luttenin’s model, and Tanner’s model are compared. It is observed that two wheelers are more aggressive than three wheelers for most of the major stream vehicular combinations observed in this study

Modeling Left-Turn Driving Behavior at Signalized Intersections with Mixed Traffic Conditions

In many developing countries, mixed traffic is the most common type of urban transportation; traffic of this type faces many major problems in traffic engineering, such as conflicts, inefficiency, and security issues. This paper focuses on the traffic engineering concerns on the driving behavior of left-turning vehicles caused by different degrees of pedestrian violations. The traffic characteristics of left-turning vehicles and pedestrians in the affected region at a signalized intersection were analyzed and a cellular-automata-based “following-conflict” driving behavior model that mainly addresses four basic behavior modes was proposed to study the conflict and behavior mechanisms of left-turning vehicles by mathematic methodologies. Four basic driving behavior modes were reproduced in computer simulations, and a logit model of the behavior mode choice was also developed to analyze the relative share of each behavior mode. Finally, the microscopic characteristics of driving behaviors and the macroscopic parameters of traffic flow in the affected region were all determined. These data are important reference for geometry and capacity design for signalized intersections. The simulation results show that the proposed models are valid and can be used to represent the behavior of left-turning vehicles in the case of conflicts with illegally crossing pedestrians. These results will have potential applications on improving traffic safety and traffic capacity at signalized intersections with mixed traffic conditions

Investigating the spatial heterogeneity of factors influencing speeding-related crash severities using correlated random parameter order models with heterogeneity-in-means

Speeding has been acknowledged as a critical determinant in increasing the risk of crashes and their resulting injury severities. This paper demonstrates that severe speeding-related crashes within the state of Pennsylvania have a spatial clustering trend, where four crash datasets are extracted from four hotspot districts. Two log-likelihood ratio (LR) tests were conducted to determine whether speeding-related crashes classified by hotspot districts should be modeled separately. The results suggest that separate modeling is necessary. To capture the unobserved heterogeneity, four correlated random parameter order models with heterogeneity in means are employed to explore the factors contributing to crash severity involving at least one vehicle speeding. Overall, the findings exhibit that some indicators are observed to be spatial instability, including hit pedestrian crashes, head-on crashes, speed limits, work zones, light conditions (dark), rural areas, older drivers, running stop signs, and running red lights. Moreover, drunk driving, exceeding the speed limit, and being unbelted present relative spatial stability in four district models. This paper provides insights into preventing speeding-related crashes and potentially facilitating the development of corresponding crash injury mitigation policies

Network-Wide Pedestrian and Bicycle Crash Analysis with Statistical and Machine Learning Models in Utah

Recent trends in crashes indicate a dramatic increase in both the number and share of pedestrian and bicyclist injuries and fatalities nationally and in many states. Crash frequency modeling was undertaken to identify crash prone characteristics of segments and non-signalized intersections and explore possible non-linear associations of explanatory variables with crashes. Crowdsourced “Strava” app data was used for bicycle volume, and pedestrian counts estimated from nearby signalized intersections were used as pedestrian volume. Multiple negative binomial models investigated crashes at different spatial scales to account for different levels of data availability and completeness. The models showed high traffic volume, steeper vertical grades on roads, frequent bus and rail stations, greater driveway density, more legs at intersections, streets with high large truck presence, greater residential and employment density, as a larger share of low-income households and non-white race/ethnicity groups are indicators of locations with more pedestrian and bicycle crashes. Crash severity model results showed that crashes occurring at mid-blocks and near vertical grades were more severe compared to crashes at intersections. High daily temperature, driving under influence, and distracted driving also increases injury severity in crashes. This study suggests potential countermeasures, policy implications, and the scope of future research for improving pedestrian and bicycle safety at segments and at non-signalized intersections

Gap acceptance for left turns from the major road at unsignalized intersections

This paper attempts to identify factors that may influence the gap acceptance behavior of drivers who turn left from the major road at unsignalized intersections. Drivers’ accepted and rejected gaps as well as their age and gender were collected at six unsignalized intersections with both two and four lanes on the major road, with and without the presence of a Left-Turn Lane (LTL), and with both high and low Speed Limits (SLs). Whether or not a driver accepts a given gap was considered as a binary decision and correlated logit models were used to estimate the probability of accepting a gap. Models with different factors were tested and the best model was selected by the quasi-likelihood information criterion. The gap duration, the number of rejected gaps, the mean and total time interval of the rejected gaps and the gender of the driver were all significant in explaining the variation of the gap acceptance probability, whereas the number of lanes of the major road, the presence of LTL, the SL and the driver’s age category were not. Gap acceptance probability functions were determined based on the best model, including both the factors of the number of rejected gaps and the mean time interval of the rejected gaps. As the values of these two factors increase, the probability of accepting a given gap rises up. The developed model can be further applied in practice to improve the analysis of traffic operations and capacity at unsignalized intersections. First published online: 10 Jul 201

Methodological Frontier in Operational Analysis for Roundabouts: A Review

Several studies and researches have shown that modern roundabouts are safe and effective as engineering countermeasures for traffic calming, and they are now widely used worldwide. The increasing use of roundabouts and, more recently, turbo and flower roundabouts, has induced a great variety of experiences in the field of intersection design, traffic safety, and capacity modeling. As for unsignalized intersections, which represent the starting point to extend knowledge about the operational analysis to roundabouts, the general situation in capacity estimation is still characterized by the discussion between gap acceptance models and empirical regression models. However, capacity modeling must contain both the analytical construction and then solution of the model, and the implementation of driver behavior. Thus, issues on a realistic modeling of driver behavior by the parameters that are included into the models are always of interest for practitioners and analysts in transportation and road infrastructure engineering. Based on these considerations, this paper presents a literature review about the key methodological issues in the operational analysis of modern roundabouts. Focus is made on the aspects associated with the gap acceptance behavior, the derivation of the analytical-based models, and the calculation of parameters included into the capacity equations, as well as steady-state and non-steady-state conditions and uncertainty in entry capacity estimation. At last, insights on future developments of the research in this field of investigation will be also outlined

Identification of factors increasing the risk of pedestrian death in road accidents involving a pedestrian with a motor vehicle

Walking is a basic form of activity for every human being and has many advantages, including health, economic and environmental benefits. Every journey made using various means of transport begins and ends on foot. As is well known, the group of road users particularly exposed to the risk of serious injury in road accidents, apart from cyclists, also includes pedestrians. These are the so-called vulnerable road users. Pedestrians are a group of road users that is often deprecated by many drivers of motor vehicles, but very important in road traffic. Pedestrian injuries and pedestrian fatalities have enormous social and economic consequences. The problem of high pedes-trian risk on Polish roads is well known and has been widely described in the scientific literature last few years. However, the reasons for this state of affairs have not been fully explained, as evidenced by the statistics of road traffic incidents. Despite many studies in this area, the causes indicated in the research often differ depending on the area of analysis, the environment in which the incident took place, location, participants of the incident, environmental conditions, behaviorism and many other features. Therefore, the main goal of the article was to determine the factors influencing the formation of fatalities in road traffic accidents among pedestrians in acci-dents involving pedestrians and motor vehicles in the Silesian Voivodeship (Poland) in 2016-2021. The logit model presented in the article allowed for the conclusion that the main attributes influencing the increasing the risk of pedestrian death in road accidents involving a pedestrian with a motor vehicle include such features as driving under the influence of alcohol by the driver, exceeding the speed limit by the vehicle driver, when the road incident involves a heavy vehicle (truck, bus), a pedestrian is a male, pedestrian is over 60 years old, is under the influence of alcohol, the incident took place outside built-up area, at night, i.e. from 10:00 p.m. up to 6:00 a.m, in other than good weather conditions. The obtained results can be used in various activities, campaigns aimed at improving the safety of pedestrian traffic in the area of the analysis