Synergy Effect of Factors Characterising Village Transition Zones on Speed Reduction

There are various traffic calming measures that can be installed in village transition zones. So far, focus was placed on diversified use of pavement markings, amounts of horizontal deflection, shape of the installed chicanes or central islands, presence of gateway, etc., and their location along the transition zone. However, the combined effect of the different transition zone factors on speed reductions has been rarely studied so far. Authors put forward a hypothesis of there being some determinants, which in combination influence speed reduction. To corroborate the hypothesis on the combined impact of the transition zone features on speed reduction in the village transition zones and to validate the established relationships the authors conducted verification study in transition zones containing chicanes or central islands. To verify this hypothesis the authors studied twenty transition zones and managed to confirm the hypothesis at 95% confidence level. The authors used previously adopted binary methods, verified the previously defined factors and added a few new determinants. The contribution of this study is a further investigation of the synergy effect of various relevant factors and the findings can assist in planning new transition zones or suggest additional measures to achieve the desired speed reduction in existing transition zones.

Leveraging LiDAR Intensity to Evaluate Roadway Pavement Markings

The United States has over 8.8 million lane miles nationwide, which require regular maintenance and evaluations of sign retroreflectivity, pavement markings, and other pavement information. Pavement markings convey crucial information to drivers as well as connected and autonomous vehicles for lane delineations. Current means of evaluation are by human inspection or semi-automated dedicated vehicles, which often capture one to two pavement lines at a time. Mobile LiDAR is also frequently used by agencies to map signs and infrastructure as well as assess pavement conditions and drainage profiles. This paper presents a case study where over 70 miles of US-52 and US-41 in Indiana were assessed, utilizing both a mobile retroreflectometer and a LiDAR mobile mapping system. Comparing the intensity data from LiDAR data and the retroreflective readings, there was a linear correlation for right edge pavement markings with an R2 of 0.87 and for the center skip line a linear correlation with an R2 of 0.63. The p-values were 0.000 and 0.000, respectively. Although there are no published standards for using LiDAR to evaluate pavement marking retroreflectivity, these results suggest that mobile LiDAR is a viable tool for network level monitoring of retroreflectivity.

High Performance Pavement Markings Enhancing Camera And LiDAR Detection

It is well known that camera and video sensors have limitations in detecting pavement markings under certain conditions e.g. glare from sunlight or other vehicles, rain, fog etc. First generations of lane keeping systems depend on visual light. Erroneous detection is also resulting from irregular road surfaces such as glossy bitumen sealing strips, rain puddles or simply worn asphalt. The role of higher performing markings and better visual camera detection has been studied with Vedecom France. LiDAR (light detection and ranging) technology could help to fill remaining gaps, as it actively sends out IR (infrared) light, that returns reliable images of the road scenario and pavement markings both day and nighttime. In order to evaluate the opportunities of LiDAR technology for the detection of road markings, 3M Company and the University of Applied Sciences in Dresden decided to work together in a joint research project. All-Weather Elements AWE, are the latest development of high-performance optics, using high index beads to provide reflectivity both in dry and wet condition. It could be determined that high performance markings help to increase the level of detection by both camera and LiDAR sensors. The AWE marking was detected from significantly longer distances, especially in wet and rainy conditions. In combination with common camera based LKA and LDW systems, the LiDAR sensors can increase the overall detection rate of pavement markings. This is especially important for vehicles with higher SAE levels of automated driving and can support the overall safety of vehicles. The research also evaluated existing test methods for wet and rain reflectivity in EN 1436 and ASTM E 2832 and how measured performance correlates with LiDAR detection.

Overview of Intelligent Transportation System Safety Countermeasures for Wrong-Way Driving

Wrong-way driving (WWD) crashes are rare but tend to cause severe injuries and fatalities. When they occur, WWD crashes are mostly head-on and tend to be more severe than any other crash type. Currently, most efforts to mitigate WWD crashes focus on the application of intelligent transportation systems (ITS) technologies. Since the mid-1960s, many agencies have proposed and tested a variety of countermeasures ranging from the conventional, including improvements in signage and pavement markings, to the more advanced, based mainly on ITS. The application of ITS in detecting and deterring WWD crashes involves a combination of several components, such as detection, warning, and action. The main objective of this paper is to provide a comprehensive overview of ITS technologies implemented by different transportation agencies in the mitigation and prevention of WWD crashes. This study proves the importance of ITS technologies that have seen widespread application in recent years. Moreover, according to the evaluation studies of different ITS-related countermeasures, it shows that the countermeasures deployed have been effective in detecting wrong-way drivers and reducing the rate of wrong-way incidents. The results will provide insights for policymakers, engineers, and researchers into the application of ITS for reducing the severity and frequency of WWD crashes and other incidents on highway facilities.

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.

Human Factors Assessment of Pavement Marking Retroreflectivity in Simulated Rain and Dry Conditions

Pavement marking retroreflectivity standards are typically developed with dry conditions in mind, however, driving at night during rainfall is seemingly one of the most challenging and stressful situations for a driver. Furthermore, existing research indicates continuous wet retroreflectivity is relatively weakly correlated with dry retroreflectivity and deteriorates differently over time, leading to the obvious conclusion that dry retroreflectivity standards alone are not enough to ensure that pavement markings meet the needs of drivers across the breadth of roadway conditions that may occur. Consequently, developing standards for minimum continuous wet retroreflectivity for new installations and for maintenance purposes represents an important area for research. This study aims to develop new installation and maintenance values for continuous wet retroreflectivity based on a multifaceted, closed-course study of detectability of pavement markings in simulated rain and dry conditions. A series of 20 pavement marking samples was evaluated in relation to detection distance and subjective rating. The results of the study indicated that pavement markings need to be maintained at a continuous wet retroreflectivity value of 50 mcd/m2/lux based on a participant pool that skewed older in age, but that likely represents something close to the 85th-percentile driver. Additional salient findings included observed wet retroreflectivity loss in the existing literature of approximately 7% per month, as well as the maximum preview time in simulated rain conditions being substantially lower than in dry conditions.