Automated Object Detection, Mapping, and Assessment of Roadside Clear Zones Using Lidar Data

This paper proposes a fully automated approach to map and assess roadside clearance parameters using mobile Light Detection and Ranging (lidar) data on rural highways. Compared with traditional manual surveying methods, lidar data could provide a more efficient and cost-effective source to extract roadside information. This study proposes a novel voxel-based raycasting approach focused primarily on automating roadside mapping and assessment. First, the scanning vehicle trajectory is extracted. Pavement surface points are then detected, and a method is proposed to extract pavement edge trajectories. Once pavement edges are extracted, guardrails were identified using a conical frustum emitted from the edge trajectory points. Target points and flexion points are then generated and located on the roadside, and a voxel-based raycasting approach is used to search for roadside obstacles and query their locations. Finally, roadside slopes and embankment heights were mapped at specific intervals, and roadside design guidelines and requirements were automatically checked against the mapping results. Noncompliant locations with substandard conditions were automatically queried. The method was tested on four highway segments in Alberta, Canada. The accuracy of the edge detection reached up to 98.5%. Furthermore, the method proved to be accurate in object detection, being able to detect all obstructions on the roadside in each tested segment. The proposed method can help transportation authorities automatically map and inventory roadside clearance parameters. Moreover, the safety performance of existing road infrastructure can be studied using collected information and crash data to support decision making on road maintenance and upgrades.

The Effects of Speed and Flow Characteristics on Crash Rates for Wasit Multi-lane Highways in Iraq

This study describes traffic crash rates in selected multilane rural highways in Wasit governorate in Iraq. The main objective of this research is to investigate relationships between total, fatal crash rates and their kinds and factors such as hourly traffic flow and average spot speed. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected to cover the locations of the accidents. The selection includes Kut – Suwera with five segments, Kut – ShekhSaad with three segments, and Kut – Hay with two segments multilane divided highways. Multiple linear regression analysis is applied to the data by using SPSS software to attain the relationships between the dependent variables and the independent variables in order to identify elements that are strongly correlated with crashes rates and severity. Seven regression models are developed which verify weak and strong statistical relationships between crashes types and average spot speed with hourly traffic flow respectively. As the hourly traffic flow of automobile grows, the need for safe traffic facilities also grown.

Automated Assessment of Passing Sight Distance on Rural Highways using Mobile LiDAR Data

Providing sufficient Available Sight Distance (ASD) that meets the minimum design requirements is crucial for highway safety. Previous work on sight distance assessment focused on Stopping Sight Distance (SSD) with little attention given to Passing Sight Distance (PSD). Insufficient PSD could lead to severe collisions such as head-on and sideswipe crashes. To address this gap, this paper introduces an automated method for PSD assessment on two-lane highways using mobile Light Detection and Ranging (LiDAR) data. The procedure involved extracting centerline lane marking, defining passing-allowed and passing-prohibited regions, computing the ASD, and comparing the existing centerline marking pattern (i.e., passing and no-passing zones) to a proposed lane marking that is based on the ASD for passing maneuvers. Regions that meet the design standards, substandard zones, and non-optimal design regions were all defined. A reallocation of PSD zones was conducted based on the ASD including modifying the existing lane marking pattern, which resulted in increasing the total length of passing zones by up to 20%, providing more, but safer, passing opportunities. A high-level safety assessment of historical collisions showed clusters of crashes along regions where passing is currently allowed at locations where the ASD is less than standard requirements. The proposed framework represents a tool by which transportation agencies could assess PSD, upgrade the design of existing highways, and investigate the consequences of PSD limitations to ensure compliance with standards during highway service life.

Characteristics of Crashes Caused by Distracted Driving on Rural and Suburban Roadways in Jordan

The objective of this study was to identify the most salient driver faults that cause crashes on some Jordanian rural and suburban roadway segments, to examine crashes with distracted driving as the driver’s fault, and to investigate the differences between crashes caused by distracted driving. Data for more than 10,200 crashes on nine roadway segments (five rural and four suburban) were accessed from the relevant government agency, but only n = 2472 were used for analysis after controlling for crashes specified as being caused by drivers’ distracted driving. IBM SPSS version 22 was used to perform descriptive analysis and independent samples’ t-tests. The results revealed that distracted driving was the second most common driver fault to cause crashes and the second main cause of fatalities and injuries on both rural and suburban roadways. Distracted driving on rural highways appears to be more fatal, whereas it caused more crashes with severe injuries on suburban roads. The variables at junction, road grade, number of lanes, weather condition, crash type, and number of vehicles involved were found to be statistically significant but with a small effect size. The following categories showed high percentages of distracted driving crashes on rural and suburban roadways: males, drivers 25–39 years old, non-holidays, weekdays, tangent sections, two-way divided roads, not at junction, level roads, two-lane roads, clear weather, dry surface, daylight, and automobile vehicles showed high percentages of distracted driving crashes on rural and suburban roadways. Differences between crashes on rural and suburban roadways caused by distracted driving were found to be small.

Evaluating the Impacts of Speed Limit Increases on Rural Two-Lane Highways Using Quantile Regression

Understanding speed selection behavior of drivers following speed limit increases is critically important. To date, the literature has largely focused on freeways and the effects of speed limit changes on two-lane highways remains under researched. Prior research has generally focused on changes to mean speeds, although the speeds of both the highest and lowest drivers are also of great interest. This study investigates trends in free-flow travel speeds following 2017 legislation that increased the posted speed limit from 55 to 65 mph on 943 mi of rural highways in Michigan. Speed data were collected for over 46,000 drivers at 67 increase segments where speed limit increased and 28 control segments where speed limits remained unchanged, before and during each of the two successive years following the speed limit increases. Site-specific traffic, geometric, and cross-sectional information was also collected. Impacts of the speed limit increases on the 15th, 50th, and 85th percentile speeds were evaluated using quantile regression. Separate analyses were conducted for passenger cars and heavy vehicles. Locations where the speed limits were raised experienced increases in travel speeds ranging from 2.8 to 4.8 mph. The control sites experienced marginal changes in speeds, which suggests that any spillover effects of the higher speed limits have been limited. Significant differences were observed across the quantiles with respect to the effects of the speed limit increases, as well as numerous site-specific variables of interest. The results provide important insights about the nature of driver speed selection and the impacts of speed limit increases.