Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments.

Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor's coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach.

Particulate matter exposure at urban traffic intersection during haze episodes: A case study in Changsha.

Urban intersection has been identified as a major contributor to the total personal exposure and short-term high exposure of particulate matter (PM) in modern cities. The main aim of this study was to get a better understanding of the determinants of traffic-related PM temporal variations and personal exposure to PMs at a viaduct-covered intersection controlled by traffic signals during the winter haze episodes. A two-day field sampling campaign was conducted with a portable device during evening rush hour and measured the PMs in the 0.3-10 µm size range both on the surface crosswalk and underground passage. PM variations and related cumulative respiratory deposition dose (RDD) along two routes with six road crossing scenarios were estimated on a severe pollution day and a typical day for both adults and children, respectively. The PM concentration on the severe pollution day ranged 59.2-67.9 µg/m3 for PM1, 163.8-257.0 µg/m3 for PM2, and 258.2-469.1 µg/m3 for PM10, respectively, as compared to 47.9-57.9 µg/m3for PM1, 112.7-199.8 µg/m3 for PM2, and 151.0-301.0 µg/m3 for PM10 on the typical day, respectively. The variability could be explained largely by the built-up environment, traffic component, signal setting, and ventilation condition. Our data suggest that an appropriate setting of the traffic signal would help reduce the personal exposure dose on the surface crosswalk at urban intersections and the ventilation condition had a significant influence on local PM distributions inside the underground passage. Results here provide possible suggestions for the future design of a walkable city.

A random parameters probit model of urban and rural intersection crashes.

Intersections are hazardous locations and many studies have been conducted to identify the factors contributing to the frequency and severity of intersection crashes. However, little attention has been devoted to investigating the differences between crashes at urban and rural intersections, which have different road, traffic and environmental characteristics. By applying a random parameters probit model to the data from the Canadian Province of Alberta between 2008 and 2012, we find that urban intersection crashes are more likely to be associated with hit and run behaviours, roads with higher traffic volume, wet surfaces, four lanes and skewed intersections, and crashes on weekdays and off-peak hours, whereas rural crashes are likely to be associated with increases in fatalities and injuries, roads with higher speed limits, special road features, exit and entrance terminals, gravel, curvature and two lanes, crashes during weekends, peak hours and night-time, run-off-road crashes, and police visit to crash scene. Hence, road safety professionals in urban and rural areas should consider these differences when designing and implementing counter-measures to improve intersection safety, especially their safety audits and reviews, enforcement activities and education campaigns, to target the more vulnerable times and locations in the different areas.