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Advancements in computer vision are rapidly revolutionizing the way traffic agencies gather roadway geometry data, leading to significant savings in both time and money. Utilizing aerial and satellite imagery for data collection proves to be more cost-effective, more accurate, and safer compared to traditional field observations, considering factors such as equipment cost, crew safety, and data collection efficiency. Consequently, there is a pressing need to develop more efficient methodologies for promptly, safely, and economically acquiring roadway geometry data. While image processing has previously been regarded as a time-consuming and error-prone approach for capturing these data, recent developments in computing power and image recognition techniques have opened up new avenues for accurately detecting and mapping various roadway features from a wide range of imagery data sources. This research introduces a novel approach combining image processing with a YOLO-based methodology to detect turning lane pavement markings from high-resolution aerial images, specifically focusing on Florida’s public roadways. Upon comparison with ground truth data from Leon County, Florida, the developed model achieved an average accuracy of 87% at a 25% confidence threshold for detected features. Implementation of the model in Leon County identified approximately 3026 left turn, 1210 right turn, and 200 center lane features automatically. This methodology holds paramount significance for transportation agencies in facilitating tasks such as identifying deteriorated markings, comparing turning lane positions with other roadway features like crosswalks, and analyzing intersection-related accidents. The extracted roadway geometry data can also be seamlessly integrated with crash and traffic data, providing crucial insights for policymakers and road users.

Left turns may generate efficiency problems, which can be solved by appropriately prohibiting left turns. The goal of this paper is to propose a method for purpose of minimizing total travel times in urban road networks by prohibiting left turns. With left turn prohibition, the signal timing plan is optimized with the lane-based method because the method can adequately handle both signal timing optimization and lane assignment. The total travel time is calculated with link flows and link travel time being estimated with signal settings. As illustrated by numerical examples, prohibiting left turns reduces the total travel time of car traffic in road networks. As the left turn prohibition results can handle the randomness in the network, these results provide potential implications for congestion management. [ABSTRACT FROM AUTHOR]

Introduction Understanding driver behavior is important for traffic safety and operation, especially at intersections where different traffic movements conflict. While most driver-behavior studies are based on simulation, this paper documents the analysis of driver-behavior at signalized intersections with the SHRP 2 Naturalistic Driving Study (NDS) data. This study analyzes the different influencing factors on the operation (speed control) and observation of right-turn drivers. Method A total of 300 NDS trips at six signalized intersections were used, including the NDS time-series sensor data, the forward videos and driver face videos. Different factors of drivers, vehicles, roads and environments were studied for their influence on driver behavior. An influencing index function was developed and the index was calculated for each influencing factor to quantitatively describe its influencing level. The influencing index was applied to prioritize the factors, which facilitates development and selection of safety countermeasures to improve intersection safety. Drivers' speed control was analyzed under different conditions with consideration of the prioritized influencing factors. Results Vehicle type, traffic signal status, conflicting traffic, conflicting pedestrian and driver age group were identified as the five major influencing factors on driver observation. Conclusions This research revealed that drivers have high acceleration and low observation frequency under Right-Turn-On-Red (RTOR), which constituted potential danger for other roadway users, especially for pedestrians. Practical applications As speed has a direct influence on crash rates and severities, the revealed speed patterns of the different situations also benefit selection of safety countermeasures at signalized intersections. [ABSTRACT FROM AUTHOR]

A simulation based on VISSIM and its external driver model dynamic link library (DLL) was used to investigate the efficiency and safety of a two-way left-turn lane (TWLTL) on roads with staggered intersections (SIs). The impacts of traffic volume, ratio of left-turn vehicles, and stagger distance on SIs and conventional cross intersection (CI) were simulated with the following results: (1) SIs with stagger distance shorter than 200 m show few advantages in terms of average delay over a CI irrespective of traffic volume and left-turn ratio; (2) In contrast, SIs with stagger distance longer than 200 m show advantages that, however, disappear with an increase in traffic volume and left-turn ratio; and (3) SIs show a significantly higher number of traffic conflicts than CIs, indicating that they have more serious safety problems. These results should help traffic researchers and practitioners decide whether it is feasible to establish SIs on existing roadways with TWLTLs and the appropriate stagger distance for SIs, as well as whether it is appropriate to change a road that has dense SIs into a TWLTL cross section. [ABSTRACT FROM AUTHOR]