What Is an Effective Way to Measure Arterial Demand When It Exceeds Capacity?

This project focused on developing and evaluating methods for estimating demand volume for oversaturated corridors. Measuring demand directly with vehicle sensors is not possible when demand is larger than capacity for an extended period, as the queue grows beyond the sensor, and the flow measurements at a given point cannot exceed the capacity of the section. The main objective of the study was to identify and develop methods that could be implemented in practice based on readily available data. To this end, two methods were proposed: an innovative method based on shockwave theory; and the volume delay function adapted from the Highway Capacity Manual. Both methods primarily rely on probe vehicle speeds (e.g., from INRIX) as the input data and the capacity of the segment or bottleneck being analyzed. The proposed methods were tested with simulation data and validated based on volume data from the field. The results show both methods are effective for estimating the demand volume and produce less than 4% error when tested with field data

Factors influencing drivers' queue-jumping behavior at urban intersections: A covariance-based structural equation modeling analysis

Queue-jumping is widely acknowledged as one of the most vexing driving behaviors and a prevalent traffic violation at urban intersections in China, exerting detrimental effects on both traffic operational efficiency and safety. To investigate the motivational factors underlying drivers' queue-jumping behavior at urban intersections, a questionnaire was designed to collect data based on an extended theory of planned behavior (TPB). A total of 427 valid responses were received through an online self-reported questionnaire survey conducted in China. The Pearson's chi-square test was employed to examine potential demographic disparities in self-reported queue-jumping behavior among drivers at urban intersections. Covariance-based structural equation modeling (CB-SEM) with bootstrapping was utilized to elucidate the impact of various factors on drivers' engagement in queue-jumping behavior. The findings revealed significant gender and age differences regarding drivers' propensity for queue-jumping at urban intersections, with male and young drivers exhibiting higher inclination compared to female and older counterparts, respectively. Furthermore, the extended TPB effectively accounted for both behavioral intention and actual occurrence of queue-jumping among drivers at urban intersections. Behavioral intention (β = 0.391, p = 0.002) and perceived behavior control (β = 0.282, p = 0.002) emerged as influential determinants of queue-jumping. Among all influencing factors shaping drivers' behavioral intention toward engaging queue-jumping at urban intersections, attitude (β = 0.316, p = 0.005) proved to be the most significant factor followed by perceived risk (β = 0.230, p = 0.001), moral norms (β = 0.184, p = 0.002), subjective norms (β = 0.175, p = 0.002), and perceived behavior control (β = 0.122, p = 0.05). These results offer valuable insights for urban road traffic managers seeking effective strategies for public awareness campaigns as well as practical intervention measures aimed at curbing improper driving behavior of queue-jumping at urban intersections