Yogeshwar V. Navandar

Behavioral Dynamics at Signalized Intersections during a Green Phase: A Data-Driven Model for Speed Prediction in Mixed-Traffic Conditions

Growing traffic is a major concern worldwide, impacting both transportation system performance and road safety. In India, these challenges are intensified by highly heterogeneous traffic, highlighting the need to examine flow characteristics and driving behavior under mixed-class, non-lane-disciplined conditions. This study analyzes vehicular behavior at signalized intersections to quantify speed and acceleration profiles in such environments, using unmanned aerial vehicle (UAV) video data collected 100 m upstream and 40 m downstream of the stop line, segmented into 20-m intervals. The UAV-based videographic method is specifically chosen to overcome line-of-sight occlusion inherent in traditional ground-based observation techniques, thereby enabling continuous, high-quality trajectory collection in dense, mixed-traffic settings. Data were collected at four signalized intersections in the cities of Nashik and Nagpur, Maharashtra, India, covering four major vehicle classes: two-wheelers, cars, three-wheelers, and heavy vehicles (including buses). The analysis revealed that two-wheelers and cars exhibited similar speed behavior across all locations, with cars consistently showing the strongest polynomial fit and highest R 2 values, often exceeding 0.9. Two-wheelers also showed good fit, with R 2 ranging from 0.58 to 0.74, with heavy vehicles and buses displaying comparable trends. Three-wheelers showed more erratic behavior, and heavy vehicles generally exhibited the weakest correlation. Descriptive statistics and ANOVA confirmed statistically significant differences among vehicle classes. Based on these findings, a universal polynomial model framework is developed to predict mean speeds as a function of distance from the stop line, with a consistent structure across vehicle classes and locations during the green phase. The framework provides a novel basis for simulating and calibrating heterogeneous, non-lane-based traffic, with green-phase speed, acceleration, and deceleration profiles enabling accurate microsimulation calibration to support signal timing design, safety evaluation, and operational planning in complex urban settings.

Real-time traffic congestion mapping and bottleneck analysis using an edge-deployable lightweight deep learning framework for UAV-assisted monitoring

Analysis of Maneuver Time due to On-street Parking Near Signalized Intersections

The present study analyzes the variation in maneuver time of different vehicle types during on-street parking near signalized intersections with mixed traffic conditions. Traffic engineers frequently overlook this type of maneuver and their impact on a signalized intersection's performance while designing and assessing one. In this study, different type of vehicles, such as car, Two-Wheeler (TW), Autorickshaw (Auto), and Light Commercial Vehicles (LCV), are identified as parked vehicles near an intersection. Data on parking and maneuver time have been gathered from multiple Indian states. In this study, LCVs have the longest average maneuver time (MT) of 12.81 seconds (sec), followed by cars (10.25 sec), auto (7.58 sec), and TW (5.01 sec). According to these observations, maneuvering time (MT) for various types of vehicles operating in mixed traffic conditions vary considerably (5 to 13 sec). Further, the study analyzes vehicle MT among different vehicle classes and different intersections with parking maneuvers in the vicinity. The maneuver time (i.e., the time for which a parking vehicle blocks the traffic) observed in the present study is less than half of the benchmark values typically cited in capacity analysis guidelines. Notably, those conventional benchmarks are often applied uniformly, without differentiation across vehicle categories, approach volumes, or traffic composition. The outcomes of the current study will be useful for transportation planners to evaluate the performance of signalized intersections considering the effect of parking in the vicinity.