Testing Connected Vehicle-Based Accident Mitigation for Red-Light Violation Using Simulation Strategies

Abstract

Simulation is often suggested as a way to analyze the safety improvements of geometric changes and operational strategies. But the results from simulations are mixed. This paper presents new ideas about how to do such assessments, especially in the context of testing the value of vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and vehicle to pedestrian (V2P) communications in preventing crashes because of red-light violation at signalized intersections. Algorithms are created that watch for impending collisions through sensing and then issue speed and trajectory changes to avoid accidents. Red-light violation is a primary focus because it increases the likelihood that collisions will occur. VISSIM is used to test these ideas, including new communication and control algorithms that link to vehicles, pedestrians, and signal controllers through the communication interface. The algorithms predict unsafe conditions, determine an appropriate crash remedial decision, and communicate those controls with the appropriate vehicles and pedestrians. The impacts of these algorithms are explored under various demand patterns, connected vehicle market penetration rates, and red-light violation rates in a hypothetical simulated environment. The simulation analysis suggests that the number of near-crash events can be reduced significantly if V2V and V2P communications are implemented. Moreover, adding V2I communication on top of these may further reduce the number of near-crash events. These results suggest that not only could such control strategies have significant impacts, but also those impacts can be assessed through simulation.

Effects of Connectivity and Automation on Saturation Headway and Capacity at Signalized Intersections

“…The driving behavior associated with HVs, CVs, AVs, and CAVs differs by automation and connectivity level. Lower automation levels aim to assist human drivers through technologies enabled by onboard computers and sensors, such as adaptive cruise control ( 27 ), collision warning ( 28 – 30 ), collision avoidance ( 31 ), or assistant braking ( 32 ). On the other hand, higher automation levels enable AVs and CAVs to take complete control of the vehicle’s movements without any assistance from the human driver by predicting the future trajectory of surrounding vehicles and avoiding any potential collisions.…”

“…In addition, CAVs can improve traffic mobility without sacrificing safety. For instance, controlling the trajectory of CAVs upstream of signalized intersections based on advanced knowledge of signal phase and timing (SPaT) increases intersection throughput and reduces the experienced delay and risk of collisions among vehicles ( 29, 38–43 ). Moreover, the trajectory of CAVs can be managed to avoid stops at the intersection and minimize fuel consumption ( 13 , 39 , 44 ).…”

Proposed CtCNet-HDRNN: A Cornerstone in the Integration of 5G mmWave and DSRC for High-Speed Vehicular Networks

“…These technologies are implemented using different supporting units at critical points, providing facil-ities such as collision warning, road construction warning, overhead heavy vehicle warning, and lane changing warning. They assist drivers through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication [3]. Further, DSRC, designated for the 5.9GHz band [4], is a significant advancement in the automotive sector as it allows data transmission directly between two devices without intermediaries.…”