Behavior-Based Assessment of Driverless Vehicles in Signalized Urban Traffic: Effects on Delay, Emissions, and Fuel Consumption

The gradual integration of driverless vehicles into urban traffic systems is expected to affect both operational performance and environmental outcomes, particularly during the mixed-automation phase of urban traffic systems, in which human-driven and driverless vehicles coexist. However, existing studies have rarely examined this phase through jointly accounting for behavioral heterogeneity among human drivers and varying levels of driverless vehicle penetration in signalized urban networks. This study addresses this gap through a behavior-based microscopic traffic simulation framework that explicitly incorporates different human driving styles together with driverless vehicles across penetration levels ranging from 0% to 100%. Network- and link-level indicators, including delay, queue length, fuel consumption, and emissions, are evaluated under coordinated signal control conditions. The results reveal a nonlinear relationship between the automation level and traffic performance. While changes remain limited at low and moderate penetration levels, more pronounced improvements emerge beyond a critical threshold of approximately 75% driverless vehicle penetration. At this level, network-wide average delay reductions of about 3–5% are observed, accompanied by consistent decreases in fuel consumption and emissions. By highlighting how behavioral interactions shape the effectiveness of automation, the findings provide practical insights for traffic engineers and urban planners, supporting the design and evaluation of signalized urban arterials under mixed traffic conditions while contributing to environmental sustainability and sustainable urban mobility through improved traffic efficiency and stability.