This manuscript proposes Socio-Traffic Thermodynamics (STT), a theoretical framework that models traffic congestion as a non-equilibrium dissipative structure generated by agents moving along social-potential gradients on transportation networks with finite capacity, finite perceptual bandwidth, and finite relaxation time. The framework integrates three mechanisms: bandwidth compression induced by vehicular encapsulation, demand refilling under endogenous cost equilibration, and attraction toward high-opportunity, high-interaction regions.The model combines stochastic car-following dynamics with an internal binary state field and derives a macroscopic continuum description involving density, mean velocity, and an order parameter representing synchronized internal orientation. Near the free-flow critical point, decentralized residual control variance is interpreted as a source of increased dissipation and reduced effective barrier against jam nucleation. The framework also suggests that coarse or privatized information can, under convex congestion costs, reduce action correlation and load variance, thereby lowering expected social cost.A minimal numerical simulation using a stochastic Optimal Velocity model on a periodic ring reproduces spontaneous symmetry breaking and persistent backward-propagating stop-and-go waves without any fixed bottleneck. This supports the interpretation of congestion as an organized non-equilibrium pattern sustained by throughput, relaxation delay, stochastic fluctuation, and dissipation.STT does not claim that congestion is universally eliminable, nor does it provide a calibrated city-scale traffic model or policy prescription. It should be read as a reduced theoretical and numerical prototype for interpreting recurring congestion in socially attractive, capacity-limited transport systems.