Constructing self-sustained highway transportation energy systems (HTESs) hinges on effective sustainable energy planning along highways. Addressing the complex spatial-temporal distribution characteristics of sources and loads presents a formidable challenge in accurately determining the siting and sizing of sustainable energy installations. In this study, we utilize a map rasterization approach and decentralized connection models for quantifying the spatial-temporal distribution characteristics of sources and loads. Leveraging these insights, the source-load-network cooperative operation models in uncertain scenarios, which seamlessly integrate highway and electricity networks, are built and embedded in the multi-objective robust planning model, enabling dynamic resource and demand management. The proposed planning model simultaneously optimizes the capacity, location, and connectivity of wind and photovoltaic power plants in HTES, while improving the robustness. Moreover, a multi-objective-oriented evaluation framework that adjusts the planning priorities based on three key dimensions – investment economy, self-sustained operation, and energy utilization efficiency – is formulated. The dynamic weight allocation mechanism enables tailored planning schemes that address diverse operational objectives effectively. Simulations of an actual HTES validate the effectiveness of the proposed planning model, demonstrating its capability to harmonize the inherent variabilities in the spatial-temporal distribution of sources and loads. The results highlight the significant variability in outcomes based on different objective orientations, underscoring the adaptability potential of the proposed planning model in designing futuristic HTES.