Sustainable impact of urban road class on smart transportation systems: A field data-informed exploration

Urban transportation systems face critical sustainability challenges, including congestion and carbon emissions, where the Speed Guidance System (SGS) serves as a key smart technology. However, systematic analysis of its context-dependent impacts—particularly across urban road classes—remains limited, hindering optimized deployment. This study addresses this gap by leveraging field data from Zibo City, collecting and processing millions of vehicle trajectory records using the SGS, categorized by road class (branch, minor arterial, arterial, expressway). Through sustainability metrics (travel comfort, delay, carbon emissions), the analysis reveals significant road-class variations in SGS optimization effects. Results show that SGS performance differs markedly by road class: it degrades sustainability on branch roads (increasing delays by 11 % and emissions by 1-16 %) but optimizes minor arterials most effectively (reducing delays by 20 % and emissions by 2 %). Crucially, driver inexperience reduces comfort by 11-29 % across classes, while emission peaks controlled near 60 km/h enable superior decarbonization on minor arterials. Based on partial correlation analysis, the study develops road-class-specific carbon emission regression models and proposes three targeted strategies (e.g., Prioritizing minor arterial deployment). This work advances intelligent transportation by providing a data-informed understanding of SGS's sustainable impacts under varying urban contexts.