Introduction. The article considers the possibility of forming a traffic optimization strategy based on a macroscopic fundamental diagram between urban areas. The zones of influence are constructed with the maximum and minimum values of border control, respectively, according to the category of different types of the zone of influence. The stability zones were formed by connecting the zones of influence and their shape changes with different values of exogenous flow and endogenous flow were analysed.Materials and methods. The data on the traffic flow of the central part of the Jinan (PRC) road network are presented and an urban bizonal system is constructed using the obtained macroscopic fundamental diagrams. The modelling of the state of traffic flows was carried out and the regularity of their changes at different parameters of border control was obtained using the mathematical modelling MATLAB complex.Results. As a result of the study, the traffic management solutions were obtained for the relatively parameter of completed and incomplete trips. The generalized options for optimizing traffic in the considered urban areas are proposed – four combinations of strategies for traffic management are performed serving the design optimization option with different traffic flow conditions.Discussion and conclusions. For each multi-zone system of the road network, it is necessary to form its own characteristic variant of optimizing the traffic flow.

Electronic toll collection (ETC) gantries generate transaction records, yet existing anomaly-detection approaches often depend on manual labels or external information, limiting multiview inconsistency ranking under restricted supervision. We propose consistency-aware weakly supervised anomaly sensing for ETC (CAWS-ETC), combining monetary, temporal, structural-pattern, and contextual-semantic evidence and transferring high-confidence references to Light Gradient Boosting Machine (LightGBM). Evaluation used 14,510,847 transactions from 2525 gantries. On joint-transfer benchmarks, CAWS-ETC achieved area under the precision–recall curve (AUPRC) values of 0.9354 for rule-aligned interventions and 0.9018 for rule-orthogonal challenges, versus 0.7609 and 0.7377 for Isolation Forest. A blinded audit of 1200 unmodified transactions by two independent reviewers yielded 777 determinate labels; CAWS-ETC achieved a sampling-weighted AUPRC of 0.7946, while Isolation Forest showed higher ranking point estimates on this temporal-only subset. Post hoc attribution showed that direct rule-created and high-confidence probabilistic labels were identical after the 0.90/0.10 selection, and matched LightGBM models produced essentially identical rankings. Thus, capability beyond direct rule activation arose primarily from discriminative transfer rather than measurable gains from probabilistic aggregation or posterior-confidence weighting. Because all experiments used one day from one provincial network, multi-day, seasonal, and cross-region generalizability remain unverified.