Vladimir Zyryanov

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.

Rapid urbanization has led to the development of intelligent transport in China. As active safety technology evolves, the integration of autonomous active safety systems is receiving increasing attention to enable the transition from functional to all-weather intelligent driving. In this process of transformation, the goal of automobile development becomes clear: autonomous vehicles. According to the Report on Development Forecast and Strategic Investment Planning Analysis of China’s autonomous vehicle industry, at present, the development scale of China’s intelligent autonomous vehicles has exceeded market expectations. Considering limited research on utilizing autonomous vehicles to meet the needs of urban transportation (transporting passengers), this study investigates how autonomous vehicles affect traffic demand in specific areas, using traffic modeling. It examines how different penetration rates of autonomous vehicles in various scenarios impact the efficiency of road networks with constant traffic demand. In addition, this study also predicts future changes in commuter traffic demand in selected regions using a constructed NL model. The results aim to simulate the delivery of autonomous vehicles to meet the transportation needs of the region.

This paper describes possibilities of the macroscopic fundamental diagram (MFD) of traffic flow to predict the conditions of operation of the road network in urban areas. This study examines relationships between traffic flow parameters on the network level. Microscopic traffic simulation has provided important data on the estimation of road capacity, velocity, trip time, and detection of congestions reasons. Data of spatial distribution density in network useful for implementing approach based on gating policy on subnetwork using MFD. It presents the results of a simulation using the example of central area of Rostov-on-Don

The paper considers capabilities of the macroscopic fundamental diagram of traffic flow to predict the conditions of operation of the road network in urban areas. This study examines relationships of traffic flow parameters on the network level. Microscopic traffic simulation has provided important data on the estimation of road capacity, velocity, trip time, and detection of congestions reasons. Data of spatial distribution density in network useful for implementing approach based on gating policy on subnetwork using MFD. It presents the results of a simulation using the example of central area of Rostov-on-Don.