Balázs Varga

Urban intersections are critical points of conflict between vehicles and cyclists; therefore, improving safety at these locations is an important part of sustainable transport planning. In this study, a Simulation-Based analysis evaluated cyclist safety at 7 four-leg intersections across 14 scenarios, including 3 bike-lane and 4 signalization cases. Using the Simulation of Urban Mobility (SUMO) and the Surrogate Safety Assessment Model (SSAM), we analyzed conflict frequency and severity using Time-to-Collision (TTC) and developed a Severity-Weighted Conflict (SWC) metric. The outcomes suggest that while traffic signals and dedicated cycling infrastructure promote safety, poorly positioned bike lanes—especially to the right of right-turning cars—worsen conflicts. These findings highlight the critical role of geometric design and context-sensitive planning in implementing cyclist-safety treatments at urban intersections.

This paper presents a comparative study between microscopic traffic simulation and vehicle dynamics simulation to evaluate their consistency and applicability for driver behavior analysis. The study focuses on four representative driving scenarios: roundabouts, four-way intersection, highway overtake, and U-turn. Traffic simulations were conducted using SUMO, while vehicle dynamics simulations utilized the double-track vehicle model in MATLAB SIMULINK, driven by the Pure Pursuit control algorithm. Trajectories were visually compared using x-y plots, and the maximum positional deviations were calculated. Speed profiles and heading angles were analyzed as functions of distance, complemented by a quantitative metric based on phase, amplitude, and topology errors. This metric, developed in earlier work, provides a method for comparing vehicle behaviors. The research results highlight the need to incorporate detailed vehicle dynamics into traffic simulations for improved realism, especially in scenarios with high lateral acceleration and abrupt steering inputs. Refining traffic simulations with improved vehicle behavior will also make road traffic flow predictions more realistic and reliable.

Increasing motorization represents a severe problem worldwide, also affecting the emission levels of the road network. Accordingly, congestion management has obtained growing importance because of its strong economic, social, and environmental implications. Macroscopic Fundamental Diagram (MFD) based traffic control is a popular and efficient approach in this scientific field. In our research work the urban network has been divided into homogeneous regions, each of them characterized by its own MFD, and they are regulated using a network-level control scheme. The proposed Multiobjective Model Predictive Control (M-MPC) takes into account the congestion and $CO_{2}$ emission levels of the urban network, modelled by the emerging Emission Macroscopic Fundamental Diagram (e-MFD). The applied strategy has been demonstrated in a realistic traffic scenario (Luxembourg City) using validated microscopic traffic simulation. According to the introduced multiobjective approach, the control method can better exploit the road network capacity while efficiently reducing traffic-induced emissions.

Road traffic simulation is becoming more important as the complexity and multi-modality of traffic grow with the evolution of novel transportation and automotive technologies. There are several ways to mimic traffic dynamics. However, realistic modeling traffic has a clear trade-off between accuracy and simulation processing time. Obviously, the more detailed traffic dynamics are sought, the more calculation is needed. The paper offers a compromise solution introducing a novel mesoscopic traffic simulation framework applicable on an arbitrary network level. The mathematical background of the simulation is provided as the extension of the shockwave profile model (SPM), distinguishing three continuous traffic states on a link. Extension of the model is done by means of a weighted directed graph where the dynamic edge weights are the queue lengths on each link. The model can handle both signalized and unsignalized road links. An important opportunity of the proposed network SPM approach is the analysis of traffic jam propagation and bottleneck detection. The presented mesoscopic simulator is validated based on both synthetic and real-world traffic networks using fleet traffic data. The simulated queue length in the mesoscopic simulation accurately matches the microscopic traffic simulation results.

Simulating automotive functions that rely on interaction with other vehicles (e.g., perception-based control or algorithms relying on inter-vehicular communication) created a demand for traffic simulation in the automotive field as well. Large-scale traffic simulation can be used to generate long, synthetic drive-cycles for EGO vehicles with realistic traffic. An EGO vehicle is defined as the vehicle the scenario revolves around, presumably running a control algorithm to be tested. On the other hand, simulating an entire district or city with thousands of vehicles present is superfluous and comes with a heavy computational burden while only the vicinity of the EGO vehicle is relevant. On the other hand, major traffic patterns can that could still influence the nearby traffic (e.g., traffic disruptions farther away) but can be simulated with lesser accuracy. Thus, simulation accuracy far from the EGO vehicle can be traded for simulation speed. This paper achieves this trade-off by co-simulating SUMO in microscopic and mesoscopic modes using Libsumo API. Microsimulated traffic is continuously spawned in an EGO-centered sub-network based on traffic states in the mesoscopic simulation. Simulation results in large urban scenarios suggest that the behavior of the EGO vehicle in terms of velocity distribution, headway distribution, and lane changes accurately matches pure microsimulation while simulation speed increased by 3 – 10 times. This result assumes linear time complexity control algorithms with respect to the vehicle number and a single EGO vehicle. Reducing the number of microsimulated vehicles with co-simulation yields even larger simulation speed gains for more computationally complex algorithms. The aggregate (macroscopic) traffic parameters match for both the micro-, meso-, and co-simulated cases. Thus coupling the two simulators does not distort the mesoscopic simulation.