Search Results (177)

Autonomous vehicles (AVs) will operate alongside human-driven vehicles for an extended transition period, during which aggressive human driving may become a major source of risk. This study proposes an integrated safety-control framework that combines real-world-data-driven behavior modeling with deep reinforcement learning to design longitudinal AV control strategies for mixed traffic. Aggressive, general, and defensive driving patterns are calibrated from the CitySim dataset, and dynamic aggressiveness is incorporated into an improved car-following model. A proximal policy optimization algorithm with a Kullback–Leibler penalty is then used to learn multi-objective strategies balancing safety, efficiency, comfort, and fuel economy in freeway and signalized-intersection scenarios. The results show that the behavior-aware strategies exhibit different strengths across traffic environments. On the freeway, the defensive-threshold strategy maintains a larger time headway, reduces positive acceleration, and lowers system-level fuel consumption, whereas the default, aggressive, and general strategies preserve higher traffic efficiency. At the intersection, signal control narrows the differences among strategies and limits the influence of longitudinal threshold settings on most evaluated indicators. These findings provide a quantitative basis for selecting behavior-aware control thresholds and designing robust AV strategies for mixed-autonomy traffic containing aggressive human drivers. Full article

Adaptive cruise control (ACC) performance is affected by multiple coupled factors, including safety margin, dynamic response, spacing regulation, target-transition behavior, and ride comfort. A single indicator is therefore insufficient for comprehensive ACC evaluation. This study proposes an adaptive cruise control comprehensive performance evaluation index model (ACC-CPEIM) for scenario-oriented ACC testing and diagnosis. The model links functional objectives, six typical ACC scenarios, measurable longitudinal indicators, hierarchical weights, and scenario-specific scoring rules into a unified evaluation chain. Time-domain response data are converted into scenario-level, criterion-level, and overall performance scores, while the results remain traceable to specific weak scenarios and performance dimensions. The proposed model was evaluated using a CarSim/Simulink co-simulation platform and further applied to vehicle-test data. The co-simulation results yielded an overall score of approximately 3.32 and identified weak acceleration-following response, insufficient spacing reserve during deceleration, and limited cut-in safety margin as the main limitations. The vehicle-test application produced an overall score of approximately 2.98 and showed that comfort and steady-state control were relatively stronger, whereas target-transition adaptability, safety margin, and dynamic response remained limiting dimensions. The results indicate that the ACC-CPEIM can provide quantitative, interpretable, and engineering-oriented support for ACC performance testing and diagnosis. Full article

To validate the validity of a driving simulation platform, this study constructs a dual dataset using field data and driving simulation experiments on an urban arterial road. Comprehensive validation is conducted from both subjective perception and objective quantification perspectives. Subjectively, questionnaires assessed simulation fidelity, driving workload, and physiological symptoms. Objectively, five core car-following indicators (speed, acceleration, relative speed, headway, and time headway) were analyzed using dynamic time warping and statistical methods. The results demonstrate that the simulated scenarios exhibit high subjective fidelity, reasonable task loads, and controllable motion sickness risks. Objectively, dynamic time warping confirms strong temporal pattern similarity, with waveform consistency proportions across core indicators ranging from 91.67% to 100.0%. Macroscopically, satisfactory relative aggregate similarity is demonstrated, with relative difference between means consistently constrained within a 20% threshold. However, strict absolute behavioral validity is unsupported due to significant statistical differences in sequence means, establishing clear boundary constraints for trend replication. It is speculated that systematic biases are primarily related to differences in risk perception within virtual environments. Full article

Traffic simulation models require calibration to capture local driving behavior, particularly in developing countries where conditions differ from Western defaults. This study calibrates three car-following models—Krauss, Wiedemann 99 (W99), and Intelligent Driver Model (IDM)—with lane-change parameters for heterogeneous traffic on six two-lane two-way roads in Egypt’s Nile Delta. A sensitivity analysis reduced eight lane-change parameters to three critical ones: lcOpposite, lcSpeedGain, and lcStrategic. A Genetic Algorithm then optimized car-following and lane-change parameters jointly across multiple seeds and generations. The best configurations achieved mean absolute percentage errors of 8.79% (IDM), 8.94% (W99), and 10.56% (Krauss), indicating that IDM and W99 performed similarly under the original four-metric validation, while Krauss was less accurate overall and exceeded the overtaking-rate threshold. suggesting calibration methodology matters more than model choice. Minimum standstill gap (minGap) treatment varied by model: freeing it improved IDM and Krauss performance, while fixing it at 1.50 m benefited W99. Constraining minGap in IDM increased error by 2.06 percentage points and altered parameter sensitivity, with lcStrategic becoming dominant and tau showing reduced sensitivity. Notably, SUMO’s uncalibrated Krauss defaults achieved 10.11% error under understaturated conditions, though this should not extend to congested scenarios. Simulated standard deviations underestimated observed variability, primarily due to aggregation mismatches rather than model inadequacy. Full article

Microscopic traffic simulation is widely used to evaluate traffic operations in continuous-flow tunnel scenarios. However, conventional calibration methods mainly rely on aggregate indicators such as average speed or traffic flow. Under constrained geometric conditions, stable lane-use patterns, and mixed passenger car and truck operations, different parameter combinations may reproduce similar macroscopic traffic states while generating different car-following behaviors. Therefore, aggregate-indicator-based calibration alone cannot ensure behavioral realism. The principal contribution of this study is a stepwise macro- and mesoscopic calibration framework that first constrains car-following behavior using the Speed Gap Function (SGF) and then refines the macroscopic traffic state using the Speed Distribution Function (SDF). The SGF characterizes the relationship between vehicle speed and net spacing, thereby capturing longitudinal interactions often overlooked in conventional calibration, whereas SDF describes the cumulative speed distribution. Latin Hypercube Sampling and VISSIM batch simulations are used to generate a dataset for 19 driving behavior parameters, and multilayer perceptron surrogate models are trained to improve optimization efficiency. Single-objective, simultaneous multi-objective, and stepwise calibration schemes are compared. The SGF-priority stepwise scheme achieves the most balanced performance, with SGF and SDF MAPE values of 12.00% and 11.53%, respectively, corresponding to average relative discrepancies of approximately 12% in reproducing the two calibration curves. An independent capacity pressure test used for external validation yields a deviation of only −1.84%, indicating that the simulated capacity is within 2% of the reference value. Overall, the proposed framework improves behavioral consistency and engineering applicability under high-demand tunnel conditions. Full article

To mitigate driving risks from brake failure on long and steep downhill sections, this study designs three deployment schemes for radar–video fusion devices: a baseline scenario with no coverage, a scenario with partial coverage in high-risk areas, and a scenario with full coverage. Corresponding information service strategies are delivered via Human–Machine Interfaces (HMIs), forming an integrated active prevention and control framework from risk perception to preventive action. Driving simulation experiments focusing on the car-following process were conducted to collect vehicle operational data and extract characteristic indicators based on the Wiedemann model. A Generalized Linear Mixed Model was employed to comprehensively examine the effects of HMIs on car-following behavior to identify the optimal active prevention strategy. Results show that drivers exhibit greater caution under the partial coverage scheme, with time headway increasing by 47.63% compared to the scheme with no radar–video fusion devices to ensure safety. Under full coverage conditions, drivers can obtain real-time information about the leading vehicle’s status and the distance between the two vehicles in key risk sections. Drivers choose to follow the leading vehicle, balancing both safety in car-following and efficiency on long and steep downhill sections. As the level of accompanying services improves, drivers engage in self-regulation to avoid rear-end collisions. Particularly under the scheme with full coverage of radar–video fusion devices, the standing distance significantly increases by 219.37% compared to the partial coverage condition. Drivers demonstrate optimal vehicle control capabilities. Furthermore, there is an interaction effect between the accompanying service strategy and drivers’ attributes on car-following behaviors. Under different schemes, more experienced drivers exhibit a certain degree of aggressiveness, providing a basis for the targeted design of information services for different types of drivers. The findings support the deployment and application of risk perception and prevention devices on long and steep downhill sections, which can effectively enhance the comprehensive safety of such special roads in the connected vehicle environment. Full article

Decision-making is a critical module for intelligent vehicles to achieve safe and efficient autonomous driving. However, most existing reinforcement learning-based decision-making methods optimize policies by maximizing the expected return, which may inadequately account for low-probability but high-cost safety risks in complex traffic interactions. To address this issue, this paper proposes a Risk-Sensitive Distributional Proximal Policy Optimization (PPO) method, termed Risk-Sensitive Distributional Proximal Policy Optimization (RSDPPO), for highway lane-changing decision-making. Within the PPO framework, a distributional state-value function is introduced to model the return distribution under the current policy, and a Wang distortion-based risk measure is further incorporated to construct a risk-sensitive advantage function. In this way, risk information contained in the return distribution can be propagated into the policy gradient update, guiding the learned policy to avoid high-risk driving behaviors while maintaining training stability. Simulation experiments are conducted in a highway lane-changing scenario with heterogeneous surrounding vehicles. The results show that, under medium-density traffic, the proposed method outperforms representative baseline algorithms in cumulative reward, success rate, and safety reward. Further evaluation under higher-density traffic demonstrates that RSDPPO maintains better overall performance, indicating stronger adaptability to denser traffic conditions. Ablation studies further show that risk-averse distortion improves the balance between safety and efficiency by increasing safety margins during car-following and lane-changing maneuvers. These results indicate that RSDPPO provides an effective risk-sensitive policy optimization framework for safety-oriented highway lane-changing decision-making. Full article

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects. Full article

This paper presents a novel approach to design an attitude motion control strategy of a vehicle to mitigate lateral or longitudinal inertial forces acting on the passenger during cornering, braking, and acceleration maneuvers. The collaboration of active suspension system and active airfoil substantially enhances the attitude motion of a vehicle. By incorporating integral control action for both the desired body attitude roll or pitch angle and zero dynamic tire deflection within the performance index, the optimal controller maintains the ideal roll or pitch angle while preserving the road holding capability. The computer simulations were conducted to evaluate the dynamic performance of the proposed system in comparison with various other suspension systems based on a 4-degree-of-freedom half-car model. Four scenarios for rolling and pitching motions were simulated as follows: the first case examines the rolling response to a one-sided bump input applied to a lateral half-car model during straight-line driving. The second case investigates the rolling performance during a cornering maneuver. The third and fourth cases analyze the pitching responses to braking and acceleration using a longitudinal half-car model. The simulation results demonstrate that the proposed system maintains the ideal body attitude, attenuates the effect of the lateral or longitudinal inertial forces and keeps an ideal road holding capability. As a result, the proposed control system substantially improves ride comfort while enhancing the dynamic safety of the vehicle. Full article

There is an important debate about the appropriate policy measures for reducing greenhouse gas (GHG) emissions in the transport sector. Strong expansion of battery electric vehicles (BEVs) following a ban on the registration of new vehicles with internal combustion engines (ICEs) by 2035 is a prominent but controversial proposal. To evaluate achievable GHG emission reductions, it is essential to understand the temporal dynamics of such a fleet transition. This study provides a time-resolved, policy-oriented quantification of annual and cumulative lifecycle GHG emissions during this process. Therefore, it uses an annual simulation model to assess GHG emissions from vehicle production and use during the transition of Germany’s passenger car fleet between 2019 and 2060. The analysis compares an ICE registration ban by 2035 with alternative scenarios and evaluates the effects of electricity decarbonization, greener BEV production, and the supply of additional Zero Emission Fuels (ZEFs). This study reveals a substantial time lag of 10–20 years between changes in new vehicle registrations and effective emission reductions. Even with a complete ICE ban by 2035, annual GHG emissions decline by only 3.7% by 2030 relative to 2025, while cumulative emissions over this period fall by just 1.6%. Larger reductions occur later, reaching 39% in 2040, 77% in 2050, and 82% in 2060 compared with 2025; cumulative emissions until 2060 decrease by 45%. Without an ICE ban and with a 75% BEV share from 2035 onward, cumulative reductions fall to 34%. Introducing additional ZEFs equivalent to 10% of 2030 fuel demand increases this value to 41%, compensating for much of the lower BEV uptake. Full article

This study develops a comprehensive carbon footprint assessment model that integrates forward and reverse logistics to evaluate and compare greenhouse gas emissions from online and offline apparel sales channels in China, with a particular focus on high return rates. The model quantifies emissions from transportation, packaging, storage, and operations, incorporating return and exchange logistics. The system boundary is limited to enterprise-controllable sales-phase activities and excludes consumer travel. Three sales models are compared: factory-to-consumer (F2C), traditional business-to-consumer (B2C) e-commerce, and brick-and-mortar retail (BMR). Within this defined boundary, BMR exhibits the lowest carbon footprint (0.296 kg CO₂e/item), followed by F2C (0.408 kg CO₂e/item) and B2C (0.602 kg CO₂e/item). Packaging dominates online emissions (55–57%), whereas store operations are the main contributor to offline emissions (43%). Return rates are identified as a decisive factor, accounting for over 31% of e-commerce emissions and potentially increasing them by 171.3% under extreme scenarios. Sensitivity analysis reveals that trunk line distance (factory to warehouse) has a greater impact on emissions than last-mile return route optimization. Relocating the factory closer to consumers reduces B2C transport emissions by 72.3%, whereas replacing conventional packaging with recycled plastic reduces total B2C emissions by 46.0%. These findings provide channel-specific sustainability strategies: return reduction and packaging innovation for online channels, and energy efficiency improvements for physical stores. These results are conditional on the defined system boundary. If consumer travel by private car were included, the relative advantage of offline channels would diminish or could reverse. Full article

Cycling is widely recognized as a sustainable urban mobility solution, and many municipalities focus on cycling infrastructure expansion to promote improved environmental sustainability. However, the current literature on cycling has predominantly focused on safety and health benefits, while the environmental benefits including GHG mitigation benefits remain less explored. To summarize findings from the current literature that explore the GHG emissions-related benefits (or costs) of cycling infrastructure, we conducted a literature review using five major scientific databases, following the PRISMA guidelines. Out of 824 screened records, 17 studies met the inclusion criteria. Most studies were published in the last decade, reflecting a limited but growing interest in this topic. The current analytical approaches include mode shift analysis, life cycle assessment, and scenario modelling. Among these, mode shift analysis (i.e., assessing the potential benefits related to replacement of car trips with cycling) remains a commonly used method. We found that cycling offers significant operational benefits by reducing GHG emissions, especially in the context of large-scale expansions of cycling infrastructure. Existing research indicates that even when embodied emissions are considered, bicycle is a more sustainable mode of transportation compared to cars or even public transit. However, emissions associated with installation and maintenance of cycling infrastructure may sometimes negate the GHG benefits associated with additional cycling. We discussed gaps in the current literature and directions for future research. Full article

The transition to autonomous driving is creating mixed traffic environments in which human-driven vehicles, partially automated vehicles, and autonomous vehicles must continuously interact, adapt, and respond to one another. This paper presents a comprehensive review of driver behavior in mixed traffic with autonomous vehicles, with emphasis on the sociotechnical nature of human–machine coexistence. The review synthesizes recent evidence on behavioral adaptation in car-following and tactical decision-making, trust calibration, situational awareness, takeover performance, internal and external human–machine interface design, surrogate safety metrics, vehicle-to-vehicle communication, operational design domains, and data-driven scenario generation. The literature shows that drivers do not respond to autonomous vehicles uniformly. Instead, behavior varies by driving style, perceived predictability of the automated vehicle, interface transparency, and traffic context. The review also emphasizes that these interaction patterns are context-dependent and may differ substantially across regions, particularly in dense mixed traffic environments. While some adaptations can improve stability and safety, others can encourage opportunistic maneuvers, overtrust, confusion, or degraded takeover quality. The review also highlights that crash data alone are insufficient to assess safety in mixed traffic, and that near-miss analysis, surrogate conflict metrics, and scenario-based evaluation are essential for understanding safety-critical interactions. Across the literature, a central inference emerges: adaptation to autonomous vehicles is real, but it is not automatically stabilizing. Safe deployment therefore depends not only on technical vehicle performance but also on behavioral legibility, transparent communication, calibrated trust, and robust evaluation under diverse real-world conditions. The paper concludes by identifying major research gaps, including the lack of longitudinal studies, incomplete standardization of surrogate metrics, limited understanding of vehicle conspicuity effects, and the need for integrated frameworks that jointly assess driver behavior, system design, and scenario-based safety. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

CAR-T cell therapy has shown substantial efficacy in haematological malignancies, but its application to solid tumours remains limited by poor effector-cell infiltration, functional exhaustion, antigenic heterogeneity, and an immunosuppressive microenvironment. In this study, we develop a new spatiotemporal mathematical model of CAR-T therapy for solid tumours that integrates these resistance mechanisms within a single reaction–diffusion framework. The model is formulated as a system of partial differential equations describing functional and exhausted CAR-T cells, antigen-positive and antigen-low tumour subpopulations, and chemokine, immunosuppressive, and hypoxic fields. Steady-state analysis and finite-difference simulations showed that therapeutic outcome is governed by the interplay between CAR-T cell infiltration, exhaustion, and antigen escape. The model reproduces partial tumour regression followed by residual tumour persistence, therapy-driven enrichment of antigen-low cells, and reduced efficacy under stronger immunosuppressive and hypoxic conditions. In the combination therapy scenario considered here, repeated simulated CAR-T cell administration together with attenuation of the suppressive microenvironment improves tumour control. The proposed model provides a mechanistic basis for analysing resistance and for future optimisation studies of CAR-T therapy in solid tumours. Full article