Search Results (466)

The operation of driver assistance systems and autonomous vehicles requires a sensor system and a control algorithm. Sensors provide information to detect people, vehicles and objects in the vehicle’s environment; however, their performance can be degraded by adverse environmental conditions and contamination. This literature review identified factors that reduce sensor visibility, such as weather conditions and external contamination. In this study, the detection efficiency of state-of-the-art neural network-based object detectors was examined in a simulation environment using a synthetic dataset. A custom dataset comprising six urban and suburban traffic scenarios was created, including clean images and ten contaminated variants per scene with increasing mud coverage. The results show that contamination leads to a measurable reduction in detection performance across all models. Smaller variants are more sensitive to degradation, while medium-complexity models provide a favorable balance between robustness and computational cost. Increasing model size yields limited additional robustness, and performance differences between architectures highlight the importance of model design. Furthermore, the spatial distribution of contamination, particularly near the image center, has a significant impact on performance in addition to its overall extent. 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

With the rapid maturation of autonomous driving technology, the hybrid traffic of Internal Container Trucks (ICTs) and External Container Trucks (ECTs) has become a major trend in Automated Container Terminals (ACTs), imposing higher demands on the interaction efficiency between trucks and Yard Cranes (YCs). This paper proposes a comprehensive optimization strategy for the coordinated scheduling of ICTs, ECTs and YCs under hybrid traffic. First, a task combination strategy for ICTs is designed to improve ICT utilization by pairing delivery and retrieval tasks across yard blocks. Second, a Chebyshev-motion-based coordination strategy for YC gantry and trolley movements is developed to reduce travel time and optimize handover points. Third, a mixed-integer programming model is formulated to minimize total energy consumption. An Improved Hybrid Genetic Algorithm (IHGA) is then developed, incorporating chaotic initialization, simulated annealing-based mutation, and dual local search to enhance convergence and solution quality. Simulation results confirm that the proposed model and strategy effectively reduce the total energy consumption of task execution, and the designed algorithm outperforms comparative algorithms in both optimization capability and convergence speed. Overall, the research provides theoretical support for future automated terminal development and practical guidance for achieving efficient and sustainable port operations. Full article

Accurate short-term traffic flow prediction in complex urban road networks is of great significance for capacity organization and dispatch optimization in intelligent transportation systems. Using publicly available historical taxi trip records released by the New York City Taxi and Limousine Commission from January to June 2016, this study develops a spatiotemporal fusion framework for short-term traffic flow prediction. To address the nonlinearity, sparsity, and complex spatiotemporal dependencies of traffic flow sequences, the raw trajectory data are first cleaned, spatially gridded, and temporally discretized. Based on the spatial adjacency relationships among grid nodes, a graph structure is then constructed, and a serially coupled graph convolutional network and long short-term memory model is developed to capture spatial dependency features and temporal dynamic features, respectively. Experimental results on the New York City taxi dataset show that, compared with baseline models including the historical average model, long short-term memory network, graph convolutional network, and Transformer, the proposed model achieves better performance in terms of mean absolute error, root mean square error, and coefficient of determination. Furthermore, the SHAP (SHapley Additive exPlanations) method is employed to ANALYZE the differences in feature contributions across nodes in different functional zones from both temporal and spatial perspectives. The results indicate that the model exhibits heterogeneous temporal dependency depths and spatial aggregation patterns across different types of regions within the study area. In addition, regions with high feature contributions show a certain degree of spatial correspondence with the major traffic corridors in Manhattan, suggesting that the model is able to capture part of the spatiotemporal correlation structure of traffic flow in this dataset. Finally, the limitations of the proposed method in terms of static graph structure, response to extreme events, and integration of external factors are discussed. It should be noted that these findings are derived from New York City taxi data from the first half of 2016, and their generalizability to other cities, time periods, or traffic scenarios remains to be further validated. Full article

As the electrification of transportation systems accelerates, incentivizing electric vehicle (EV) participation in vehicle-to-grid (V2G) operations is becoming increasingly crucial. This paper introduces a dynamic EV charging and discharging strategy that incorporates integrated energy station (IES) congestion and electricity purchase and sale scenarios. The proposed strategy seeks to facilitate orderly EV charging and discharging within a real-time simulation framework that integrates the transportation network (TN), IES, and the external grid (EG). First, we develop a real-time collaborative simulation framework that combines microscopic traffic flow (MTL) and IES–grid energy interaction models to account for mutual feedback among these components. Second, we propose an EV IES selection strategy aimed at maximizing discharge revenue, which takes into account various factors, including driving distance, time costs, battery degradation, discharge benefits, and government subsidies. Finally, we design a dynamic discharge pricing model based on real-time vehicle arrival patterns at the IES and the status of electricity purchases and sales. Simulation results show that the EV IES selection strategy, optimized for discharge revenue, reduces average user waiting time by 5.36%, decreases network time loss by 3.86%, and increases EV discharge revenue by 6.79%. Furthermore, the introduction of dynamic pricing leads to additional reductions in waiting time and network time loss by 3.46% and 4.80%, respectively. The proposed mechanism and pricing strategy effectively mitigate traffic congestion, enhance user discharge revenue, and provide flexible scheduling options for IES operations. Full article

Autonomous battery electric vehicles (BEVs) have the potential to reshape urban mobility systems, yet their sustainability impacts remain underexplored in Gulf-region cities where traffic dynamics, land-use structures, and environmental conditions differ substantially from Western contexts. This study introduces a Saudi-specific assessment framework that integrates monetised externalities with empirically calibrated traffic dynamics to evaluate how automation influences safety, congestion, land use, emissions, and noise. To the best of our knowledge, this is the first Riyadh-calibrated monetised external-cost evaluation of autonomous BEVs that couples externality valuation with simulation-validated time-varying traffic dynamics (SAR per vkm and SAR per pkm), enabling realistic peak-period sustainability assessment. The framework’s key contribution is linking external-cost modelling with spatiotemporal traffic behaviour derived from Riyadh’s 2023 mobility patterns, providing a more realistic basis for sustainability evaluation. Using national datasets from transport, energy, and statistical authorities, the model estimates substantial reductions in external costs when transitioning from human-driven to autonomous BEVs, driven primarily by lower crash exposure and smoother traffic flow. To validate these findings under real operating conditions, a dynamic analysis incorporating hourly and seasonal traffic variability was developed, revealing that automation delivers its strongest improvements during peak-demand periods where congestion externalities are highest. The integrated results demonstrate the relevance of autonomous BEVs for dense rapidly growing Saudi cities and provide actionable insights for future mobility planning. The study highlights the policy importance of coordinated transport, land-use, and energy strategies to ensure that automation contributes meaningfully to national sustainability goals under Vision 2030. Full article

Heavy-duty trucks, especially those used in urban areas, are responsible for a disproportionally large share of the external costs of the transportation sector. Policy instruments that target these trucks could thus be efficient measures to reduce negative impact from the traffic sector. This paper presents how heavy-duty trucks operated in Sweden’s two largest cities, Gothenburg and Stockholm, in the year 2022 and how zero-emission zones or environmental zones with an entrance fee targeting heavy-duty trucks could affect not only urban traffic but all trucks on Swedish roads. The analysis is based on GPS data from 69,000 trucks in operation in Sweden in the year 2022. Of these trucks, 4% visited the two cities for more than 100 days (frequent visitors) and 40% visited at least once during the year. Although zero-emission zones would have the strongest impact, environmental zones with an entrance fee could be a more flexible way to create a strong enough incentive for frequent visitors to electrify. An entrance fee of 100 Euro per day in combination with current investment subsidies would make electric trucks competitive for frequent visitors and still allow for others to continue using conventional trucks during a transition period. Full article

Multi-task learning (MTL) is essential for satellite internet systems requiring simultaneous optimization of beam management, interference mitigation, resource allocation, and traffic prediction. However, existing evaluation methods rely predominantly on external performance metrics, neglecting internal dynamics governing task interactions. We propose TDS-Mamba (Task-Aware Decoupled State-Space Model), integrating selective state-space models with task-specific modulation for satellite networks. Our contributions include: (1) Task-Aware Decoupled S6 (TA-DS6) with hypernetwork-generated task-conditioned projection matrices; (2) Shared–Private State Decomposition disentangling cross-task representations from task-specific features; (3) Value-at-Risk (VaR) Gating for risk-sensitive optimization under varying orbital conditions; and (4) an internal diagnostic framework with Task-Specific Entropy and Interference Coefficient metrics. Experiments on LEO satellite constellation benchmarks show consistent improvements over the selected baselines and provide enhanced interpretability of multi-task dynamics via internal diagnostics. Full article

This study is based on the practical context of the livestream e-commerce industry’s shift from “traffic competition” to “quality competition”. Addressing the limitations of existing research that predominantly focuses on streamers’ external traits while overlooking intrinsic qualities and frequently employs linear models that oversimplify the decision-making processes of consumer purchasing behavior (CPB), a theoretical framework grounded in the Elaboration Likelihood Model (ELM) is developed to explain how streamer traits drive consumer trust and identification through dual pathways. This study adopted a mixed-method approach combining structural equation modeling (SEM) and artificial neural networks (ANNs). By analyzing 408 valid questionnaires, it systematically investigated the driving mechanisms through which streamer traits affected consumers’ trust and identification. The study found that streamers’ integrity significantly enhanced perceived trust and perceived identification via the central route. While awareness could strengthen identification, it had no significant effect on trust building, revealing the inherent tension between “traffic” and “quality”. ANN analysis further demonstrated that the nonlinear combination of traits more effectively predicts consumer responses than traits. This study provided empirical support for the “quality transformation” of livestream e-commerce from both theoretical and methodological perspectives, offering important implications for platforms to develop a quality assessment system centered on trust and identification and to optimize the streamer cultivation mechanism. Full article

Evaluating urban mobility transitions is essential to determine whether local transport interventions support broader sustainability goals. Cities increasingly implement initiatives to promote public transport, active mobility, and low-carbon transport systems. Still, assessing their impact on city-scale structural change remains challenging. Existing evaluation approaches often rely on project-level monitoring or fragmented indicators, which limits cross-city comparison and the assessment of long-term system transformation. This paper proposes a dual-track methodology to evaluate sustainable urban mobility interventions. The first track uses city-defined key performance indicators to capture local implementation processes, governance dynamics, and perceived outcomes. The second track relies on publicly available open data to assess city-scale changes in mobility indicators, including public transport accessibility, cycling infrastructure provision, and traffic-related air pollution. The methodology is applied to ten European cities using open data and satellite-based environmental indicators. Results indicate that while cities report progress at the project level, external indicators show limited short-term structural change in city-wide mobility systems. These findings highlight the value of open data as an independent evaluation layer that contextualises local results and supports transparent assessment of urban mobility transitions. Full article

Urban traffic networks are highly vulnerable to external disturbances such as heavy rainfall, which can induce capacity degradation, non-periodic congestion, and delayed system recovery. To address the limitations of existing perimeter control strategies that primarily focus on demand-side fluctuations and assume fixed network capacity, this study proposes a distributed coordinated perimeter control framework that explicitly incorporates rainfall-induced capacity degradation into system feedback. The proposed framework adopts a two-layer control structure, in which a main controller regulates global network accumulation near the critical macroscopic fundamental diagram (MFD) state, while sub-controllers dynamically adjust perimeter control rates in response to localized traffic conditions and water accumulation. A case study based on real taxi trajectory data from Wuhan City, combined with SUMO-based microscopic traffic simulation, is conducted to evaluate the proposed approach under heavy rainfall conditions. The results show that the distributed coordinated control framework reduces peak network accumulation by 39.6%, increases average vehicle speed by 35.28%, and significantly accelerates post-disturbance recovery. These findings indicate that integrating environmental disturbances into distributed perimeter control can effectively enhance the stability and resilience of urban traffic systems under adverse weather conditions. Full article

Urban business-to-business distribution in Casablanca relies heavily on light commercial vehicles (LCVs) operating in a constrained street environment where loading/unloading access, intersection capacity, and recurring bottlenecks jointly shape performance and environmental impacts. However, high-resolution freight origin–destination (OD) observations and junction calibration data are limited, which complicates direct estimations of congestion and externalities attributable to commercial activity. This study develops a reproducible, large-scale modeling workflow that couples tour-based freight demand generation in order units with simulation-based traffic assignment (SBA) on a metropolitan network and translates network performance into emissions and monetary losses. Warehouses are modeled as primary producers and commercial activity zones as attractors via sector-tagged production and attraction functions; the resulting order distribution is converted to OD vehicle trips using the tour-based trip generation procedure with the mean targets-per-tour fixed to one to ensure numerical stability, yielding a direct-shipment approximation appropriate for stress–response analysis. Junction impedance is represented through turn-type volume–delay relationships and node-level impedance procedures, and congestion is evaluated using vehicle kilometers traveled/vehicle hours traveled (VKT/VHT)-based indicators, delay-intensity measures, and link/node bottleneck rankings. Across demand-scaling scenarios, VKT increases from 302,159 to 1,017,686 veh·km/day, while network delay rises nonlinearly from 392.5 to 2738.4 veh·h/day, indicating saturation-driven amplification of time losses. The Handbook of Emission Factors for Road Transport (HBEFA)-compatible emission estimates scale with activity: total carbon dioxide (CO₂) increases from 154.1 to 519.5 t/day, and nitrogen oxides (NOx) and particulate matter (PM_2.5) totals rise proportionally under fixed fleet assumptions. Monetizing delay with a purchasing-power-adjusted value-of-time range yields a congestion cost per trip that increases from approximately 0.20 to 0.41 Moroccan dirham, MAD/trip (at 60 MAD/veh·h), consistent with rising delay intensity. Bottleneck extraction shows welfare losses to be structurally concentrated on a small persistent corridor set, led by ‘Boulevard de la Résistance’, with recurrent hotspots including ‘Rue d’Arcachon’ and ‘Rue d’Ifni’. The framework supports policy-relevant reporting of congestion, emissions, and welfare impacts under data scarcity, with explicit sensitivity bounds. Full article

Digital twin systems are becoming an important tool in intelligent transportation management, as they provide simulation-based environments for monitoring, analyzing, and predicting traffic behavior. However, the predictive performance of traffic digital twins is often limited by the quality and temporal consistency of sensor-level data generated from microscopic simulations. Most current calibration methods focus mainly on matching macroscopic traffic indicators, such as vehicle count and speed, without explicitly addressing the requirements of multi-horizon forecasting. This creates a gap between achieving realistic simulations and building reliable predictive models. This research proposes a forecasting-aware digital traffic twin framework that integrates microscopic SUMO simulation, controlled sensor-level observation modeling through geometric misalignment and noise injection, behavioral calibration, and deep temporal forecasting within a unified end-to-end structure. Unlike traditional calibration approaches, the proposed Genetic Algorithm (GA) reformulates calibration as a multi-step predictive optimization task. Simulation parameters are optimized by minimizing forecasting error produced by a lightweight proxy sequence model embedded within the calibration loop. In this way, calibration moves beyond simple statistical matching and instead emphasizes temporal learnability and forecasting stability, enabling the digital twin to generate traffic patterns more suitable for long-term prediction. Based on the calibrated traffic time series, both convolutional and recurrent deep learning models are evaluated under single-step and multi-step forecasting scenarios. To further examine generalizability, external validation is performed using the real-world PEMS-BAY dataset. The experimental findings demonstrate that forecasting-aware calibration reduces macroscopic traffic signal errors by around 50% for vehicle count and around 40% for average speed, improves temporal stability, and significantly enhances forecasting accuracy across both short-term and long-term horizons. Full article

This study investigates the Wider Economic Impacts (WEIs) of railway infrastructure in Italy by analysing how station characteristics and surrounding urban contexts are capitalized into residential property values. A nationwide cross-sectional dataset covering 985 railway stations is used to estimate a Hedonic Price Model (HPM) combining observed variables and latent constructs derived from Confirmatory Factor Analysis (CFA). Results show that railway centrality, long-distance service provision, and multimodal integration are positively associated with housing prices. In particular, shared mobility services generate significant value uplift effects, especially around Local and Local Plus stations. Conversely, car-oriented accessibility is negatively associated with residential values, reflecting the capitalization of traffic-related externalities. Socioeconomic and tourism-related characteristics further contribute to heterogeneous capitalization patterns across the national territory. The findings provide systemic empirical evidence to support investment prioritization, multimodal integration, and value uplift of station areas within the Italian railway network. Full article

With the advancement of autonomous driving technologies, automated container terminals (ACTs) are transitioning toward mixed-traffic operations involving unmanned internal container trucks (ITs) and manned external container trucks (ETs). However, the complex interactions in mixed traffic present frequent conflicts and challenges for system performance evaluation. To address this, this study focuses on mixed-traffic ACTs and establishes a comprehensive performance evaluation model based on multi-agent simulation. First, three lane configurations are defined, including segregated unidirectional, mixed unidirectional, and mixed bidirectional. Then, a path interaction point (PIP) modeling method is proposed under traffic conflicts to characterize lane-level motion behaviors and conflict relationships represented by a conflict matrix, based on which an IT access strategy is developed. Second, a multi-level performance evaluation model for ITs is established, and a 3D multi-agent simulation model is developed to support performance evaluation under different lane configurations. Finally, the three lane configurations are evaluated through simulation at both individual and system levels under a baseline scenario, followed by a sensitivity analysis across varying IT–ET task ratios to assess adaptability. Simulation results indicate that the mixed bidirectional lane configuration achieves superior overall performance and robustness. Full article