Search Results (191)

Traditional routing algorithms optimizing for distance or travel time are inadequate for electric vehicles (EVs), which require energy-aware planning considering battery constraints and charging infrastructure. This work presents an energy-optimal routing system for EVs that integrates personalized consumption modeling with real-time environmental data. The system employs a Long Short-Term Memory (LSTM) neural network to predict State-of-Charge (SoC) consumption from real-world driving data, learning directly from spatiotemporal features including velocity, temperature, road inclination, and traveled distance. Unlike physics-based models requiring difficult-to-obtain parameters, this approach captures nonlinear dependencies and temporal patterns in energy consumption. The routing framework integrates static map data, dynamic traffic conditions, weather information, and charging station locations into a weighted graph representation. Edge costs reflect predicted SoC drops, while node penalties account for traffic congestion and charging opportunities. An enhanced A* algorithm finds optimal routes minimizing energy consumption. Experimental validation on a Nissan Leaf shows that the proposed end-to-end SoC estimator significantly outperforms traditional approaches. The model achieves an RMSE of 36.83 and an $R^2$ of 0.9374, corresponding to a 59.91% reduction in error compared to physics-based formulas. Real-world testing on various routes further confirms its accuracy, with a Mean Absolute Error in the total route SoC estimation of 2%, improving upon the 3.5% observed for commercial solutions. Full article

Amid the increasing demand to reduce carbon emissions, replacing diesel buses with electric buses has become a key development direction in public transportation. However, a significant challenge in this transition lies in developing efficient charging strategies and accurately determining the required fleet size, as existing research often fails to adequately account for the impact of real-time traffic congestion on energy consumption. To address this gap, in this study, an optimized charging strategy is proposed, and the necessary fleet size is calculated using a deep reinforcement learning (DRL) approach, which integrates actual route characteristics and dynamic traffic congestion patterns into an electric bus operation model. Modeling is conducted based on Beijing Bus Route 400 to ensure the practical applicability of the proposed method. The results demonstrate that the proposed DRL method ensures operational completion while minimizing charging time, with the algorithm showing rapid and stable convergence. In the multi-route scenarios investigated in this study, the DRL-based charging strategy requires 40% more electric buses, with this figure decreasing to 24% when fast-charging technology is adopted. This study provides bus companies with valuable electric bus procurement and route operation references. Full article

Short-term traffic prediction is vital for intelligent transportation systems, enabling adaptive congestion control, real-time signal management, and dynamic route planning for autonomous vehicles (AVs). This study introduces a custom Transformer-based deep learning framework for joint forecasting of traffic flow and vehicle speed, leveraging handcrafted positional encoding and stacked multi-head attention layers to model multivariate traffic patterns. Evaluated against baselines including Long Short-Term Memory (LSTM), Support Vector Machine (SVM), Random Tree, and Random Forest on the Next-Generation Simulation (NGSIM) dataset, the model achieves 94.2% accuracy (Root Mean Squared Error (RMSE) 0.16) for flow and 92.1% accuracy for speed, outperforming traditional and deep learning approaches. A hybrid evaluation metric, integrating RMSE and threshold-based accuracy tailored to AV operational needs, enhances its practical relevance. With its parallel processing capability, this framework offers a scalable, real-time solution, advancing AV ecosystems and smart mobility infrastructure. Full article

Traffic flow prediction is a fundamental component of intelligent transportation systems and plays a critical role in traffic management and autonomous driving. However, accurately modeling highway traffic remains challenging due to dynamic congestion propagation, lane-level heterogeneity, and non-recurrent traffic events. To address these challenges, this paper proposes an improved attention-mechanism spatio-temporal graph convolutional network, termed AMSGCN, for highway traffic flow prediction. AMSGCN introduces an adaptive adjacency matrix learning mechanism to overcome the limitations of static graphs and capture time-varying spatial correlations and congestion propagation paths. A hierarchical multi-scale spatial attention mechanism is further designed to jointly model local congestion diffusion and long-range bottleneck effects, enabling an adaptive spatial receptive field under congested conditions. To enhance temporal modeling, a gating-based fusion strategy dynamically balances periodic patterns and recent observations, allowing effective prediction under both regular and abnormal traffic scenarios. In addition, direction-aware encoding is incorporated to suppress interference from opposite-direction lanes, which is essential for directional highway traffic systems. Extensive experiments on multiple benchmark datasets, including PeMS and PEMSF, demonstrate the effectiveness and robustness of AMSGCN. In particular, on the I-24 MOTION dataset, AMSGCN achieves an RMSE reduction of 11.0% compared to ASTGCN and 17.4% relative to the strongest STGCN baseline. Ablation studies further confirm that dynamic and multi-scale spatial attention provides the primary performance gains, while temporal gating and direction-aware modeling offer complementary improvements. These results indicate that AMSGCN is a robust and effective solution for highway traffic flow prediction. Full article

Accurate multivariate traffic flow forecasting is critical for intelligent transportation systems yet remains challenging due to the complex interplay of temporal dynamics and spatial interactions. While Transformer-based models have shown promise in capturing long-range temporal dependencies, most existing approaches compress multidimensional observations into flattened sequences—thereby neglecting explicit modeling of cross-dimensional (i.e., spatial or inter-variable) relationships, which are essential for capturing traffic propagation, network-wide congestion, and node-specific behaviors. To address this limitation, we propose TSAformer, a novel Transformer architecture that explicitly preserves and jointly models time and dimension as dual structural axes. TSAformer begins with a multimodal input embedding layer that encodes raw traffic values alongside temporal context (time-of-day and day-of-week) and node-specific positional features, ensuring rich semantic representation. The core of TSAformer is the Two-Stage Attention (TSA) module, which first models intra-dimensional temporal evolution via time-axis self-attention then captures inter-dimensional spatial interactions through a lightweight routing mechanism—avoiding quadratic complexity while enabling all-to-all cross-node communication. Built upon TSA, a hierarchical encoder–decoder (HED) structure further enhances forecasting by modeling traffic patterns across multiple temporal scales, from fine-grained fluctuations to macroscopic trends, and fusing predictions via cross-scale attention. Extensive experiments on three real-world traffic datasets—including urban road networks and highway systems—demonstrate that TSAformer consistently outperforms state-of-the-art baselines across short-term and long-term forecasting horizons. Notably, it achieves top-ranked performance in 36 out of 58 critical evaluation scenarios, including peak-hour and event-driven congestion prediction. By explicitly modeling both temporal and dimensional dependencies without structural compromise, TSAformer provides a scalable, interpretable, and high-performance solution for spatiotemporal traffic forecasting. Full article

Spatiotemporal traffic flow prediction is a fundamental task in intelligent transportation systems and is crucial for promoting efficient and sustainable urban mobility, especially under increasingly complex and rapidly evolving traffic conditions. To overcome the challenges of modeling high-order spatial dependencies and heterogeneous temporal patterns, this study develops a novel Hierarchical Spatiotemporal Graph–Hypergraph Network (HSTGHN). For spatial representation learning, a hypergraph neural module is employed to capture high-order interactions across the road network, while a hypernode mechanism is designed to characterize complex correlations among multiple road segments. Furthermore, an adaptive adjacency matrix is constructed in a data-driven manner and enriched with prior knowledge of bidirectional traffic flows, thereby enhancing the robustness and accuracy of graph structural representations. For temporal modeling, HSTGHN integrates the complementary strengths of Gated Recurrent Units (GRUs) and Transformers: GRUs effectively capture local sequential dependencies, whereas Transformers excel at modeling global dynamic patterns. This joint mechanism enables comprehensive learning of both short-term and long-term temporal dependencies. Extensive experiments on multiple benchmark datasets demonstrate that HSTGHN consistently outperforms state-of-the-art baselines in terms of prediction accuracy and stability, with particularly significant improvements in long-term forecasting and highly dynamic traffic scenarios. These improvements provide more reliable decision support for intelligent transportation systems, contributing to enhanced traffic efficiency, reduced congestion, and ultimately more sustainable urban mobility. Full article

Intelligent Transportation Systems (ITSs) aim to improve mobility and reduce congestion, yet current solutions still struggle with scalability, sensing bottlenecks, and inefficient computational resource usage. These limitations impede the shift towards environmentally responsible mobility. This work introduces ORQCIAM (Orchestrated Reasoning based on Quantum Computing and Intelligence for Advanced Mobility), a modular framework that combines Quantum Computing (QC) and Large Language Models (LLMs) to enable real-time, energy-aware decision-making in ITSs. Unlike conventional ITS or AI-based approaches that focus primarily on traffic performance, ORQCIAM explicitly incorporates sustainability as a design objective, targeting reductions in travel time, fuel or energy consumption, and CO₂ emissions. The framework unifies cognitive, virtual, and federated sensing to enhance data reliability, while a hybrid decision layer dynamically orchestrates QC–LLM interactions to minimize computational overhead. Scenario-based evaluation demonstrates faster incident screening, more efficient routing, and measurable sustainability benefits. Across tested scenarios, ORQCIAM achieved 9–18% reductions in travel time, 6–14% lower estimated CO₂ emissions, and around a 50–75% decrease in quantum-optimization calls by concealing QC activation during non-critical events. These results confirm that dynamic QC–LLM coordination effectively decreases computational overhead while supporting greener and more adaptive mobility patterns. Overall, ORQCIAM illustrates how hybrid QC–LLM architectures can serve as catalysts for efficient, low-carbon, and resilient transportation systems aligned with sustainable smart-city goals. Full article

Transportation management within university campuses presents distinct challenges due to highly fluctuating traffic patterns. King Abdulaziz University (KAU), which attracts over 350,000 trips daily, is experiencing substantial congestion-related issues. This study focuses specifically on King Abdulaziz University Hospital (KAUH), a major trip generator on campus characterized by significant temporal variations in travel demand. The objective of this research is to develop a validated and operational traffic demand model using PTV VISUM 2025. A four-step framework was implemented, where campus gates were defined as trip production sources and 13 parking areas were designated as trip attractions. The morning peak-hour, identified as 7:15 AM to 8:15 AM, was selected for analysis due to the highest observed inflow of vehicles. Traffic surveys were conducted at seven bidirectional stations along key links to support Origin–Destination (O–D) matrix estimation and calibration. Both static and dynamic traffic assignment methods were applied to assess model performance. Model validity was evaluated using the R² statistic, percentage deviations, and the GEH measure of fit. The results demonstrate that both the equilibrium static assignment and the dynamic stochastic assignment achieved strong levels of accuracy, with R² = 0.98 and 86% of links exhibiting GEH values below 5, alongside average GEH scores of 3.2 and 2.7, respectively. This dual-model approach provides a robust analytical foundation for KAU, enabling long-term strategic planning through static assignment outputs and supporting short-term, peak-hour operational management through dynamic assignment results. Full article

Highway interchanges are vulnerable components of transport networks, often prone to congestion and crashes. Traditional monitoring methods like loop detectors or travel time queries often fail to capture the granular spatiotemporal distribution of bottlenecks in detail. To address this gap, this study introduces a new approach to quantify congestion and analyze bottleneck dynamics at Atlanta’s Tom Moreland Interchange, one of the nation’s most congested sites. A percent Traffic Congestion (pTC) metric was developed from the Google Maps Traffic Layer for twelve directional routes and validated against observed travel times obtained independently through the Google Maps Routes API. Traffic imagery collected every ten minutes for four months and 746 crash records were analyzed. Findings reveal distinct spatial patterns and temporal dynamics of congestion, with northbound I-85 and eastbound I-285 most affected during afternoon peaks. A quadratic model provided the best fit between pTC and travel times (R² = 0.85), confirming pTC as a reliable congestion indicator. An LSTM model using pTC time series also accurately predicted mobility trends at the I-285 west to I-85 north bottleneck. Additionally, Seasonal-Trend decomposition using LOESS (STL) identified congestion anomalies, and their association was analyzed with crashes. The proposed methodology offers transportation agencies a cost-effective framework for monitoring, measuring, and understanding congestion in complex interchanges. Full article

This study investigates the impact of high-resolution spatiotemporal profiles of road transport emissions on urban air quality simulations for Thessaloniki, Greece. Dynamic spatiotemporal emission profiles were developed based on real vehicle speed data and implemented in an integrated air quality modeling system to improve the representation of temporal and spatial traffic activity patterns. The new profiles captured the variability of emissions across hours, days, and months, reflecting differences in congestion intensity and seasonal mobility behavior. Zero-out air quality simulations, in which road transport emissions were entirely removed from the model domain, revealed that road transport is a dominant source of urban air pollution, contributing by up to 47 μg/m³ to daily NO₂ and up to 15 μg/m³ to daily PM_2.5 concentrations during winter, while remaining significant in summer. The speed-based spatiotemporal profiles affected NO and NO₂ concentrations by up to +20 μg/m³ and +3.8 μg/m³, respectively, during the rush hours in winter. The use of dynamic spatiotemporal profiles improved model performance with a maximum daily BIAS reduction of –5 μg/m³ for NO and an increase in the index of agreement of up to 0.13 during the warm period, demonstrating a more accurate representation of traffic-related air pollution dynamics. Improvements for PM_2.5 were smaller but consistent across most monitoring sites. Overall, the study demonstrated that incorporating detailed traffic-derived spatiotemporal profiles enhances the accuracy of urban air quality simulations. The proposed approach provides valuable input for municipal action plans, supporting the design of effective traffic management and emission reduction strategies tailored to local conditions. Full article

Smart city initiatives highlight the vital role of Intelligent Transportation Systems (ITS), which remain underexplored with limited AI-driven solutions integration in real-time urban traffic management across African cities. ITS is crucial to enhance urban mobility efficiency and sustainability to address growing mobility challenges in the era of swift African urbanization. This paper proposes an AI-driven predictive model for the Travel Time Index (TTI), a key metric quantifying urban traffic congestion and mobility performance. Using spatio-temporal analysis, neural networks, and advanced machine learning algorithms, the model processes real-time, multimodal traffic data, capturing congestion patterns, TTI fluctuations, and complex urban travel dynamics, focusing on Casablanca, Morocco, as a smart city case study. Five predictive modeling approaches were carefully selected and rigorously evaluated: Multivariate Linear Regression (MLR), Random Forest (RF), Gradient Boosting, Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Their performance was assessed using standard evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). All models achieved high accuracy, with Random Forest ranking highest (MAE = 0.315, R² = 0.985). Beyond prediction, the methodology incorporates feature importance analysis and hyperparameter tuning via GridSearchCV to improve operational performance and practical applicability across evolving traffic ecosystems. Hyperparameter optimization further enhanced Random Forest’s accuracy (MAE = 0.220, R² = 0.988). The findings demonstrate improved travel time estimation and congestion management capabilities, offering a scalable, adaptable framework to guide data-driven mobility strategies in diverse urban settings and provide actionable insights for urban planners, policymakers, and mobility stakeholders. Full article

Driven by government subsidies and advertising revenue, air taxis present an innovative solution to alleviate traffic congestion and are poised for growth. However, at their current stage of development, air taxi companies primarily operate short-distance routes within cities and rarely offer intercity services. Moreover, as a new mode of transportation, air taxis experience low levels of consumer trust at present. This study, grounded in the Hotelling model, examines differentiated decision-making scenarios between two competing air taxi service providers. It systematically analyzes how service expansion (specifically, the introduction of intercity services) and advertising strategies affect pricing, market share, and profits. Furthermore, it explores optimal decision-making patterns under external disturbances, providing theoretical support for service providers formulating operational strategies. We constructed a differentiated decision-making game model to simulate competition between Service Provider 1 (which does not offer intercity services but may advertise) and Service Provider 2 (which advertises but may choose whether to offer intercity services). By comparing game equilibrium outcomes under different decision combinations, we identify threshold conditions for key variables (e.g., additional price for intercity services and the advertising discount coefficient). The model is further expanded to incorporate external disturbance factors, allowing for analysis of how such environments influence the profitability of each decision pattern. Research has revealed that 1. offering intercity services can increase a provider’s optimal price and market share, but only if the “additional price for intercity services exceeds the threshold”; 2. both providers choosing advertising services is the optimal strategy, but if the advertising discount coefficient exceeds a reasonable range, it will intensify vicious competition. Therefore, it must be controlled within the optimal threshold to avoid adverse effects; 3. under external disturbance conditions, service providers prefer models that do not involve intercity services, and the “both parties advertise (NTX)” combination is more optimal. If intercity services are necessary, disturbance risks must be carefully assessed, or flexible cost and operational strategies should be implemented to hedge against negative impacts. Full article

Liquefied natural gas (LNG) container trucks are regarded as clean energy vehicles with the potential to reduce air pollution. However, their CO₂ emissions remain relatively high and are not yet well understood. In this study, the actual CO₂ emissions of LNG container trucks in Shanghai were measured using a portable emissions measurement system (PEMS). This study quantitatively analyzed the relationship between traffic congestion levels and CO₂ emissions on elevated roadways, providing new insights into the impact of urban traffic conditions. In addition, distinct emission patterns were revealed under different uphill, downhill, and level road conditions, highlighting the substantial effects of roadway geometry on vehicle carbon emissions. Based on these findings, engine-related factors were identified as the dominant contributors, explaining 74% of the emission variance, while road slope analysis showed that uphill driving increased emissions by 13.41% compared with flat roads, whereas downhill driving reduced them by 76.22%. Finally, an efficient carbon emission prediction model for LNG container trucks was developed using machine learning methods. This study enriches the understanding of carbon emissions from LNG container trucks and provides theoretical support for their future applications in sustainable freight transportation. Full article

Nowadays, we live in the smart mobility era in which vehicles are equipped with small sensing devices to collect various road information. With such sensors, we are able to provide an overview of what is happening on the road and offer an efficient solution for transport problems such as congestion, accidents, avoiding traffic lights, fuel consumption, etc. Particularly, driver drowsiness is one of the most important problems that transportation systems face and mostly leads to severe accidents, injuries, and deaths. In order to overcome such a problem, a set of sensor devices has been integrated into vehicles to monitor driver and driving behaviors, and then to evaluate the driver’s situation, e.g., drowsy or awake. Unfortunately, most of the proposed drowsiness detection techniques are dedicated to analyzing one behavior type, but not both, which may affect the accuracy rate of the detection. In this paper, we propose an efficient drowsiness detection framework (RDDF) that may analyze one behavior or be adapted to both of them in order to increase the accuracy of drowsiness detection. Mainly, RDDF periodically monitors the driver and driving behaviors, extracts important patterns, and then uses and compares a set of supervised learning models to detect drowsy drivers. After that, RDDF proposes a modified version of the K-nearest neighbors (KNN) model called Jaccard-KNN (JKNN) that increases drowsiness detection accuracy and overcomes several challenges imposed by traditional models. The proposed framework has been preliminarily validated through real sensor data, and we show the effectiveness of our framework in detecting real-time drowsy drivers with an accuracy rate of up to $99\%$. Full article

Long-term traffic flow prediction (LTFP) is crucial for intelligent transportation systems but remains challenging due to complex spatiotemporal dependencies and multi-scale temporal patterns. While recent models like Autoformer have introduced decomposition techniques, they often lack tailored mechanisms for traffic data’s unique characteristics, such as strong periodicity and long-range spatial correlations. To address this gap, we propose STLLformer, a novel spatiotemporal Transformer that establishes a seasonal-dominated, multi-component collaborative forecasting paradigm. Unlike existing approaches that merely combine decomposition with graph networks, STLLformer features: (1) a dedicated encoder–decoder architecture for separate yet synergistic modeling of trend, seasonal, and residual components; (2) a seasonal-driven autocorrelation mechanism that purely captures cyclical patterns by filtering out trend and noise interference; and (3) a low-rank graph convolutional module specifically designed to capture dynamic, long-range spatial dependencies in road networks. Experiments on two real-world traffic datasets (PEMSD8 and HHY) demonstrate that STLLformer outperforms strong baseline methods (including LSTGCN, LSTM, and ARIMA), achieving an average improvement of over 10% in MAE and RMSE (e.g., on PEMSD8 for 6-h prediction, MAE drops from 36.87 to 30.34), with statistical significance (p < 0.01). This work provides a more refined and effective decomposition-fusion solution for traffic forecasting, which holds practical promise for enhancing urban traffic management and alleviating congestion. Full article