Search Results (22)

Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments. Full article

Accurate identification of traffic bottlenecks is a fundamental task in the operational analysis of urban expressways. Traditional bottleneck detection methods rely heavily on fixed-location sensors, which suffer from limited spatial coverage and high infrastructure costs. This study proposes a comprehensive Floating Car Data (FCD)-based framework for detecting, characterizing, and ranking recurrent bottlenecks on urban expressways using segment-level speed data alone. The methodology integrates normalized speed fields, a spatially aware moving window algorithm with adaptive downstream verification, and temporal persistence criteria to distinguish active bottlenecks from transient congestion events. To prioritize detected bottlenecks by their operational significance, a Speed-only Bottleneck Severity Index (S-BSI) is introduced. The S-BSI is a 0–100, five-component severity index that ranks active bottlenecks using segment-level speed data only, without requiring flow or density measurements; its components capture upstream spatial extent, temporal persistence, speed deficit magnitude, queue length, and queue growth rate. This research presents a structured methodology for identifying, characterizing, and ranking bottlenecks on urban expressways using Floating Car Data. Detection parameters are selected through a systematic grid search over 128 combinations. The framework is validated using one year of weekday commercial FCD (248 days) obtained from an urban expressway corridor. Empirical results demonstrate that the proposed algorithm reliably identifies spatially concentrated and temporally recurrent bottlenecks; in total, 32 distinct critical bottleneck locations (S-BSI ≥ 35) are detected, of which 31 (96.9%) coincide with an identifiable physical or operational congestion-generating feature of the corridor. By providing a scalable, cost-effective, and transferable detection-and-severity methodology, this research contributes to the growing body of literature on probe vehicle data applications for corridors where fixed-sensor infrastructure is limited or absent. Full article

With the rapid growth of distributed Internet of Things (IoT) services and edge-intelligence applications, conventional cloud computing is increasingly limited in latency-sensitive scenarios. Mobile Edge Computing (MEC) reduces latency by moving computation closer to end devices, while Unmanned Aerial Vehicles (UAVs) further extend MEC services to remote, emergency, or congested areas through flexible aerial deployment. However, UAV-enabled MEC still faces coupled challenges caused by heterogeneous tasks, limited resources, device energy constraints, time-varying channels, and UAV mobility. To address these challenges, this paper develops a Lyapunov-based joint optimization framework for UAV-enabled MEC systems. A dual-queue model is established to characterize local task uploading and UAV-MEC task execution, and a long-term stochastic energy minimization problem is formulated under queue-stability, resource-capacity, and energy constraints. By applying the drift-plus-penalty principle, the problem is transformed into online per-slot control decisions that jointly coordinate MEC scheduling, uplink power control, and UAV flight decisions. Structure-matched solutions are then developed, including a Lyapunov-drift-based MEC scheduling scheme, a queue-weighted closed-form water-filling power-control policy, and a gradient-based UAV flight controller with exponential smoothing and a fly-or-hover gate. A power–position alternating optimization algorithm is further introduced to handle the coupling between transmit power and UAV position. The simulation results demonstrate that the proposed framework maintains queue stability while reducing system energy consumption under heterogeneous and bursty workloads. It also achieves a balanced tradeoff between delay, energy consumption, and UAV flight activity, supporting energy-efficient and delay-aware UAV-MEC operation. Full article

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a periodic $M/M\left(t\right)/1$ queue whose service rate follows from a signal-to-noise-ratio (SNR)-to-rate map and construct an edge-resident controller that exploits this periodic structure for real-time quality-of-service (QoS) control. From a harmonic-balance (Fourier–Galerkin) solution of the periodic regime, the controller derives backlog and tail-probability indicators and uses them to drive admission, redundancy and handover decisions on the device. The method rests on a stability criterion and a quantitative error bound for the spectral truncation, under stated regularity and stability conditions, and is validated against Monte Carlo simulation along a ∼650 km geo-anchored corridor: on the periodic backbone, the solver matches simulation to within about $1.6\%$, and a coefficient-driven admission rule lowers the 99th-percentile delay by about $28\%$ relative to a reactive baseline at high load. On the full map-derived profile with aperiodic coverage gaps, the proposed proactive controller—spectral backbone admission combined with a radio-map look-ahead—attains the lowest mean and tail delay, about $27\%$ and $21\%$ below the reactive baseline and $54\%$ and $42\%$ below uncontrolled DropTail, with buffer overflow cut from $2.2\%$ to $0.1\%$, at a deliberate admitted-load cost (goodput ≈0.84 vs. $0.94$). An operation-count analysis indicates compatibility with sub-$100$ms control deadlines on a Cortex-A55-class system-on-chip. The controller runs on the device itself, without cloud or GPU, and the architecture is realised in a granted patent; end-to-end hardware benchmarking and an extension to non-Poisson traffic are left for future work. Full article

Autonomous vehicles operating at signalized intersections face fundamental challenges arising from queue dynamics, signal-phase transitions, and tightly coupled multi-vehicle interactions. Conventional motion-planning methods, which rely primarily on instantaneous perception, are inherently reactive and struggle to reason about short-term traffic evolution. This paper presents a Transformer-based predictive motion-planning framework that embeds short-term traffic state prediction directly into the structure of the planning problem. A lightweight spatial–temporal Transformer model is designed to forecast traffic occupancy, queue evolution, and interaction patterns using historical trajectories, signal-phase information, and road topology. By converting predicted traffic dynamics into explicit spatial–temporal constraints, a hierarchical motion planner jointly optimizes path geometry and speed profiles through dynamically constructed feasible corridors. The proposed framework is evaluated using a joint SUMO–CARLA simulation platform under realistic traffic conditions derived from real-world datasets, including pNEUMA and CitySim. The experimental results across straight-through, queueing, and turning scenarios show that prediction-aware planning significantly reduces high-risk driving time and intersection travel time while maintaining stable real-time computational performance. Beyond scenario-level improvements, the results indicate that transforming traffic prediction into planning constraints provides a generalizable paradigm for proactive, feasibility-aware autonomous driving at signalized intersections. From a methodological perspective, the proposed framework can be interpreted through the lens of symmetry and asymmetry in intelligent transportation systems: the conventional symmetric decoupling between prediction and planning modules is deliberately broken by embedding predicted traffic states as time-varying, directionally asymmetric constraints, while the permutation symmetry of the multi-head attention mechanism is preserved over lane-segment tokens to provide a structured inductive bias for traffic state forecasting. This symmetry-aware design highlights how controlled symmetry breaking in modeling and optimization can yield safer, more efficient, and more adaptive autonomous driving behaviors in signalized urban environments. Full article

Past Citi Bike rebalancing research has relied on optimization and geospatial models but has treated spatial and temporal structures separately, leaving a gap in understanding stations as long-term behavioral entities. This study exploits the frequent spatiotemporal structure in Citi Bike daily trip data and treats the station’s bike net flow rate (NFR) time-series as the study object. Stations are grouped into regimes using time-series clustering, cluster stability, and the spatial context surrounding each station. Stations were assigned operational roles based on their hourly NFRs and potential contribution to the rebalancing truck. A priority-queue-based heuristic routing (PQHR) algorithm is introduced to design a single-vehicle route that accounts for stations’ regimes, roles, rebalancing urgency, and priority during rush hours. Therefore, this study formally introduces the Station Behavior Regime Model (SBRM) that defines station regimes, rebalancing roles, and routing. The result achieved a >90% reduction in the number of stations with extreme bike accumulation or unavailability and reduced the NFR of affected stations by >30% in busy areas. The spatial context derived from station behavior modes suggests new ways to define neighborhood boundaries. The methodologies provide new avenues for rebalancing operations and routing plans across a broad range of station-centric transportation network studies. Full article

Multi-runway sequencing of unmanned aerial vehicles (UAVs) at temporary disaster relief aerodromes presents a priority-heterogeneous scheduling problem under class-asymmetric wake turbulence constraints. We formulate this as a priority-weighted Markov decision process with a deliberately minimalist reward—per-step class weights for completed landings, with no shaping or hand-crafted safety logic—and extend it with per-UAV operational deadlines (encoding en-route endurance consumption) and per-runway queue capacity constraints that produce a non-trivial action mask. We train a Proximal Policy Optimisation (PPO) agent and benchmark it against six baselines spanning deterministic optimisation (Joint-LA-1), stochastic lookahead (Stochastic-LA), and online tree search (MCTS). Across 100 paired evaluation episodes, PPO matches the operational standard Priority-FCFS within 2.7% (p = 0.124, not significant); Joint-LA-1, the strongest non-learned baseline, outperforms PPO by 3.2% (p = 0.043). Despite near-identical aggregate throughput, PPO autonomously develops a runway specialisation pattern—concentrating 60% of high-priority landings on a single strip while routing 93% of emergency arrivals to the remaining strips—that emerges entirely from the reward signal. Under looser deadlines, the PPO–PFCFS gap narrows to −0.5%, and wake symmetry ablation reveals that PPO outperforms Priority-FCFS by 46.5% when the asymmetric wake structure is removed. These results demonstrate that priority-aware capacity reservation can emerge without embedded domain knowledge, and that simple heuristics are near-optimal under tight operational constraints—a finding with direct implications for autonomous scheduling in disaster relief aviation. Full article

This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission–reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor–critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security–latency trade-off in complex urban vehicular scenarios. Full article

Modern electric vehicle (EV) charging stations must increase their adaptability to dynamic demand patterns driven by users’ heterogeneous charging behaviors, which often result in high spatial–temporal fluctuations. This study develops an agent-based model to accurately evaluate the potential of partial-charging strategy in addressing this issue, taking into account the influence of drivers’ heterogeneous waiting patience. The simulating results indicate that the operational efficiency of the charging station and the level of crowding are most sensitive to changes in vehicle arrival rates and the total number of charging stations. However, individual-level heterogeneity in waiting patience emerges as the core factor preventing limitless queuing increase. Compared with other strategies, the partial-charging strategy improves the turnover of charging stations by reducing per-vehicle charging duration, allowing stations to adapt to varying charging demand conditions without capacity expansions. Setting the charging threshold at 80% state of charge allows the stations to efficiently serve twice the demand level as under full-charging strategy, while a 70% threshold may increase this adaptability by approximately 2.5 times. This study provides structured recommendations for the strategic and adaptive deployment of the partial-charging strategy in alleviating queue-related inefficiencies of charging stations. Full article

The increasing electrification of urban transportation has formulated a tightly coupled energy-mobility nexus. Under extreme disaster events or grid faults, rapidly restoring power supply capacity and re-dispatching shared electric vehicle (EV) fleets are critical for enhancing system resilience. Existing co-optimization methods face the curse of dimensionality when dealing with high-dimensional discrete grid reconfigurations and continuous spatio-temporal EV queuing dynamics. While multi-agent deep reinforcement learning (MADRL) offers real-time responsiveness, it inherently struggles to satisfy strict physical constraints, frequently generating infeasible and unsafe actions. To bridge this gap, this paper proposes a heuristic-guided safe multi-agent reinforcement learning (Safe-MADRL) framework for the resilient dispatch of the energy-mobility nexus. Instead of relying solely on black-box neural networks, the framework structurally embeds physical models and heuristic solvers into the learning loop. A quantum particle swarm optimization (QPSO) algorithm acts as a heuristic action refiner to ensure that grid topology actions strictly comply with non-linear power flow and voltage constraints. Simultaneously, a mixed-integer linear programming (MILP) model coupled with a single-queue multi-server (SQMS) model serves as a safety projection layer. This layer mathematically guarantees EV battery energy continuity and accurately quantifies spatio-temporal queuing delays at charging stations. Case studies on a coupled IEEE 33-node distribution system and a regional transportation network demonstrate that the proposed Safe-MADRL framework achieves zero physical violations during training and significantly outperforms traditional mathematical optimization and pure learning-based methods in computational efficiency, system power loss reduction, and overall operational economy. Full article

Urban traffic signal control heavily impacts vehicle emissions, yet most reinforcement learning models falsely assume flat terrain, ignoring the energy penalties of uphill stop-and-go driving. This omission creates a structural misalignment between generic, delay-focused rewards and the energetic realities of hilly corridors. In this work, we propose a topography-aware deep reinforcement learning framework that mitigates this hidden ecological cost. Our Context-Specific Reward Design procedure selects, normalizes, and calibrates reward terms based on physical conditions and traffic composition. The controller was trained using a microscopic simulation calibrated from video-derived traffic data, featuring a 3.8-degree uphill approach, 14,800 vehicles over 9 h, and a 20% heavy-vehicle fleet. In the uphill setting, the specialized controller reduced total CO₂ emissions to 256.97 million milligrams, corresponding to 8.6% and 4.7% reductions relative to a pressure-based and a standard deep Q-learning controller, respectively. The proposed method also achieved the lowest mean trip duration of 72.09 s and a queue length of 1.31 vehicles. Welch’s t-tests confirmed that these CO₂, duration, and queue improvements were significant. Overall, treating topography as a foundational design variable is crucial for sustainable urban mobility. Full article

Freight-dominant port collection and distribution road networks exhibit strong spatial congestion, early spillback, and heterogeneous vehicle dynamics that challenge conventional traffic signal control strategies. Although Max-Pressure (MP) signal control provides strong decentralized stability properties, its classical queue-based formulation lacks sensitivity to incipient spatial congestion and performs poorly when heavy-duty vehicles (HDVs) dominate traffic composition. This paper proposes a gain-modulated Max-Pressure (Gain-MP) control framework, in which conventional pressure computation is augmented by an occupancy-dependent feedback gain that dynamically adjusts phase priorities according to real-time spatial congestion states and current right-of-way conditions. Without altering the decentralized structure of MP, the proposed method introduces a nonlinear feedback mechanism that enhances system responsiveness to congestion formation while suppressing excessive phase switching. The approach is evaluated using microscopic simulation on a signalized grid network representing port access corridors under time-varying demand and high HDV penetration. Results demonstrate that the dynamic Gain-MP controller performs better than classical queue-based MP, PCU-weighted MP, and fixed-time control. Moreover, constant-demand experiments indicate that the dynamic Gain-MP controller maintains bounded vehicle accumulation over a wider empirical demand range than the benchmark MP-based methods under the tested settings. Full article

Quantum computing offers transformative potential to solve complex optimisation problems in transportation and logistics, particularly those that involve large combinatorial decision spaces such as vehicle routing, traffic control, and supply chain design. Despite theoretical promise and growing empirical interest, its adoption remains limited. This systematic literature review synthesises fifteen peer-reviewed studies published between 2015 and 2025, examining the application of quantum and quantum-inspired methods to transport optimisation. The review identifies five key problem domains (vehicle routing, factory scheduling, network design, traffic operations, and energy management) and categorises the quantum techniques used, including quantum annealing, variational circuits, and digital annealers. Although several studies demonstrate performance gains over classical heuristics, most rely on synthetic datasets, lack statistical robustness, and omit critical operational metrics such as energy consumption and queue latency. Four cross-cutting barriers are identified: hardware limitations, data availability, energy inefficiency, and organisational readiness. The review identifies limited real-world deployment, a lack of standardised benchmarks, and scarce cost–benefit evaluations, highlighting key areas where further empirical work is needed. It concludes with a structured research agenda aimed at bridging the gap between laboratory demonstrations and practical implementation, emphasising the need for pilot trials, open datasets, robust experimental protocols, and interdisciplinary collaboration. Full article

Drive-through establishments are becoming increasingly prominent in urban transport systems; however, their impacts on traffic generation, spatial form, and sustainability remain insufficiently understood. Conventional trip generation manuals often rely on static predictors, such as gross floor area, which can misrepresent demand in high-turnover, convenience-driven contexts and fail to capture operational, behavioral, and environmental effects. This knowledge gap underscores the need for an integrated framework that supports both effective planning and congestion mitigation, particularly in cities experiencing rapid motorization and shifting mobility behaviors. This study investigated the evolving dynamics in trip generation associated with drive-through services and their influence on urban development patterns. A mixed-methods approach was employed, combining a systematic literature review, meta-analysis of queue data, cross-comparison of trip generation rates from international and Asian datasets, and case-based scenario modeling. The results revealed that drive-throughs intensify high-frequency, impulse-driven vehicle trips, thereby causing congestion, reducing pedestrian accessibility, and reinforcing auto-centric land use configurations, while also enhancing consumer convenience and commercial efficiency. This study contributes to the literature by synthesizing inconsistencies in regional datasets; introducing a systems-based framework that integrates structural, behavioral, and environmental determinants with road network topology; and outlining policy applications that align trip generation with zoning, design standards, and sustainable infrastructure planning. Full article

Urban traffic congestion poses significant economic and environmental challenges worldwide. To mitigate these issues, Adaptive Traffic Signal Control (ATSC) has emerged as a promising solution. Recent advancements in deep reinforcement learning (DRL) have further enhanced ATSC’s capabilities. This paper introduces a novel DRL-based ATSC approach named the Sequence Decision Transformer (SDT), employing DRL enhanced with attention mechanisms and leveraging the robust capabilities of sequence decision models, akin to those used in advanced natural language processing, adapted here to tackle the complexities of urban traffic management. Firstly, the ATSC problem is modeled as a Markov Decision Process (MDP), with the observation space, action space, and reward function carefully defined. Subsequently, we propose SDT, specifically tailored to solve the MDP problem. The SDT model uses a transformer-based architecture with an encoder and decoder in an actor–critic structure. The encoder processes observations and outputs, both encoded data for the decoder, and value estimates for parameter updates. The decoder, as the policy network, outputs the agent’s actions. Proximal Policy Optimization (PPO) is used to update the policy network based on historical data, enhancing decision-making in ATSC. This approach significantly reduces training times, effectively manages larger observation spaces, captures dynamic changes in traffic conditions more accurately, and enhances traffic throughput. Finally, the SDT model is trained and evaluated in synthetic scenarios by comparing the number of vehicles, average speed, and queue length against three baselines, including PPO, a DQN tailored for ATSC, and FRAP, a state-of-the-art ATSC algorithm. SDT shows improvements of 26.8%, 150%, and 21.7% over traditional ATSC algorithms, and 18%, 30%, and 15.6% over the FRAP. This research underscores the potential of integrating Large Language Models (LLMs) with DRL for traffic management, offering a promising solution to urban congestion. Full article