In developing countries, urban tourism systems increasingly rely on large-capacity buses, whose stopping, boarding, and manoeuvring operations often create localised congestion and traffic bottlenecks. However, contemporary routing algorithms can create a paradox whereby solutions that are optimal for individual tourist buses may collectively induce emergent network-wide congestion when deployed simultaneously across the entire fleet. This paper introduces a cross-scale evaluation framework that explicitly links vehicle-centric routing decisions with macroscopic transport network performance. A microscopic traffic simulation of Nha Trang, Vietnam, developed in SUMO and calibrated using empirical intersection signal timings and stochastic bus dwell times, serves as the experimental testbed. Three routing paradigms are systematically benchmarked: a fixed-route baseline, a metaheuristic approach based on a Genetic Algorithm (GA), and an AI-driven method using Double Deep Q-Network (Double DQN). Stress testing with near-critical background traffic, evaluated across multiple random seeds to account for stochastic demand, reveals that uncoordinated static routing results in a severe 71% loss in network throughput. While static metaheuristic optimisation reduces this loss to 37%, it remains vulnerable to dynamic bottlenecks. Conversely, the Double DQN agent continuously redistributes the spatial load, suppressing average vehicle time loss by over 60% and limiting throughput degradation to a statistically significant 24% (p < 0.01). These findings demonstrate that optimising routes independently for individual buses is insufficient; real-time adaptive routing is a potential mechanism to help preserving network-level capacity, sustain Macroscopic Fundamental Diagram (MFD) flows, and mitigate localised CO2 emission hotspots.
Traffic congestion often arises from intrinsic traffic instabilities that generate stop-and-go waves and reduce traffic efficiency. While nonlocal perception and connected vehicle technologies have each been shown to improve traffic stability, their combined macroscopic effects in heterogeneous traffic streams remain incompletely understood. This paper develops a nonlocal cooperative multiclass macroscopic traffic model within a second-order continuum framework. The model integrates heterogeneous vehicle classes, nonlocal density perception, and vehicle-to-vehicle (V2V) communication. Cooperative interaction is represented through a density-normalised communicated velocity that distinguishes instantaneous sensing of vehicle presence from delayed reception of velocity information. Communication delays are incorporated into the velocity alignment term, enabling their influence on stability to be examined analytically. A linear stability analysis yields explicit conditions describing the combined effects of nonlocal perception, cooperative communication, and communication delay on traffic stability. Nonlocal perception weakens the amplification of density disturbances, while cooperative communication further suppresses traffic oscillations through velocity alignment and spatial averaging. Although communication delays reduce effective damping, stability is maintained for moderate delay magnitudes. Numerical simulations support the analytical results and illustrate the distinct roles of anticipation and cooperation. Nonlocal perception mitigates oscillations but does not fully suppress stop-and-go waves near capacity, whereas cooperative V2V communication provides substantial stabilisation even at moderate penetration rates. Strong cooperative regimes produce the greatest reduction in traffic oscillations, although these benefits gradually diminish as communication delays increase. These findings highlight the complementary roles of nonlocal anticipation and cooperative connectivity in improving stability in mixed-autonomy traffic streams.
This study presents a community-applicable approach for stabilizing expansive soil subgrades using locally available cellulose-based plant residues in Ethiopia. An integrated methodology combining laboratory testing, field monitoring, and full-scale driving experiments was employed to evaluate the performance of ensete residue (EN) and sugarcane bagasse (SB). The results demonstrate that plant-derived fibers improve soil behavior through coupled mechanical and hydraulic mechanisms. Fiber reinforcement enhances shear strength and ductility via interparticle bridging and friction, while the water absorption capacity of cellulose modifies soil–water interactions, reducing shrink–swell behavior. Although the plasticity index remained high, sufficient bearing capacity was achieved at low mixing ratios, and the target CBR value was satisfied using SB alone or in combination with a small amount of lime. Field observations revealed that SB-treated layers alter crack morphology and act as a near-surface moisture barrier, limiting infiltration and stabilizing subsurface moisture conditions. Full-scale driving tests confirmed improved trafficability with reduced rutting and deformation under repeated loading. These findings highlight the potential of locally sourced plant residues as practical, scalable materials for community-based improvement for rural road infrastructure in expansive soil regions.
Energy efficiency in electric vehicles can be improved through eco-driving optimal control strategies. To enhance driver acceptance, recent work has incorporated driver preference models, leading to naturalistic eco-driving optimisation. However, this framework has primarily been developed at an algorithmic level and has not been evaluated using physically realistic electric vehicle models. Moreover, many existing approaches assume simplified regenerative braking behaviour, without examining how such assumptions affect optimisation performance when applied to different drivetrain configurations. To address this gap, this paper presents the first evaluation of driver-preference-based eco-driving optimisation using a validated, forward-facing electric vehicle simulation model. The study further investigates the impact of drivetrain regenerative braking assumptions. An all-wheel-drive (AWD)-based formulation, which assumes distributed regenerative braking, is applied to a front-wheel-drive (FWD) vehicle and compared with an FWD-specific formulation that explicitly enforces front-axle regenerative limits within the optimisation. The results show that both optimisation formulations reduce energy loss relative to the IDM baseline; however, the FWD-specific formulation achieves consistently greater reductions. The generalised AWD-based formulation produces braking demands that exceed the feasible regenerative capability of the FWD drivetrain, leading to increased reliance on mechanical braking and higher electrical losses. For equivalent car-following performance, the drivetrain-inconsistent regenerative braking assumption results in 3%–11% higher total energy losses in urban driving and 7%–12% in rural conditions, with smaller differences (2%–4.1%) in mixed cycles dominated by high-speed operation.
This paper presents a fully automated workflow for road surface imagery acquisition and zero-shot distress mapping from Google Street View (GSV). In a single run, the system samples a user-defined area of interest, retrieves forward-looking lane images, and queries a vision-language model (VLM) to detect pavement distress types and severities. The workflow outputs raw and labeled images, along with an interactive georeferenced map of pavement condition for each sampled location. We evaluate four GPT-based VLMs against three pavement engineering experts on 364 GSV images using standard reliability and accuracy metrics. The analysis quantifies VLM repeatability and agreement with a human consensus reference within this proof-of-concept dataset. Results show almost deterministic behavior, strong performance for the coarse distress-versus-no-distress decision, and high within-one-level severity agreement, although exact severity and full multi-class agreement remain below the human consensus. These findings support the feasibility of an automated, GSV-driven workflow for network-level pavement screening without task-specific training, while broader validation is still needed.
