Search Results (581)

This paper presents an integrated techno–economic framework for coordinated grid-to-vehicle and vehicle-to-grid (G2V–V2G) operation in unbalanced distribution networks. A hardware-compatible bidirectional charger with nested AC/DC and DC/DC control loops, together with a rule-based energy management system (EMS), enables seamless mode transitions while enforcing state-of-charge (SoC) and network constraints. A probabilistic Monte Carlo study on the IEEE 13-bus feeder shows that uncoordinated G2V charging induces adverse grid impacts such as voltage stress, line-ampacity violations, and transformer overloading, whereas EMS-driven V2G support improves voltage by 2–4%, reduces line loading by 15–25%, and lowers transformer stress by up to 10%. To align these technical benefits with economic incentives, a bi-level Stackelberg model is formulated where the utility updates locational energy prices based on combined voltage, line ampacity, transformer loading stress indices and EVs choose profit-maximizing nodes, modes and power levels. The interaction converges to a Stackelberg equilibrium with a clear win–win situation; the feeder’s average locational energy price falls entirely within the win–win region, yielding positive per-session profits for both the EV (≈$0.80) and the utility (≈$0.48) while reducing feeder stress. These results demonstrate that stress-aware locational pricing, combined with detailed converter-level control provides a technically robust and economically sustainable pathway for large-scale EV integration. Full article

Reliable short-term prediction of bus travel time on signalized urban arterials is essential for improving service reliability and may provide a useful forecasting basis for prediction-informed transit signal priority (TSP) and arterial coordination applications. However, bus operations on urban arterials are highly variable due to stop dwell times, signal delays, and interactions with mixed traffic, leading to nonlinear and nonstationary travel time patterns with strong spatiotemporal dependence. This study proposes a hybrid KNN–ConvLSTM framework for short-term arterial bus travel time prediction using real-world field data. A K-nearest neighbors (KNNs) module is first employed to retrieve historical operation sequences that are most similar to the current corridor state, thereby reducing interference from mismatched traffic regimes and improving robustness. Smart-card (IC card) transaction data are incorporated as demand-related features to represent passenger activity and its impact on dwell time and travel time variability. The selected sequences are then organized into a corridor-ordered spatiotemporal representation and further refined by lightweight temporal enhancement operations, including relevance gating, multi-scale aggregation, adaptive feature fusion, and residual enhancement, before being fed into the convolutional long short-term memory (ConvLSTM) predictor. The proposed approach is evaluated using weekday service-hour data extracted from 30 days of real-world bus operation records collected from a typical urban arterial corridor in Changchun, China, and is compared with several benchmark models, including ARIMA, KNN, LSTM, CNN, ConvLSTM, Transformer, and DCRNN. The results indicate that the proposed KNN–ConvLSTM framework achieves an MAE of 40.1 s, an RMSE of 55.8 s, a SMAPE of 10.7%, and an R² of 0.878, outperforming all benchmark models. Specifically, compared with the Transformer baseline, the proposed framework reduces MAE by 1.5%, RMSE by 5.1%, and SMAPE by 7.0%, while increasing R² by 0.014. Compared with the DCRNN baseline, it reduces MAE by 10.7%, RMSE by 1.9%, and SMAPE by 2.7%, while increasing R² by 0.008. These findings demonstrate that similarity-aware retrieval combined with spatiotemporal deep learning can substantially enhance short-term bus travel time prediction on signalized urban arterials. More accurate short-term forecasts may support prediction-informed transit signal priority and arterial coordination by providing more reliable downstream arrival-time estimates. However, the generalizability of the reported results is still constrained by the relatively short 30-day observation period and the single-corridor case setting, and the operational and environmental effects of downstream applications remain to be validated through dedicated closed-loop control evaluation in future work. Full article

This paper addresses the optimal management of photovoltaic (PV) systems and distribution static synchronous compensators (D-STATCOMs) in modern electrical distribution networks. A mixed-integer nonlinear programming (MINLP) model is formulated which co-optimizes device placement, sizing, and multi-period dispatch to minimize the total annualized system costs while satisfying AC power flow and operational constraints. To solve this challenging problem, a decomposition methodology is proposed, wherein the binary location decisions for the PVs and D-STATCOMs are treated as predefined inputs, upon the basis of site selections commonly reported in the literature. With the integer variables fixed, the problem is reduced to a continuous nonlinear programming (NLP) subproblem for optimal capacity sizing and operational scheduling, which is solved using the interior point optimizer (IPOPT) via the Julia/JuMP environment. The core contribution of this work lies in its comprehensive demonstration of the economic superiority of variable reactive power injection over conventional fixed compensation schemes. Through numerical validation on standard 33- and 69-bus test systems, it is shown that a variable D-STATCOM operation yields substantial and consistent economic gains. Compared to optimized fixed-injection solutions, variable injection provides additional annual savings averaging USD 120,516 (33-bus feeder) and USD 125,620 (69-bus grid), corresponding to a further 3.4% reduction in total costs. These benefits prove robust across different device location sets identified by various metaheuristic algorithms, and they scale effectively to larger network topologies. The results demonstrate that transitioning to variable power injection is not merely an incremental improvement but a fundamental advancement for achieving techno-economic optimality in distribution system planning. The proposed methodology provides utilities with a computationally efficient framework for determining near-optimal PV and D-STATCOM management strategies by first fixing deployment locations based on established planning insights and then rigorously optimizing sizing and dispatch, in order to maximize economic returns while ensuring reliable network operation. Full article

The present study proposes a full-scale experimental methodology for testing and quantifying the trajectory deviations induced in road vehicles. A full-scale articulated bus was employed in this work and tested under real operating conditions. In its foreseen exploitation use, the vehicle will, under certain conditions, be automatically guided (to cross bridges and tunnels and to approach and stop at bus stops). A series of tests was conducted on a bridge under different transverse wind conditions. It is important to note that the deviation measured by the laser system includes both the inherent deviations of the optical guidance system (OGS) and those induced by wind. It was observed that, despite trajectory variability, when measured at high spatial resolution (±1 mm) during the approach phase upstream of the test zone, the optical guidance system corrected deviations from the ideal trajectory within a short time interval and over a short distance. The system’s response shows reasonable agreement with the manufacturer’s results reported in the OGS study. The results also show some degree of dispersion, given multiple sources of uncertainty inherent to full-scale testing under real operating conditions. The findings show that the OGS’s dynamic response is adequate to reduce disturbances to the vehicle’s trajectory caused by crosswind. Full article

Global transition toward sustainable micro-mobility is an essential aspect of Saudi Vision 2030; however, high car dependency remains a significant barrier to public health and safety targets. In this context, this study explores the factors determining the adoption of electric bicycles (e-bikes) in Al-Qunfudhah, Saudi Arabia. The present research used a convenience sampling strategy through an online survey conducted via social media and texting, utilizing a designed questionnaire of 10 sections delivered to 171 participants, alongside a 5-point Likert scale. Additionally, the scientific validation and analysis were conducted utilizing internal consistency, validity and scale reliability via statistical analysis. The findings indicated a significant intention–action disparity; while respondents demonstrate a strong psychological intention to adopt e-bikes within 12 months (an average of 3.51), real household ownership was relatively low at 11.1%. In addition, a significant 71.9% of participants use private vehicles for short-distance travel (<5 km), influenced by an average bus stop distance of 21.22 km. The hierarchy of barriers indicates infrastructure and security as the main barrier, particularly the absence of dedicated bike lanes, and concerns regarding traffic safety. In contrast, a perception of physical fitness, and interpersonal interaction behave as significant facilitators. Public health data reveals an average weekly activity of 109.77 min, significantly lower than worldwide recommendations; however, 66.7% of individuals believe e-bikes may address the difference. The statistical evaluation acknowledged the questionnaire’s robustness, with significant Pearson correlation coefficients (p < 0.01) demonstrating internal consistency validity and Cronbach’s alpha values between 0.71 and 0.88 indicating high scale reliability, demonstrating a scientifically stable framework for assessing the measured behavioral determinants. The research recommends the establishment of shaded, dedicated micro-mobility networks and the enforcement of safety regulations to promote a healthy, multi-modal urban ecosystem. Full article

This paper develops a probabilistic Available Transfer Capability (ATC) framework that quantifies export headroom for renewables across transmission-distribution interfaces under time-varying uncertainty. Static transmission reliability margins can unnecessarily curtail exports. A dynamic transmission reliability margin (TRM) is embedded within ATC using rolling window statistics and adaptive confidence factor scheduling to release capacity in calm periods and tighten margins during volatile transitions. Uncertainty is modeled as net nodal power imbalance variability from load and renewable deviations, together with stochastic thermal limit fluctuations. Correlated multivariate scenarios are generated via Latin Hypercube Sampling with Iman-Conover correlation preservation and propagated through full AC power flow analysis. Validation on the IEEE 39-bus system and New Zealand’s HVDC inter-island corridor recovers 93.31 MW of usable transfer capacity on the IEEE system relative to the pooled Monte Carlo P95 constant-margin baseline, with 78.11 MW attributable to rolling window volatility tracking and 15.20 MW to adaptive confidence factor scheduling, and 59.51 MW (+7.6%) on the New Zealand corridor relative to the corresponding pooled Monte Carlo P95 baseline, with the gain arising primarily from rolling window volatility tracking. Relative to a 95% one-sided reliability target, achieved coverage is 93.9% for IEEE and 91.8% for New Zealand, translating into increased export headroom and reduced curtailment. Full article

Post-quantum cryptography (PQC) artifacts are one to three orders of magnitude larger than their classical counterparts and must be segmented via ISO-TP across a shared CAN-FD bus while coexisting with periodic safety-critical traffic. No prior work has quantitatively mapped the transport-level feasibility of these artifacts under realistic multi-electronic control unit (ECU) contention. This paper presents a validated discrete-event simulator and evaluates 29 parameter sets from nine algorithm families—spanning the KpqC final portfolio, NIST FIPS 203–205 standards, and the draft FIPS 206—across 534 scenarios classified as feasible, borderline, or infeasible. Results show that key encapsulation mechanism (KEM) feasibility is scenario-dependent: domain scale and startup coordination dominate over algorithm choice, with 4-ECU staggered deployments feasible for all Level-1 candidates, while 16-ECU simultaneous startup is universally infeasible. For digital signatures, FN-DSA achieves the best transport feasibility due to its compact signature, while HQC is uniformly infeasible and SLH-DSA is nearly uniformly infeasible, quantifying the CAN-FD bandwidth premium of algorithmic diversity. System-side traffic shaping—staggered startup and reserved bus windows—outperforms algorithm substitution as a mitigation strategy. To the best of our knowledge, these findings constitute the first design-space map of PQC artifact transport on CAN-FD and provide actionable deployment guidelines for post-quantum transition. Full article

Amid the global decarbonization of urban transportation, the large-scale deployment of electric buses faces major challenges, including concentrated charging demand, increased peak electricity demand, and inefficient energy utilization at transit depots. Existing studies usually optimize depot energy system configuration and bus scheduling separately, which often leads to biased system-level decisions. To address this limitation, this study proposes a collaborative optimization framework that integrates cross-line scheduling with the configuration of photovoltaic–storage–charging systems at depots to improve overall resource utilization. Specifically, this study formulates a mixed-integer linear programming (MILP) model to minimize the total daily system cost. The proposed model comprehensively captures multiple factors, including the costs of bus investment, charging infrastructure, photovoltaic deployment, energy storage deployment, and carbon emissions. In this study, Benders decomposition is used as a solution framework to handle the coupling structure of the model. Case studies show that, compared with conventional operation modes, the combination of cross-line scheduling and fast charging technology produces a significant synergistic effect. This combination reduces the required fleet size from 17 to 14 buses and substantially lowers investment in depot infrastructure, thereby minimizing the total system cost. Sensitivity analysis further shows that the deployment scale of photovoltaic systems has a clear threshold effect on electricity costs, whereas the core economic value of energy storage systems depends on peak shaving and arbitrage under time-of-use electricity pricing. Overall, this study demonstrates the critical role of integrated planning in improving the economic efficiency and operational feasibility of electric bus systems. It provides important theoretical support and practical guidance for depot design and resource scheduling in low-carbon public transportation networks. Full article

Urban transportation is a crucial aspect of modern societal development, with bus route optimization playing a central role in urban transit planning. Well-designed bus routes can enhance the efficiency and attractiveness of public transportation, alleviate traffic congestion and pollution, and ultimately contribute to the overall growth of a city. This study investigates the selection of bus stop locations and route optimization from three perspectives: population density, facility distribution, and route length. The main methodological contribution lies not in the Pareto filtering itself, but in the development of a unified pipeline. This pipeline first generates and prunes candidate stops by applying road-network and intersection-safety constraints. It then constructs feasible routes using a constraint-driven heuristic that enforces stop spacing, ensures monotonic progress away from the origin and toward the destination, and maintains route smoothness. Finally, it integrates population-grid and POI indicators into a tri-objective evaluation framework prior to non-dominated sorting. The proposed method for bus stop location and route optimization is universally applicable to urban bus routes and can be validated through case studies in different cities. An empirical analysis is conducted using Route 119 in Kunming City, Yunnan Province, as a case study. Compared with the original bus route, the optimized route demonstrates improvements of 18.26% in route distance, 15.79% in Points of Interest (POI) accessibility, and 10.53% in population coverage. Full article

Achieving the EU 2030 target of a 30% CO₂ reduction requires transitioning intercity buses to CNG- or fuel-cell-driven vehicles, and urban buses to electric vehicles. The increasing mass of roof-mounted energy systems, such as battery packs, creates additional loads on the body frame. This study investigates the integration of a welded handrail system into the bus body frame as an additional load-bearing element. A combined approach based on dynamic modeling and finite element analysis was applied to evaluate the structural body response under the UNECE R100 and R110 regulations. The results demonstrate that the structural concept significantly improves the stress–strain state of the body frame. Maximum roof displacements under 5g loading decreased by 34% for the gas-powered model and by 50% for the electric model, enhancing passive safety by reducing window-rack intrusion. Maximum stress decreased by 20%, shifting the stress state below the ultimate strength of S235 steel and preventing rupture. Uniform strength under vertical loading increased significantly (by 58%) due to a more favorable stress distribution within the structure. Overall, the results indicate that integrating a welded handrail truss into the bus body frame can effectively improve structural stiffness and redistribute loads within the frame. Full article

Public bus systems are vital to achieving sustainable urban mobility in developing countries; yet, the quality of bus stops, a critical interface between users and transit services, remains widely overlooked. This study evaluates bus stop quality in Sulaymaniyah, Iraq, from bus users’ perspectives by integrating importance–performance analysis (IPA) and the customer satisfaction index (CSI) with level of conformity analysis (CR) using extensive, real-world survey data. The objective was to identify priority areas to help improve the quality of public bus stop provision in the city and ensure the most efficient allocation of resources by focusing on the quality attributes that matter most to bus users. The results highlight six critical service quality attributes that require immediate improvement due to their high importance to users and low service quality performance: (i) safety barriers to prevent traffic accidents while waiting at bus stops; (ii) accessibility of bus stops for elderly and disabled users; (iii) availability of signage and timetables/maps; (iv) overall bus stop quality; (v) narrow bus stop platforms; and (vi) waiting time at bus stops. Addressing these gaps is essential to enhance user satisfaction and ensure that users have a safer, more inclusive, and reliable PT experience. This study offers evidence-based recommendations to enhance bus stop design and service quality, thus contributing to improved user satisfaction and increased ridership. More broadly, the results can be applied to other rapidly urbanizing developing cities seeking to provide equitable, safe, and user-centered bus transit systems. Full article

While bus transit plays a critical role in promoting urban transport sustainable development, the phenomenon of bus bunching has brought severe challenges. To alleviate bus bunching, speed control strategies have been widely used to improve the stability of bus headway distribution. However, existing research mainly focuses on developing optimized models with more flexible speed adjustments; a critical yet often ignored fundamental assumption behind these models is that all bus drivers can strictly adhere to the speed instructions issued by the bus dispatch center. To further explore how the compliance of bus drivers affects the implementation of speed adjustment instructions, this study designs a driving simulation experiment under different driving conditions. Modeled after a real bus line in Changsha, China, the designed simulator study incorporates three external variables, weather conditions, road conditions and command types, with behavioral data from 48 professional drivers analyzed via linear mixed-effects models. The results have shown that road conditions and command types emerged as main factors affecting compliance patterns. Specifically, congestion reduced average speeds by 5.1 km/h, especially affecting female drivers who showed 15.9% Command Compliance Index (it has been designed to quantify execution efficiency and will be referred to as CCI hereafter) reduction versus 10.6% for males. Compared to high-speed instructions, the execution efficiency of low-speed instructions increased by 12.3%, with drivers exceeding target speeds during 45.69% of sections to balance speed profiles. It is notable that the fog density had a minimal impact on efficiency, with only about 2% difference in efficiency. Despite standardized operational norms minimizing individual behavioral heterogeneity, significant group-level demographic variations persisted. Male drivers consistently maintained higher compliance with speed adjustment commands across all driving conditions; drivers under 40 and over 50 had a 3.3% higher CCI than middle-aged drivers; and prior bus bunching exposure increased compliance by 3.3%. High-CCI bus drivers strategically balanced headway distribution through controlled overspeeding. These findings provide empirical foundations for optimizing speed control strategies based on road sections. This study explores ways to enhance the attractiveness of public transit and promote sustainable development. Full article

Prior studies have typically analyzed the injury severity of bus or taxi passengers at the crash level or single-vehicle level, neglecting vehicle-level interdependence between them. To address the gap, this research sets out to analyze the factors contributing to the vehicle-level injury severities of transit bus–taxi crashes, with consideration of their interdependence and heterogeneities. The random-parameters bivariate probit model, which can capture both unobserved heterogeneity and within-crash correlation between bus and taxi injury outcomes, was advocated for the joint analysis. In the model, the factors related to the two vehicles and their drivers, together with other factors (e.g., roadway, environment, and crash configuration), were used as the explanatory variables. A total of 3404 two-vehicle bus–taxi crash records in Hong Kong, China, from 2009 to 2019 were used for model estimation. The results indicate that taxi driver age, taxi age, crash location, and collision manner resulted in heterogeneous effects on bus injury severity, and the time of day yielded a heterogeneous effect on taxi injury severity. In addition, bus driver error and street light resulted in fixed yet moderate (less than 6%) effects on bus injury severity, while taxi driver gender, speed limit, rainfall, and collision manner resulted in fixed effects on taxi injury severity, where female drivers and front collisions significantly increased the likelihood of fatality and severe injury with their marginal effects more than 20%. Based on the findings, tailored strategies pertaining to safety education, law enforcement, vehicle safety devices, and traffic management and control were proposed to mitigate crash outcomes involving public buses and taxis. Full article

Quantitative evaluation of public transit networks (PTNs) with complex-network models informs route optimization and operational adjustments. Prior studies emphasize large cities and pay limited attention to small-sized urban systems. This study examines the bus network of Cangzhou City, Hebei Province, China, to broaden the empirical scope and characterize PTNs in smaller cities. The dataset for this study comprises route and stop records, passenger boarding logs, and bus GPS traces. We develop a general workflow for bus data cleaning and completion. To characterize the dynamic bus network and compare it with the static network, we construct a static network and Directed Weighted Dynamic Network I (DWDN I) using the L-space method, and we construct Directed Weighted Dynamic Network II (DWDN II) using the P-space method. We calculated network metrics including degree, weighted degree, clustering coefficient, path length, network diameter, network efficiency, and small-world coefficient. The principal results show that: (1) at the macroscopic level, the dynamic PTN tracks passenger demand, as the average degree, weighted average degree, and clustering coefficient fluctuate in concert with passenger flows; (2) key stations concentrate in the urban core, and stations with high weighted degree display pronounced spatial autocorrelation; (3) the exponential form of the weighted-degree distribution indicates that the examined bus network is not scale-free, while the dynamic network’s small-world coefficient exceeds that of the static network across time periods, reflecting stronger small-world characteristics. This study integrates network and spatial attributes of the PTN to offer an exploratory case for investigating public transit networks in third-tier cities. The findings can inform comparable studies and offer practical guidance for bus operators. Full article

Distributed Energy Resource (DER)-integrated power grids are vulnerable to cascading effects under concurrent cyber–physical attacks, where even minor disruptions in system states accumulate and amplify over time, leading to significant system failures. Traditional static risk assessment methods are insufficient for modeling these time-varying, dynamic scenarios, particularly in the context of concurrent attacks. This paper presents a dynamic risk assessment framework leveraging time-synchronized co-simulation, which integrates power system and communication network simulations within a unified time framework. Cyber-attack actions in the communication layer are mapped to corresponding physical disturbances in the distribution network, including voltage, frequency, and power variations. Using the resulting system state evolution trajectories, a Markov Decision Process (MDP)-based state transition tree captures the progression of system risk under concurrent attacks. This framework accounts for cumulative risk across different attack paths and identifies critical nodes and high-risk propagation paths within the network. By incorporating a concurrent event detector into the MDP model, the method quantifies evolving risk dynamics, overcoming the limitations of traditional static methods. Case studies on the IEEE 13-node test feeder and IEEE 14-bus system demonstrate that concurrent attacks result in a security risk metric 2.3 times higher than single-point attacks, validating the effectiveness of the proposed approach in identifying vulnerable nodes whose compromise could lead to cascading failures, supporting the risk-aware prioritization of defensive resources. Full article