<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?cdatein=1year" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>The Influence of Oblique Views and Ground Control Point Distribution on Unmanned Aerial Systems Photogrammetry Precision for Pavement Assessment</title><link>http://pubsindex.trb.org/view/2796353</link><description><![CDATA[Over the last two decades, unmanned aerial systems (UAS) have gained increased traction as a tool for pavement inspection, particularly for evaluating rutting, apparent lift thickness, and surface irregularities. Despite their widespread use, a lack of consensus remains on the optimal number and distribution pattern of ground control points (GCPs) for accurate georeferencing. In this study, six test configurations were created using two types of imagery (nadir and oblique) and varying GCP patterns to assess their influence on the accuracy of digital elevation models over a 50 × 50 m pavement test area. A combination of geostatistical methods, including kriging and inverse distance weighting, along with metrics such as root mean square error, mean absolute error, and bias, was used to evaluate performance. The results show that oblique imagery significantly improves model accuracy when GCPs are limited. While increasing the number of GCPs beyond five did not yield notable improvements, error metrics and semivariogram behavior remained consistent across patterns. However, spatial analysis tools, including Getis-Ord Gi* and Moran’s I, revealed localized clusters of high error, particularly in configurations lacking oblique coverage. These findings underscore the importance of evaluating not only global accuracy measures, but also spatial patterns of error when designing UAS-based pavement surveys. The results support the conclusion that a minimum number of strategically distributed GCPs, in the order of approximately five per 2,500 m2, combined with oblique imagery, can provide reliable elevation modeling for small-scale pavement assessment.]]></description><pubDate>Wed, 04 Nov 2026 13:59:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796353</guid></item><item><title>Effects of Ultraviolet Radiation and Temperature on the Daytime Optical and Colorimetric Properties of Pavement Markings</title><link>http://pubsindex.trb.org/view/2796347</link><description><![CDATA[Pavement markings are important components of roadway infrastructure, delineating lane boundaries and providing guidance for traffic. However, long-term exposure to the natural environment leads to progressive deterioration of their visibility. This study investigated the degradation patterns of four optical and colorimetric indicators related to the daytime visual performance of pavement markings: luminance factor (β), yellowness index (YI), yellow axis (𝑏), and color difference (ΔE). Accelerated-aging tests were conducted using a Q-SUN Xe-3 xenon arc chamber under different combinations of ultraviolet (UV) irradiance and temperature. The performance changes of white and yellow thermoplastic and two-component pavement markings were examined under varying temperature levels (20°C, 40°C, 60°C, and 80°C) and UV irradiance intensities (0 W/m², 0.51 W/m², 1.01 W/m², and 1.50 W/m2). Based on the experimental results, separate multiple linear regression models were developed for white and yellow markings. Results indicate that the measured daytime optical and colorimetric properties are affected by temperature and UV irradiation. As temperature and UV irradiance increase, the magnitude and rate of changes in the measured indicators increase significantly. Within the tested product set, the methyl methacrylate based two-component formulations exhibited smaller changes in the measured optical and colorimetric indicators than the thermoplastic formulations, while the white formulations generally exhibited smaller colorimetric changes than the yellow formulations. These findings provide an experimental basis for material weathering assessment and daytime optical and colorimetric deterioration evaluation under varying environmental conditions, offering valuable insights for pavement-marking material evaluation and optimization.]]></description><pubDate>Wed, 04 Nov 2026 13:59:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796347</guid></item><item><title>Hierarchical Cloud-Edge Cooperative Control Framework for Multi-Vehicle Autonomous Driving Using Reinforcement Learning and Model Predictive Control</title><link>http://pubsindex.trb.org/view/2796340</link><description><![CDATA[This study presents a hierarchical cooperative control framework for hybrid electric vehicles in intelligent transportation systems, termed “cloud-coordinated cooperative driving with deep reinforcement learning.” The framework integrates reinforcement learning with model predictive control (MPC) to balance adaptability and stability. A cloud-level coordinator utilizes the multi-agent deep deterministic policy gradient (MADDPG) algorithm for global policy optimization, while each vehicle executes a local Soft Actor–Critic (SAC) controller for real-time adaptive control. A predecessor–leader–follower-based cooperative economic MPC module is incorporated to ensure stable spacing and energy-efficient trajectory tracking. Furthermore, a hybrid offline–online learning mechanism with state synchronization and experience replay enhances convergence speed and generalization capability. Under three representative driving cycles: highway fuel economy test, US06 (a high-speed, high-acceleration cycle), and China light-duty vehicle test cycle with an initial state-of-charge (SOC) of 0.60, the proposed MADDPG-SAC algorithm consistently outperforms deep deterministic policy gradient (DDPG) and MADDPG. This method attains lower fuel consumption (17.93–20.62 L/100 km), higher minimum SOC (0.33–0.35), reduced speed tracking error (0.22–0.26 m/s) and spacing error (0.29–0.35 m), smoother control (mean jerk of 0.041–0.049 m/s3), and faster convergence (11–13 episodes compared with 12–29 episodes for DDPG and MADDPG). Overall, the framework exhibits strong cooperative capability, energy efficiency, and driving stability, demonstrating its potential for large-scale energy-aware autonomous driving applications.]]></description><pubDate>Wed, 04 Nov 2026 13:59:05 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796340</guid></item><item><title>Strategic Decision Making to Improve Traffic Safety</title><link>http://pubsindex.trb.org/view/2796215</link><description><![CDATA[Transportation research and government agencies often operate in separate disciplines, creating organizational silos that hinder the development of coordinated, multidisciplinary strategies to eliminate traffic fatalities. The Safe System Approach seeks to overcome these barriers by integrating countermeasures across disciplines and introducing redundancy to improve transportation safety. This report, NCHRP Web-Only Document 470, presents updated versions of the predictive models developed in earlier research and describes the Safety Countermeasure Benefits Estimator (SCBE), a web-based tool developed as part of the research, and its accompanying user guide. The tool is designed to help practitioners estimate the potential effects of safety programs and projects. By combining these estimates with the anticipated impacts of vehicle safety improvements, the updated models can help states establish realistic targets for reducing traffic fatalities and assess the investments needed to achieve those goals.]]></description><pubDate>Wed, 04 Nov 2026 13:19:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796215</guid></item><item><title>Validation of Internal-Restraint Damage Mechanism in Asphalt Mixtures Using Thermal Conditioning and Mechanical Testing</title><link>http://pubsindex.trb.org/view/2796264</link><description><![CDATA[Thermal distresses like transverse and block cracking continue to be major failure mechanisms in asphalt pavements, especially in cold climates. Although external-restraint damage mechanisms have been well studied, the internal-restraint damage mechanism (IRDM), caused by differential thermal contraction between asphalt mastics and the aggregate skeleton, needs more research for full understanding. This study focuses on investigating the IRDM by subjecting asphalt mixture specimens to extreme cooling and evaluating their response using the Ohio CTE (Coefficient of Thermal Expansion) Device and dynamic modulus testing. The Ohio CTE Device test was performed first to monitor thermally induced strain during a controlled cooling cycle from 20°C to −65°C at a rate of 20°C/h. The transition point, captured as the critical cracking temperature, reflected the onset of damage resulting from internal restraint caused by differential thermal contraction between binder and aggregate. To validate this observed transition behavior, dynamic modulus tests were conducted at 0°C on the same specimens before and after thermal conditioning. This temperature was selected to minimize healing effects. Results showed a consistent decrease in |E*| and an increase in phase angle after cooling across all mixtures, with the most significant changes observed at low frequencies. The outcome suggests that finer gradation and the absence of reclaimed asphalt pavement (RAP) tend to enhance low-temperature performance, whereas higher RAP content and coarser gradations increase vulnerability to thermal cracking. The study provides strong mechanical evidence of IRDM effects and validates the Ohio CTE Device’s ability to capture internal damage in asphalt mixtures.]]></description><pubDate>Tue, 03 Nov 2026 15:49:41 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796264</guid></item><item><title>Digital Twin–Enabled Safety Training and Route Rehearsal for Precast-Component Transportation</title><link>http://pubsindex.trb.org/view/2796263</link><description><![CDATA[Precast components are widely used in modern construction due to their high quality and efficient manufacturing under factory-controlled conditions. However, transportation from the plant to the construction site remains a vulnerable phase, where components may be damaged because of unexpected road conditions or operational errors. These incidents can result in costly delays, material waste, and disturbances to nearby communities. To address these challenges, this study proposes a simulation-based training and planning framework for precast-component transportation. Using Unity, a virtual environment is developed to recreate representative delivery scenarios with varied road constraints and operational risks. Drivers can rehearse transportation tasks under various simulated conditions, helping to build route familiarity and reduce operational mistakes. The simulator also records time-stamped vehicle states, user-control inputs, and safety-related events for post-session review. Building on these recorded data, a conceptual monitoring architecture is presented to demonstrate how acceleration and global positioning system information could be visualized within the three-dimensional environment. The proposed approach supports driver education, route planning, and safety communication, providing a digital foundation for improving logistics efficiency and decision-making in precast-component transportation.]]></description><pubDate>Tue, 03 Nov 2026 15:49:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796263</guid></item><item><title>An Extensive Field-Based Assessment of Exterior and Interior Noise and Vibration Across Longitudinal Rumble-Strip Designs</title><link>http://pubsindex.trb.org/view/2796260</link><description><![CDATA[Rumble strips (RS) are cost-effective safety countermeasures that reduce certain types of crashes by providing audible and vibratory warnings when vehicles drift from the travel lane. However, they often cause annoyance to nearby residents, resulting in complaints to transportation agencies such as departments of transportation. Prior studies have evaluated only a limited number of RS designs and configurations, relying primarily on A-weighted decibels (dBA), which have been shown to be insufficient for assessing annoyance responses. This study comprehensively evaluated 26 RS designs, including one at a complaint location, covering sinusoidal, raised, and cylindrical shapes with different configurations: continuous shoulder rumble strips (SRS; 10 designs), bicycle-gap SRS (6 designs), and centerline rumble strips (CLRS; 10 designs). RS designs were tested using two vehicle types at three speeds. The experimental design included assessing exterior noise following AASHTO TP-98-18 at two distances using different metrics, one-third-octave-band analysis, and the annoyance-focused low-frequency corrected sound pressure level (LA′) employed in RS studies. To ensure safety effectiveness, interior noise was assessed following SAE J1477. Additionally, vibration was evaluated using ISO 2631-1 frequency-weighting filters (Wd, Wk), which better account for human vibration perception. Approximately 792 valid runs were conducted. Results showed that sinusoidal RS produced the lowest exterior noise increments relative to the no-rumble-strip baseline, consistently below 10 dBA, while maintaining sufficient interior noise and vibration levels. At the complaint location, the measured exterior noise increment exceeded the 10 dBA above-baseline threshold at 480 ft. This study reinforces the effectiveness of sinusoidal RS as a superior alternative to other RS designs, despite implementing low-frequency content penalties.]]></description><pubDate>Tue, 03 Nov 2026 15:49:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796260</guid></item><item><title>Understanding Disparities in Activity Participation in California: A Clustering Analysis Approach</title><link>http://pubsindex.trb.org/view/2796259</link><description><![CDATA[This study utilizes data from a survey administered in Fall 2023 to explore activity participation patterns in California. Previous literature underscores the significance of activity participation in meeting an individual’s need for social interactions and contributing to well-being and highlights the substantial effects of the COVID-19 pandemic on essential and nonessential activities. While extensive research has examined activity–travel behavior during the pandemic, fewer studies have focused on disparities in activity participation and travel frequencies for discretionary and utilitarian purposes across population segments that differ in socioeconomics and mobility profiles. This study employs a latent class cluster analysis approach to categorize individuals into distinct activity participation groups based on their activity frequency in Fall 2023. Then, it analyzes their characteristics for personal attitudes, sociodemographics, physical and other mobility limitations, and residential environment characteristics. By applying an equity lens centered on mobility limitations and socioeconomic status, this study aims to provide a systematic assessment of heterogeneity in activity participation. The findings reveal significant disparities in activity participation and travel behavior across population groups in California, informing policy development by highlighting transportation challenges faced by disadvantaged communities and individuals with limited resources. The results identify individuals experiencing accessibility barriers and offer guidance for improving transportation options, enhancing active travel infrastructure, and fostering equitable access for disadvantaged groups. The study provides an empirical characterization of activity participation patterns in Fall 2023, offering valuable information for equitable transportation policies.]]></description><pubDate>Tue, 03 Nov 2026 15:49:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796259</guid></item><item><title>Wear Prediction and Profile Design for Critical Sections of Heavy-Haul Turnout Frog Zones</title><link>http://pubsindex.trb.org/view/2796258</link><description><![CDATA[Heavy-haul railway turnouts are subjected to high traffic volume. The fixed frog area within the turnout, characterized by its variable cross-section structure, collectively leads to severe wear of the frog. To mitigate frog wear and extend its service life, a novel method for predicting frog wear is proposed. This method forecasts the wear amount of the frog under varying traffic volumes based on factors such as wheel wear, plastic deformation at frog nodes, and wheelset lateral displacement. The accuracy of the method is verified through comparison with measured data. Based on the results of the simulation analysis, a profile design is conducted for the cross-section of the standard frog at the location +0.3 m from the reference point. Results indicate that wheels at the midstage of wear exhibit better compatibility with the frog, leading to reduced wear on the frog under the same traffic volume. A comparison of wear volume is conducted using the measured profiles at +0.1 m and +0.3 m and the predicted profiles. The wear volume of the measured profiles shows minimal discrepancy compared with that of the predicted profiles at a passing tonnage of 75 million tonnes, indicating the satisfactory accuracy of the wear prediction method. The designed frog profile demonstrates a reduction in equivalent stress and a significant decrease in maximum plastic deformation compared with the standard profile, thereby providing a theoretical reference for extending the service life of the frog.]]></description><pubDate>Tue, 03 Nov 2026 15:49:40 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796258</guid></item><item><title>Lateral Avoidance or Lane-Keeping? Analyzing Decision-Making and Driving Risk on Sand-Covered Roads during Sand and Dust Storms</title><link>http://pubsindex.trb.org/view/2796257</link><description><![CDATA[During sand and dust storms (SDS), wind-blown sand reduces visibility distance and accumulates on the road surface, decreasing tire–road friction and posing challenges to driving safety. This study aims to investigate how drivers respond to sand-covered roads during SDS and to identify the associated driving risks with different maneuvers. We conducted an experiment with 45 participants who experienced simulated SDS events under six visibility conditions. Driving maneuvers were classified into lane-keeping and lateral avoidance. A generalized linear mixed model was used to examine higher-risk and lower-risk levels using the K-means algorithm, analyze their temporal and spatial characteristics, and use Fisher’s exact test to identify factors correlated with driving risk. The results show that extremely low visibility conditions (100 and 50 m) significantly reduced the percentage of drivers choosing lateral avoidance, while drivers with longer driving experience were more likely to adopt it. Lateral avoidance involved more higher-risk cases than lane-keeping, and vehicles showed greater lateral acceleration, yaw rate, and displacement on sand-covered roads. Risk levels were strongly influenced by visibility, rising notably when visibility dropped to 100 m or below, whereas driver characteristics showed no significant correlation with risk level. Therefore, staying in your lane and avoiding sudden lane changes or aggressive maneuvers may be a safer strategy to maintain vehicle stability and ensure safety under extremely low visibility. The findings offer evidence-based driving suggestions for drivers in extreme SDS scenarios to reduce driving risk.]]></description><pubDate>Tue, 03 Nov 2026 11:46:54 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796257</guid></item><item><title>Enhancing Maritime Domain Awareness on Inland Waterways: A You Only Look Once-Based Fusion of Satellite and Automatic Identification System for Vessel Characterization</title><link>http://pubsindex.trb.org/view/2796256</link><description><![CDATA[Maritime domain awareness (MDA) in inland waterways is constrained by vulnerabilities in cooperative monitoring systems. This study presents a data fusion framework that integrates high-resolution satellite imagery with vessel trajectory data from the automatic identification system (AIS) to enhance detection, characterization, and tracking of maritime activity. The proposed approach leverages noncooperative satellite sensing to complement AIS data, enabling the identification of dark vessels, validation of cooperative traffic, and support for advanced MDA capabilities. A You Only Look Once (YOLO) v11-based object detection model is developed to detect and characterize vessels and barges by type, cover status, operational state, barge count, and direction of movement. The model is trained on a manually annotated dataset comprising 4,550 instances extracted from 5,973 mi² of Lower Mississippi River imagery. Evaluation on a held-out test set demonstrates strong performance across multiple tasks, with vessel classification achieving an F1-score of 95.8%, barge cover detection attaining an F1-score of 91.6%, and operational status classification reaching an F1-score of 99.4%. Direction-of-movement classification attains an accuracy of 98.1%, while barge-count estimation yields a mean absolute error of 2.4 barges. Spatial transferability assessment across a geographically distinct river system indicates minimal performance degradation, with accuracy levels remaining as high as 98%. These results demonstrate the effectiveness of integrating satellite imagery with AIS data for inland waterway monitoring. The framework supports near-real-time fleet inventory generation, anomaly detection, and improved situational awareness. Future work will focus on expanding dataset diversity, incorporating temporal modeling, and exploring multimodal learning to further enhance scalability and operational deployment.]]></description><pubDate>Tue, 03 Nov 2026 11:46:54 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796256</guid></item><item><title>Novel Deep Learning Architecture for Modeling Crash Severity Using Satellite Imagery Considering Land Use and Geometry</title><link>http://pubsindex.trb.org/view/2796255</link><description><![CDATA[Crash severity prediction has been significantly enhanced by deep learning (DL) techniques, which overcome the limitations of traditional statistical and machine learning models that rely on manually engineered features and fixed relationships. These conventional approaches often fail to account for the complex spatial and structural factors influencing crash severity. In contrast, convolutional neural networks (CNNs) can automatically extract hierarchical features from high-dimensional data, such as satellite imagery, allowing for more scalable and accurate analysis. However, most existing deep-learning-based models use a single-input architecture, which restricts the integration of multiple interacting factors. To address this limitation, this study introduces ArroNet42, a novel dual-branch CNN designed to classify crash severity into four categories—no crash, simple collision, injury crash, and fatal crash—by concurrently processing land use and road geometry imagery. The model demonstrates superior performance, achieving a validation accuracy of 80.8%, outperforming established architectures such as GoogleNet (73.3%), ResNet-50 (72.9%), EfficientNet-B0 (72.8%), and DarkNet-53 (72.2%), which were limited to single-input land use imagery. An extended model, ArroNet43, which integrates roadway curvature as an auxiliary numerical input, achieved a slightly lower validation accuracy (79.3%), suggesting that curvature-related spatial features are already implicitly learned from the imagery inputs. These results underscore the advantage of multi-branch DL architectures in capturing complex crash-related factors. ArroNet42 provides a robust framework for proactive safety analysis and supports data-driven interventions by reducing dependence on historical crash records.]]></description><pubDate>Tue, 03 Nov 2026 11:46:54 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796255</guid></item><item><title>Joint State Estimation of Real-Time Transit Travel Time and Reliability Bounds</title><link>http://pubsindex.trb.org/view/2796254</link><description><![CDATA[Uncertainty in bus travel times because of congestion, signal delays, dwell times, and data gaps poses significant challenges for passengers and operators. Existing real-time information systems primarily disseminate point estimates of arrival times, which fail to convey uncertainty, undermining passenger trust and complicating operational planning. This study develops a hazard-based framework for the joint real-time estimation of bus travel time and its reliability bounds to address these limitations. The approach leverages accelerated failure time (AFT) models to dynamically update mean and variability estimates while handling censored observations from incomplete Global Positioning System bus trajectories. Among alternative parametric forms evaluated using multi-criteria performance metrics and the technique for order preference by similarity to ideal solution (TOPSIS) ranking method, the log-normal AFT model applied to pace (travel time per unit distance) delivers the most reliable predictions, achieving near-nominal coverage, narrower intervals, and interpretable hazard structures aligned with real-world transit dynamics. Comparative analysis against alternative regression benchmarks confirmed that ignoring the dependence between mean and variability leads to biased reliability estimates. Empirical insights highlight pronounced spatial and temporal heterogeneity, with congestion during peak hours and route-specific factors significantly shaping uncertainty. Sensitivity analysis underscored the operational significance of reliability management: a 30% reduction in median travel time produced ∼21% improvements in both mean travel time (∼17 min) and reliability interval width (∼9 min). In comparison, a 30% reduction in variability lowered interval width by ∼9% (∼4 min). These findings establish the hazard-based framework as a robust, interpretable basis for delivering reliability-aware traveler information and supporting data-driven operational strategies in heterogeneous urban transit environments.]]></description><pubDate>Tue, 03 Nov 2026 11:46:54 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796254</guid></item><item><title>Expanding Stability Domains in Connected Vehicle Platoons: A Safety-Oriented Framework for Cooperative Control Parameter Optimization</title><link>http://pubsindex.trb.org/view/2796253</link><description><![CDATA[Cooperative adaptive cruise control (CACC) has great potential to enhance the stability and safety of connected and automated vehicle (CAV) platoons. However, gaps remain in understanding how damping behavior and connectivity-enabled information flow jointly affect the relationship between platoon stability and collision risk. This study proposes a safety-oriented CACC framework that investigates the relationship between stability and collision risk under three damping states: underdamped, critically damped, and overdamped. Numerical simulations are performed on five-vehicle platoons with a fixed time headway (τ = 0.6 s) subjected to periodic disturbances. Additionally, varying connectivity weights are examined to evaluate the influence of vehicle-to-vehicle (V2V) communication on stability and safety. Safety performance is evaluated using multiple surrogate safety metrics, including deceleration rate to avoid crash (DRAC), rear-end collision risk index (RCRI), and time-to-collision (TTC). The results show that damping ratios within 1.0 to 1.2 provide favorable safety performance under fixed-headway conditions, while increased upstream information flow expands the stability domain and improves downstream safety margins. Additional sensitivity analyses on platoon size and communication delay further indicate that the proposed safety-oriented damping region is not restricted to the original five-vehicle setting, although delayed V2V information increases dynamic fluctuation and reveals a safety–smoothness tradeoff. These findings provide interpretable guidance for the design of robust and safety-oriented cooperative longitudinal control strategies for connected vehicle platoons.]]></description><pubDate>Tue, 03 Nov 2026 11:46:54 GMT</pubDate><guid>http://pubsindex.trb.org/view/2796253</guid></item><item><title>Spatial Decision Support System for Evaluating and Optimizing Smart-Locker Networks</title><link>http://pubsindex.trb.org/view/2793832</link><description><![CDATA[Parcel-locker networks are increasingly used to address last-mile delivery inefficiencies, yet their performance depends strongly on whether locker locations provide accessible and spatially balanced service. Although previous studies have examined parcel-locker siting through facility-location models, multicriteria decision-making, simulation, or accessibility analysis, fewer studies provide an integrated decision-support workflow that evaluates an existing real-world network, statistically diagnoses spatial underservice, and uses these diagnostics to guide scalable expansion. This paper addresses this gap by developing a cloud-based spatial decision support system (SDSS) that combines spatial-statistical evaluation with genetic algorithm-based (GA) p-median optimization. The framework first uses descriptive accessibility metrics and kernel density estimation to characterize current siting patterns, then applies local Moran’s I (LMI) to identify statistically significant clusters of high population-weighted access burden. These underserved clusters are used both as diagnostic outputs and as a reduced candidate pool for optimization. The method is tested on a real smart-locker network in the Athens Metropolitan Area, comprising 858 locker locations and 36,268 population parcels. Results show that adding 40 lockers, approximately a 5% network expansion, increases population coverage within a 300 m service radius by about 3%. The proposed LMI + GA workflow reduces the candidate search space from 36,268 parcels to 5,346 underserved parcels and reduces runtime from about 2 min to under 20 s while maintaining comparable solution quality. The SDSS provides an accessible, data-driven environment for identifying coverage gaps, prioritizing expansion sites, and supporting strategic smart-locker planning.]]></description><pubDate>Sat, 31 Oct 2026 10:50:29 GMT</pubDate><guid>http://pubsindex.trb.org/view/2793832</guid></item></channel></rss>