This paper proposes to estimate the travel time at each edge of an urban street network using Artificial Neural Networks (ANNs). To improve the ANN performance and minimize errors in manual design, an Algorithm Auto-Design ANN Topology (A-DANNT) is introduced that automatically determines the most suitable architecture for regression problems. The methodology implements an algorithm based on Tabu Search, called the Best R-Value Determination algorithm (BR-vD), which optimizes the topology obtaining a lower Mean Square Error (MSE) and a higher correlation coefficient. The process is developed in three phases: first, the variables that impact the travel time are analyzed; then, the proposed algorithm is used to find the best topology; and finally, the travel times are estimated. The proposal is evaluated in two case studies: in the first, the algorithm automatically designs the architecture, and a 0.99366 correlation coefficient is achieved between the results and the objectives. In the second case, the performance of the algorithm is compared with a fuzzy travel time model, achieving a 0.99898 correlation coefficient. In both cases, the proposed algorithm is capable of designing topologies with coefficients greater than 0.99 and Mean Absolute Errors (MAEs) of 3.2765 and 0.6957 s, respectively.

Urban traffic congestion, along with the resulting fuel waste and pollution, poses increasing challenges as city populations grow. There is a critical need for techniques that mitigate these effects while maintaining traffic efficiency. This study proposes an integrated framework that combines the Internet of Things (IoT) infrastructure and the Internet of Drones (IoD) to enhance urban traffic management. IoT sensors monitor real-time traffic conditions, while Roadside Units (RSUs) collect and process data to deliver timely updates to vehicles. Drones dynamically extend communication coverage by acting as mobile relay nodes, accelerating traffic information dissemination across wider areas, particularly those with sparse connectivity. Drone placement was optimized for maximum coverage using the Particle Swarm Optimization (PSO) algorithm. Experiments conducted in two urban scenarios-Dammam (Saudi Arabia) and Doha (Qatar)-used the SUMO simulator, Traffic Control Interface (TraCI), and Python to implement communication protocols and adaptive rerouting mechanisms. The findings suggest that integrating IoT and drones can substantially reduce travel delays and vehicular emissions while maintaining real-time operational efficiency. In Dammam, emissions and travel times decreased by up to 40.99% and 32.05%, respectively, and in Doha by up to 48.78% and 43.92%. These results indicate that coordinated IoT-IoD systems can support sustainable urban mobility by improving traffic flow and contributing to cleaner air.

Abstract Local leakage processes and potential migration pathways of modern water (< 60 years) from the shallow aquifer, into the underlying semiconfined Memphis aquifer, were evaluated to assess the vulnerability of groundwater in Memphis Light, Gas and Water’s (MLGW) Sheahan well field. To identify the source(s) and pathways of modern water, integrated hydrostratigraphic analysis, numerical modeling, hydrologic tracers, and geochemical modeling were utilized. The percentage of modern water present in Memphis aquifer production wells is estimated using inverse geochemical modeling, lumped parameter modeling, and solute transport modeling with Modular Transport, 3-Dimensional, Multi-Species model (MT3DMS). The mixing percentages determined from lumped parameter modeling and MT3DMS are generally in agreement, yielding maximum values of 14.3% and 15.3% young water, respectively; however, the estimate diverge at well 87 A. The mixing fraction difference at 87 A might account for a missing hydrogeologic connection in the groundwater model on the eastern part of the well field. Estimates for the apparent age of modern water derived from MT3DMS are generally consistent with those from lumped parameter models (LPM) for most wells, except for well 96. However, both MT3DMS- and LPM-derived mean ages are younger than tracer-based apparent ages, likely due to age mixing, unsaturated zone travel time, and possible helium loss during sampling. Thus, the MT3DMS model, calibrated with long-term tracer data could simulate the mean age and mixing percentage of modern water while emphasizing the importance of accurate hydrogeologic conceptualizations at the Sheahan well field. As a result, tracer data and solute transport modeling can identify vulnerabilities and ensure the long-term sustainability of the Sheahan well field.

Traffic congestion is increasingly recognized not only as a transportation issue but also as a significant source of economic inefficiency, ‎particularly in developing and medium-sized cities. This study estimates congestion-related productivity losses at a major intersection in ‎Batangas City, Philippines, with emphasis on variations across days of the week. Using a value-of-time (VOT) framework, congestion ‎costs were estimated from observed travel time delays and additional fuel consumption during morning (AM) and afternoon (PM) peak ‎hours over seven consecutive days. Traffic data were obtained from closed-circuit television (CCTV) footage provided by the Batangas City Transportation Development Regulatory Office (TDRO) and supplemented by a brief key-informant interview to contextualize ‎the observed traffic conditions. Results show clear day-of-week variation in congestion costs, with weekdays, particularly Mondays, incurring the highest economic and productivity losses. Across all days, time-related productivity loss constituted the dominant share of total ‎congestion cost. The findings highlight how routine traffic delays translate into measurable economic losses and underscore the importance of integrating economic and productivity considerations into local traffic management and policy decisions in growing urban ‎areas‎.

A convex reformulation for speed planning of a vehicle under the travel time and energy consumption objectives

Measuring maternal healthcare accessibility in Florida by a data-driven extension of V2SFCA.

Optimization of electrified railway operations: Energy efficiency, travel time, and regenerative energy utilization

Current understanding of students’ mobility patterns often overlooks the integration of perceptual experiences with spatial behavior, particularly in rapidly urbanizing Global South contexts where informal infrastructures and cognitive factors critically shape mobility. This study addresses this gap by analyzing route preference determinants at Politeknik Negeri Ujung Pandang using mental map sketches (n=165), questionnaires, and GIS-based spatial analysis. Key findings reveal: 1) dense morning commutes within 500m of campus versus afternoon dispersal into alternative routes up to 2km; 2) distance (r = 0.876, p < 0.01) and travel time (r = 0.699, p < 0.01) dominate choices, outweighing convenience (r = 0.196, p < 0.05); and 3) gendered behaviors, with 65% of alternative route users were males. The findings validate Hägerstrand’s Time-Geography model and recommend arterial corridor optimization and safety retrofits, advancing an equitable framework for campus mobility planning.

Multi-class freight tour synthesis model incorporating environmental, entropy, cost, and travel time objectives

Exploring the robustness of metro-bus composite networks considering the heterogeneity of travel time and passenger flow

Exact reliability of cold chain networks with multi-state travel time and transport capacity

Ride-hailing demand elasticities and value of travel time savings: An empirical analysis with massive data including sociodemographic characteristics

This study aims to track changes in tissues due to neoadjuvant chemotherapy with a transmission-based microwave imaging system that scans the entire breast and is fast, comfortable and suited to point-of-care implementation. Microwave scans are collected with patients undergoing chemotherapy treatment. Specifically, both breasts are scanned prior to each cycle of chemotherapy. The microwave scanner consists of planar transmit and receive arrays that are placed in contact with the breast. Signals with frequency content from 0.1 to 8 GHz are transmitted through the tissue, and the travel time of these signals is used to estimate the microwave frequency properties (permittivity). Property estimates at each sensor pair are mapped to the imaging plane. Case studies are presented for 6 patients. Comparison of the scans of left and right breasts suggests increased properties related to the presence of the tumor at the initial scan. Consistent properties are observed for the contralateral breast at different time points, while changes in the breast with the tumor are noted. In addition, differences between the breasts are more clearly depicted in dense breasts. This study demonstrates the potential for a transmission-based microwave scanner to provide feedback on treatment progress. Point-of-care microwave imaging has the potential to offer regular, quantitative feedback on treatment progress as an alternative to palpation.

In recent years, it has become evident that there may be a gross underestimation of the true disease burden of cystic fibrosis (CF) in low-and-middle-income countries. Data on incidence of CF in India are sparse, and optimal CF care is impeded by lack of standardized epidemiological data, limited awareness on the disease impact of CF, and inadequate diagnostic and clinical infrastructure to manage CF. Lack of universal neonatal screening, insufficient sweat testing equipment, and expensive genetic testing preclude timely diagnosis and treatment. Restricted access and costs prohibit consistent use of many CF-specific treatments, leading to high morbidity and early mortality. Early diagnosis is crucial and may be achieved by providing CF-specific education to physicians, ancillary staff, medical trainees, and the community, and by initiating neonatal screening. CF clinical diagnostic scores and indigenously developed sweat collection and analysis are inexpensive diagnostic options when c resources are limited. Targeted mutation panels based on regional heterogeneity of CFTR variants need further investigation. Increasing the number of specialized CF centers will improve access to timely care and reduce travel time significantly. Cost-effective clinical protocols following standardized guidelines (with resource-based modifications) for nutritional health, preventive care, and airway clearance through shared care models are invaluable. Ongoing support for the CF registry initiated by the Indian Council of Medical Research is important for epidemiological assessments and policy change, and patient advocacy through organizations such as CF Trust is vital to increase referrals and diagnostic access, subsidize medications, and procure modulator therapies.

Groundwater travel time distributions in two adjacent competing Springs

This study presents a comprehensive methodology to improve monorail route selection in Kirkuk city, Iraq using a quantitative GIS-based multi-criteria decision analysis. The primary aim of this study is to develop and apply a quantitative, GIS-based multi-criteria decision analysis framework for optimizing and objectively selecting the most suitable monorail route in Kirkuk city, Iraq, by integrating spatial, topographic, accessibility, service coverage, and economic factors in order to support sustainable and cost-effective urban transport planning in a post-conflict context. The research area covers 460.89 km², including an extensive road network of 5,533.27 km and 3,193 points of interest (POI). Four different monorail routes were analysed and evaluated using OpenStreetMap data derived from an analytic hierarchy process, digital elevation models and site suitability analysis. The approach uses weighted graph construction, various shortest path methods, and a wide range of performance metrics, including cost-effectiveness, service coverage, topographic conditions, and accessibility. Route optimization includes terrain suitability (scores from 0.50 to 0.78), elevation profile (288–393m), gradient limitations (maximum score 0.20 to 0.32), and points of interest (POIs) within a service radius of 500 m to 1 km. Based on this, Results shows Route 1 is the ideal choice as it found to be the most economical route (4.15 km, US$214.01 million), had the highest suitability score (0.70), and required the shortest travel time (9.61 minutes). On the contrary, Route 0 had the widest service coverage (31.37 km² service area, 66.1 points of interest within 500 m).

Spatial and social inequities in access to essential healthcare services: a case study of a fast-growing, diverse Canadian city.

Predictors of missed hemodialysis in end-stage renal disease patients presenting to the emergency department.

International travel time prediction for China–Europe Express trains via interpretable deep learning models

Exploring within-and-between differences in cycling travel time: A comparative study of İstanbul, Braga, and Tallinn