Interplay of chemical toxicants and urban microenvironments in the oxidative potential and comprehensive risk of road dust.

Multi-phase PAH transport across the land-sea continuum: Mechanistic drivers of source-sink dynamics and socioeconomic modulation.

Distribution efficiency is a critical aspect of bakery product distribution systems, as delivery delays affect product freshness, operational costs, and service reliability to customers. This study aims to develop a mathematical model to analyze product distribution time at UD. Win Win Bakery by integrating Petri Nets and Max-Plus Algebra. Petri Nets are used to represent the sequential, event-based distribution process, including vehicle preparation, goods loading, distribution travel, goods unloading, and the return trip to the factory. The Petri Net structure is then transformed into a Max-Plus Algebra model and matrix to calculate the total distribution time for each team. Data were collected through observations and interviews regarding distribution schedules, routes, number of vehicles, loading time, unloading time, and travel duration. The results show significant variations in distribution times among the nine delivery teams. The longest durations were found on the Toboli–Parigi, Ampibabo, and Kotamobagu routes, indicating workload imbalance and potential bottlenecks in the distribution system. The main contribution of this study lies in the application of Max-Plus Algebra supported by Petri Nets as a structured framework to identify time inefficiencies in regional-scale distribution. The implications of this study suggest that Max-Plus Algebra can be effectively used in discrete event-based distribution systems and supports route evaluation, workload balancing, and operational decision-making in perishable product logistics.

Under the background of accelerated urbanization， the continuous increase in the number of motor vehicles has led to a rapid rise in road traffic carbon emissions， becoming an important factor restricting the green transformation of cities. In order to systematically identify the key driving factors of road traffic carbon emissions， a "bottom-up" approach was used to construct a road traffic carbon emission inventory for the urban agglomerations in Fujian Province from 2003 to 2022， with motor vehicles being subdivided into 13 types for accounting purposes. Secondly， correlation analysis and Lasso regression were combined for variable selection， and multiple machine learning algorithms were used to build carbon emission prediction models. Finally， SHAP values were employed to enhance model interpretability and quantify the contributions of driving factors. The results show that： ① The output value of the transportation industry， urban green space area， and the number of invention patents were the main factors affecting carbon emissions. ② Among all models， XGBoost performed the best， with the test set R2 of 0.992 and MAE and RMSE of 7.393×104 t and 8.803×104 t， respectively. ③ SHAP analysis revealed the positive and negative impact pathways of key factors on carbon emissions. This study， starting from the perspective of interpretability， reveals the driving mechanisms of urban road traffic carbon emissions dominated by motor vehicles， providing scientific support for the formulation of low-carbon transportation policies.

As time has progressed, road transportation has become essential in today’s world. This evolution has been driven by economic growth, increased mobility and intensified communication activities. The increase in number of vehicles on the roads has been tremendous. Also, ill-constructed roads, rash driving, negligence of traffic rules and sudden obstacles have caused many accidents. We have made great progress in artificial intelligence, deep learning and computer vision. These developments can be used for road hazard detection and driver assistance. This research presents an intelligent multi-hazard road detection and driver assistance framework is presented based on deep learning and computer vision technologies to boost the transportation safety and driver awareness. The framework is intended to analyze the dashcam video streams and identify major road hazards such as potholes, traffic signs, speed breakers etc. The framework utilizes deep learning models like YOLOv8 and Convolutional Neural Networks (CNNs) for hazard identification and classification. Furthermore, the framework includes an intelligent audio alert system to inform drivers when hazardous road conditions are detected, enhancing driving awareness and safety. The architecture also comprises GPS-based hazard localization for transportation monitoring and future intelligent transportation applications. The goal of this research is to improve road monitoring, situational awareness and intelligent driver assistance by combining different hazard detection capabilities into a common framework. Overall, the proposed work demonstrates the increasing importance of artificial intelligence and computer vision technologies in the development of safer, smarter, and more efficient transportation systems.

Modeling heterogeneity in fault attribution of Pedestrian-Vehicle crashes using a Random parameter Binary Logit approach.

The rapid growth of vehicles in urban areas has increased the need for intelligent traffic monitoring and automated vehicle analysis systems. Manual vehicle monitoring methods are time-consuming, less efficient, and prone to human errors. To overcome these limitations, this project proposes an AI-Based Vehicle Counting and Crossing Time Analysis System using Computer Vision and Deep Learning technologies. The proposed system uses the YOLOv8 object detection model to detect and track vehicles from traffic video streams in real time. Different types of vehicles such as cars, buses, trucks, and motorcycles are identified automatically using Artificial Intelligence techniques. A virtual checkpoint line is created within the video frame to monitor vehicle movement and count vehicles whenever they cross the predefined region. The system also integrates EasyOCR technology for optional license plate recognition operations. The OCR module extracts alphanumeric characters from vehicle number plates and converts them into machine-readable digital text. The detected vehicle information, crossing activity, and monitoring statistics are displayed through a Streamlit-based dashboard interface.The proposed system improves traffic monitoring efficiency, reduces manual effort, minimizes counting errors, and supports intelligent transportation applications. The project can be used in smart traffic monitoring systems, parking management, highway analysis, toll gate monitoring, and smart city surveillance applications. The developed system demonstrates the successful integration of Artificial Intelligence, Computer Vision, OCR, and Real-Time Video Processing technologies for intelligent vehicle monitoring and analysis. Keywords – Artificial Intelligence, YOLOv8, Vehicle Detection, Vehicle Counting, OCR, OpenCV, Traffic Monitoring, Computer Vision

The number of motor vehicles have experienced significant growth over the past few decades as the economy continues to grow and urbanization accelerates. This phenomenon creates an urgent need for smart transportation technology. This paper aims to explore a prediction method suitable for predicting traffic flow in public transportation systems and the challenge of coping with the impact of abnormal traffic events on prediction. To this end, we propose a deep learning approach using dynamic graph networks and multi-head attention mechanisms to develop a traffic flow prediction method based on spatio-temporal multi-source information fusion. In addition, this paper also proposes an urban road traffic prediction method suitable for normal and abnormal traffic conditions. This method employs a deep learning framework to process traffic and accident data through dynamic graph networks and multi-task learning. The experimental results on three real traffic datasets, namely PEMS04, PEMS08 and Highways England, show that the proposed model has significantly better MAE than the optimal baseline AGCRN on PEMS04. On the England dataset, RMSE was 18.7% lower than DCRNN. In terms of long-term prediction, the MAE predicted by the model at 60min is still lower than the baseline. Extensive experiments on a real-world dataset validate the effectiveness of our model. This study provides a novel perspective and solution for smart city traffic prediction, while developing a predictive tool for abnormal events, thereby enhancing the intelligence level of traffic management.

IntroductionMotorization has undeniably enhanced mobility and convenience, but it comes at a significant cost, as the increasing number of vehicles has led to a surge in road traffic injuries (RTIs) now a major global cause of disability and death. Over the past decade, the significant growth in two-wheelers has coincided with a rise in RTIs, creating an increasing concern for young individuals. This mixed-method study aims to explore epidemiology, assess their impact on undergraduate students, and gather key recommendations from stakeholders to improve road safety and reduce injury rates.MethodologyThe quantitative data collection was conducted among undergraduate students in the four colleges in Kathmandu district. The total number of participants in this study was 1217 students of 17–31 years of age. Furthermore, for the qualitative data collection, 5 Key informant interviews were conducted among various stakeholders.ResultsThe prevalence of RTI in 2021–2022 was 23%. The most common injuries were bruises in the upper and lower limbs. It was seen that only 78% of the respondents had a driving license. The injuries led to academic loss and caused financial strain due to healthcare and vehicle repair expenses. 5 key recommendations to decrease the ever-increasing burden of RTI have emerged from the qualitative data analysis.ConclusionThe study highlighted a high prevalence and impact of RTIs among undergraduate students along with 5 key recommendations that came forward from this study could be instrumental for road safety in Nepal.

Artificial light at night (ALAN) is an increasingly significant environmental disturbance, as it disrupts natural light-dark cycles that regulate daily and seasonal physiological processes and phenological events of all organisms. The use of artificial lighting in urban areas is rapidly increasing each year due to the rising number of unregulated vehicles, as well as the widespread installation of decorative lights, digital advertising boards, and streetlights. The objective of this research was to determine the impacts of artificial light at night (ALAN) on various ornamental garden plants such as Dieffenbachia seguine, Lawsonia inermis, Alocasia cucullata, Cynodon dactylon and Dypsis lutescens through the analyses of chlorophyll fluorescence transients, specific and phenomenological energy fluxes, density of functional PSII RCs, quantum yields (Fv/Fm, ϕE0), non-photochemical quenching (Kn) and photochemical quenching (Kp), superoxide dismutase (SOD) activity, and concentrations of chlorophylls, malondialdehyde (MDA) and starch content. The results of the present study highlight that plant responses to ALAN vary among species. The present investigation demonstrates that D. lutescens and C. dactylon exhibit pronounced sensitivity to ALAN, whereas D. seguine, L. inermis, and A. cucullata display a comparatively higher degree of tolerance. These findings underscore the need to preferentially select ALAN-tolerant species for urban plantation programs to minimize the ecological consequences associated with light pollution. Moreover, the study identifies specific photosynthetic parameters (OJIP transients, ET/CS, RC/CS, Kp, Kn, and PICS) along with key biochemical indicators (SOD activity, MDA accumulation, and chlorophyll content) as reliable diagnostic markers for distinguishing ALAN-sensitive and ALAN-tolerant species, thereby supporting informed species selection for sustainable urban greening.

Abstract. As the global automotive industry continues to grow rapidly, the increasing number of passenger vehicles has contributed to worsening air pollution. However, previous studies have insufficiently addressed nationwide hourly vehicle emissions. This study firstly utilized big data of ride-hailing services and traffic flow model to obtain nationwide hourly gridded speed and traffic volume. Then we established a high spatiotemporal resolution (0.01° × 0.01°; 1 h) emission inventory by using multiple correction factors. The annual amounts of CO, VOCs, NOx, PM and NH3 emitted from national passenger vehicles in 2019 were 4087.8, 1069.4, 211.7, 1.9, 77.5 kt, respectively. Despite occupying merely 0.8 % of the national territory, urban areas generated 35.3 % of the country's total vehicle emissions, due to high local traffic volumes and relatively low vehicle speeds. From a temporal perspective, passenger vehicle emissions exhibit significant holiday effect and weekend effect. In addition, hourly average emissions on workday exceeded those of weekend and holiday by 8 % and 5 % during the morning peak, with these differences increasing to 12 % and 18 % during the evening peak. Current traditional emission methodology might underestimate emissions by 31.5 %. We also used the WRF-Chem model for simulation validation. This hourly-scale inventory provides quantitative support for the precise implementation of pollution control and early warning.

This study explores sustainable energy management approaches for a smart distribution network that combines multiple infrastructures, such as electric vehicle charging stations, hydrogen refueling facilities for fuel cell vehicles, and renewable energy systems integrated with hydrogen storage. These components are managed in a coordinated manner to satisfy both operational requirements and security criteria defined by the distribution system operator. A key feature of the hydrogen storage unit is its dual functionality, as it not only stores electrical energy but also supplies hydrogen to end users. The primary objective is to reduce overall energy losses within the distribution system. To accomplish this, the research considers several important factors, including AC power flow modeling, grid voltage operational and security constraints, system flexibility, environmental restrictions, operational characteristics of electric vehicles charging and hydrogen stations, and performance models of renewable energy systems coupled with hydrogen storage. Furthermore, the proposed framework accounts for uncertainties related to load demand, renewable generation, and variations in the number of electric vehicles by applying a scenario-based stochastic optimization technique. The findings demonstrate significant enhancements in both system performance and security. In particular, the proposed method decreases voltage deviations, power losses, and peak load capacity by approximately 24.4%, 32.8%, and 38.3%, respectively, compared to conventional load flow analyses. Moreover, voltage security within the network is improved by nearly 10.2%, confirming the efficiency of the proposed integrated energy management strategy.

As global supply chains increasingly prioritize environmental sustainability and operational efficiency, battery electric freight vehicles (EFVs) have emerged as a pivotal alternative to traditional diesel-powered logistics fleets. This paper addresses the integrated planning and scheduling problem for multi-modal logistics systems utilizing EFVs. An integrated model is proposed to determine the number of electric freight vehicles and optimize dispatch and charging schedules, considering deadheading, and time-of-use electricity pricing. The model is formulated as an integer linear programming (ILP) problem solvable by commercial solvers. A branch-and-price framework and a heuristic algorithm are developed to handle large-scale instances. A case study using real data from a logistics provider in China demonstrates that the EFV system achieves a 45.5% reduction in total monthly costs compared to traditional diesel freight vehicle systems, even after accounting for higher vehicle and infrastructure costs. Sensitivity analyses offer practical insights for EFV adoption in multi-modal logistics.

Estimating traffic from crowdsourced location activity: A new data source perspective

A queue-battery model for electrified traffic flow considering endogenous congestion propagation

Abstract The increase in carbon dioxide released due to the growing number of cars and other vehicles that run on internal combustion engines is considered a significant source of pollution contributing to global warming. Adopting electric vehicles (EVs), which use electric motors instead of internal combustion engines, is a smart approach to mitigating the greenhouse effect.Batteries, specifically LiFePO4 batteries, are a crucial power source for EVs. However, these batteries can reach high temperatures after prolonged use, necessitating designing and constructing a battery temperature management system (BTMS). Various techniques are used to manage battery temperature, including a promising new technology, which is the forced air thermoelectric cooler-thermoelectric generator (TEC-TEG) model. This new technology is investigated both computationally and experimentally. In the voltage produced by the TEG is used as feedback voltage for the TEC. The simulation of the BTMS is realized using the ANSYS 2021R1 software. A single battery cell and BTMS utilize 6,197,879 and 12,697,173 mesh numbers, respectively. In the experiment, the TEG electric potential created at an input voltage of 1 V is equivalent to 0.03 V and increases to approximately 0.115, 0.225, 0.4, and a maximum of 0.55 V for input voltages of TEC of 2, 3, 4, and 5 V, respectively. In the other hand, the difference between the TEG's current without the use of a voltage-controlled current VCC system and that using a VCC system for TEC input voltages of 1, 2, 3, 4, and 5 V, are 0.0257, 0.058878, 0.10607, 0.18691, and 0.2664 A, respectively, and 0.203, 0.3704, 0.586969, 0.8244888, and 1.0608 A respectively. These results indicate that using the VCC system remarkably increases the current generated through the TEG, thus increasing the overall efficiency of the TEC-TEG model. The TEG generally reduces the BTMS temperature by converting the heat absorbed from the hot surface of TEC into electrical potential. Also, the efficiency of TEC and TEG increases with increasing hot surface temperature. The battery surfaces are temperature-regulated and maintained at 25°C to 31°C at an ambient temperature of 25°C.

State-of-charge-aware charging opportunity detection for electric vehicles using data-driven learning and digital twin simulation

• Advanced estimation framework for on-board comfort from incomplete APC data. • Real-time performance of complex multi-line public transport networks. • Case study on Helsinki commuter train network with high estimation accuracy. • Reliable comfort estimates even at low APC coverage levels. • Guidance for optimal APC deployment and service quality monitoring. Comfort on-board public transport vehicles is a critical metric of user experience and service performance. The quantification of this metric requires knowledge of the number of passengers on-board every time a vehicle arrives at or departs from a stop or station. Automatic Passenger Counting (APC) systems allow obtaining such knowledge in real-time, but the information is often incomplete due to system malfunctions, or, more commonly, a lack of the relevant equipment in some vehicles. This study develops an advanced method for passenger estimation that fills gaps in incomplete APC datasets, with computational performance allowing real-time application, and calculates comfort levels on-board public transport vehicles in complex networks where stations are served by multiple lines. The proposed method is tested on a case study considering the Helsinki commuter train network, comprising 6 service lines and 20 stations. The results indicate that the proposed framework can achieve comfort level estimations with high precision across the different cases evaluated. Furthermore, the study provides insight into the key practical question of the number of vehicles that need to be equipped with APC devices in order to obtain sufficiently accurate on-board passenger comfort estimates, and it is shown that it is possible to obtain these estimates even when only a small subset of the runs of any single day are performed by equipped vehicles. Finally, the proposed estimation approach is a valuable tool for operators to obtain a better understanding of daily mobility patterns, evaluate their services through quantifying user experience, and enhance their operations.

• Monte Carlo simulations capture uncertainty in EV fast-charging station demand. • Charging protocols are compared under realistic power and queuing constraints. • The optimized OPT-FDP protocol achieves shorter charging times. • Optimal station sizes: 200 kW (urban) and 900 kW (highway) The paper reports a comparative study on the performance of fast-charging protocols for electric vehicles (EVs), evaluated through a stochastic simulation framework, representing both urban and highway charging stations. Three charging protocols are modeled and compared: two derived from commercially available vehicles (Nio and Tesla Model 3) and one optimization-based protocol (OPT-FDP), developed by the Authors, that balances charging speed and battery degradation. The framework accounts for queue management and power-allocation strategies, and Monte Carlo simulations reproduce variability in daily traffic and initial state-of-charges for realistic operating conditions. Key performance indicators include total delivered energy, number of vehicles served, and median station time, representing both user −and operator- oriented perspectives. Two case studies have been developed. In urban contexts, station power capacity is the dominant variable affecting throughput, with 200 kW identified as the minimum rating to ensure user-perceived fast charging, keeping median station time below 30 min and serving approximately 100% of daily arrivals. In highway scenarios, power and sockets number jointly determine service rate, with 900 kW and 10 sockets identified as the ideal size for 200 EVs per day, delivering about 9000 kWh/day and serving approximately 99% of vehicles. Among the tested protocols, OPT-FDP consistently minimizes charging time, achieving median station times of approximately 15 min in properly sized highway configurations, compared to about 20–25 min for Tesla and Nio protocols, without compromising energy delivery. These findings provide quantitative insight into planning future high-power charging infrastructure, highlighting ideal station sizing and management strategies for large-scale EV adoption.

Black carbon (BC) aerosols in urban areas affect human health and climate, leading to increased monitoring efforts. This study presents a comprehensive assessment of equivalent BC (eBC) in a Mediterranean urban environment (Rome, Italy). Harmonized protocols by ACTRIS and RI-URBANS were adopted to enhance data comparability. The study examined: (i) temporal eBC variability contributing to submicrometer aerosol mass; (ii) source apportionment into fossil fuel (eBC ff ) and biomass burning (eBC bb ) using the “aethalometer model”; (iii) local Mass Absorption Cross-section (MAC exp ); (iv) eBC trend analysis with and without MAC corrections. Results indicate traffic emissions are the primary source, with eBC ff approximately 70% of the total eBC mass. The findings underscore the importance of considering the local MAC to assess eBC: the difference between eBC estimated using MAC exp (eBC MAC exp ) and uncorrected eBC varies with time. The decline in eBC inferred using a constant MAC ( − 10 % yr −1 , p < 0 . 001 ) disappears when accounting for MAC variability, while MAC exp itself decreases at an exceptional rate of − 12 % yr −1 (-1 m 2 g − 1 y r − 1 ) . The intraannual eBC MAC exp -to-eBC ratio is on average close to 1, but it ranges from 0.7 in summer to 1.2 in winter. Temporal changes in meteorological conditions and emission patterns were found to influence both MAC exp and BC aerosol properties. The eBC ff -to -eBC ratio showed a significant positive trend ( + 0 . 01 y r − 1 ), linked to increased vehicle numbers and complemented by the eBC bb -to-eBC ratio exhibiting a similar but negative trend, associated with a +1.2 ∘ C rise in temperature ( + 0 . 3 ∘ C yr − 1 ). Accordingly, the observed decrease in MAC exp is coherent with a negative trend in the absorption Å ngström exponent (AAE) ( − 0 . 018 y r − 1 ). These findings highlight BC dynamics in urban areas, aiding efforts to harmonize measurements and improve climate and air quality models, guiding public health strategies. • Equivalent black carbon (eBC) in Rome primarily driven by traffic emissions. • Nominal MAC biases eBC; site-specific MAC (MAC e x p ) significantly influences eBC. • Local factors (meteorology, emissions) strongly affect eBC and MAC from daily to yearly scales. • Winter-summer eBC ratio rises from 2.17 to 2.76 when MAC variability is included. • Variable MAC removes the apparent −10% y r − 1 eBC decline caused by absorption changes.