High Traffic Volume Research Articles

Trends in Atmospheric Nitrogen Deposition in Macedonia Studied by Using the Moss Biomonitoring Technique

This study examined the nitrogen content in moss samples collected across Macedonia over a 15-year period (2005, 2010, 2015 and 2020) from 72 consistent sampling locations. The nitrogen content was determined using the Kjeldahl method, providing insight into the trends of atmospheric nitrogen deposition across different regions. Descriptive statistical analyses, including spatial distribution maps, were used to compare the temporal variations and regional nitrogen levels. In addition, box-plots (P25–P75) and whiskers (P5–P95) were constructed to provide a comprehensive view of the variability across different tectonic units and zones, allowing for an in-depth understanding of the spatial distribution of nitrogen across the country. The study revealed that the median nitrogen content in moss samples decreased from 1.21% in 2005 to 1.04% in 2015, followed by a slight increase to 1.07% in 2020. The highest nitrogen concentrations were consistently found in areas with heavy agricultural activities and high traffic volumes, indicating the direct impact of these anthropogenic factors. The comparisons across regions and geological zones also highlighted the substantial variation in nitrogen levels, reflecting the diverse environmental pressures in different parts of Macedonia. This long-term analysis not only offers valuable insights into the trends in nitrogen pollution but also underlines the necessity for targeted policy interventions, particularly in the regions where nitrogen levels remain persistently high.

Can the digital economy empower the transportation sector to improve green total factor productivity?

The digital economy has the potential to facilitate digitization and green transformation within the transportation sector, but further research is warranted on how it can enhance the sector’s green total factor productivity (GTFP). Utilizing provincial data from China spanning 2013 to 2021, this study investigates the impact of the digital economy on the transportation sector’s GTFP. The findings are as follows: (1) The transportation sector exhibits a U-shaped relationship between the digital economy and GTFP. The inflection point of the U-shaped curve is at 0.441, with 81.11% of the samples falling to the left of this point. It is primarily through technological progress and innovation, rather than changes in technological efficiency, that the digital economy influences the improvement of GTFP. (2) The U-shaped relationship exhibits heterogeneity and varies depending on the mode and content of transportation. The U-shaped relationship is more pronounced in regions with lower total traffic volume on highways and railways or higher traffic volume on waterways. For transportation content, DEEI and GTFP show a significant U-shaped relationship in areas with high traffic volumes of passengers and freights. (3) Government intervention can attenuate the U-shaped relationship, which leads to a flatter curve. The market-driven level has the capability to strengthen the U-shaped relationship, resulting in a steeper curve. In conclusion, these findings contribute to deeper insights into the U-shaped relationship between the digital economy and GTFP, offering valuable guidance for policymakers seeking to improve GTFP in the transportation sector.

Fisher activity patterns show potential for behavioral adaptations to human modified landscapes

Animals alter their diel activity in response to physiological constraints and ecological conditions. Fisher (Pekania pennanti) activity is known to vary through the diel cycle and change in response to cold stress and generally through both the climatic and biological seasons. However, less is known whether thermoregulatory effects impact fisher activity in milder climates and in areas of high human disturbance. We focused on two distinct research objectives to understand the 1) physiological constraints, and 2) ecological components of fisher activity in a highly disturbed landscape with a relatively mild climate. We used accelerometer data from 34 individual live-captured fisher in Rhode Island, USA from 2021 to 2023. We found that fisher activity patterns were primarily driven by diel cycle with higher activity levels at night than during the day. We did not observe any physiological influence of ambient temperature on fisher activity; daily minimum temperatures did not constrain fisher activity in the colder months, nor did daily maximum temperatures in warmer months. We did find that female activity levels differed by breeding status with non-pregnant females having higher activity levels than pregnant females. Considering ecological components, we found fisher decreased activity levels in higher road density areas during warmer months that coincide with higher traffic volumes. For fisher living in areas with lower road densities, we saw higher activity in the breeding season and summer than in winter. In contrast, fisher living in areas with high road densities had lower activity in the breeding season and summer than in winter. We conclude that fisher largely do not shift their activity to mitigate thermoregulatory costs in areas where temperatures do not reach extremes for extended periods of time. However, our findings suggest that behavioral shifts in activity are impacted by human disturbance and fisher minimize activity in risky areas.

Sustainability of Asphalt Mixtures Containing 50% RAP and Recycling Agents

The substitution of virgin asphalt binder with reclaimed asphalt pavement (RAP) has environmental and economic merits, however, cracking susceptibility arises due to the aged asphalt binder within RAP. The objectives of this study are to (1) enhance the cracking resistance of asphalt mixtures containing 50% RAP utilizing recycling agents (RAs) derived from six petroleum-based and bio-based materials, (2) conduct an environmental impact assessment (represented by global warming potential “GWP”) for high-RAP mixtures including RAs, and (3) estimate the cost effectiveness of including high-RAP content in asphalt mixtures. Based on the RAP asphalt binder performance grade (PG), base asphalt binder PG, and RAP content, the RA contents were determined to achieve a target asphalt binder of PG 76-22. A control mixture was benchmarked for comparison, specified for high-traffic volume roads, and contained PG 76-22 polymer-modified asphalt binder. The engineering performance of studied asphalt mixtures was evaluated using the Hamburg wheel-tracking (HWT), semi-circular bend, Illinois flexibility index, Ideal cracking tolerance, and thermal stress-restrained specimen tensile strength tests. It was found that petroleum-derived aromatic oil, soy-based oil, and tall oil fatty acid-based RAs demonstrated a successful restoration of aged RAP asphalt binder without compromising the permanent deformation resistance. The 50% RAP mixtures emitted less GWP by 41% and 42.9% using petroleum- and bio-oil RAs, respectively, and achieved a 31% cost reduction compared to the control mixtures.

Exploring and identification of road traffic crash hotspots in Kigali-city, Rwanda

Road traffic crashes are on the rise worldwide, particularly in African countries. Rwanda, in this context, faces the challenge of an increasing frequency of crashes each year. Kigali, the capital city of Rwanda, witnesses over three-quarters of the total population as daily road users, leading to high traffic volumes and congestion during peak hours. Identifying dangerous locations and providing insights to transport policymakers is crucial to minimise the severity of crashes in the city. This study focuses on identifying crash hotspots through spatial analysis and explores the various factors contributing to crash severity in the capital city of Rwanda. The study utilises crash data recorded between 2015 and 2022 by the Rwanda National Police, conducting temporal, spatial, and statistical analyses. A multinomial regression model was employed to examine the significance of contributing factors to crash severity. Additionally, six supervised machine learning classifiers were employed to assess their predictive capabilities for accident severity in Kigali city. The findings indicate that the Gasabo district recorded the highest number of crashes among all districts. Time series analysis reveals a decrease in fatal accidents from 2020 to 2022 in Kigali city, while the number of injury and property damage crashes progressively increased from 2019 to 2022. Among the predictive models, the Random Forest model demonstrated strong predictive abilities, achieving an accuracy of 88.11% compared to other models. These results provide valuable insights for road safety policymakers to proactively make decisions and take precautions to prevent crashes before they occur.

Analisis Pelaksanaan Tugas Ramp Handling dalam Menjaga On Time Performance (OTP) Pada Maskapai Garuda Indonesia di Bandara Soekarno Hatta Cengkareng

Soekarno-Hatta Airport is the main gateway for international and domestic air travel in Indonesia. As one of the busiest airports in Southeast Asia, Soekarno-Hatta Airport handles more than 60 million passengers per year (Soekarno-Hatta Airport Authority, 2023). According to the operational management theory by Suryanto (2020), efficiency in managing all operational processes, including ramp handling, is crucial to ensure smooth and punctual flights. With high traffic volume, the main challenge is to maintain On Time Performance (OTP) to minimize flight delays. This study aims to determine the implementation of ramp handling tasks during pre-flight activities, as well as to determine the obstacles experienced by Ramp Handling officers in pre-flight activities at Soekarno Hatta Cengkareng International Airport and to determine how efforts are made by PT Gapura Angkasa in improving the quality of On Time Performance (OTP) at Soekarno Hatta International Airport, Cengkareng. This study uses a qualitative method, namely through interviews, observations, and documentation of ramp handling personnel units, this study was conducted for 1 month at Soekarno Hatta International Airport The results of this study are that ramp handling officers at Soekarno-Hatta Cengkareng International Airport are responsible for checking the readiness of ground equipment, flight documents, and coordinating with the ground crew team. The use of checklists ensures that all pre-flight steps are carried out correctly and efficiently, which is important for smooth boarding and loading.

Analysis of pedestrian second crossing behavior based on physics-informed neural networks

Pedestrian two-stage crossings are common at large, busy signalized intersections with long crosswalks and high traffic volumes. This design aims to address pedestrian operation and safety by allowing navigation in two stages, negotiating each traffic direction separately. Understanding crosswalk behavior, especially during bidirectional interactions, is essential. This paper presents a two-stage pedestrian crossing model based on Physics-Informed Neural Networks (PINNs), incorporating fluid dynamics equations to determine characteristics such as speed, density, acceleration, and Reynolds number during crossings. The study shows that PINNs outperform traditional deep learning methods in calculating and predicting pedestrian fluid properties, achieving a mean squared error as low as 10–8. The model effectively captures dynamic pedestrian flow characteristics and provides insights into pedestrian behavior impacts. The results are significant for designing pedestrian facilities to ensure comfort and optimizing signal timing to enhance mobility and safety. Additionally, these findings can aid autonomous vehicles in better understanding pedestrian intentions in intelligent transportation systems.

Studying the factors that affect urban road noise-case study: El-Tahrir Street, Al-Dokki Cairo, Egypt.

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter.Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent.

Pengaruh Parkir pada Badan Jalan terhadap Kinerja Ruas Jalan MT Haryono di Kota Balikpapan

This study aims to examine the impact of on-street parking on the performance of MT Haryono Road in Balikpapan City. The research employs a qualitative descriptive method. The analysis reveals that the presence of on-street parking significantly affects road performance. On Tuesday, the day with the highest traffic volume, the degree of saturation during peak afternoon hours reached 0.89 with on-street parking and decreased to 0.85 when parking was moved off-street. The Level of Service (LOS) on Monday and Tuesday was categorized as E, indicating congested traffic conditions with very high side friction. The LOS improved to category C on Wednesday and Sunday, indicating unstable traffic flow with limited speeds and considerable side friction. The LOS was categorized as D on Thursday, Friday, and Saturday, indicating saturated traffic flow with starting low speeds and high side friction. The worst traffic conditions occurred on Tuesday, April 2, 2024, with a LOS of E and a degree of saturation of 0.89. Conversely, the best traffic conditions occurred on Sunday, April 7, 2024, with a LOS of C and a degree of saturation of 0.62. The study's findings highlight the importance of parking management to enhance road performance in urban areas.

Polycyclic aromatic hydrocarbons (PAHs) in blood serum of adults living in high and low-traffic volume areas in Malaysia: A comparative cross-sectional study

Ambient polycyclic aromatic hydrocarbons (PAHs) originate predominantly from fuel combustion of motor vehicles and have the potential to affect human health. However, there is insufficient knowledge regarding serum PAHs health risks among the Malaysian population. This study aims to compare PAH concentrations, distributions, correlations, and health risks in 202 blood serum samples drawn from residents living in high-traffic volume areas (Kuala Lumpur) and low-traffic volume areas (Hulu Langat) in Malaysia. Solid phase extraction and gas chromatography-mass spectrometry (GC-MS) were employed to extract and analyze blood serum samples. Questionnaires were distributed to obtain sociodemographic and contributing factors of serum PAHs. The mean total PAHs concentration in serum of the Kuala Lumpur group was 54.44 ng g−1 lipids, double the Hulu Langat group's concentration (25.7 ng g−1 lipids). Indeno(1,2,3-cd)pyrene (IcP) and acenaphthene (ACP) feature the most and least abundant compounds in both study groups. The mean concentrations of IcP and ACP in the Kuala Lumpur and Hulu Langat groups were 26.8 vs 12.68 and 0.27 vs 0.14 ng g−1 lipids, respectively. High-molecular-weight PAHs (HMW-PAHs) composed 85% of serum total PAHs in both groups. Significant correlations were found (i) between the individual serum PAH congeners (p < 0.01) and (ii) between serum PAHs and total lipids (p < 0.01). According to the questionnaire data, high traffic volume and outdoor hobbies were the only contributory factors that confirmed significant relationships with serum PAHs (p < 0.001). Health risk assessment was computed using benzo(a)pyrene (BaP) equivalent (BaPeq) and demonstrated that the Kuala Lumpur group has twofold greater carcinogenic risk than the Hulu Langat group (16.11 vs 7.76 ng g−1 lipids). Our study reveals that traffic volumes notably impact serum PAH levels and general health among the Malaysian population.

Speed and lateral distance behavior on highly frequented bicycle facilities in Germany

Due to increasing bicycle traffic demands in Germany, more and more bicycle facilities are designed with widths beyond the standard values given in the existing design guidelines. To date, only few findings are available regarding the traffic flow at bicycle facilities with high volumes, especially when comparing different facility types and percentages of specific bicycle types. For this, the driving behavior of cyclists at high traffic volumes was analyzed, considering different bicycle types. Traffic flow was examined on segments of bicycle facilities with different geometric characteristics, including facility type, gradient, and width. The speed and lateral distance behavior of cyclists was analyzed by using radar and video measurements. It was found that the speed and lateral distance behavior in bicycle traffic is particularly influenced by the gradient and the composition of bicycle types. Furthermore, the results show that the number of bicyclists in the cross section, the width of the facility, the type of interaction (meeting, passing or riding next to each other at the same speed) as well as the type of the constructional boundary influence the lateral distance behavior. In general, it could be observed that bicyclists make use of the available width of the bicycle facility and choose larger distances in case of passing or meeting. Furthermore, it was observed that a minimum lateral wheel distance of 0.80 m is maintained during passing both in one- and two-directional traffic. This minimum lateral distance is already included in the current German design guidelines.

Impacts of connected autonomous vehicle platoon breakdown on highway

Connected Autonomous Vehicles (CAVs) are seen as an opportunity to improve traffic efficiency and safety. However, further studies are needed to prove such positive effects. Particularly, there is a lack of quantitative research on the impacts of CAV breakdown in mixed traffic flow conditions (i.e., CAVs and Human Driven Vehicles, HDVs). The aim of this research is to explore the impacts generated by the breakdown of the leading vehicle of a CAV platoon in mixed traffic conditions, which in turn generates CAV platoon breakdown (i.e., platoon dispersion), by considering several CAV market penetration rates (MPRs) and platoon size. In this perspective, a control algorithm (“avoidance algorithm”) for modelling CAVs and HDVs behaviour to avoid obstacles (i.e., breakdown CAV platoon) has been developed and tested on some simulation scenarios in order to derive key traffic flow parameters. The dynamic characteristics of traffic flow with CAV platoon breakdown have been explored for both low and high traffic flow volumes. Finally, potential conflicts, congestions, as well as energy consumption and CO2 emissions resulting from the breakdown of CAV platoons in mixed traffic streams have been assessed and discussed. Results suggest that (1) CAV platoon breakdown can reduce the traffic capacity by about 20 %; (2) higher CAV MPRs are more suitable for enhancing highway safety even in breakdown conditions, decreasing energy consumption, and reducing CO2 emissions; (3) platoon size should be limited to 4, since larger sizes affect traffic safety, increases vehicle average delay time as well as energy consumption. The obtained results provide useful insights for transportation planners and transport infrastructure management companies to design and apply policies aimed at improving driving conditions, traffic quality, and safety on highways.

Peningkatan Kinerja Simpang Ciceri, Kota Serang dengan Model Penerapan Ruang Henti Khusus

The Ciceri intersection is in a densely populated urban area characterized by heavy traffic, particularly motorbikes. This is due to the nearby educational institutions, shops, offices, and a shopping center. The intersection faces numerous traffic conflicts, including crowded vehicle queues, irregular queuing by motorbikes, and improper lane usage by two- and four-wheeled vehicles. The phase setting of traffic signaling devices (APILL) at the Ciceri intersection has been implemented but has not solved the problem. This research aims to analyze the use of RHK to improve the performance of the Ciceri intersection using the Vissim model. The method is to test or simulate various RHK models at the research location. The results obtained can be applied to the RHK at two intersection legs, namely east and west, with a box-type RHK measuring 8m long and 2 x 3.5m wide, increasing the level of Service (LOS) from E to F. The results show a reduction in queues of 5.64 m and delays of 1.52 seconds on the north leg, as well as a reduction in queues of 7.85 m and a delay of 13.48 seconds on the south leg. These results show that RHK can effectively improve the performance of intersections with high traffic volumes without the need to increase physical road capacity.

Trip Assignment Modeling for Transportation in Denpasar City Using PTV Visum Software

Mapping and macro transportation modelling are methods used to obtain an overview and analysis of transportation conditions at the macro level, encompassing all road segments in urban areas. Critical constraints on road segments can be identified through this process. Macro transportation modelling serves as a tool for studying the implementation of problem-solving policies, allowing the anticipated outcomes to be analysed through simulations in transportation modelling. This research has a specific goal: to understand the travel patterns of the population in the city of Denpasar. Calibration and validation processes in the PTV Visum modelling are conducted to test the validity of the created macrosimulation model. The trip assignment results are generated through PTV Visum modeling. The research methodology involves quantitative analysis using household interview surveys, traffic counting, correlational studies, and macrosimulation modelling. Calibration is performed by calculating the beta coefficient value (with specific calculated values), time matrices, and distance matrices to measure travel impedance functions. Validation is conducted by evaluating the Generalised Exponential Horn (GEH) value, which is less than 5, to ensure the alignment between travel output in the model and field survey data from traffic counting. From the analysis of a sample of 2,725 households, a total of 2,165,876 daily trips were obtained. Types of trips include movements from internal to internal zones (2,115,602 trips), from internal to external zones (28,799 trips), from external to internal zones (19,319 trips), and from external to external zones (2,159 trips) per day. Roads with the highest traffic volume in a single day include Hasanudin Street (42,524 pcu/hour), Diponogoro Street (42,282 pcu/hour), Sudirman Street (31,062 pcu/hour), Sutoyo Street (30,180 pcu/hour), and Sumatra Street (23,515 pcu/hour).

Simulation-Oriented Analysis and Modeling of Distracted Driving

Distracted driving significantly affects the efficiency and safety of traffic flow. Modeling distracted driving behavior in microscopic traffic flow simulation is essential for understanding its critical impacts on traffic flow. However, due to the influence of various external factors and the considerable uncertainties in behavior characteristics, modeling distracted driving behavior remains a challenge. This study proposed a model which incorporates distraction features into the microscopic traffic flow model to simulate distracted driving behavior. Specifically, the study first examines the characteristics of distracted driving, including the intervals and durations of distraction events, as well as the patterns and environments of distraction. It then introduces distraction parameters into the Intelligent Driver Model (IDM), including reaction time delays and perception deviations in both speed difference and following distance. These parameters are quantified by probabilistic distributions to reflect the uncertainty and individual differences in driving behavior. The model is calibrated and validated using 772 distracted following events from the Shanghai Naturalistic Driving Study (SH-NDS) data. Three patterns of distraction (excessive, moderate, mild) are distinguished and modeled separately. The results show that the model’s accuracy surpasses that of the IDM under various road types and traffic volumes, with an average improvement in model accuracy of about 11.30% on expressways with high traffic volume, 4.54% on expressways with low traffic volume, and 4.46% on surface roads. Meanwhile, the model can effectively simulate the variations in reaction times and perceptual deviations in both speed and following distance for different distraction modes at the individual level, maintaining consistency with reality. Finally, the study simulates distracted driving behavior under different road environments and traffic volumes to explore the impact of distracted driving on traffic flow. The simulation results indicate that an increase in the proportion of distraction reduces the efficiency and safety of traffic flow, which is consistent with real-world observations. Since the model considers human distraction factors, it can generate more dangerous driving scenarios in simulations, which holds significant importance for safety-related research. The findings from this study are expected to be helpful for understanding distracted driving behavior and mitigate its negative influence on the efficiency and safety of traffic flow.

Assessing Level of Service, Capacity and Future Optimization of Roundabouts in Rajshahi City Using Sidra Intersection

This study investigates traffic flow challenges at the bustling Mintu Chattar roundabout Situated in Laxmipur Mor, Rajshahi, Bangladesh. Located in close proximity to a hub of hospitals and medical establishments, this intersection routinely witnesses traffic congestion, becoming a familiar occurrence in the area. From dawn to dusk, traffic officers are required to step in to manage the traffic flow of countless individuals commuting to hospitals, workplaces, educational institutions, and other destinations. Our capacity analysis using Sidra Intersection software reveals significant bottlenecks, particularly during peak hours, necessitating traffic police intervention due to inadequate control measures. The research identifies key contributing factors: insufficient entry and circulatory lanes, uneven traffic distribution, and high overall traffic volume. A detailed case study of the south leg highlights the severity of the situation, with Level of Service (LOS) dropping to F during evening hours, resulting in long queues (over 61 vehicles) and delays exceeding 82 seconds per vehicle. The integration of the design life model from SIDRA software has also illustrated the correlation between Level of Service (LOS) and capacity with the projected rise in traffic volume for the next five years. In order to improve traffic flow efficiency, the study proposes exploring design optimization of the roundabout. This includes investigating the impact of geometric redesigning, such as gradual expansion of approach lanes width, on capacity at the roundabout leg, considering both existing and future traffic scenarios.

An adaptive coupled control method based on vehicles platooning for intersection controller and vehicle trajectories in mixed traffic

AbstractConnected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 &lt; R &lt;1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

A low-carbon route optimization method for cold chain logistics considering traffic status in China

In the paper of cold chain logistics, it is necessary to consider both traffic status and carbon reduction simultaneously. In this paper, an improved BPR (Bureau of Public Roads) road impedance function is designed, which reflects the traffic status in China under congested status more accurately. A low-carbon cold chain logistics route optimization model considering the traffic status in China is constructed, considering the multiple impacts of traffic status on total delivery cost, freshness of fresh products, and carbon emissions. To address the challenges posed by this multidimensional and discrete model, an improved discrete firefly algorithm (IDFA) is proposed. The numerical result demonstrates that IDFA has higher solving efficiency and better quality of the obtained solutions. Moreover, this paper also analyzes the impact of traffic status, vehicle types, and carbon trading prices on cold chain logistics delivery, as well as the comprehensive impact of traffic status and carbon trading prices. The proposed model is suitable for cold chain logistics enterprises with a single delivery center, active emission reduction willingness, and mostly delivering in congested road sections with high traffic volume. It offers valuable guidance for decision making and management in low-carbon cold chain logistics delivery.

Separable contextual graph neural networks to identify tailgating-oriented traffic congestion

Identifying largescale traffic congestion with thousands of robots is a complicated task involving structured-data graphical modeling, and it has recently gained more research attention. This study aims to model traffic metrics that exhibit negative correlations in specific spatiotemporal contexts of automated material handling systems in semiconductor manufacturing. Numerous studies have focused on detecting vehicle congestion using multivariate time-series data. However, they faced challenges regarding the understanding of the complex graph structural joint spatiotemporal dynamics of the traffic rails. Existing methods fail to distinguish between congestion caused by high traffic volumes and nondisruptive delivery robot operations, such as parking and hoisting. This study introduces a novel approach to detect tailgating-oriented congestion using separable contextual graph neural networks (SC-GNNs). The proposed method employs deviation-based anomaly detection with attentive GNNs to capture spatial correlations and temporal dependencies. SC-GNNs were proposed for dual metrics with distinct characteristics, and dynamic thresholds were applied for each GNN to ensure reliable and interpretable anomaly detection. To demonstrate the outperformance of the proposed method, we conducted simulation experiments via a high-fidelity simulator similar to actual semiconductor fabrication. The experimental results showed that our method achieved both a 36% reduction in the false positive rate and a 32% decrease in the equal error rate compared to the best performance of existing state-of-the-art anomaly detection models. Overall, the proposed approach significantly improved the accuracy and interpretability of anomaly detection in multivariate time series, particularly effective in the context of identifying anomalies related to traffic congestion tailgating.

Studying the effectiveness of project solutions using the transport modeling

One of the factors that lead to an increase in congestion on the road network is the growth of motorization levels in the city. Under such conditions, the number of private vehicles on city streets is increasing as delays at intersections and queues. As a result, the passage of the intersection is delayed, as drivers are forced to wait longer for their turn. When reconstructing a street or road, it is proposed to change the traffic management, the number of lanes and the configuration of some intersections to solve the problems described above. However, after the project is implemented, traffic flows are mostly redistributed, and the proposed changes may not be effective. It is imperative to conduct transport modeling, which allows checking the effectiveness of decisions and adjusting the project in the early stages, but not after implementation, to prevent the negative impact of such situations. When performing transport modeling, unnecessary investments that may arise after implementing the project can be avoided, or the feasibility of its implementation can be checked. The object of research is a section of the road network located in a settlement. It is worth noting that all approaches to intersections have high traffic volumes, which should be taken into account when creating project documentation. This article presents a design solution that includes reconfiguring the intersection, increasing the number of lanes, and conducting transport microscopic modeling of the existing and projected conditions to determine the effectiveness of changing the traffic management on the studied section of the road network. According to the results of the microscopic modeling, delays and queues on the approaches to the intersection have been reduced, which indicates that the decision was correct. PTV VISSIM software was used to perform transport modeling. After testing the simulation model and analyzing the results, it is possible to assess the impact of design decisions on the road network and, if necessary, make adjustments.