Transport Data Research Articles

Prediction of vertical transport of microplastics: Shape- and aging-dependent drag models

The prediction of vertical transport of microplastics (MPs) is essential for understanding their multidimensional transport, fate, and environmental risks, but drag models applicable to aging MPs are currently understudied. In this study, pristine and UV-aged polyethylene terephthalate (PET) and polystyrene (PS) MPs were used for settling experiments. Combining physicochemical properties and transport data, a shape-dependent drag model based on the Corey shape factor was optimized with average errors of 9.73% and 10.42% and coefficients of determination of 0.6878 and 0.8359 for predicting the settling terminal velocities (ut) for PET and PS MPs, respectively. Meanwhile, aging-dependent drag models were constructed by incorporating the carbonyl index as functional forms of the newly defined aging index, which can be used to differentiate the effects of shape and aging characteristics on the vertical transport of MPs. These aging-dependent models showed better predictive abilities with average errors of 3.97% and 4.56% in predicting ut for PET MPs, and of 5.89% and 6.91% for PS MPs. Additionally, the drag models in this study improved applicability to predict vertical transport of environmentally-collected weathered MPs. With the continuous improvement of the transport database of diverse MPs, this study is expected to provide scientific support for predicting the environmental behaviors of MPs and formulating targeted pollution control strategies.

Transport challenges in Brazil and research opportunities

Located in South America, Brazil has continental dimensions and a population of approximately 203 million. As expected, the country has many mobility challenges and issues, as well as various research gaps and opportunities to promote improvements in urban transportation planning. This article aims to present some of these mobility challenges, such as the lack of transportation surveys and data in many Brazilian cities, decreased demand for public transport, increased demand for ridesourcing, telecommuting, COVID-19, and freight transport. We also mentioned the National Association for Transportation Research and Teaching (ANPET in Portuguese) and its important role in promoting annual conferences and supporting transportation research and education in the country. After making some critical reflections, we have outlined various research challenges that aim to improve the future of mobility in Brazil.

Cyclic Generative Adversarial Networks with KNN-transformers for missing traffic data completion

In the face of the huge amount of intelligent transportation data, it is necessary and important to collect and statistically process it. Due to adverse weather conditions, sensor malfunctions and other reasons, the collected data inevitably contains missing data. Aiming at the phenomenon of missing traffic data, we propose an interpolation method of missing traffic data based on Cyclic Generative Adversarial Networks with hybrid KNN-Transformer method (KT-CyclicGAN). This method effectively utilizes K-Nearest Neighbor as prior knowledge to guide network training, employs transformers to extract the spatiotemporal relationships present in traffic data, and reconstructs missing data. In GANs, it uses a multi weight sharing cyclic structure to thoroughly learn the spatiotemporal sequences in the traffic data, resulting in accurate imputed data. We assess the performance of the proposed model using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, and the Concordance Correlation Coefficient (CCC), while simulating three missing data scenarios on the PEMSD4 dataset. The experimental results show that the algorithm proposed in this paper can deal with various missing scenarios and missing rates more effectively than the other five algorithms. Even with a high missing rate of up to 90%, the data imputed by KT-CyclicGAN can still fit the real data quite well.

Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace

Urban transportation data are crucial for smart city development, enhancing traffic management’s intelligence, accuracy, and efficiency. This paper conducts a comprehensive investigation encompassing policy analysis, a literature review, concept definition, and quantitative analysis using CiteSpace from both domestic and international perspectives. Urban transportation data comprise multiple dimensions, such as infrastructure status, real-time monitoring, policy planning, and environmental assessment, which originate from various sources and stakeholders. Highly influential authors and active institutions, particularly in the USA, China, Canada, and England, contribute significantly to extensive and collaborative research. Key areas include intelligent transportation, traffic flow prediction, data fusion, and deep learning. Domestic research focuses on practical applications, while international studies delve into interdisciplinary research areas. With advancements in intelligent systems and big data technology, research has evolved from basic data collection to sophisticated methodologies, such as deep learning and spatiotemporal analysis, driving substantial progress. This paper concludes by recommending enhanced data integration, improved privacy and security, fostering big data and AI applications, facilitating policy formulation, and exploring innovative transportation modes, thereby underscoring the importance of urban transportation data in shaping the future of smart cities. The findings provide theoretical and practical guidance for the future intelligence, efficiency, and sustainability of urban transportation systems.

Traffic safety at level crossings in the Czech Republic

The article deals with the issue of evaluation of the causes of accidents at level crossings in the Czech Republic, including the prediction of the development and proposals of measures for prevention and thus reduction of their number. The goal of this work is to verify the hypothesis that the number of accidents at level crossings in the Czech Republic is decreasing. The authors use available data from the Ministry of Transport and process statistical data for ten years (2013–2022) of railway operation. Accidents are divided into groups related to accidents at level crossings and the result is expressed in graphical form. These data are then processed and evaluated using the method of regression data analysis. The results of the work are also new proposals and recommended measures to reduce the accident rate at level crossings.

Modulating the Electrical Transport in Superconducting NbC Crystals by Fractal Morphology.

The self-similar fractal morphology mediated by nonequilibrium processes is widely observed in low-dimensional materials grown by various techniques. Understanding how these fractal geometries affect the physical and chemical properties of materials and devices is crucial for both fundamental studies and various applications. In particular, the interplay between superconducting phase fluctuations and disorder can give rise to intriguing phenomena depending on the dimensionality. However, current experimental studies on low-dimensional superconductors are limited to two- and one-dimensional systems, leaving fractional dimensional systems largely unexplored. Here, we use chemical vapor deposition to successfully synthesize ultrathin NbC crystals with a well-defined fractal geometry at the nanoscale. By performing electrical transport measurements, we find that both the superconducting and normal-state properties are strongly affected in the fractal samples, where the intrinsic and geometric disorder is induced. In contrast to the 2D crystal, the fractal NbC crystals show a significant low-temperature resistive upturn before the onset of superconducting transition, which can be attributed to the disorder-enhanced electron-electron interaction effect. From transport data analysis, we demonstrate that the superconducting transition in NbC is correlated to the strength of disorder and the fractional dimensions, revealing that nanoscale fractal structures can significantly modify the electronic properties of low-dimensional superconductors. Our work paves the way for the explorations of mesoscopic transport and intriguing superconducting phenomena in fractional dimensions.

Life cycle assessment of concrete incorporating all concrete recycling products

This study evaluates the environmental impact of concrete incorporating locally available raw materials through life cycle assessment (LCA). It explores the integration of all concrete recycling products in the concrete mixes, including recycled concrete aggregates (coarse and fine) as alternative aggregates and ternary blended cements made with recycled concrete powder and waste perlite powder. With the declining availability of conventional supplementary cementitious materials like fly ash and blast furnace slag, alternative resources are essential. The developed concrete mixes reduced the climate change impact by up to 12 % when ternary blended cements were used instead of ordinary Portland cement with the same aggregate mix. This can be attributed to the utilisation of industrial waste in the binder, which are locally available without any environmental burden other than transportation. The study maintains the same workability class and assesses the competitiveness of the developed mixes across strength classes, with notable environmental benefits recognised for the C50/60 strength class. A comprehensive assessment of the life cycle inventory (LCI) of the raw materials revealed significant variability between different sources for recycled aggregates, demonstrating the necessity for adopting a range of LCI data for alternative raw materials in the absence of specific data. Transparent reporting of production and transportation data is essential to improving LCA accuracy. The study shows the feasibility of developing circular concrete mixes that incorporate all products from the concrete recycling process, highlighting that optimising material performance and reducing reliance on conventional materials are crucial for increasing their attractiveness to the construction industry.

From roads to roofs: How urban and rural mobility influence building energy consumption

Understanding the relationship between travel behavior and building energy use at an urban scale is crucial for developing effective energy management strategies. Mobility patterns significantly impact building occupancy, which in turn affects energy consumption. However, existing methods often focus on individual buildings, whereas geographical influences on energy usage are not adequately examined. This study addresses this gap by using transportation origin-destination (OD) data to estimate building occupancy and energy. The proposed method assigns OD trips from census block groups to the building level, incorporating building, travel survey, and census data to derive building occupancy profiles. This method was applied to urban and rural areas with 4062 buildings in 70 census block groups. We found that the OD-informed occupancy profile exhibits smoother energy consumption patterns compared with that of Department of Energy reference occupancy profiles. Our analysis reveals distinct building energy consumption patterns among groups with long and short commutes, emphasizing the effect of commute times and work schedules on residential energy usage. This framework is useful for practitioners in transportation agencies and utility companies, enabling the estimation of building energy based on mobility patterns. Overall, this study shows the potential of integrating transportation and building energy data to inform cross-sector energy management strategies.

Burden of Non-Protocolized Patient Transport Outside of Response Area on a Rural Emergency Medical Services System

Objectives Transport destination decisions by prehospital personnel depend on a combination of protocols, judgment, patient acuity, and patient preference. Non-protocolized transport outside the service area may result in unnecessary time out of service and inappropriate resource utilization. Scant research exists regarding clinician rationale for destination decisions. Methods We retrospectively reviewed one year of scene transports by a single rural, hospital-based emergency medical services (EMS) system. We collected dispatch, patient demographic, primary impression, and transport data from prehospital records and matched them to emergency department (ED) data. We characterized rationale for transport decisions and compared rates of hospital admission and specialist consultation in the ED as surrogates for decision appropriateness. Results We reviewed 2,223 patient transports, 281 of which were transported out of the service area. The most common reasons for out-of-area transport were patient preference NOT related to prior medical care (40%) and clinician judgment (24%). Admit rates were highest for per protocol (85%) and patient preference related to prior medical care (67%) groups and lowest for no explanation (41%) and clinician judgment (47%) groups. Rates of in person specialist consultation in the ED were highest in per protocol (69%) and clinician judgment (47%) groups and lowest in no explanation (23%) and patient preference NOT related to prior medical care (30%) groups. Clinician judgment was less predictive of admission and specialist consultation for non-trauma and pediatric patients than for all patients. Median time out of service was more than twice as long for out-of-area transports (140 min) compared to patients transported to the nearest facility (62 min). For out-of-area transports discharged from the ED without specialty consultation (n = 104), ambulances traveled an additional 52 miles/patient compared to theoretical transport to nearest facility. Conclusions Unit out of service time more than doubled for non-protocolized transports outside of the service area and rationale for destination decisions variably predicted admission and specialist consultation rates. Patient preference NOT related to prior medical care and, in pediatric and non-trauma populations, clinician judgment, were less predictive of admission and specialist consultation. Transport guidelines should balance rationale for transport destination and patient characteristics with resource preservation, especially in low-resource systems.

In-situ activation of persulfate by emplaced magnetite nanoparticles for degradation of 1,2-dichloroethane in porous media

In this study, column experiments were conducted to explore on the method of emplacement of magnetite nanoparticles (MNPs) for in situ activation of persulfate (PS) in sand porous media to degrade 1,2-dichloroethane (DCA), a typical recalcitrant chlorinated compound. Different molar ratios between PS and DCA (0:1, 2:1, 5:1 and 20:1) and mass contents of MNPs in the sand (0 %, 1.9 % and 5.4 %) were tested. In the absence of MNPs, degradation of DCA was negligible for a hydraulic retention time of 7 h. Presence of MNPs at the content of 1.9 % enhanced degradation of DCA and the highest removal efficiency (34.2 %) was observed at the PS-to-DCA molar ratio of 5:1. At the MNPs content of 5.4 %, increase of the PS-to-DCA molar ratio from 2:1 to 20:1 lead to not only increase in DCA removal efficiency but also substantial enhancement in chloride production, indicating that high PS concentration could cause significant degradation of the Cl-containing intermediates. In contrast to MNPs, Fe3O4 solids with much larger size (∼1 μm) were much less effective in activating PS for DCA removal even at a significantly higher content in the medium. The transport data could be well fitted by the one-site chemical nonequilibrium model, which showed kinetic DCA sorption to the MNPs as a key process for the transport. In the long-term injection experiment, a stable and significant removal of DCA (∼50 %) was observed for 254 days at the MNP content of 1.9 %. The results of this study show the potential of MNPs as a sustainable PS activator in injection-based in situ chemical oxidation for groundwater remediation.

Deconvolution of light- and heavy-hole contributions to measurements of the temperature-dependent Hall effect in zincblende copper iodide

This study presents a detailed experimental investigation of conductivity and Hall effect measurements in copper iodide (CuI), which is complemented by density-functional theory and Boltzmann-transport calculations. We have evaluated the temperature dependence of the transport data of solution-grown bulk single crystals, textured thin films grown by pulsed laser deposition, and additional data published in the literature. The density of compensating donorlike defects is determined from fits comprising ionized-impurity scattering. This is considered for determining acceptor densities and corresponding activation energies. In the course of this investigation, meticulous attention has been paid to the intricate interplay between light-hole and heavy-hole contributions, as well as the experimental values observed in zincblende CuI. This study has yielded two distinct ionization energies for acceptors in unintentionally doped CuI, with values of EI,1=95 meV and EI,2=330 meV, respectively. Published by the American Physical Society 2024

Public Transport Delay Prediction using Deep Learning

A system of trains, buses, subways, ferries, and other vehicles that are accessible to the public is referred to as public transportation, also known as public transit or mass transit. These networks are usually run by private or public organisations are made to efficiently transport large numbers of people in urban and suburban areas. It usually operates on set schedules and routes and has a listed fare for each journey. Long wait periods and lengthy travel times resulting from delays are two problems that frequently plague public transportation services. There are several things that can cause a delay in public transport, such as an excess of passengers, heavy traffic, accidents, and other unforeseen circumstances. The availability of a more accurate delay prediction for public transportation might increase users’ confidence and their willingness to pay more for transit services. Over the past two decades, a number of studies on prediction algorithms for transportation data have been proposed. Most of the work is on machine learning model development, focusing on delay prediction and taking into account several factors such as weather conditions and infrastructure issues. This paper proposes a deep learning model to predict public transportation delays using data from public transportation and the weather. The results obtained from this research work are compared with several other existing works. Our experiment has demonstrated that the deep neural network (DNN) is the best model to predict transit delay compared to several other machine learning and deep learning models.

Spatial and Temporal Variations in Offshore Crude Oil Transportation: Insights From China's Coastal Ports

ABSTRACTAnalyzing the spatial and temporal variations of offshore crude oil transportation at China's coastal ports is critical for the country's energy security. The automatic identification system provides high‐resolution real‐time data on China's offshore crude oil transportation, offering more precise spatial and temporal insights than existing statistical studies. In this study, we propose a novel method for calculating offshore crude oil transportation volumes by considering the operational status of crude oil tankers as “empty” or “full.” This approach enhances reliability and provides detailed port‐level transport data. Our findings reveal notable similarities between the centrality measures of maritime oil transportation and oil tanker networks, but significant differences in community divisions. Specifically, in the tanker networks, Chinese ports are categorized into three regions, with one region being dominant and the other two regions containing only four ports. In contrast, the oil flow networks show China divided into two major regions and two minor ones. This study offers valuable contributions to strengthening energy security in China and supports informed policy development and implementation.

Bus arrival and departure time updates in the Greater Sydney Area

Public Transport Authorities generate large quantities of data as part of their daily operations, including vehicle positions, arrival times, and dynamic routing options. This information is essential feedback into the system for planning routes and timetables, modelling passenger demand growth and evaluating operational performance. The General Transit Feed Specification (GTFS) is a data format that allows public transport data to be consumed by a wide variety of software applications. There are barriers to widespread consumption of GTFS data related to location-specific data extensions, non-human-readable formats and error cleaning. This paper describes a flexible dataset of actual bus arrivals and departure times created with a pipeline for GTFS realtime feeds designed to address these challenges. The paper describes the pipeline, verifies the quality of the data and presents an output of 25 months of actual bus arrival and departure times for Sydney, Australia. We conclude by discussing relevance to researchers and practitioners of the pipeline outputs in general and the sample data specifically.

Traffic and transportation management data storage terminal based on Internet of Things

Traditional data storage models are inadequate in the face of the growing demand for big data in transportation and transportation management. Its poor horizontal scalability makes it difficult to deal with the growth of massive data; on the other hand, its complex management makes it challenging to achieve unified management and effective resource utilization due to the differences in equipment from different manufacturers. In order to effectively store and manage this huge amount of information, it is urgent to rely on advanced technical tools. In this context, while ensuring data security and simplifying data management, it is also necessary to meet the terminal’s demand for high real-time performance, and these factors jointly promote the continuous attention and improvement of data storage terminal performance. This paper proposes a Hadoop solution based on distributed computing. As a distributed system infrastructure, Hadoop allows users to develop distributed programs without a deep understanding of distributed details, fully using the high-speed computing and storage capabilities of Hadoop clusters, which is especially suitable for big data processing tasks on the Internet of Things (IoT) platform. The experimental results show that for a 10 GB data file, the traditional terminal (Terminal 1) can store 7.8 GB, while the Hadoop-based terminal (Terminal 2) can store 9.9 GB. For 50 GB of data files, Terminal 1 and Terminal 2 can store 40.4 GB and 49.8 GB of data respectively. These results show that Hadoop terminals have significant advantages in processing large-scale data, especially in terms of data storage efficiency, and can use storage resources more effectively to meet the high-performance requirements of traffic and transportation management for data storage terminals.

Self-organisation and megaregional transport planning along the North Coast of Java

This article explores the intricate relationship between self-organisation and planning in megaregional transportation dynamics, using the North Coast of Java as a case study. Focusing on circular migration during the Eid holiday season, the study identifies the crucial roles of symmetry breaks, feedback loops and interventions in shaping transportation patterns. Specifically, the article employs qualitative methods, including interviews with key stakeholders and analysis of transportation data, to investigate the dynamics of circular migration. The findings reveal how planning interventions influence outcomes, emphasise the importance of multilevel governance, and offer insights for planning in complex systems. This work enhances our understanding of self-organisation and planning, providing valuable guidance for urban practitioners dealing with complex socio-spatial systems.

Exploration of the Application of Aesthetics in Transportation Teaching

Transportation is the foundation and prerequisite for the development of human society and economy, and also has aesthetic significance in beautifying human life. The development of transportation is a shift from extensive resource consumption to coordinated resource intensive use, and it is also a development in aesthetics. Guiding students to discover the beauty of transportation vehicles, the harmonious integration of transportation infrastructure and nature, the beauty of transportation data, and the beauty of the behavior and soul of transportation participants, igniting more students' curiosity for transportation majors, and inspiring their enthusiasm for the transportation industry, is the central goal of the construction of the Beauty of Transportation course.

Effect of Check Dams on Sediment Concentration in a Watershed in the Loess Hilly Area During a Heavy Rain Event

ABSTRACTUnderstanding the changes of sediment concentration in rivers, especially under heavy rains, is of great significance to accurately evaluate the effectiveness of soil and water conservation measures in the Loess Hilly Area. On July 26, 2017, an extremely heavy rain (maximum rain intensity of 66.6 mm/h, the highest rainfall of 256.8 mm in 10 h, and a return period of 100 years) occurred over the 821 km2 Xiaoli River watershed in the Wuding River watershed. In order to understand the sediment concentration during the heavy rain event, 10 dam‐controlled catchments in the center of the rainstorm (3 in the Xiaoli River watershed and 7 in its adjacent watersheds) without drainage and damage were selected. All the retained runoff and sediment in the check dams were measured by the cross‐sectional survey according to the flood marks left by the heavy rain. Combined with the observed runoff and sediment transport data by the hydrological station at the outlet of the Xiaoli River watershed as well as the deposited sediment investigated in the watershed during the heavy rain event, the change of sediment concentration and the role of check dam were analyzed. Results showed that under heavy rain, all the sediment concentrations of runoff in the dam‐controlled catchments ranged from 344 to 552 kg/m3, suggesting that the sediment retaining effect of slope measures (woodland, grassland, and terrace) is limited and cannot prevent the occurrence of hyperconcentrated flows. The gully measures (check dams) in the Xiaoli River watershed can hold a large amount of sediment under heavy rain and make the runoff no longer a hyperconcentrated flow at the watershed outlet. In order to control the transport of hyperconcentrated flows to the downstream, more attention should be paid to the construction of check dams while carrying out vegetation ecological construction in the Loess Hilly Area.

Application of Historical Comprehensive Multimodal Transportation Data for Testing the Commuting Time Paradox: Evidence from the Portland, OR Region

There have been numerous theoretical and empirical transportation studies contesting the stability of commuting time over time. The constant commuting time hypothesis posits that people adjust trip durations, shift across modes, and sort through locations, so that their average commuting time remains within a constant budget. There is a discrepancy between studies applying aggregate analysis and those using disaggregate analysis, and differences in data collection may have contributed to the varying conclusions reported in the literature. This study conducts both aggregate and disaggregate analyses with two travel surveys of the Portland region. We employ descriptive analysis and t-tests to compare the aggregate commuting times of two years and use regression models to explore factors affecting the disaggregate commuting time at the individual trip level to examine whether the stability of the commuting time remains after substantial changes in the transportation and land use systems. Our study indicates that the average commuting time, along with the average commuting distance, increased slightly, as the mode share shifted away from driving during the examined period. The growth in shares of non-driving modes, which are slower than driving, coupled with an increased travel distance, contributed to the small increase in the average commuting time. Our analysis also indicates that the average travel speed improved for transit riders as well as drivers, contradicting earlier research that claims that public transit investment has worsened the congestion in Portland.

Incident reporting and data monitoring of sexual violence and harassment on public transport

IntroductionSexual violence and harassment occur in different settings, including public transport, and cause significant harm to victim-survivors' mental and physical health. While sexual violence and harassment on public transport is a documented problem, the public transport sector uptake has been slow and the true prevalence of sexual violence and harassment on public transport is yet to be determined. We aimed to investigate existing public transport and key stakeholder data systems, and explore the views of service providers, to ultimately improve sexual violence and harassment incident reporting and data monitoring. MethodsUsing a mixed methods design, researchers worked with public transport providers and other interested stakeholders in one Australian state. Twelve organizations were involved in the project and six provided current data on public transport safety for analysis. An audit of public transport and stakeholder websites was also undertaken to assess sexual violence and harassment incident reporting pathways. In addition, seven managers from five public transport organizations were interviewed to capture views on gender sensitive data collection and monitoring, and the challenges and enabling factors facing the sector. ResultsThe results uncovered varied sexual violence and harassment reporting formats and subsequent limited reporting of sexual violence and harassment incidents on public transport. In addition, poor-quality data sets lacked gender sensitivity, comprehensiveness, comparability, reliability and accessibility. Three key themes from public transport providers, on sexual violence and harassment data collection included increasing incident reporting, barriers and enablers of collecting and analysing quality data and governance frameworks. A minimum data set collection tool has been developed and could be tested in future. ConclusionTo improve women's safety on public transport, enhanced gender-sensitive data collection and monitoring is required. Long term government investment in sexual violence and harassment primary prevention strategies on public transport are urgently needed.