Daily Transport Research Articles

Sediment load prediction in Johor river: deep learning versus machine learning models

Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life’s natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison.

Simulating daily sediment transport using the Water Quality and Sediment Model (WQSED)

IntroductionHigh erosion and sediment yield rates continue to pose a significant threat to the environment globally. Information on erosion and sediment rates is key for supporting effective and sustainable mitigation measures. Models that estimate sediment yield are vital in providing information about erosion and sediment yield rates, as empirical studies are prohibitive over large spatial and temporal scales.MethodsIn this study, we simulate daily sediment transport using the WQSED model and assess the effectiveness of the tool in providing crucial estimations of sediment yield. The model structure links the Modified Universal Soil Loss Equation (MUSLE) to a simple sediment storage component. The model was applied to the Odzi River catchment in Zimbabwe and The Rio Tanama River catchment in Puerto Rico, where daily observations of sediment yield exceeding a decade were available for calibration and validation.ResultsIn both catchments, we achieved a coefficient of efficiency and R2 and NSE of > 0.7 during model calibration and > 0.6 during model validation. The percentage bias remained below 45% for both calibration and validation periods.ConclusionThese results indicate that the WQSED model can be applied to provide estimates of sediment yield that are reliable for erosion, sediment yield and water quality management. An effective and relatively simple sediment yield model incorporating sediment storage is essential for catchment management in erosion-prone areas.

Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot

With the rapid development of the Internet of Things (IoT), the logistics and transportation industry is booming. At the same time, with the advancement of AI technology, intelligent logistics is also gradually emerging, and the purpose of intelligent logistics is to use different types of automatic guided transport machines to replace people to handle and move products. At the same time, with the help of big data, cloud computing, artificial intelligence, sensor technology, and other technologies, we can achieve logistics automation. However, the current logistics robot creation platforms are diverse, which makes intelligent logistics robots diverse in variety and wide in application and also makes the creation and use of robots more challenging. Robot Operating System (ROS) is an open-source software platform that supports programming in multiple languages and has excellent adaptability. In addition, most of the currently used path planning focuses on a single target point, which is insufficient to support the current needs of multitasking in intelligent logistics. Therefore, this paper aimed to design an intelligent logistics management system based on ROS robot and proposed to use the A-star algorithm to calculate the shortest path of the robot so as to achieve the optimal path. In the simulation experiment, 20 ROS robots were selected and divided into two groups. In the logistics warehouse of different transportation nodes, 20 ROS robots were set up to transport goods of different weights in the experiment, and the transportation data were collected at last. The final simulation results have shown that the power consumption and response delay performance of the ROS robot are good, and the logistics transportation speed is significantly improved. In addition, compared with the traditional transportation method, the daily transportation weight of each robot is up to 310.1% and the monthly profit is up to 171%, which shows that the intelligent logistics management system designed in this paper is more efficient in logistics and transportation and can bring more profits.

Techno-economic analysis of hydrogen storage and transportation from hydrogen plant to terminal refueling station

Economical hydrogen storage and transportation contribute to hydrogen energy utilization. In this paper, for economically distributing hydrogen from the hydrogen plant to the terminal hydrogen refueling station, considering the daily hydrogen demand and transportation distance, firstly a comprehensive techno-economic analysis of the point-to-point hydrogen storage and transportation system based on compressed gas hydrogen (GH), liquid hydrogen (LH), pipeline hydrogen (PH), and liquid organic hydrogen carriers (LOHC) is investigated, respectively. The optimal economical point-to-point energy storage and transportation modes under various transportation distance and daily hydrogen demand are obtained. Then the economic analysis of different hydrogen storage and transportation modes under 1-to-N hydrogen storage and transportation scenario is conducted, which can significantly decrease the cost of LH storage and transportation mode. To step further, the 1-to-N relay hydrogen storage and transportation scenario is constructed to improve the applicability and reduce the unit cost. The economic analysis of 14 kinds of feasible combinations of hydrogen storage and transportation modes is systematically assessed. Regarding to the transportation distance and daily hydrogen demand, the optimal economical 1-to-N relay energy storage and transportation modes are identified. This study may contribute to rationally design and application for economic hydrogen storage and transportation systems.

Exploring the effects of COVID-19 on motorcycle riding patterns and its importance.

A motorcycle refers to a two-wheeled, personal mobility vehicle used for daily transportation and leisure activities. Leisure enables social interaction, and motorcycle riding could be an activity that facilitates social interactions and distancing. Therefore, grasping the importance of riding motorcycles during the pandemic-which involved social distancing and limited leisure activities-can be valuable. However, researchers have yet to examine its potential importance during the pandemic. Therefore, this study aimed to determine the importance of personal space and time spent with others in the context of motorcycle riding during the COVID-19 pandemic. We specifically explored the effects of COVID-19 on riding patterns and importance of riding motorcycle by examining whether these factors differed regarding changes in the frequency of motorcycle riding before and during the pandemic in daily and leisure-oriented transportation. Data were collected from 1,800 motorcycle users in Japan using a web-based survey conducted in November 2021. Respondents replied to questions concerning the importance of personal space and time spent with others attributed to motorcycle riding before and during the pandemic. Following the survey, we conducted a two-way repeated measures analysis of variance (two-factor ANOVA) and performed a simple main effect analysis using the SPSS syntax editor in case of interactions. The valid samples for motorcyclists with leisure motive (leisure-oriented users) and daily transportation motive (daily users) numbered n = 890 and n = 870, respectively (total n = 1,760, 95.5%). Each valid sample was divided into three groups based on the differences in motorcycle riding frequency before and during the pandemic: unchanged, increased frequency, and decreased frequency. The two-factor ANOVA results showed significant differences in the interaction effects for leisure-oriented and daily users regarding personal space and time spent with others. The mean value of the "increased frequency" group during the pandemic indicated significantly higher importance of personal space and time spent with others than the other groups. Motorcycle riding could enable daily transportation and leisure-oriented users to practice social distancing while simultaneously spending time with companions and alleviating loneliness and isolation during the pandemic.

Transportation Service Report Information System Using Prototype Method

CV. Femarse Inti Mulia, a company engaged in the distribution of products, still processes reporting data, especially payments for transportation services, manually using Microsoft Excel. Problems arising from the old system are errors in the calculation of total wages for transport services for each pen, as well as the occurrence of differences in recording by the warehouse and the finance department. In addition, when using Microsoft Excel formulas, typographical errors can occur, causing calculations to be different than they should be. In light of these problems CV. PT. Femarse Inti Mulia needs an information system for daily transportation service wages reports that is capable of calculating daily transportation service wages, storing this data securely, and managing daily transportation service wages reports easily, quickly, accurately, and in a well-organized manner. The use of information systems for daily wage reports is the right solution to solve existing problems. With the prototype software development method and primary data collection, it makes it easier to design the daily wage report information system.

Automatic pothole detection

Road irregularities, such as potholes and road cracks, are a common hazard of daily commute and transportation. Automobile owners everywhere have long worried about the high price of fixing damage caused by potholes. Many accidents caused by potholes can be avoided if road repair agencies can respond quickly. Thus, an automated pothole detection system may help improve this issue. In this work, automatic pothole detection is performed using Faster R-CNN, Sparse R-CNN, YOLOv3, YOLOv5, and YOLOv7 computer vision algorithms. For this purpose, the dataset of road damage images collected in the Slovak region was used. The use of country-specific data is of great importance since the accuracy of pothole detection is dependent on the country of origin of the data. In this work, YOLOv7 achieved the highest detection accuracy of 0.884 (mAP@0.5) followed by YOLOv5 with the accuracy of 0.854 (mAP@0.5) on the pothole dataset.

Testing the resistance of bicycle locks as part of the property crime prevention

Cycling is environmentally friendly, noiseless and emission-free. Cycling is good for your health, it provides you with healthy movement during the day. Cycling is fun, but it is also a lifestyle. The current trend where we are trying not to pollute the environment is just using bicycles for daily transportation is suitable. The use of bicycles gradually increased during the Covid-19 pandemic, but with the increase in bicycle use, the number of bicycle thefts gradually increased. The price of bicycles ranges from €50 to several thousand €. Therefore, it is necessary to protect parked bicycles in public spaces but also at home in the garage by locking the bicycle with a bicycle lock, for example. Just as there is a wide variety of bicycles, there is also a wide variety of bicycle locks. This article deals with testing the durability of different bicycle locks. The result of the tests was that only the lock of class 4 innumerable resisted up to the specified time limit, but only with the hacksaw.

Методика адаптивного регулирования и управления тяговыми ресурсами на полигоне железных дорог

In the context of introduction of railway test site technologies for organization of train traffic, the issue of eliminating the main reasons for inefficiency of freight transportation is acute - uncontrolled delays of locomotives under technological operations, difficulty in forming a single schedule, instability of the daily transportation plan and lack of a suitable train at the time of formation at the locomotive stations. It is necessary to optimize the specific processes of organizing and managing the fleet of traction resources. The methodological framework of the research is based on modern perception about provisions for the synthesis of correct adaptive mechanisms with identification, principles of comprehensive assessment, organizational and economic action of intelligent and automated transport systems of organization and management in production, the methods of expert assessments, Monte Carlo, Delphi, graphic and analytical planning and evaluation of indicators are used, which enable to organize the supply of traction and locomotive crews at the request of carriers by independent commercial structures and individuals on a competitive basis. The mechanism of predictive reduction of losses in the management of traction resources at the railway test site, ensuring the fulfillment of established parameters of the use of locomotives, is given, and the method of adaptive regulation and management of traction resources is proposed, which allows analyzing the production and economic indicators of the use of traction to develop and evaluate the proposed management solutions to improve efficiency of a transportation process.

Hourly forecasting of traffic flow rates using spatial temporal graph neural networks

Traffic congestion forms a large problem in many major metropolitan regions around the world, leading to delays and societal costs. As people resume travel upon relaxation of COVID-19 restrictions and personal mobility returns to levels prior to the pandemic, policy makers need tools to understand new patterns in the daily transportation system. In this paper we use a Spatial Temporal Graph Neural Network (STGNN) to train data collected by 34 traffic sensors around Amsterdam, in order to forecast traffic flow rates on an hourly aggregation level for a quarter. Our results show that STGNN did not outperform a baseline seasonal naive model overall, however for sensors that are located closer to each other in the road network, the STGNN model did indeed perform better.

Do commuters intend to avail electric street cars as public transport? Evidence from urban India

PurposeElectric street car (ESC) is a globally popular clean and safe electric transport system for urban agglomeration. India envisions achieving “all-electric transport” by 2030, yet ESC as a modal transport alternative is not distinct in the policy discussion. The emerging market for electric transportation in urban spaces requires a detailed demand study at the service user level to remove behavioural barriers and design integrated energy planning in developing economies. This paper explores the probabilistic uptake intentions of the daily public transport commuters for ESCs over e-buses from the only Indian city with operational ESCs, Kolkata.Design/methodology/approachUsing a random utility model on primary survey data from daily commuters, the authors identify demographic, psychometric and socio-economic factors influencing probabilistic uptake of ESC over e-buses.FindingsIt estimates that 38% of the commuters demand ESC over e-buses, given the alternatives' comparative details. Factors like frequent availability and technological upgradation would increase the uptake of ESCs.Social implicationsThe study highlights that even though there are infrastructural challenges in the implementation of ESC, so does any other electric transport system; it is worth considering as a decarbonising transport alternative, given the high up-take intension of the users.Originality/valueThis is the first attempt to study the demand for ESC in developing economies, identifying the factors which may be considered in the sustainable urban transportation policy perspective.

Using the IPCC Formula to Calculate CO2 Emissions from Everyday Motorized Vehicles as the Baseline for Climate Change Mitigation Policies

Climate change has attracted the attentions of every nation on earth, including Indonesia. Indonesia’s commitment to minimize greenhouse gas emissions through the ratification of the Kyoto decree in Law no. 17 / 2004. Other regulations for the National Action Plan for Reducing Carbon dioxide are regulated in Presidential Regulation No. 61/2011 and No. 7/2021 for the National Greenhouse Gas Inventory. The East Java provincial government adopted this policy through East Java’s Province Regulation No. 67/2012. This article predicted an increase in carbon dioxide from the daily vehicles. The study will be done in Jember, a third city in East Java Province. Data is calculated using IPCC formula. The secondary data from 2018 to 2020 was analyzed, as well as predictions for 2030. The number of motorized vehicles growth is 4.5% each year. CO2 emissions from the daily transportation sector will reach 3,846,049.49 tons in 2030. Motorcycles contribute 2,055,244.87 tons. CO2 gas emissions from the 8 main streets of the Jember Gold Triangle are 62,190.52 tons.

Estimating the steps made by public transport commuters using a synthetic population enriched with smart card data

BackgroundPublic transport use can contribute to daily physical activity recommendations through its associated walking components. However, without using travel surveys, it is challenging to estimate walking associated with public transport use for the population of large cities. MethodsBig data, in the form of smart card data, combined with a synthetic population, was used to estimate the access, transfer and egress distance of public transport commuter journeys in Sydney, Australia. A spatial network analysis of the access, transfer and egress segments of commuter journeys was performed using Geographic Information Systems (GIS). ResultsA mean step count of approximately 2400 steps (1915 m) weighted across modes was estimated for daily public transport commutes. The largest mean number of steps corresponds with ferry (3066 steps, 2436 m) and train use (2933 steps, 2340 m). ConclusionWalking to public transport is an important contributor to meeting physical activity recommendations. The findings of this study can aid planners and health practitioners to understand the pattern of physical activity associated with public transport commuter use and can influence future decision-making in land use planning and infrastructure provision, or for developing targeted interventions for the promotion of the benefits of physical activity and public transport use.

Walking Behavior of Older Adults in Temuco, Chile: The Contribution of the Built Environment and Socio-Demographic Factors.

The amount of walking for daily transport has decreased significantly over the last decades in Temuco, Chile. Moreover, the percentage of older adults (aged over 65) who did not meet the recommendations of at least 150 min of physical activity per week has increased during this time. In this regard, the present study examines the contribution of socio-demographic and built environment factors on the walking behavior of older adults in Temuco, Chile, with a view to improving their level of physical activity. A cross-sectional study was conducted among 463 older adults aged 60 years and over. Travel Diary Data taken from "Encuesta Origin Destino" (EOD) 2013, Geographic information systems (GIS), audits (PEDS with certain revisions) and finally, multiple regression analysis, were used to examine the objectives. Associations were found between the walking behavior of older adults and several socio-demographic factors, as well as several built environment factors including destination (the number of parks and the land use mix), functionality (street connectivity, length of street sections and off-street parking lots) and aesthetics (views of nature, building height, and articulation in building design). These findings should be considered by urban/transport policymakers to improve the walking behavior of older adults in this city.

Impact of Floods on Sediment Trap Efficiency of a Small Shallow Reservoir—A Case Study

Silting is the main factor limiting the ability to perform the tasks that small reservoirs are intended for. Therefore, the change in sediment trap efficiency was studied for a small shallow reservoir, Krempna, which, due to high silting intensity, was desilted twice (2005, 2018). Siltation measurements were performed in two periods (1987–2005, 2006–2018) after the reservoir was desludged. It was found that the sediments were composed of fine-fraction sediment; therefore, a series of measurements of suspended sediment transport were performed. These data allowed us to calculate the daily sediment transport flowing into and out of the reservoir and water-level measurements. Then, the sediment trap efficiency was calculated. The aim of this study was: (1) to determine the impact of flood flows on the sediment trap efficiency (STE) of a small shallow reservoir, (2) to determine changes in the value of the sediment trap efficiency of a small shallow reservoir in two different periods of its operation, and (3) to demonstrate whether it is possible to determine the value of the initial sediment trap efficiency and changes in the STE values during operation using empirical formulas. Finally, during flood flows, the amount of sediment retained in the studied reservoir was several times lower than during freshets with a much smaller flow. It is these small freshets that reduce the capacity of the reservoir. A correlation relationship was developed for 18 data—flood flows (Q) and sediment trap efficiency (STE).

A shared‐transportation mobile app continuance model: The moderating effects of brand awareness

AbstractIn light of the increasing development of technological innovation and information and communication technologies, features of mobile apps functions are also increasingly developed to meet people's needs and wants. Amongst the variety, shared transportation apps (STA) have emerged to optimize people's daily transportation needs. This study modified the expectation‐confirmation theory and the post‐acceptance model of information system continuance to examine the effects of customers' perceived STA performance (service performance, content provision, and system performance) and benefits (function, money worthiness, pleasure, and social interaction) on customer satisfaction with the usage of STA, which is expected to have a positive effect on customer retention on STA in a new theoretical framework, called the performance‐benefit of the STA continuance model (PBCM). Moreover, brand awareness was expected to be an essential moderator on the effect of perceived performance and perceived benefits on customer satisfaction. 397 respondents were recruited online through a convenient sampling technique in the Vietnam market. Results show that service performance, money worthiness, pleasure, and social interaction are four important determinants of customer satisfaction with STA. Moreover, brand awareness significantly moderates the effects of service performance, system performance, money worthiness, and social interaction on customer satisfaction. Findings also contribute rational strategies for e‐commerce managers in general and STA managers in particular.

Immunotherapy and Modern Radiotherapy Technique for Older Patients with Locally Advanced Head and Neck Cancer: A Proposed Paradigm by the International Geriatric Radiotherapy Group.

The standard of care for locally advanced head and neck cancer is concurrent chemoradiation or postoperative irradiation with or without chemotherapy. Surgery may not be an option for older patients (70 years old or above) due to multiple co-morbidities and frailty. Additionally, the standard chemotherapy of cisplatin may not be ideal for those patients due to oto- and nephrotoxicity. Though carboplatin is a reasonable alternative for cisplatin in patients with a pre-existing hearing deficit or renal dysfunction, its efficacy may be inferior to cisplatin for head and neck cancer. In addition, concurrent chemoradiation is frequently associated with grade 3-4 mucositis and hematologic toxicity leading to poor tolerance among older cancer patients. Thus, a new algorithm needs to be developed to provide optimal local control while minimizing toxicity for this vulnerable group of patients. Recently, immunotherapy with check point inhibitors (CPI) has attracted much attention due to the high prevalence of program death-ligand 1 (PD-L1) in head and neck cancer. In patients with recurrent or metastatic head and neck cancer refractory to cisplatin-based chemotherapy, CPI has proven to be superior to conventional chemotherapy for salvage. Those with a high PD-L1 expression defined as 50% or above or a high tumor proportion score (TPS) may have an excellent response to CPI. This selected group of patients may be candidates for CPI combined with modern radiotherapy techniques, such as intensity-modulated image-guided radiotherapy (IM-IGRT), volumetric arc therapy (VMAT) or proton therapy if available, which allow for the sparing of critical structures, such as the salivary glands, oral cavity, cochlea, larynx and pharyngeal muscles, to improve the patients' quality of life. In addition, normal organs that are frequently sensitive to immunotherapy, such as the thyroid and lungs, are spared with modern radiotherapy techniques. In fit or carefully selected frail patients, a hypofractionated schedule may be considered to reduce the need for daily transportation. We propose a protocol combining CPI and modern radiotherapy techniques for older patients with locally advanced head and neck cancer who are not eligible for cisplatin-based chemotherapy and have a high TPS. Prospective studies should be performed to verify this hypothesis.

A Study on Safety Driving of Intelligent Vehicles Based on Attention Mechanisms

Intelligent vehicles attempt to improve the daily transportation of people, but their safety has been questioned after numerous trafic accidents. This paper proposes a Safety Driving Framework (SDF) to improve the ability of intelligent vehicles to avoid risks during emergencies. When performing autonomous driving tasks, the SDF interacts with the environment using a camera, makes decisions using convolutional neural networks, and can perform an emergency stop if it encounters an obstacle. In this paper, we examine the potential of attention mechanisms to enhance the performance of convolutional neural networks and construct four convolutional neural networks with attention mechanisms to use in experiments. Additionally, we extend a dataset and enhance the robustness of the model by implementing data augmentation (DA) techniques. We train the model using 10-fold cross-validation. In this article, we build an intelligent driving platform and a simulation track for simulation testing. The experimental results show that CNNs using data reinforcement and with an attention mechanism perform better than existing models. In particular, ENetb0-SE has an average recognition rate of 95.6% for obstacles and an accident rate of 2%, which is much better than existing models.

Eddy Induced Cross-Shelf Exchanges in the Black Sea

Cross-shelf exchanges in the Black Sea were investigated using remote sensing data and an ocean circulation model to which an eddy-tracking algorithm and Lagrangian particle tracking model was applied. An anticyclonic eddy in 1998 and a cyclonic eddy in 2000 were investigated in detail. Eddy-induced cross-shelf transport of low salinity and high Chl-a waters reached a maximum in the presence of filaments associated with these eddies. The daily mean volume transport by the eddies was comparable with the previously documented transport by eddies of similar size in the north-western shelf region. Lagrangian particle tracking results showed that 59% of particles initially released over the shelf were transported offshore within 30 days by the 1998 anticyclone and 27% by the 2000 cyclone. The net volume transport across the Black Sea shelf-break reached the maxima in winter, coinciding with the increase in wind stress curl and mean kinetic energy that is a measure of the intensity of the boundary current. Ekman transport directly influences the cross-shelf exchanges in the surface layer. The south-eastern Black Sea is presented as an important area for cross-shelf transport. The total cross-shelf transport can be divided into its “large-scale” and “eddy-induced” components. Eddy-induced transport was 34% and 37% of the total cross-shelf transport (1998–2014) in the Black Sea in the off-shelf and on-shelf directions, respectively, but these values ranged between 25% and 65% depending on the eddy activity over time.

Characteristics and comparison between e-scooters and bicycle-related trauma: a multicentre cross-sectional analysis of data from a road collision registry

BackgroundUrban mobility has drastically evolved over the last decade and micromobility rapidly became an expanding segment of contemporary daily transportation routines. E-scooter riders and bicyclists may share similar trauma characteristics, but this has been little explored. The objective was to describe and compare the characteristics of e-scooter and bicycle-related trauma.MethodsWe conducted a cross-sectional analysis of data from the Rhône road collision registry (January 1, 2019 to December 31, 2019). We included all e-scooter or bicycle riders injured in traffic collisions during the study period; there were no exclusion criterion.ResultsA total of 2,779 patients were included; 825 (29.7%) were e-scooter riders and 1,954 (70.3%) were bicyclists. E-scooter riders were younger (median [IQR]: 24 [20–32] vs 29 [20–45] years, p < 0.001) and less frequently male (64.2% vs 73.4%, p < 0.001). Most e-scooter and bicycle road collisions were consequent to a fall or loss of vehicle control (74.2% vs 67.7%, p < 0.001). E-scooter riders were less frequently wearing a helmet at the time of the road collision (6.1% vs 30.7%, p < 0.001) and had more frequently head (24.2% vs 19.9%, p = 0.01) and face (30.6 vs 20.5%, p < 0.001) injuries compared to bicyclists. The median injury severity score was 2 [1–4] in both groups with no significant difference (p = 0.77).ConclusionsE-scooter and bicycle-related trauma patients were mainly young males with minor injuries and most of them sustained a road collision with no third-party. However, they suffered from different injury patterns; e-scooter riders suffered more frequently face and head injuries than bicycle riders, which may be at least partly the consequence of less frequent helmet use among e-scooter riders compared to bicyclists. Hence the two groups of users should not be considered as a single trauma entity. This issue should be promptly addressed to bring down the incidence of preventable injuries and avoid healthcare costs.