Search

The last-mile delivery challenges and complexity are increasing simultaneously with the e-commerce growth, the e-commerce growth constitutes a threat to urban transport, especially for big cities and developing countries. This paper examines the economic impact of crowd-shipping based on public transport collaborated with the automated parcel locker and the impact of traditional delivery, as two different methods of last-mile delivery under the same circumstances. This paper aims to assess crowd-shipping as an alternative solution for last-mile delivery compared to traditional delivery. Data has been simulated based on Cairo reality, one of the largest cities in the world. The mathematical models were formulated for the problem, where the first model objective function minimizes the costs of vehicle possession and vehicle kilometers traveled, while the second model objective function minimizes the costs of establishing lockers, vehicle possession, and vehicle kilometers traveled. A genetic algorithm has been used due to the complexity of the problem. The results show that the economic impact of the crowd-shipping delivery system is better than the traditional delivery system under the conditions of large-sized and medium-sized demand, while the economic impact of traditional delivery is better than the crowd-shipping delivery system under the conditions of small-sized demand. [ABSTRACT FROM AUTHOR]

This paper presents the design, implementation, and practical use of a specialized online data repository and an interactive visualization of a transportation dashboard. Specifically tailored to handle and interpret large-scale transportation data within the Qatari context, the combined platform serves as a comprehensive solution for managing extensive datasets, including GPS traces from taxis and e-scooters, which are examined as primary use-cases in this paper. The online data repository provides a centralized hub for efficient data storage and management of public transport including Mobility-as-a-Service (MaaS) data. Concurrently, the visualization dashboard fosters an intuitive, user-friendly interface for data exploration and analysis. Through real-world applications within Qatar's transportation ecosystem, we elucidate how these innovative developments can inform data-driven policy decisions in crucial areas such as infrastructure development, resource allocation, and safety measures. Ultimately, this study underscores the pivotal role of effective data management and advanced visualization in maximizing the potential of big data, providing valuable insights for urban mobility planning and enhancing the landscape of policy-making in Qatar and worldwide. [ABSTRACT FROM AUTHOR]

Curbside parking in a residential area may be induced by the presence of a public transport stop. Travelers may park their car near the stop and continue their trip to the city center by other means. This is called informal park-and-ride. The magnitude of the phenomenon is estimated by simulation. Parking demand is derived from the history of parking time (tickets) sold at vending machines. For each ticket, an activity location is determined by stochastic sampling from a buildings (facilities) database based on the position of the vending machine. The activity timing is derived from the parking duration specified by the ticket. Suitable parking spots for an activity are determined for the cases (i) drive-park-walk and (ii) drive-park-publicTransport-walk respectively. The generalized cost (based on money and travel duration) is determined for both options. The decision is sampled by means of a behavioural model. Several scenarios are considered and the results allow to evaluate the complaints issued by residents of a study area because microsimulation enables the generation of probability densities for parking occupation in such area in each scenario. This paper reports how the method is applied to a study area in Amsterdam. [ABSTRACT FROM AUTHOR]

Station-pair passenger flow forecast modeling is crucial for public transportation to address emerging needs. The accurate prediction and estimation will provide backbone support for various features of transport, viz. (a) Demand forecasting for resource allocation during peak travel periods; (b) Service planning to determine transportation frequency on specific routes; (c) Resource allocation for vehicles and capacity adjustments, including planning for new fleet size; (d) Infrastructure planning to identify areas requiring additional development; (e) Enhancing passenger experience with ample seating and amenities; and (f) Emergency planning for crowd control and passenger safety. Recent advancements in transit information station-pair estimation methods can be implemented with Data Science. Over the years, there has been significant progress in developing innovative modeling approaches to estimate travel behaviour, taking into account various factors such as timeslots, holidays, and other relevant features. In this study, we have synthesized ticket data from Thane Municipal Transport (TMT), collected from e-ticket machines, spanning from January 1 to June 23, 2023, covering all routes and comprising more than 2.80 crore tickets. This paper presents a comprehensive station-pair short-term passenger flow forecast by utilizing three boosting models: extreme Gradient Boosting (XGBoost), Light Gradient Boosting model (LightGBM), and Categorical Boosting model (CatBoost). Their implementation is well-suited based on statistical parameters. The study concludes by comparing the results of the models, considering computation time and statistical parameters, to validate their superiority. By looking into day to day requirements, the LightGBM model is most appropriate for implementation with an accurate result. The research findings contribute to providing valuable data support and a robust scientific foundation for managing passenger flow through route planning and dispatching in urban transit systems. [ABSTRACT FROM AUTHOR]

Shared micro-mobility services are increasingly becoming popular in many cities. These services can help shape sustainable cities and reduce car dependency. To unlock their full potential, a deep understanding of these services' operation, adoption, and integration within existing mobility systems is required. This paper uses a newly introduced agent-based simulation framework to study the implications of shared bikes and e-scooters in Lyon, France. The paper describes data collection, processing, and input data generation for the simulation of shared bikes and e-scooters. Simulation outcomes evaluate the impact of introducing these services on existing travel modes. The introduction of shared bikes and e-scooters can compete against public transit and walking. The simulation framework can be used to assess various aspects of shared bikes and e-scooters and help policy-makers take advantage of their full potential to improve urban mobility. [ABSTRACT FROM AUTHOR]

Improving energy efficiency in public passenger transport is key to achieving sustainability goals. In many developing countries, public passenger transport relies heavily on fossil fuels, which necessitates a strong focus on reducing emissions from energy consumption. Surprisingly, few studies have addressed this issue in the context of daily operations, with most research focusing on operational and infrastructural changes. This paper aims to fill this gap by introducing detailed energy consumption variables into a public transport simulation model using SUMO, a tool known for its seamless integration with Python. The development of two Python scripts underpins this endeavour. The first script facilitates the automatic generation of scenarios, allowing the manipulation of parameters such as station spacing, line length, passenger capacity and waiting times, among others. The second script is dedicated to estimating energy consumption for each scenario. Together, these tools allow us to calculate average energy consumption for different scenarios and modes of public transport. As a result, they provide valuable insights for the formulation of public policies aimed at saving energy in different public transport environments and lay the foundation for the integration and development of intelligent transport systems and simulation-based digital twins. [ABSTRACT FROM AUTHOR]

This paper presents the results of primary research involving a survey of travelers in Poland during the COVID-19 pandemic. The survey was conducted in January 2022 using the CAWI method on a representative sample of n =1,129 Polish adults aged 18 to 60. As many as 74% of respondents believe that the COVID-19 pandemic has changed traveler preferences with regard to urban, regional and trans-regional transport. Two-thirds of respondents (67%) cited a car as the most common mode of transportation, 40% said they walk, and 25% use buses. Just over 10% chose a bicycle (14%) or tram (11%). The least preferred mode of transportation was the train (7%). The three most frequently cited features of transportation services that respondents considered most important included easy ticket purchasing, ticket prices, and cost of travel. The aim of this paper is to discuss the decisions of Polish passengers using public transport during the COVID-19 pandemic. The results of the research may help to influence the passengers' purchasing decisions in accordance with the objectives of long-term transport policy of both the European Union and Poland related to sustainable transport development, as well as the decisions of transport operators as this is a stakeholder group that may be interested in applying them in their daily work and carriers themselves, competing for passengers. [ABSTRACT FROM AUTHOR]

Autonomous-rail Rapid Transit (ART) is a new type of road public transportation with an automatic driving function, and it is one of the main places for epidemic spread. Scientific wearing of masks has been proven to be an extremely effective way to prevent an epidemic. However, drivers wearing masks make it difficult for traditional driver alert detection methods based on facial information to operate effectively. This paper proposes an ART driver alert detection system based on multimodal information fusion and deep learning, which can be applied to drivers wearing masks. The detection system is mainly divided into three modules: data acquisition module, preprocessing module, and vigilance detection module. The data acquisition module collects real-time face thermal imaging data, and environment temperature information of 13 people in the vigilance and fatigue state of the ART simulated driving environment. The preprocessing module proposes a face thermal imaging key information extraction method based on the constraints between Yolov5 and thermal image interframe constraints and extracts the mean temperature information and the hot voxel information (HV) of the eyes, forehead, and mask areas. Then the extracted information and the environment temperature information are transmitted together to the vigilance detection module for learning and classification. The vigilance detection module proposes a lightweight hybrid-attention optimization network, and this paper extracts training sets and testing sets from the data of 13 subjects. And the training set data is enhanced. Each sample is 90 frames. The maximum accuracy rate of the testing set reaches 99.57%. The feasibility of the detection system proposed in this paper is proved, which makes the thermal imaging and attention model show great potential in the application of traffic safety operation and maintenance. • A contactless vigilance detection method is proposed for ART drivers with face masks. • The multi-modal fusion method uses facial thermal image and environmental data. • A lightweight network model that integrates hybrid-attention mechanism is proposed. • A method for extracting face key information from thermal images is proposed. • The testing set shows that the system is feasible and the accuracy rate is 99.57%. [ABSTRACT FROM AUTHOR]

This paper proposes a new method for assessing the attractiveness of a city for different types of workers depending on their skills and industry. The method uses job vacancy data to obtain information on the qualifications required for each type of worker and to determine the wage levels in different industries. It then calculates the value of an urban livability index, which reflects the spatial and financial correspondence between jobs and housing for each type of worker. It also reflects the connectivity between them by public transport rather than spatial proximity. In this task, transport connectivity affects a worker's quality of life more than the spatial remoteness of the workplace. The value of the indicator can be used to assess the well-being and quality of life in the city for workers with different qualifications. This approach improves the jobs-housing mismatch indicator, by using NLP methods to consider wage differentials across occupations. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive architecture for a Mobility-as-a-Service (MaaS) platform, focusing on service browsing, ticketing, and payment, with a specific emphasis on integrating MaaS into bus payment systems. The proposed architecture aims to enhance user experience and convenience in accessing and paying for various transportation services while providing support for MaaS startups, offering a novel approach to MaaS, addressing challenges related to service discovery, ticketing, and payment through a unified system. The proposed MaaS platform holds the potential to improve mobility, reduce congestion, and promote sustainable transportation. [ABSTRACT FROM AUTHOR]

Transportation agencies face escalating challenges in forecasting the traffic demand. Traditional prediction methods focused on individual transportation sectors and failed to study the inter-dependencies between the different transportation systems. Hence, there is a need for more advanced and holistic modeling techniques. To this end, this paper models and analyses an urban transportation system-of-systems incorporating seven various systems: population and GDP, CO2 emission, gasoline price and total vehicle trips, traffic demand, public and private transportation, transportation investment, and traffic congestion. Accordingly, this research simulates transportation networks as a collection of task-oriented systems that combine their resources to form a complex system with increased functionality. The goal of this paper is to understand the traffic complex behavior of urban transportation networks and to study the interdependencies between the different variables. The proposed framework could be implemented to any urban city, county, state, or country. The developed model incorporates a hybrid modeling approach that includes: logistic model, system dynamics, stochastic cellular automata, chaos theory, and Lotka-Volterra model. The final model is demonstrated using a case study. The contribution of this paper lies in modeling the transportation network as a dynamic system of systems rather than as static model as provided in previous studies. [ABSTRACT FROM AUTHOR]

Driven by the massive number of connected vehicles and the stringent requirements of data-intensive applications, logistics transportation systems have evolved to fully comprehend its effectiveness and quality to meet public transportation needs. As a result, to meet public transportation requirements and analyze the data efficiently at the edge of the networks, an advanced artificial intelligent technique needs to be introduced to make the transportation system intelligent by supporting efficient decision making, intelligent traffic control, and intrusion and misuse detection.Motivated by the challenges mentioned above, in this paper, we develop a logistic agent-based model for analyzing public transports such as cars, bus or trains in the intelligent transportation system. The intelligent logistic framework is built on a parallel neural network structure, known as a Swarm-Neural Network (SWNN). The proposed SWNN model analyzes the sensory data and recognizes the public transportation at the edge of the networks. The SWNN model is constructed so that it fits within the intelligent logistic transportation framework, and the proposed model shortens the transit time of every small-scale logistics delivery to its destination. The performance of the proposed SWNN model is evaluated using a standard TMD dataset, where the SWNN model is trained using data, retrieved multiple sensors such as accelerometer, gyroscope, magnetometer, and audio sensors. The features of the sensory data are extracted based on a 5-s time interval. The performance of the proposed SWNN model is studied over various standard machine learning techniques such as Random Forest, XGBoost, and Decision Tree. As per the simulation results, the proposed technique achieves 78–98% accuracy over a real-time dataset's different sets of features. [ABSTRACT FROM AUTHOR]

Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low-structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real-life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real-life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators. • Significant subgroup discovery rules on sequences of multidimensional events. • Process discovery and conformance checking on low-structured processes. • Real-life case study based on smartcard travel validations of a transport network. • Characterization of mobility flows over multiple aspects. • Identification of unexpected behaviors in the flow of commuters. [ABSTRACT FROM AUTHOR]

With the expansion of the city and the rapid development of public transport, the subway as a mass public transport means to bear a very large number of passengers, so that in the morning and evening rush hour, some subway stations are almost saturated. Once a disaster happens in a subway station, the saturated space environment makes it difficult for passengers to evacuate. In addition, the subway station is usually located underground, buried depth of about 10m-30m, the accident station passengers have to overcome their own gravity to the ground to escape, which increases the difficulty of passenger evacuation. Fire is one of the biggest security hidden dangers in subway operation. Once it happens, it will bring huge loss to the safety of social resources and people's life and property. On the basis of summarizing the present situation of fire evacuation in urban rail transit stations at home and abroad, this paper firstly analyzes and studies the factors that affect the fire evacuation ability of stations. Then through the station fire evaluation index to build the safety evacuation evaluation index system. Finally, through the VR technology, computer simulation, field investigation and other methods to quantify the index, the Fuzzy Network analysis of the station fire safety evacuation capacity comprehensive evaluation and put forward targeted countermeasures and suggestions. [ABSTRACT FROM AUTHOR]

This paper analyzes how the same commuters use public transport and short-term rental cars to get to work and universities. Its purpose is to identify the specific contextual elements (benchmarks for valuations, activities, and assets used) of the two practices - public transport and short-term car rental - and investigated whether commuters create urban mobility practices rather than simply adopt new offers. We discuss how the providers inspired the changes in the repertoire of mobility practices, how other stakeholders contribute or not to this change and how commuters create their practices independent of the providers of the services. We conceptualize the consumer enrichment as their ability to perform a diverse repertoire of practices. Based on a qualitative research method (33 individual interviews with public transport and car-per-minute users, aged 19-25) and the map of practices we suggest that researchers and policy makers interested in transformations of mobility practices should consider practices that are mediated by the mobility and not to be limited to modality problems. Transformations of non-mobility social practices can inspire changes in mobility, including its reduction. [ABSTRACT FROM AUTHOR]

• A vibration prediction model of long-span steel truss cable-stayed bridge was established, and validated by test results. • The vibration reduction mechanism of damping pad floating slab (DPFS) on the long-span steel truss cable-stayed bridge was analyzed in this paper. • The vibration reduction effect of the damping pad floating slab (DPFS) on the long-span steel truss cable-stayed bridge was discussed in this paper. With the development of urban rail transit, long-span steel truss cable-stayed bridges have become increasingly favored. Therefore, train-induced bridge-borne vibration and noise are occurring more often. The damping pad floating slab (DPFS) has been widely used as an effective vibration damping track structure; however, there is a lack of research on the vibration mechanism and effects of the DPFS for long-span steel truss cable-stayed bridges. This study focuses on the steel truss cable-stayed bridge in urban rail transit. First, the theoretical model of the train-track-bridge coupling interaction is established in the frequency-domain (the track structure is DPFS, and the interaction of multiple wheels is considered). Using this model, the wheel–rail dynamic force, force transmission rate of the track, and the force transmitted to the bridge are calculated. Then, the vibration prediction model of the long-span steel truss cable-stayed bridge is established using the plate-beam finite element (FE) method and statistical energy method. The force transmitted to the bridge is applied to the vibration prediction model to obtain the vibration transmission characteristics of the typical plate of the bridge. The prediction model is verified by comparing the obtained results with the field test of a long-span steel truss cable-stayed bridge, and the damping mechanism of DPFS track is discussed. Finally, the force transmitted to the bridge is calculated based on the embedded-sleeper (ES) track structure (the ES has no vibration damping facilities). The values of the overall vibration level are calculated from the prediction models using ES and DPFS, and the vibration reduction effect of DPFS is studied by comparison with the ES. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) proliferation relies on robust charging infrastructure deployment and operational advances that require cooperation among multiple stakeholders invested in supporting growth in EV adoption. While growth in EV adoption is influenced by the availability of charging infrastructure, the decisions necessary to successfully roll out such infrastructure are multi-dimensional and cross individual stakeholder's comfort zone. In this paper, we present experiences and lessons learned of a multi-year multi-institution effort to deploy advanced charging infrastructure for increasing EV adoption in the state of Utah. Three distinct charging infrastructure cases are studied in this paper: residential charging, public charging in electrified highways, and electrified public transportation through electric buses. Each of these charging infrastructure present unique challenges and decision criteria for different stakeholders involved in the process. This paper highlights the role of decision makers in addressing and overcoming the challenges to EV proliferation and provides charging infrastructure utilization data to support such strategies. [ABSTRACT FROM AUTHOR]

Abstract In this paper we will consider generalizations of the class of so-called windmill graphs, which were recently introduced by Estrada [6]. A windmill graph W (η , k) consists of η copies of the complete graph K k , with every node connected to a common node. Estrada [6] showed that the clustering coefficient and the transitivity index of windmill graphs diverge, when the graph size tends to infinity. In addition [6] studied the spectra of the adjacency and the Laplacian matrices of these graphs. In this paper we will generalize the family of windmill graphs in three ways. We will study properties for all three types of generalized windmill graphs. In particular we will focus on the behavior of the two clustering metrics. We will quantify the difference between the two metrics, under various conditions. In addition, we give the spectra of the adjacency and the Laplacian matrices of these graphs. We will also derive analytic expressions for several other graph metrics, such as average path length, heterogeneity index and a variety of robustness metrics. We also show how the generalized windmill graphs can be used to construct pairs of non-isomorphic graphs with the same number of nodes and links. Finally, we will show how generalized windmill graphs occur quite naturally in the study of public transportation networks. [ABSTRACT FROM AUTHOR]

Few studies have calculated the magnitude of the modal accessibility gap between public and private transportation (MAG) by taking into account both population characteristics and time-varying characteristics. To incorporate the aforementioned characteristics, this study computed MAG within each population grid using the anticipated travel time between two grids in real time. Moreover, a representative case study focuses on the MAG in the urban area of Xi'an during three time periods. Research results indicate that the average travel time for private transportation in the study area is significantly lower than that of public transportation. Further, the subway line has an obvious impact on improving MAG. In addition, the development of public transportation in the peripheral regions lags behind that of the city center, and the subway network is inadequately serviced. As a result of utilizing the MAG method presented in this paper, the problem related to urban transportation planning can be addressed by incorporating both the population and time-varying factors. At the end of the paper, the limitations and future works are discussed. [ABSTRACT FROM AUTHOR]

Many countries are increasing their research interest in multi-modal passenger transportation design, including hub, interchange, and other typologies. Multi-modal transportation is the act of connecting and moving people and goods using at least two different modes of transportation. As a result, expanding the country's transportation system is part of its economic reform strategy. The problem of the paper is the lack of guidelines for pedestrian circulation requirements in public transportation stations in Egypt. This paper analyzed and evaluated the pedestrian circulation requirements in public transport stations in Egypt. With the aim of suggesting the design guidelines for pedestrian circulation requirements in multi-modal hub stations in Egypt. After identification, the findings are formed by the suggested design guidelines for pedestrian circulation requirements of multi-modal hub stations in Egypt. This paper recommends a framework for the design aspects of multi-modal hub stations. [ABSTRACT FROM AUTHOR]

The impacts of using downscaled inputs in mobility simulation involving taxi fleets have not been well studied. Downscaled inputs, that is, inputs with a fraction of the full-scale population, are often used for large-scale scenarios in order to keep run times short and to avoid using high-performance computing resources. In this paper, a large-scale multi-modal scenario of the Munich metropolitan region, with a taxi fleet that serves the first/last mile legs of public transit passengers, is used to systematically quantify the impacts of downscaling. The results show that the full-scale population is required to obtain accurate spatio-temporal estimations of the fleet's performance. Sample sizes of at least 30% of the full-scale population can thus be used if only an approximate knowledge of spatially aggregated performance metrics (such as average waiting time, fleet utilization and empty mileage) is desired. However, with downscaled inputs, different characteristics during peak hours, such as a shift in the peak utilization, can result. Moreover, we find evidence that downscaling can affect the spatial distributions of trip densities of taxi fleets. [ABSTRACT FROM AUTHOR]

The modal choice, the third step of the historical four-stage model (FSM), determines the flows for each mode of transportation. It is generally stimated from the discrete choice models, which require re-building trips conditions for all alternatives, even those marginal. This paper proposes a new alternative based on machine learning methods and resampling techniques to estimate the modal choice by considering the extreme minority mode of transportation, such as intermodal combinations (e.g., car + public transit, bike + public transit). We have proposed two learning algorithms: the decision tree (DT) and the multinomial logistic regression (MNLR). Afer resampling the dataset, the DT presents better predictions (70% of global accuracy and f1-score closes to 70% for all modes of transportation) than the MNLR (35% of global accuracy). [ABSTRACT FROM AUTHOR]

The global context of cooperative intelligent public transport systems (C-ITS) in Smart Cities calls for a solution that integrates the perspectives of the travelers, public administration, vehicle manufacturers and transport operators in the joint effort of standardization, interconnection and assimilation of emerging technologies to meet operational, safety and environmental objectives at metropolitan, national or international level. The article proposes a definition for the capabilities maturity levels of the mobility ecosystem, and functional architecture for the collaborative decision-making system for implementing the C-ITS in the future Smart Cities. The paper contributes to the literature and practice in four manners. The first one is enhancing the awareness and understanding of maturity capability model (CMM) for all the stakeholders throughout the mobility ecosystem, emphasizing the key capabilities required for each maturity level. The second way is manifested throughout public and private companies acting on the same territory in the mobility ecosystem assess the existing capabilities and collaborate for achieving a superior maturity level of mobility processes, stable and repeatable results. In addition, the third one, based on CMM, is that governments regulate, conduct and control, establishing a stable and mature processes institutionalization for C-ITS. Finally, the fourth practice is defining a C-ITS functional architecture for the future Smart Cities to support collaborative decision-making applied for public transport implementation in big Smart Cities with the final objective of increasing the citizen's quality of life. [ABSTRACT FROM AUTHOR]

In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology-based mobile ticket application-based system. We formulate the problem as a multi-agent reinforcement learning framework for biometric ticketing in multi-transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger's mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi-agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems. • A novel biometric technology-based mobile ticket application. • Reinforcement learning: Multi-agent solution for diverse transport. • A3CBTF framework: Parallel learning in hybrid transport modes. • Unified ticketing: A3CBTF enables single-ticket use across public transport. • Integrated validation: A3CBTF unifies mobile, transport, edge, and cloud servers. [ABSTRACT FROM AUTHOR]

With the continuous evolution and refinement of urban functional spaces, the escalating reliance of commuters on public transportation for work-related travel has surged with time. This paper offers a multidimensional overview of research pertaining to identifying, analyzing, and forecasting of urban public transportation commuting patterns. Initially, emphasis is placed on the identification of commuting patterns. We delineate the characteristics of four data sources, summarize the establishing of travel chains, and classify the approaches involved in identifying commuting patterns. Subsequent to this, we delve into the type of determinants influencing commuters, exploring the ramifications of spatial–temporal heterogeneities. Thereafter, we classify and expound upon forecasting methodologies for commuter flow, elucidating principles behind predictive algorithms and composite models grounded in statistical and time-series analyses, discrete choice modeling, conventional machine learning, and advanced deep learning techniques. Concurrently, a comparative assessment of the strengths, limitations, and applicability of each method is presented. Concluding our exposition, predicated on current research landscapes and inherent challenges, prospective trajectories for future exploration are proposed, including multi-source data fusion, real-time commuting pattern detection, precise public transit for commuting, and commuting travel under shared transportation. [ABSTRACT FROM AUTHOR]

In this paper, we employed Data Envelopment Analysis (DEA) to assess how efficiently a sample of 22 European cities meets the SDG 11.2.1 indicator, which measures the proportion of the population with convenient access to public transport. We examined a range of cities across different sizes and regions. After using bootstrapping methods, we found that the public transport systems in southern European cities arrange their stop networks more efficiently to provide convenient access for inhabitants than those in northern cities. To reach the DEA performance benchmark, the low-efficient systems would need to reorganize their stop network and increase the population's access to public transport by an average of 58%. However, since almost all users of these systems can already walk acceptable distances to stops, these inherent inefficiencies are mainly observed due to unfavorable built environment conditions, which require a relatively extensive network of road and rail public transport stops. We estimated conditional order-m measures to unravel such exogenous influence on public transport performance. After analyzing various built environment factors, we found that cities with increased population density and a higher proportion of urbanized and built-up areas facilitate more convenient access to public transport. Cities that prioritize car-centric designs tend to have less favorable accessibility conditions. Our findings are useful for policymakers in achieving UN SDG 11 in modern cities. Expanding public transport stop networks and rail modes is important in low-density and less urbanized districts. Urban planning that promotes a more pedestrian-focused design can also improve public transport accessibility. • Spatial efficiency DEA models evaluate SDG 11.2.1 progress in 22 European cities. • Public transport is easily accessed but often inefficient, with excessive stops. • Conditional nonparametric analysis explained built environment impact on efficiency. • Denser and more urbanized cities achieve the SDG 11.2.1 goals more easily. • Diverse and less car-related land uses reduce walking distances to public transport. [ABSTRACT FROM AUTHOR]

Transport is one of the sectors of the energy consumption where the presence of renewable energies is lower and its increase is hard to achieve especially in countries that import all the fuels that they consume. This paper addresses the progress that Portugal is making toward a more sustainable transport sector. A near- and medium- term scenario analysis is performed, based on the energy consumption in the past and on a detailed evaluation of the trends for renewable energy in the transport sector. The near-term scenarios aim at portraying the conditions that allow the country to fulfill the target of 10% renewable share in transportation established by the European Union. The medium-term scenarios highlight the implications, in terms of emission reduction and of energy independence, deriving from the use of electric vehicles and natural gas, synthetic and fossil, as alternatives to liquid fuels. The results show that electric vehicles would substantially reduce the total energy consumption and the greenhouse gas emissions, while the promotion of routes for synthetic natural gas would contribute to taking advantage of local resources. The paper also provides some insights about the design of future policies to build a more sustainable transport sector. [ABSTRACT FROM AUTHOR]

Urban planners face a few challenges in making urban freight transport more sustainable: reduce urban congestion, provide reliable delivery windows, decrease logistic costs, reduce emissions, improve safety. New data may provide a key in tackling these issues. This paper presents an agent-based urban freight modeling framework: MASS-GT. Objective of the project is to develop a comprehensive simulation framework that describes logistic decision making in the context of urban transport planning. Empirical basis is provided by a large dataset with observed freight transport data for The Netherlands. Part of the data has been collected using an automated procedure to report complete freight trip patterns from the transport management system. This provides more dense and complete data compared to conventional internet surveys. The paper describes the design principles for agents, markets and logistic decisions. Furthermore we elaborate on the incremental development path of building a comprehensive agent-based simulation system. We describe the first baseline prototype of the agent based modeling framework that simulates all urban freight transport patterns for an urban area, in the case the agglomeration of Rotterdam. This baseline model applies a data driven simulation approach; future work will consists of further improving this framework with the implementation of discrete choice models for logistic decisions. [ABSTRACT FROM AUTHOR]

This paper examines the economic and policy implications of the charging, network and auxiliary infrastructure required for a fully electrified metropolitan Melbourne bus network using overnight depot charging. We introduce the concept of minimum and maximum fleet charging capacities, as well as a charging infrastructure augmented total cost of ownership that is faced by bus fleet owners. Our analysis provides policy makers with a range of viable fleet charging capacities and a cost component breakdown of public bus fleet electrification using battery electric vehicles. We find that a minimum of half the fleet's rated charging capacity is needed to maintain operation, which provides a lower bound for network and charging infrastructure costs. Considering the augmented total cost of ownership (per km), charging, network and auxiliary infrastructure costs contribute 10–19%. A gradual transition to battery electric buses over 12 years yields further TCO savings of between 4.6 and 5%. We recommend streamlining the network augmentation process to reduce uncertainty and project delays. • Calculation of viable charging capacities to operate a metro BEV bus fleet • BEV buses operate using an overnight charging strategy with real world schedules • Fleet can continue operating with half its rated charging capacity • TCO is augmented with charging and network infrastructure costs • Charging, network and auxiliary infrastructure contributes 10–19% to the TCO [ABSTRACT FROM AUTHOR]

Modern technologies provide new opportunities for the industry but raise the expectations of the customers as well. This is valid also for the postal, courier, and logistics industries. The biggest challenge for the companies in the field is to improve the efficiency of the final phase in the transfer of shipments – last-mile delivery. A contemporary solution relates to collaboration between delivery companies and citizens who are willing to perform the delivery tasks as an additional activity to their established job or routine. Such a concept is known as crowdsourcing. There are several approaches within crowdsourcing, for example, transportation by car, utilizing public transport, etc. To make an appropriate choice, multiple criteria should be considered. This paper aims to propose an original methodology for solving the explained multi-criteria decision-making problem. The proposed framework is based on the application of the Sugeno–Weber nonlinear functions in a fuzzy environment. The application of the fuzzy Sugeno–Weber weighted assessment methodology showed that the proposed methodology has adaptability, a high degree of generalization, and stability of results. This enables the applicability of the methodology to various tasks from a broad spectrum of areas, which represents an additional value. A real-life case study is provided to solve the prioritization of crowdsourcing models in suburban municipalities of Belgrade, Serbia. The available alternatives and the most important criteria for the crowdsourced delivery model are identified. All criteria are divided into four groups, which define the dimensions of sustainability, including the technical aspect. The results of the conducted research indicate a high level of convenience in using one's car or daily bus lines for crowdsourced delivery to optimize the entire process of shipment transfer in the observed territory. [ABSTRACT FROM AUTHOR]

Traffic congestion is a problem that becomes government concern in many big cities like Surabaya. Some initiatives have been carried out by the government to decrease the intensity of traffic congestion, ranging from various policy like special lane for motorcycle, odd – even license plate, etc. However, it is yet to be successful to decrease the heavy traffic in many big cities in Indonesia. The right solution tends to encourage people to use public transport instead of private cars. Within the framework of Intelligent Transport System, this paper proposes an advanced traveller information system with itinerary optimisation by Angkot, a common public transport mode in Indonesia. The itinerary optimisation is modelled as an orienteering problem and is solved using iterative local search – hill climbing algorithm. The experiment results show that generally the proposed algorithm could solve the problem fast and effectively. In addition to propose the initial algorithm, the main contribution of this paper is a new dataset for orienteering problem that may encourage researcher to come up with more sophisticated algorithm. Eventually, the proposed system could encourage more people to use public transport. [ABSTRACT FROM AUTHOR]

This paper investigates the theoretical capacity of the Swiss peri-urban dwelling stock to meet energy efficiency requirements through incremental scenarios. The notion of “typical dwelling” allows the assessment of housing energy efficiency at the country scale. The “2000-W society” concept and federal policies provide the assessment framework of the daily-mobility and housing operational energy demand along with embodied energies of the dwelling stock. The typical dwellings’ current performance for several territorial entities highlight the extent of the challenge the peri-urban housing stock is facing today. Therefore, the paper investigates which conditions would allow the peri-urban typical dwelling to meet “2000-W society” intermediary targets. Eight incremental scenarios present theoretical improvements of mobility and housing. They assume an evolution of social practices and individual behaviours, as well as the development of improved technologies. Results show a drastic reduction of primary non-renewable energy (PNRE) demand and greenhouse gas (GHG) emissions for both mobility and dwelling-related consumption. The main findings are that the current MINERGIE-A label is sufficient to build energy efficient buildings, but the current mobility practices remain very far from targets. An optimization of trips and a broader recourse to low carbon conveyances are required to reduce the overall environmental footprint. [ABSTRACT FROM AUTHOR]

This paper empirically demonstrates the value of quasi-experimental study designs to evaluate the direct impacts of new public transit services on ridership within its corridor. Using a new bus rapid transit (BRT) service, CMAX, in Columbus, Ohio, USA, as an example, we compare its impact on ridership based on a pre-post and quasi-experimental analysis framework. We conduct the pre-post analysis using a ridership space-time cube exploring a massive Automatic Passenger Counter (APC) database. Differences in total passenger counts before and after the BRT intervention indicate a 36% increase in ridership within its corridor. However, this patronage increase may not be attributable solely to the new public transit service. Potential confounding effects include systemwide ridership trends and a new unlimited transit pass program for downtown workers. To address these issues, we adopt a quasiexperimental study design with a difference-in-differences (DiD) identification strategy. We use propensity score matching (PSM) to match a counterfactual control group with the treatment group when implementing DiD model. After accounting for confounding effects, we find a less than 5% increase but not statistically significant impacts of CMAX on ridership. Our results support the argument that a simple pre-post analysis ignoring confounding effects can lead to a misleading evaluation of a new public transit service's direct impact on ridership. [ABSTRACT FROM AUTHOR]

This paper reports on the study of public support for transit in Southeast Michigan. The goal was to assess Willingness to Support Public Transit (WTST) among likely voters in the region. The study developed a new tool (WTST Index) that would allow planners, policy makers, and advocates to measure the likelihood of support for transit based on a reduced set of questions, through a formula that captures the gradient from attitudes (opinion) to behaviors (action). The study investigated how sociodemographic, sociopolitical, geographical, and opinion related variables impact WTST Index. The results highlighted how wealth, education, and political and ideological beliefs impact willingness to support transit. The U-shaped distribution of the WTST Index shows the divisiveness in support and uncovers the political and ideological dimensions of transit. The results provide guidance for understanding support for transit in comparable regions where transit initiatives and policies seek to expand or improve underused transit systems. [ABSTRACT FROM AUTHOR]

In this paper we explore how enactive methods may support and enhance the design of mobile solutions for people with intellectual and developmental disabilities. Our project deals with supporting independent public transport and applies enactive methods in order to account for the interdependent nature of technology, disability, and environment. We staged three iterations of a bus workshop and one theatre workshop where real, everyday scenarios were acted out to gain tacit yet crucial insights. These enactive workshops and the prototype testing showed that transport activities are context dependent and unique to each individual. In our case, enactive methods revealed that independent public transport goes beyond a need for reminders, time management and communication, towards the management of unforeseen events. Our work shows how a closer realistic setting may provide more nuanced, personal, and detailed design insights that support emotional and situational understandings of user experiences. [ABSTRACT FROM AUTHOR]

Low carbon economy, energy conservation and environmental protection is one of the important tasks of current and future economic and social development. The large-scale development and utilization of all kinds of clean energy has accelerated the speed of China's energy transformation. Rail transit system is a large power consumer. In recent years, the transportation system has been facing the triangle contradiction of new energy, cost and environmental protection. Connecting photovoltaic power generation to rail transit power supply system has many advantages: (1) it can reduce the operation cost of transportation system; (2) it can reduce the use of traditional thermal power; (3) it can reduce carbon emissions and protect the environment; (4) it can also promote the application of new energy. It makes a lot of sense. However, due to the randomness and uncertainty of photovoltaic power generation, the direct access of photovoltaic power generation to rail transit power supply system will bring a certain impact on rail transit power supply system. In this paper, the LSTM neural network is used to predict the load of photovoltaic power generation, which effectively ensures the accuracy of prediction, and then improves the stability of photovoltaic power generation when connected to the rail transit power supply system. [ABSTRACT FROM AUTHOR]

• Solves the practical problem of fleet allocation for disadvantaged travelers with limited public resource. • Designs an algorithm to maximize the positive social impact. • Performs a case study considering the geographic restrictions to test the developed method. The COVID-19 lockdown has reduced public transportation service to the disadvantaged and disabled people who urgently need adequate mobility to obtain essential suppliers. This paper aims to improve the life quality of people with disabilities and elderly people by addressing social exclusion, accessibility, and mobility issues. Demand responsive transport services are frequently offered in the context of door-to-door transportation of the elderly and persons with disabilities. We study and compare two frameworks. We apply both Sample average approximation (SAA) and Rolling Horizon (RH) to optimize a car sharing system for the total cost, including initiation cost and operation cost after fleet size is determined. The model is implemented with given geographic conditions and other local information to be tailored for specific applications for local communities. Given that no historical data is available, random sample data is generated to simulate expected demands. We consider three types of probability distributions for daily demand data, and the results generated using three different distributions are being examined and compared. The research shows that the demand data following a normal distribution results in the minimum total cost. Additionally, we study the impact of several factors on total cost, including demand fulfillment rates and operation hours. Our results suggest that the impact of fulfillment rate on fleet size is exponential after a threshold under all three types of daily demand data, and extended operation hours can significantly reduce the total cost. Finally, the paper provides applicable frameworks for city planners, NPOs, and policymakers to better allocate limited resources to implement the carsharing system when little to no historical travel information is available for low-density population areas. It is anticipated that the outcome from this research would benefit disadvantaged and disabled travelers during COVID-19 or similar difficult situations in the future. [ABSTRACT FROM AUTHOR]

A vehicular ad hoc network (VANETs) is transforming public transport into a safer wireless network, increasing its safety and efficiency. The VANET consists of several nodes which include RSU (Roadside Units), vehicles, traffic signals, and other wireless communication devices that are communicating sensitive information in a network. Nevertheless, security threats are increasing day by day because of dependency on network infrastructure, dynamic nature, and control technologies used in VANET. The security threats could be addressed widely by using machine learning and artificial intelligence on the road transport nodes. In this paper, a comparison of trust and cryptography was presented based on applications and security requirements of VANET. [ABSTRACT FROM AUTHOR]

Commuting could be costly, while acceptable costs vary across populations. This way, it might be easier for high-income commuters to accept a longer-distance commute having more flexibility in transportation choices and costs. Previous researches provided a statistical description of the gap in the commuting distance in different groups and modelled transportation choices based socioeco-nomic characteristics, but lacks experiments in measuring the capability of distance in discouraging commuting in different income groups. This paper quantifies the heterogeneity of distance's sensitivity on urban commuting networks by modelling flow volume among different income groups. We analyze the gravity models of the commute flows in 12 major US cities and describe the inconsistency in high-income and low-income workers' commute behavior. By comparing coefficients in the best fit models, we find that commute decay with distance in 11 out of 12 cities is significantly slower for high-income groups than for low-income groups. Results could help urban planners and transportation engineers to assess equitability in planning public transit and land use. [ABSTRACT FROM AUTHOR]

This paper deals with the competitive and economically sustainable model of public transportation service in the Upper Šariš region. The paper is focused on both the intraregional transport needs as well as the connection of the region with the outside area – especially with the city of Prešov as crucial transport node in terms of connection of the region with the rest of Slovakia. The proposal takes into consideration current state of transport infrastructure, transportation needs of inhabitants, present state of the transport service as well as operational aspects of proposed backbone transport system. [ABSTRACT FROM AUTHOR]

Abstract: Crowd sourcing and sensing are relatively recent paradigms that, enabled by the pervasiveness of mobile devices, allow users to transparently contribute in complex problem solving. Their effectiveness depends on people voluntarism, and this could limit their adoption. Recent technologies for automating context-awareness could give a significant impulse to spread crowdsourcing paradigms. In this paper, we propose a distributed software system that exploits mobile devices to improve public transportation efficiency. It takes advantage of the large number of deployed personal mobile devices and uses them as both mule sensors, in cooperation with beacon technology for geofecing, and clients for getting information about bus positions and estimated arrival times. The paper discusses the prototype architecture, its basic application for getting dynamic bus information, and the long-term scope in supporting transportation companies and municipalities, reducing costs, improving bus lines, urban mobility and planning. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose a new approach for routing and planning of hazardous materials transportation in an urban environment. The aim of this paper is to find safest and shortest itineraries for hazmat transportation. This allows us to reduce risk and to minimize damage and ultimately keeping people, property and the environment as far away as possible from the effects and consequences of hazardous materials. Indeed, we propose a calculation process based on Voronoï spatial modeling of the transportation network of hazardous materials. The development of such spatial model will make it possible to assess the proximity of vulnerable areas to risk by calculating the distance separating these spatial objects from the vehicles itineraries. The weighting of these spatial structures with socio-economic data constitutes an important spatial support for spatially analyzing the urban environment and geo-governing the transportation network of hazardous materials. [ABSTRACT FROM AUTHOR]

Abstract: Nowadays, data sets are spreading continually, generated by different devices and systems. In most cases, these data are stored, and the service providers don't use the information they contain, what even more they delete these data to save space. However, these data are processable with the modern devices and methods, and we can use them for obtaining information. This paper presents a possible application of the digital raw materials, taking the public transport passengers boarding and alighting information as a base. Based on these, we are able to deduce the characteristics of the stop point's environment since the different land usage yields dissimilar stop usage with well-defined peak hours. With the help of distance measurement and classification techniques it is possible to define how similar are the stop points to predefined patterns and we can create stop point groups which define separated zones. The paper shows a usage method of distance measurement methods and classification in public transportation and presents the background of this kind of land-use zone distribution technic. [ABSTRACT FROM AUTHOR]

In recent years, China has been gradually consolidating the establishment of Low Carbon Communities (LCC) to minimize the emission of CO 2 and other greenhouse gasses. Guangdong Province, as one of the low-carbon pilot provinces, has also paid great attention to the building of LCC. However, currently Guangdong province, or even China as a whole, has no official evaluation system in place which can be employed to evaluate the degree of low carbon of urban LCC. This paper aimed to establish a system for evaluating the urban LCC of Guangdong Province based on their actual situations. The system divided the evaluation into six categories, applied the SMART criteria to select suitable indices, and set the reference values for two zones, i.e., the Pearl River Delta zone (PRD) and the non-PRD zone. Monte Carlo and AHP were combined for the calculation of index weights, thus ensuring that the weight judgment matrixes could all pass the consistency check. In addition, this paper also laid down detailed evaluation regulations to improve the operability of this system and guarantee its wide application and better generalization. Finally, one typical urban community was selected to test the operability of this system, and the One-At-a-Time (OAT) method was used to analyze the sensitivity of each index and explore the degree of influence of these indices on evaluation results. The results showed that three indicators corresponding to green building percentage, new energy vehicles percentage and low carbon public transportation had the highest sensitivity. After field test evaluation, the evaluation system was found to have strong operability, and a high accessibility to indicator data. This system cannot only be used as a tool by the Provincial People's Government of Guangdong to evaluate the construction progress and degree of low carbon in LCC, but can also provide a reference point and experience for the Chinese government to draw upon in establishing the evaluation system of LCC at a national level. [ABSTRACT FROM AUTHOR]

The aim of the paper is to evaluate four public transport systems using different criteria, in order to reduce congestion, pollution and to facilitate traffic flow. The paper is part of a complex Opportunity Study aimed to solve the public transport system in the Bistrita Nasaud area and to connect the main city with two adjacent urban areas: Unirea and Viisoara. The study was built on Multiple-Criteria Analysis decision-making, in order to find the correct approach to meet all the requirements of a new ecologically friendly public transportation line. The evaluation of the five public transport vehicles was performed according to the following criteria, both quantitatively and qualitatively: investment cost, transport capacity, passengers flow, transport regularity through service speed and commercial speed of the vehicle, urban impact (the possibility of urban regeneration on the public transport line and urban integration) and noise pollution (based on the existing noise levels measured on the site and the admissible noise levels indicated in the standards). [ABSTRACT FROM AUTHOR]

An integrated life cycle approach framework, including material flow analysis (MFA), Cumulative Energy Demand (CED), exergy analysis (EXA), Emergy Assessment (EMA), and emissions (EMI) has been constructed and applied to examine the energy efficiency of high speed urban bus transportation systems compared to conventional bus transport in the city of Xiamen, Fujian province, China. This paper explores the consistency of the results achieved by means of several evaluation methods, and explores the sustainability of innovation in urban public transportation systems. The case study dealt with in this paper is a Bus Rapid Transit (BRT) system compared to Normal Bus Transit (NBT). All the analyses have been performed based on a common yearly database of natural resources, material, labor, energy and fuel input flows used in all life cycle phases (resource extraction, processing and manufacturing, use and end of life) of the infrastructure, vehicle and vehicle fuel. Cumulative energy, material and environmental support demands of transport are accounted for. Selected pressure indicators are compared to yield a comprehensive picture of the public transportation system. Results show that Bus Rapid Transit system (BRT) shows much better energy and environmental performance than NBT, as indicated by the set of sustainability indicators calculated by means of our integrated approach. This is because of the higher efficiency of such modality (less affected by traffic, higher vehicle occupancy, suitability for large distance transportation). The study suggests that the higher economic and resource investments performed in order to provide dedicated roads, more modern transport technology and higher speed, translated into a better use of resources and lower environmental pressure, also because of the attraction of an increased number of passengers, who would have otherwise used car transportation modalities. This study also provides a clear evidence that more than one criterion is needed to address a fully reliable and sustainable urban transportation policy. An integrated approach is therefore suggested to support decision making in the presence of complex systems and different kinds of concerns to be taken into proper account. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the impact of planned road network development on the dynamics of the automobile transportation system during the departure of visitors after the semifinal match of the 2018 FIFA World Cup, which will take place in the newly built stadium on Krestovsky Island. To perform such an analysis, we utilize an agent-based traffic flow simulation, which requires the construction of several models. This paper covers the following progress: (1) modeling the road network in Saint Petersburg, Russia (2) a population synthesis based on data provided by the Russian Federal Migration Service (FMS) (3) a comparative analysis of the simulated system behavior using the current and improved road network and (4) a sustainability analysis of the transportation system of the studied area. We estimate the impact of road improvements on traffic flow by simulating various scenarios and determining changes in distribution of agents travel time and load redistribution among exits from the area of interest. This paper is part of the project which aims to develop a large-scale agent-based traffic flow model of Saint Petersburg urban area applicable for a wide range of research tasks from transportation planning to disaster management. [ABSTRACT FROM AUTHOR]

"Wash hands frequently, reduce aggregation, and wear masks" is an important measure for the prevention and control of the new crown pneumonia epidemic. Rail transit is the basic means of transportation to ensure the daily travel of citizens. Carriages and stations are both densely populated places. Reducing the density of carriages and platforms is an urgent problem for rail transit operations in the epidemic. Therefore, this paper proposes a method for dynamic marshalling of trains based on virtual coupling in a major epidemic situation, describes in detail the operation mode of virtual coupling trains, and establishes a marshalling planning model based on passenger flow to optimize the scheduling of virtual coupling trains to reduce passenger density at stations. Then, combined with the virus infection probability model and social force-based passenger movement model, the infection risk of the entire process of passenger subway travel under virtual coupling was analyzed. After that, Matlab was used to simulate the infection analysis under the virtual coupling system and compare it with the traditional communication based train control system. The risk of infection during the entire journey of a passenger on the subway is less than that of CBTC. Finally, according to the results of simulation analysis, effective measures can be given that can be used in conjunction with virtual coupling to reduce the risk of infection. [ABSTRACT FROM AUTHOR]