Infrastructure Characteristics Research Articles

Nation-wide touristic flow prediction with Graph Neural Networks and heterogeneous open data

Tourism has become a very active ecosystem to deploy solutions based on Information and Communication Technologies. Indeed, it is now possible to analyse the mobility behaviour of tourists in great detail. However, current solutions aimed at anticipating tourist flows usually follow a limited approach based on the local (e.g., to predict the next landmark to visit) or regional (e.g., to predict the incoming number of tourists in a city) level. This paper states a novel approach to solve the problem of tourist inflow forecasting on a broader nationwide scale by defining it as an edge prediction task. To do so, we model the tourist mobility of a country as a graph which fuses heterogeneous tourism data obtained from multiple sources related to the country’s mobility and infrastructure features. Then, as a major contribution, an ensemble of Graph Neural Networks are fed with the graph models to provide the final prediction. The proposed solution has been tested in Spain showing a F1 score higher than 0.7.

The impact of natural resource endowment and transportation infrastructure on China's regional green economic performance in the context of COP26

In the context of the 26th UN Climate Change Conference of the Parties (COP26), this paper proposes at COP26 that to address climate change and promote world economic recovery, it is necessary to maintain a multilateral consensus, focus on pragmatic actions and accelerate green transformation. Therefore, the improvement in green economic performance has become the focus of academic attention, but existing research mainly measures its value, there are few studies carried out in the context of COP26, and this study is a supplement and extension to the existing research field. This paper explores the impact of transportation infrastructure and natural resource endowment on the green economic performance (GEP). The study selects the slacks-based measure (SBM) model to measure China's regional green economic performance from 2007 to 2020, analyze the temporal and spatial differences in the GEP in each region, and then uses the system Gaussian mixture model (GMM) to verify the relationship among the three. The conclusions are as follows: (1) The annual overall average of the national green economic performance was 0.538, the overall level was not high, and there were significant differences between regions, showing a gradually decreasing trend from east to west. (2) Natural resource endowment had a negative impact on the GEP of the whole country and the central and western regions; the eastern region showed significant positive characteristics on the “U”-shaped features of the transportation infrastructure on the GEP across the country and the west. The impact on the green economic performance in the eastern region had an obvious inhibitory effect and negative impact on the GEP in the central region. (3) The first-order coefficients of the intensity of the environmental regulations at the national level and in the three regions were significantly negative and showed U-shaped characteristics; foreign direct investment can improve GEP at the national level and have the same effect in eastern and western China but will not be significant in the central region. Industrial structure's impacts on GEP at the national level and in the three regions were all negative, which hindered the improvement of green economic performance. Urbanization had an obvious inhibitory effect on GEP at the national level and in central and western China, but urbanization promote eastern GEP. The research in this paper has important significance as a reference for improving the performance of China's regional green economic performance and achieving the Sustainable Development and COP26 goals.

The Role of Information Infrastructures in Scaling up Video Consultations During COVID-19: Mixed Methods Case Study Into Opportunity, Disruption, and Exposure.

Until COVID-19, implementation and uptake of video consultations in health care was slow. However, the pandemic created a "burning platform" for scaling up such services. As health care organizations look to expand and maintain the use of video in the "new normal," it is important to understand infrastructural influences and changes that emerged during the pandemic and that may influence sustainability going forward. This study aims to draw lessons from 4 National Health Service (NHS) organizations on how information infrastructures shaped, and were shaped by, the rapid scale-up of video consultations during COVID-19. A mixed methods case study of 4 NHS trusts in England was conducted before and during the pandemic. Data comprised 90 interviews with 49 participants (eg, clinicians, managers, administrators, and IT support), ethnographic field notes, and video consultation activity data. We sought examples of infrastructural features and challenges related to the rapid scale-up of video. Analysis was guided by Gkeredakis et al's 3 perspectives on crisis and digital change: as opportunity (for accelerated innovation and removal of barriers to experimentation), disruption (to organizational practices, generating new dependencies and risks), and exposure (of vulnerabilities in both people and infrastructure). Before COVID-19, there was a strong policy push for video consultations as a way of delivering health care efficiently. However, the spread of video was slow, and adopting clinicians described their use as ad hoc rather than business as usual. When the pandemic hit, video was rapidly scaled up. The most rapid increase in use was during the first month of the pandemic (March-April 2020), from an average of 8 video consultations per week to 171 per week at each site. Uptake continued to increase during the pandemic, averaging approximately 800 video consultations per week by March 2021. From an opportunity perspective, participants talked about changes to institutional elements of infrastructure, which had historically restricted the introduction and use of video. This was supported by an "organizing vision" for video, bringing legitimacy and support. Perspectives on disruption centered on changes to social, technical, and material work environments and the emergence of new patterns of action. Retaining positive elements of such change required a judicious balance between managerial (top-down) and emergent (bottom-up) approaches. Perspectives on exposure foregrounded social and technical impediments to video consulting. This highlighted the need to attend to the materiality and dependability of the installed base, as well as the social and cultural context of use. For sustained adoption at scale, health care organizations need to enable incremental systemic change and flexibility through agile governance and knowledge transfer pathways, support process multiplicity within virtual clinic workflows, attend to the materiality and dependability of the IT infrastructure within and beyond organizational boundaries, and maintain an overall narrative within which the continued use of video can be framed.

A taxonomy of pedestrian evacuation infrastructure for urban areas; an assessment of resilience towards natural hazards

Many people in the world live in hazardous environments and are susceptible to disasters. In the time of a destructive event, a resilient community must be prepared to mitigate the event and quickly respond. An effective mitigation plan can lead to fewer fatalities and damages. One of the most critical tasks for mitigation is the evacuation process. Wherein short notice time, overcrowding, bottlenecks in infrastructure and challenging terrain and topography may worsen the situation. Amongst other things, the evacuation process encompasses transportation infrastructures referred to as corridors, signs, pedestrian footpaths, and/or shelter infrastructures for keeping people safe. Evacuation infrastructure can also become damaged after the event; therefore, it’s imperative to have a robust assessment of different evacuation infrastructures. This study will investigate the characteristics of the available evacuation infrastructure and outline the general drawbacks. A systematic methodology for reviewing articles has been implemented to understand how vulnerable cities can be more prepared, especially for pedestrian evacuation. An evacuation scoring system for pedestrians will be developed to investigate evacuation infrastructure in terms of different resilience features, such as redundancy, safe to fail, readiness, capacity. The most practical evacuation system will be estimated, with a final output being to provide the features of a successful pedestrian evacuation system for future policy use.

Factors propelling fatalities during road crashes: A detailed investigation and modelling of historical crash data with field studies

One of the major concerns in developing countries like India is to maintain traffic safety under mixed and heterogenous scenario. Although zero accidents is the need of the hour, the first step to attain it is ensuring zero deaths and no serious long-term disabling injuries in road crashes. To reduce the road crash fatalities, explicit and detailed studies have been conducted by utilising historical road crash data of two emerging smart cities of India - Bhubaneswar and Visakhapatnam. Traffic flow data and characteristics of road infrastructure has also been collected by performing field studies at accident prone locations. Various factors including vehicular characteristics, road user characteristics, and road infrastructure have been analyzed using various non-parametric tests to identify the contributing factors resulting in fatalities. It is observed that out of 14 variables used for study, 8 factors were significantly related to fatal crashes. These included categories of victim and accused, 85th percentile speed, presence of road markings, availability of sight distance, etc. The significant factors were subjected to binary logistic regression to determine the odd’s ratio of significant factors. The logistic regression predicted 79% of deaths correctly. Crash fatality prediction models are developed using both Classification and Regression Tree (CART) classification tree with 83% accuracy. Although CART classification led to higher accuracy, binary logistic regression is more robust as it considered more significant factors as compared to CART. Subsequently, a severity index has been proposed based on proportions of actual fatal crashes and usage of K-means clustering technique. The proposed indices shall be really helpful in traffic safety management, specifically in reduction of fatalities during road crashes.

Comparing the Accuracy of sUAS Navigation, Image Co-Registration and CNN-Based Damage Detection between Traditional and Repeat Station Imaging

The application of ultra-high spatial resolution imagery from small unpiloted aerial systems (sUAS) can provide valuable information about the status of built infrastructure following natural disasters. This study employs three methods for improving the value of sUAS imagery: (1) repeating the positioning of image stations over time using a bi-temporal imaging approach called repeat station imaging (RSI) (compared here against traditional (non-RSI) imaging), (2) co-registration of bi-temporal image pairs, and (3) damage detection using Mask R-CNN, a convolutional neural network (CNN) algorithm applied to co-registered image pairs. Infrastructure features included roads, buildings, and bridges, with simulated cracks representing damage. The accuracies of platform navigation and camera station positioning, image co-registration, and resultant Mask R-CNN damage detection were assessed for image pairs, derived with RSI and non-RSI acquisition. In all cases, the RSI approach yielded the highest accuracies, with repeated sUAS navigation accuracy within 0.16 m mean absolute error (MAE) horizontally and vertically, image co-registration accuracy of 2.2 pixels MAE, and damage detection accuracy of 83.7% mean intersection over union.

Geospatial characteristics of medical workforce and infrastructure to combat COVID-19 in Kazakhstan

Abstract Background After cessation of initial quarantine in Kazakhstan, the COVID-19 outbreak peaked in July 2020, imposing dramatic stress on the country's healthcare system. This study was focused on calculation of updated epidemiological characteristics, on evaluation of available medical workforce and infrastructure and the impact of workforce density on infected and dead individuals via ArcGIS platform. Methods The national and local incidence rate (IR), mortality (M) and case-fatality rates (CFR) were calculated along with the population-weighted densities of beds, physicians, general practitioners, resuscitators, nurses and healthcare budget. Associations between the density of different health workers, infected and dead individuals were investigated using Poisson regression. Finally, we constructed vector maps of country regions clustered by IR and CFR to depict the density of beds and those health workers that were significantly associated with infection and death rates. Results There is much heterogeneity between the country regions in terms of CFR (range from 0.28 to 2.57) and IR (range from 1.62 to 12.04), while density of beds was characterized by a relatively greater stability (range from 3.47 to 6.66) and so did density of physicians (range from 0.79 to 2.76) and density of nurses (range from 5.73 to 8.26). Densities of beds, physicians, general practitioners, resuscitators, and nurses have been linked significantly with infection and death rates. Conclusions As COVID-19 epidemic is still far from ending, findings of this study could be of interest for policy makers to formulate an appropriate action plan in the view of possible repeated outbreaks. Key messages

6.M. Workshop: Models of care in prison: addressing infectious diseases during and after the pandemic in EU/EEA

Abstract According to the latest data, in 2019 about 497,000 people were held in prison on any given day in the EU. However, the number of people who pass through European prisons each year is considerably higher. Due to infrastructural and population characteristics, individuals in contact with the criminal justice system face multiple and complex health care issues, including a higher prevalence of communicable diseases than the general population, and severe clinical outcomes when infected. The high turnover of people coming from the most disadvantaged segments of the population, together with the daily inflow/outflow of prison staff and facilities characterized by overcrowding, poor ventilation increase the risk of air-borne virus outbreaks. People living in prison are substantially more likely to experience drug-related problems than their peers in the community. Incarceration is associated with increases in blood-borne diseases-related risk behaviour among people who inject drugs. People living in prisons, because of the scarcity or lack of availability of needles, syringes and condoms, often share injecting equipment, tattooing and shaving materials, and practice unprotected sex. Individuals in contact with the criminal justice system often come from marginalized groups of society with a higher burden of poverty and discrimination, and with limited access to healthcare. Despite tailored preventive interventions should be implemented among vulnerable groups at the community level. Prisons can represent a point of access to integrated prison-community healthcare and social services. Delivering health protection and harm reduction programmes in prisons not only benefits the prison population but also has the potential to reduce the risk of transmission of some infectious diseases in the community, intervening earlier in the natural history of disease. The WHO has long supported the concept of prison health as an inseparable component of public health. However, a number of challenges hampers the successful implementation of such a concept, including the need for evidence-based decision making, inter-sectoral partnerships and adequate monitoring systems. Due to structural and operational reasons, conducting solid research and monitoring activities in prison settings is challenging and often available studies are mono-centric. This workshop will provide attendees with a comprehensive overview at European level of infectious diseases prevalence in prison populations and health services provided in detention facilities. The discussion of three European project collecting successful models of care for hepatitis elimination and implementation of vaccination services in prisons will create the context for an in-depth analysis of key challenges for prison health implementation and may help promote awareness that targeted interventions are feasible and effective in reducing infectious diseases burden among people living in prison and the community at large. Key messages • Existing European initiatives contribute to building the evidence for tailored prevention & control interventions in prison settings. • Adequate and effective prisons healthcare contribute to achieving the UN’s SDGs through improving health, reducing health inequalities and providing a fairer and safer society for all.

Hurricane wind disaster assessment methods on coastal structures based on area and radial distribution integration

Early studies have shown that hurricane disaster assessment indicators that only consider the intensity-related single factor cannot well represent the actual degree of damage caused by hurricanes to coastal structures. In the current study, for hurricane wind damage, in addition to the three wind disaster index formulas (corresponding to the wind resistance characteristics of infrastructures in developed, developing, and underdeveloped regions, respectively) that are related to hurricane intensity, the spatial scale characteristics of hurricanes are also considered. Thus, two kinds of hurricane wind disaster assessment methods by combing the wind disaster index and spatial scale of hurricanes are carried out, area and radial integration, respectively. Compared with the traditional single-factor evaluation index, the method based on area integral can more accurately characterize the degree of hurricane wind disaster. However, the peripheral small wind speed of the hurricane has a greater impact on wind disaster assessment; for medium-strength and intense hurricanes, the highest proportion is up to 61.5–67.7%; and that of weak hurricanes is up to 85.7–94.1%. To solve the challenging problems of hurricanes with small wind speed but the large size and large wind speed but small size, the area integral method with the concept of cut-in wind speed is introduced. The impact of small wind speed on wind disaster assessment is significantly reduced; the highest proportion of medium-strength and intense hurricanes is 14.5–22.5%, and that of weak hurricanes is 27.5–33.3%. Because the three-dimensional wind field is not easy to capture, the structure along the coastline is the most severely damaged, and the linear radial integral is less sensitive to small wind speeds than the quadratic area integral, a hurricane wind disaster assessment method based on radial integral is proposed. The results show that the method has good spatial region insensitivity. Both methods show that the numerical results can accurately represent the hurricane wind disaster when the horizontal grid size is 185 m or 555 m. However, the wind disaster assessment trends of the two methods are not linear because of the asymmetry of the hurricane structure. The two hurricane wind disaster assessment methods proposed in this study can better reflect the degree of disaster. For the comprehensive hurricane wind disaster assessment for large-scale wind farms in the deep sea, the area-based method can be used; for the infrastructure structures along the coastline, the radial-based method is more suitable. Since the measured meteorological data contain different characteristic radius and the corresponding wind speed of hurricanes, the method based on radial integral is more practical.

Developing neighbourhood typologies and understanding urban inequality: a data-driven approach

ABSTRACT Neighbourhoods affect people’s livelihoods, and therefore drive and mediate intra-urban inequalities and transformations. While the neighbourhood has long been recognized as an important unit of analysis, there is surprisingly little systematic research on different neighbourhood types, especially in the fast-growing cities of the Global South. In this paper we employ k-means clustering, a common machine-learning algorithm, to develop a neighbourhood typology for South Africa’s eight largest cities. Using census data, we identify and describe eight neighbourhood types, each with distinct demographic, socio-economic, structural and infrastructural characteristics. This is followed by a relational comparison of the neighbourhood types along key variables, where we demonstrate the persistent and multi-dimensional nature of residential inequalities. In addition to shedding new light on the internal structure of South African cities, the paper makes an important contribution by applying an inductive, data-driven approach to developing neighbourhood typologies that advances a more sophisticated and nuanced understanding of cities in the Global South.

AUTOMATED REAL-TIME TRAFFIC FORECASTING SYSTEM

Based on the analysis of the vehicles total number growth rates, which exceed the rates of expansion and optimization of the transport infrastructure, the need for the introduction of real-time traffic forecasting and control systems is shown. The factors that make it possible to detect the probability of potentially dangerous situations on the road, such as traffic jams, accidents and lack of parking spaces, respectively, in certain urban areas, based on the data of sensor networks and surveillance cameras combined within the global system of the Internet of Things, have been determined. It is proposed to build a sensor network based on magnetic sensors, which allows for high-precision geolocation with refinement of the received data by using ultrasonic sensors and optical monitoring tools, while identification is carried out by reading RFID tags. It is shown that the task of optimal organization of the relay system includes the determination of the features of the city infrastructure and statistical indicators of the city's traffic flows, and for a multi-level communication system, protocols are determined depending on the distance between nodes, requirements for the level of data protection, data transmission speed, minimum radio signal amplitude, as well as restrictions on the power supply of a separate node. The presented topology of the relay network includes the organization of sensor nodes into clusters, transmission from the main node of the cluster to the gateway node, and from the gateway nodes to the base station. On the basis of the specified model, a scheme for building cluster self-organization algorithms can be presented by forming clusters in real time according to the topology of the cluster tree, which allows simplifying the data transfer subsystem and reducing the time of processing input data. The developed scheme for analyzing the traffic flow at the intersection and the availability of parking spaces can be used in the development of methodological recommendations for the implementation of the "Smart City" concept and the creation of software applications that provide drivers with information about the state of traffic and predicted changes within a certain time interval. Keywords: automaticregulation of auto-traffic, controlled intersection, sensor network, Internet of Things, geolocation, identification, self-organization of clusters.

Real-time Accelerometer-Based Event Detection Through KNN Classification Method

Real-time pattern and activity recognition techniques have experienced a recent surge in the use of advanced statistical techniques for analyzing data. More explicitly, Machine Learning classification methods have been used in a variety of applications and for different purposes, such as recognizing different types of events from accelerometer data (e.g. in smart watches, wearables, and vibration analysis). The objective of this work is to present a novel use of accelerometer-based pattern recognition techniques to detect the urban space infrastructure characteristics based on their effect on a vehicle’s body frame dynamics. For this study, we focus on detecting speed bumps, potholes and curves based on a real-time data streaming with the objective of rendering a street digitalization to automatically update an urban space Digital Twin. The present study has achieved real-time event detection for an urban trajectory with high accuracy which yields data that can be later fused with other sensors data. This approach is effectively contributing to the infrastructure layer of a multi-layered approach of digital modelling. Furthermore, the results contribute to expand the body of knowledge of real-time accelerometer event recognition techniques.

Spatial distribution modeling of customer complaints using machine learning for indoor water leakage management

Customer complaints reflect the needs of citizens and provide valuable information for the efficient management of urban problems. Indoor water leakage management is required to achieve a sustainable water infrastructure and urban development. In this study, a machine learning (ML)-based modeling framework was developed for predicting the spatial distribution of customer complaints about indoor water leakage in the downtown area of Daegu Metropolitan City, South Korea. Two ML algorithms (XGBoost and LightGBM) were used with six resampling methods (e.g., undersampling, oversampling, and hybrid sampling) to compare the prediction performances. The combination of LightGBM and hybrid sampling showed the highest prediction performance. Post hoc analysis using Shapley Additive Explanations indicated that, among the various input features, the land cover type, building and water infrastructure characteristics were of primary importance. High-resolution gridded mapping clearly revealed the spatial pattern of complaint probabilities. These results provide a decision support tool for indoor water leakage management. The proposed modeling framework encompasses data preprocessing and integration, prediction, interpretation, and spatial mapping, and it is applicable to a wide variety of urban problems that require in-depth analysis of their spatial characteristics.

Bus Rapid Transit in Australasia: Performance, Lessons Learned and Futures

This paper presents a review of Bus Rapid Transit (BRT) systems in Australasia. It describes the major systems operating in Adelaide, Brisbane and Sydney, outlining their infrastructure, operations and development characteristics. The performance of these systems in terms of patronage, markets, operations and overall urban development impacts is described. Lessons learned in their implementation and operation are also reviewed. The paper concludes with an outline of future prospects for BRT development in Australasia and a discussion of the major findings of this review.

Bus Rapid Transit Features and Deployment Phases for U.S. Cities

Bus Rapid Transit (BRT) systems are becoming popular in congested cities around the world. Since this mode of transportation is still evolving, there is a lack of clear definition of what constitutes a BRT system. This paper reviews the BRT systems around the world and characterizes their infrastructure and operational features. The most common features found are those that lead to travel time reduction or ridership attraction relative to regular bus services. However, not all the features must be implemented for a BRT system to be successful. Based on the features reviewed, this research recommends three sets of features that correspond to three phases of deployment in U.S. cities, depending on the project budget, time frame, users, and traffic and corridor characteristics.

Clinical expert consensus document on the use of percutaneous left ventricular assist devices during complex high-risk PCI in India using a standardised algorithm.

Over the past decade, percutaneous left ventricular assist devices (pLVAD), such as the Impella microaxial flow pump (Abiomed), have been increasingly used to provide haemodynamic support during complex and high-risk revascularisation procedures to reduce the risk of intraprocedural haemodynamic compromise and to facilitate complete and optimal revascularisation. A global consensus on patient selection for the use of pLVADs, however, is currently lacking. Access to these devices is different across the world, thus, individual health care environments need to create and refine patient selection paradigms to optimise the use of these devices. The Impella pLVAD has recently been introduced in India and is being used in several centres in the management of high-risk percutaneous coronary intervention (PCI) and cardiogenic shock. With this increasing utilisation, there is a need for a standardised evaluation protocol to guide Impella use that factors in the unique economic and infrastructural characteristics of India's health care system to ensure that the needs of patients are optimally managed. In this consensus document, we present an algorithm to guide Impella use in Indian patients: to establish a standardised patient selection and usage paradigm that will allow both optimal patient outcomes and ongoing data collection.

ONTOLOGICAL SUITABILITY ANALYSIS OF RAILWAY TRACKS FOR HIGH-SPEED TRAFFIC

High-speed railways are developing rapidly in the world and, in particular, in Europe, and are promising in the territory of Ukraine. The purpose of the work is to check the consistency of the existing railway characteristics with the high-speed rail transport regulations by ontological means. The ontology was developed in the Protégé and MRcube software environments in the OWL DL profile using the Extreme methodology. Ontology population is automated in OpenRefine and INCEpTION based on texts and tables semantic annotation. Data extraction is done in Tabula for tabular data resources and FineReader for drawings. The ontological support was developed according to the model of multi-level concretization employing a distinction between data and rules modules. The ontology of resources is based on a tabular knowledge representation model. The usability of the proposed approach is experimentally evaluated on the example of the sections of the «Mariupol Port-Volnovakha» railway in Ukraine, «Plovdiv-Burgas» in Bulgaria and «Coswig-ESig G» in Germany and the corresponding regulations. The ontology can be used for conventional railways (e.g., 160 km/h), as well as further integration with the railway train model can be done. The ontology was evaluated by the domain expert and using the OntOlogy Pitfall Scanner. The scientific novelty of the work lies in the integration of heterogeneous resources of railway infrastructure characteristics by ontological means. The semantic annotation and formalization of railway regulations have been employed. Railway transport data extraction and validation processes were improved. The practical significance of the work lies in the possibility of improving the safety of train traffic by checking the suitability of railway tracks to ensure the declared speeds of high-speed and conventional railways.

Damming news: Geospatial media discourse analysis of dams.

Dams are a globally important social-ecological issue, and the practice of removing aging or obsolete dams is increasing in many countries where rivers have been used to fuel industrial growth. News media play an important role in providing information and raising awareness about dam-related decision making and patterns in news media coverage can shape public sense-making about potentially controversial dam decisions. This research focuses on spatial patterns of news media references to "dam removal" georeferenced to a New England dams database, the types of dam removal characteristics that contribute to newsworthiness, and specific media framing strategies. We develop a method, known as DAMMDA, that combines a large geodatabase of dam features and corpus of news media articles. We find that spatial patterns of "dam removal" news media coverage do not necessarily coincide with the actual occurrence of removed dams, nor the distribution of extant dams, in the landscape. Instead, a minority of dams with specific characteristics make up the majority of dam media references. Such "newsworthy dams" are capable of generating hydroelectricity, are situated on large rivers, are located in urban areas, or have already been removed. Further, references to newsworthy dams are often used to frame discussions about future dam decisions in other locations in New England and across the United States. We conclude by reflecting on how this approach is relevant for understanding complex and interconnected factors that can shape controversial sustainability issues, such as the relationships between news media and social-ecological characteristics of infrastructures within landscapes.

Staged Transition Process from Driving to Bicycling Based on the Effects of Latent Variables

With the worsening of urban traffic congestion and environmental pollution, cities around the world have become conscious of the importance of the bicycle. The key to promoting the transition from driving to bicycling for short-distance travel is to understand the transition process and the effect of latent variables. This paper divides the transition from driving to bicycling into five stages according to the transtheoretical model of behavior change and analyzes the staged transition process in terms of influences from latent variables. First, based on survey data from ten communities in Beijing, China, a multiple-indicator and multiple-cause (MIMIC) model of transition intention was developed, and then the influential relationships were analyzed between latent variables such as riding attitude and the causal relationships between latent variables and exogenous variables such as infrastructure characteristics. Subsequently, four transition behavior hybrid-choice models for different stages were developed to describe the phased transition process. Finally, the key influencing factors in different stages were identified. The findings reveal the mechanism of the transition from driving to bicycling, which can support policy makers’ decisions at the level of bicycle travel promotion, guidance and facility planning.

The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach

Introduction: Walking is an active way of moving the population, but in recent years there have been more pedestrian casualties in traffic, especially in developing countries such as Serbia. Macro-level road safety studies enable the identification of influential factors that play an important role in creating pedestrian safety policies. Method: This study analyzes the impact of traffic and infrastructure characteristics on pedestrian accidents at the level of traffic analysis zones. The study applied a geographically weighted regression approach to identify and localize all factors that contribute to the occurrence of pedestrian accidents. Taking into account the spatial correlations between the zones and the frequency distribution of accidents, the geographically Poisson weighted model showed the best predictive performance. Results: This model showed 10 statistically significant factors influencing pedestrian accidents. In addition to exposure measures, a positive relationship with pedestrian accidents was identified in the length of state roads (class I), the length of unclassified streets, as well as the number of bus stops, parking spaces, and object units. However, a negative relationship was recorded with the total length of the street network and the total length of state roads passing through the analyzed area. Conclusion: These results indicate the importance of determining the categorization and function of roads in places where pedestrian flows are pronounced, as well as the perception of pedestrian safety near bus stops and parking spaces. Practical Applications: The results of this study can help traffic safety engineers and managers plan infrastructure measures for future pedestrian safety planning and management in order to reduce pedestrian casualties and increase their physical activity.