Agent-based modeling to estimate exposures to urban air pollution from transportation: Exposure disparities and impacts of high-resolution data

Better understanding of the complex links between urban transportation, land use, air quality, and population exposure is needed to improve urban sustainability. A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. We aimed to characterize exposure and exposure inequality for traffic-related air pollution, and to investigate the impacts of high-resolution information on estimated exposure. To do these, we developed and applied a modeling framework that combines the DaySim activity-based travel demand model, the MATSim dynamic traffic assignment model, the MOVES mobile source emissions estimator, and the R-LINE dispersion model. Resulting spatiotemporal distributions of daily individual human activity and pollutant concentration were matched to analyze population and subgroup exposure to oxides of nitrogen (NO_x) from passenger car travel for an average winter day in 2010. Four scenarios using data with different spatiotemporal resolutions were considered: a) high resolution for both activities and concentrations, b) low resolution for both activities and concentrations, c) high resolution for activities, but low resolution for concentrations, and d) vice versa. For the high-resolution scenario, the mean daily population exposure concentration of NO_x from passenger cars was 10.2 μg/m³; individual exposure concentrations ranged from 0.2 to 145 μg/m³. Subgroup mean exposure was higher than the population mean for individuals living below-poverty (by ~16%), those with daily travel time over one hour (8%), adults aged 19–45 (7%), blacks (6%), Hispanics (4%), Asians (2%), combined other non-white races (2%), people from middle income households (2%), and residents of urban areas (2%). The subgroup inequality index (a measure of disparity) largely increased with concentration up to the 90th percentile level for these groups. At higher levels, disparities increased sharply for individuals from below poverty households, blacks, and Hispanics. Low-resolution simulation of both activities and concentrations decreased the exposure estimates by 10% on average, with differences ranging from eight times higher to ~90% lower.     

Working hours as a risk factor in the development of musculoskeletal complaints

The length of daily working hours as a risk factor for the development of musculoskeletal complaints was studied by comparing the sick leave statistics of 408 sewing machine operators on full-time schedules (8 h working day) with 210 operators on part-time schedules (5 h working day). Working part-time was shown to postpone the occurrence of sick leave due to musculoskeletal disorders by approximately half a year. There was no lasting effect on the reduction in working hours on sick leave due to shoulder-neck complaints, but a reduction in low back complaints was indicated. It is suggested that any reorganization of work activities to counteract musculoskeletal injuries from repetitive work should aim to break up the muscular activity patterns over time periods considerably shorter than the 5 h working day of the part-time workers in the present study.     

YUTPA as a design tool for public participation

Engaging the public in decision-making processes is commonly accepted as an effective strategy for a better policy making, a better policy support and for narrowing the gap between government and the public. In today’s digitised society, participation via online media is becoming more important. But is this so-called e-participation being used optimally? Or is a better design possible? In my opinion, the answer to these questions is a ‘yes’. Despite numerous efforts in engaging the public with policy deliberation, the actual amount of participants remains low. In this article, I have used the YUTPA model (Nevejan 2009) to analyse some existing e-participation projects. Additionally, I derived ten characteristics of ‘play’ to make proposals for a more designerly e-participation approach.     

Efficient distribution-free population learning of simple concepts

We consider a variant of the ‘population learning model’ proposed by Kearns and Seung [8], in which the learner is required to be ‘distribution-free’ as well as computationally efficient. A population learner receives as input hypotheses from a large population of agents and produces as output its final hypothesis. Each agent is assumed to independently obtain labeled sample for the target concept and output a hypothesis. A polynomial time population learner is said to PAC-learn a concept class, if its hypothesis is probably approximately correct whenever the population size exceeds a certain bound which is polynomial, even if the sample size for each agent is fixed at some constant. We exhibit some general population learning strategies, and some simple concept classes that can be learned by them. These strategies include the ‘supremum hypothesis finder’, the ‘minimum superset finder’ (a special case of the ‘supremum hypothesis finder’), and various voting schemes. When coupled with appropriate agent algorithms, these strategies can learn a variety of simple concept classes, such as the ‘high–low game’, conjunctions, axis-parallel rectangles and others. We give upper bounds on the required population size for each of these cases, and show that these systems can be used to obtain a speed up from the ordinary PAC-learning model [11], with appropriate choices of sample and population sizes. With the population learner restricted to be a voting scheme, what we have is effectively a model of ‘population prediction’, in which the learner is to predict the value of the target concept at an arbitrarily drawn point, as a threshold function of the predictions made by its agents on the same point. We show that the population learning model is strictly more powerful than the population prediction model. Finally, we consider a variant of this model with classification noise, and exhibit a population learner for the class of conjunctions in this model. This revised version was published online in June 2006 with corrections to the Cover Date.     

An unconstrained statistical matching algorithm for combining individual and household level geo-specific census and survey data

The Population Census is an important source of statistical information in most countries that is capable of producing reliable estimates of population characteristics for small geographic areas. One limitation of a census is that there are many population characteristics that cannot be collected due to respondent burden or cost. This means that statistical agencies have to conduct population based surveys to provide social, economic and demographic characteristics for a target population which are not captured by a large-scale census. These surveys are usually capable of producing direct estimates at the national level and high level regions but often cannot produce reliable estimates for smaller areas. Due to the increasing demand for comprehensive statistical information not only at the national level but also for sub-national domains, there is a wide discussion in the literature about the use of statistical techniques that combine survey with census data to provide more detailed, finer-level estimates.Where censuses and sample surveys are based on the same reporting units, statistical matching techniques can be employed to link the records from survey and census data where exact matching of reporting units is impossible due to confidentiality restrictions. These techniques can then provide the detailed social, economic and demographic information required for small areas.An approach is developed in this paper in which a close-to-reality synthetic population of individuals and households is generated from available census tables using an iterative proportional updating (IPU) method. Statistical matching using a nearest neighbour method is then used to impute survey data to the individuals and households in the synthetic population. To evaluate this approach, 2011 Bangladesh census data is used to generate a district-specific synthetic population of individuals and households. Matching is then performed by imputing the nearest possible records among the 2011 Bangladesh Demographic and Health Survey to estimate the wealth index for each household within the synthetic population. The results show that using the method presented in this paper helps with achieving more representative estimates (comparing with direct survey estimates) particularly for areas with small sample sizes where not many population units with different socio-demographic characteristics are included.     

User-adaptive explanatory program visualization: evaluation and insights from eye movements

User-adaptive visualization and explanatory visualization have been suggested to increase educational effectiveness of program visualization. This paper presents an attempt to assess the value of these two approaches. The results of a controlled experiment indicate that explanatory visualization allows students to substantially increase the understanding of a new programming topic. Furthermore, an educational application that features explanatory visualization and employs a user model to track users’ progress allows students to interact with a larger amount of material than an application which does not follow users’ activity. However, no support for the difference in short-term knowledge gain between the two applications is found. Nevertheless, students admit that they prefer the version that estimates and visualizes their progress and adapts the learning content to their level of understanding. They also use the application’s estimation to pace their work. The differences in eye movement patterns between the applications employing adaptive and non-adaptive explanatory visualizations are investigated as well. Gaze-based measures show that adaptive visualization captivates attention more than its non-personalized counterpart and is more interesting to students. Natural language explanations also accumulate a big portion of students’ attention. Furthermore, the results indicate that working memory span can mediate the perception of adaptation. It is possible that user-adaptation in an educational context provides a different service to people with different mental processing capabilities.     

Research of localization approach for the new comer in wireless sensor networks

When some sensor nodes of wireless sensor networks (WSN) can not work forever because of long-term work or failure caused by attack, a few new comers need to be put into the network. For the application of the new comer in WSN, an accurate and effective localization algorithm based on received signal strength indicator (RSSI) is proposed. Through a few necessary nodes’ participation and the collaboration between the new comer and its one-hop and two-hop neighbor nodes, the accurate localization of the new comer is achieved. Simulation results show that the localization accuracy is about 17% of sensor node’s radio frequency (RF) transmission range, when the measurement error is 10% and the standard deviation for Gauss error of original sensor nodes’ coordinate is about 20% of sensor node’s RF transmission range. Simulation results also verify nice stability and adaptability of the new comer’s location algorithm.     

Network-based Accessibility Measures for Vulnerability Analysis of Degradable Transportation Networks

Accessibility is a fundamental concept in transportation analysis and urban planning. Typically, accessibility refers to the ‘ease’ of reaching opportunities for activities and services and can be used to assess the performance of a transportation and urban system. In this paper, we present network-based accessibility measures for assessing vulnerability of degradable transportation networks. The network-based accessibility measures consider the consequence of one or more link failures in terms of network travel time or generalized travel cost increase as well as the behavioral responses of users due to the failure in the network. To model different dimensions of travel behavioral responses, a combined travel demand model is adopted to estimate the long-term equilibrium network condition due to network disruptions. Numerical examples are conducted to demonstrate the feasibility of the proposed vulnerability measures for assessing degradable transportation networks. The results indicate that the accessibility measures derived from the combined travel demand model are capable of measuring the consequences of both demand and supply changes in the network and have the flexibility to reflect the effects of different travel choice dimensions on the network vulnerability.     

Collaborative virtual sculpting with haptic feedback

This paper introduces a haptic virtual environment in which two users can collaboratively sculpt a virtual clay model, working from different physical locations connected by the internet. They view their virtual sculpting tools and the clay model in 3D, feel the tool’s pressure on the clay as they work, and have their hands co-located with the view of the tool and model. Since the sculptors have independent views of the same logical environment, they can work at different zoom levels, and be in different coordinate systems, even spinning ones, at the same time. This provides them with the capability to explore new styles of collaborative creativity, working off each other’s initiative where appropriate. The system was designed to allow unrestrained, asynchronous behaviour by the collaborating sculptors. The paper describes the hardware as well as the algorithms behind the deformability of the clay surface and the communications model enabling the distance collaboration. It gives an explanation of the simple conflict resolution mechanism that haptic feedback facilitates and also reports on the results of a qualitative study into the creativity benefits of such a collaborative system.     

Fuzzy synthetic rating and a satisfying solution for Lee–Tanaka’s LP problem

Fuzzy synthetic rating is a mapping to capture the relationship between the characteristics of an object in a group and its overall fuzzy rating. This paper presents a general treatment on the problems of fuzzy synthetic rating based on factor space, fuzzy clustering and Lee–Tanaka’s idea of transferring the learning task of fuzzy regression NN by means of solving a kind of linear programming, called Lee–Tanaka’s LP problem by the author here. The author presents a satisfying solution for the LP problem. The satisfying solution is not necessarily an optimal solution in the traditional sense. That is, the fuzzy optimal methodology could be applied even when the feasible region is empty.     

Discrete Working Time Choice in an Applied General Equilibrium Model

We present a model that integrates the discrete working time choice of heterogenous households into a general equilibrium setting where wages are determined by sectoral bargaining between firms and trade unions. The model is calibrated to German micro and macro data. We then use it to analyse a stylised policy reform designed to stimulate labour supply. JEL Code: D58, J22, J51 An erratum to this article can be found at     

Combining case-based reasoning systems and support vector regression to evaluate the atmosphere–ocean interaction

This work presents a system for automatically evaluating the interaction that exists between the atmosphere and the ocean’s surface. Monitoring and evaluating the ocean’s carbon exchange process is a function that requires working with a great amount of data: satellite images and in situ vessel’s data. The system presented in this study focuses on computational intelligence. The study presents an intelligent system based on the use of case-based reasoning (CBR) systems and offers a distributed model for such an interaction. Moreover, the system takes into account the fact that the working environment is dynamic and therefore it requires autonomous models that evolve over time. In order to resolve this problem, an intelligent environment has been developed, based on the use of CBR systems, which are capable of handling several goals, by constructing plans from the data obtained through satellite images and research vessels, acquiring knowledge and adapting to environmental changes. The artificial intelligence system has been successfully tested in the North Atlantic Ocean, and the results obtained will be presented in this study.     

Modeling the progression of Alzheimer’s disease for cognitive assistance in smart homes

Smart homes provide support to cognitively impaired people (such as those suffering from Alzheimer’s disease) so that they can remain at home in an autonomous and safe way. Models of this impaired population should benefit the cognitive assistance’s efficiency and responsiveness. This paper presents a way to model and simulate the progression of dementia of the Alzheimer’s type by evaluating performance in the execution of an activity of daily living (ADL). This model satisfies three objectives: first, it models an activity of daily living; second, it simulates the progression of the dementia and the errors potentially made by people suffering from it, and, finally, it simulates the support needed by the impaired person. To develop this model, we chose the ACT-R cognitive architecture, which uses symbolic and subsymbolic representations. The simulated results of 100 people suffering from Alzheimer’s disease closely resemble the results obtained by 106 people on an occupational assessment (the Kitchen Task Assessment).     

Building a community among teachers, researchers and university students. A blended approach to training

In this paper we present a case study about a community of practice’s foundation and development among Italian teachers, researchers and university students who participated in a European project aimed at developing and testing innovative pedagogical models and technologies for collaborative knowledge building. Forty-five people (34 teachers, five researchers and six university students) participated in the community of adults that interacted for a school year both face to face and online. We analyzed interactions in order to study the roles, forms and distribution of participation in that community, and the content of teachers’ reflections about the activity. The analysis focuses particularly on different modalities of participation between expert teachers (involved in the project from the beginning) and novices, novice and expert being treated as relevant dimensions according to Wenger’s model. Conversations were transcribed and a qualitative analysis of face-to-face and online discussion performed. The diversity of roles and different modalities of participation between social factors involved in the community, in particular between novice and expert teachers, emerged from the analysis. In final focus groups, teachers underlined innovative potentialities as well as difficulties related to computer-supported collaborative learning, both in classroom activities and in teacher training. In these final focus groups, novice teachers participated in the community, becoming more competent and conscious partners in shared planning with the expert teachers.     

A multi-agent model of urban processes: Modelling relocation processes and price setting in housing markets

This paper introduces an agent-based micro-simulation model of housing market processes. The model describes aggregate housing market developments, such as price and turnover, as the outcome of households’ decisions to search for a new dwelling, accept an offered dwelling or sell their dwelling. An important feature of the model is that households’ decisions are based on perceptions of housing market probabilities. Households update these perceptions based on observed bargaining outcomes in the market. The model was tested in a simulation experiment and appeared to respond plausibly to different market settings in terms of prices and households’ perception of the market.     

A Dynamic Travel Time Model for Spillback

In this paper we introduce travel time models that incorporate spillback and bottleneck phenomena. In particular, we study a model for determining the link travel times for drivers entering a link as well as drivers already in the link but whose travel times are affected by a significant change in traffic conditions (e.g. spillback or bottleneck phenomena). To achieve this goal, we extend the fluid dynamics travel time models proposed by Perakis (1997)and subsequently by Kachani (2002), and Kachani and Perakis (2001), to also incorporate such phenomena. These models utilize fluid dynamics laws for compressible flow to capture a variety of flow patterns such as the formation and dissipation of queues, drivers’ response to upstream congestion or decongestion and drivers’ reaction time. We propose variants of these models that explicitly account for spillback and bottleneck phenomena. Our investigation considers both separable and non-separable velocity functions.     

Study on IAC networks models: learning and stability

This paper presents a methodology that uses evolutionary learning in training ‘A’ model networks, a topology based on Interactive Activation and Competition (IAC) neural networks. IAC networks show local knowledge and processing units clustered in pools. The connections among units may assume only 1, 0 or −1. On the other hand, ‘A’ model network uses values in interval [−1, 1]. This feature provides a wider range of applications for this network, including problems which do not show mutually exclusive concepts. However, there is no algorithm to adjust the network weights and still preserve the desired characteristics of the original network. Accordingly, we propose the use of genetic algorithms in a new methodology to obtain the correct weight set for this network. Two examples are used to illustrate the proposed method. Findings are considered consistent and generic enough to allow further applications on similar classes of problems suitable for ‘A’ model IAC Networks.     

Cognitive and synthetic behavior of avatars in intelligent virtual environments

In intelligent virtual environments (IVEs), it is a challenging research issue to provide the intelligent virtual actors (or avatars) with the ability of visual perception and rapid response to virtual world events. Modeling an avatar’s cognitive and synthetic behavior appropriately is of paramount important in IVEs. We propose a new cognitive and behavior modeling methodology that integrates two previously developed complementary approaches. We present expression cloning, walking synthetic behavior modeling, and an autonomous agent cognitive model for driving an avatar’s behavior. Facial expressions are generated using our own-developed rule-based state transition system. Facial expressions are further personalized for individuals by expression cloning. An avatar’s walking behavior is modeled using a skeleton model that is implemented by seven-motion sequences and finite state machines (FSMs). We discuss experimental results demonstrating the benefits of our approach.     

Ergonomic evaluation of the angle of abduction in a computer numerically controlled electro discharge machine environment

Dynamic situations are not fully controlled and affected by uncertain human factors. Anthropometric considerations are important in the design of systems. Present research work have considered the ‘angle of abduction’, whose effect on operator’s performance has been ergonomically evaluated in a CNC-EDM environment. In this work, the experimental data are analyzed through an ANOVA using SPSS statistical software. The result indicates that the angle of abduction significantly affects the operator’s performance in a CNC-EDM interaction environment. Further analysis revealed that a 45 degree abduction angle gives the optimal performance as far as a human–machine interaction environment is concerned.     

An informational divergence geometry for stochastic matrices

Summary The informational divergence between stochastic matrices is not a metric. In this paper we show that, however, consistent definitions can be given of ‘spheres’, ‘segments’ and ‘straight lines’ using the divergence as a sort of ‘distance’ between stochastic matrices. The geometric nature of many ‘reliability functions’ of Information Theory and Mathematical Statistics is thus clarified. This work has been done within the GNIM-CNR research activity.