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This paper aims to present a novel scheme of multiple vanishing points (VPs) estimation and corresponding lanes identification.

The scheme proposed here includes two main stages: VPs estimation and lane identification. VPs estimation based on vanishing direction hypothesis and Bayesian posterior probability estimation in the image Hough space is a foremost contribution, and then VPs are estimated through an optimal objective function. In lane identification stage, the selected linear samples supervised by estimated VPs are clustered based on the gradient direction of linear features to separate lanes, and finally all the lanes are identified through an identification function.

The scheme and algorithms are tested on real data sets collected from an intelligent vehicle. It is more efficient and more accurate than recent similar methods for structured road, and especially multiple VPs identification and estimation of branch road can be achieved and lanes of branch road can be identified for complex scenarios based on Bayesian posterior probability verification framework. Experimental results demonstrate VPs, and lanes are practical for challenging structured and semi-structured complex road scenarios.

A Bayesian posterior probability verification framework is proposed to estimate multiple VPs and corresponding lanes for road scene understanding of structured or semi-structured road monocular images on intelligent vehicles.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

The purpose of this paper is to outline the key determinants of innovation performance of a firm in a cluster. This paper probes the role of absorptive capacity in furthering the cluster linkages and thereby enhancing the innovation performance of a firm.

This study adopts stratified random sampling technique to choose sample firms from the identified population of firms in a cluster. Further, it employs primary data collection method to collect data from sample firms through a semi-structured questionnaire based in-depth interviews with the top level management of sample firms. It uses multiple linear regression (MLR) techniques to ascertain the influence of absorptive capacity on degree of cluster linkages (both intra-cluster and extra-cluster linkages), and degree of cluster linkages (both intra-cluster and extra-cluster linkages) on innovation performance of a firm.

On the one hand, internal factors of absorptive capacity of a firm have a significant positive influence on the degrees of both intra-cluster linkages and extra-cluster linkages. On the other hand, external factors of absorptive capacity of a firm significantly impact the degree of intra-cluster linkages (DICL). But, they have no significant influence on the degree of extra-cluster linkages (DECL). Further, both the DICL and the DECL drive innovation performance of a firm in a cluster. Notably, subsidiaries of externally based firms exhibit superior innovation performance compared to those firms based in a cluster.

This paper contributes to the extant literature in two ways. First, it empirically validates the effect of absorptive capacity of a firm on its degree of cluster linkages (both intra-cluster and extra-cluster linkages) taking into account both internal and external factors of absorptive capacity. Second, it ascertains the influence of degree of cluster linkages (both intra-cluster and extra-cluster linkages) on the innovation performance of a firm in a cluster.

The south of Chad has seen an influx of many tens of thousands of refugees within the last three years. After the president of the neighbouring Central African Republic (CAR), Felix Patasse, was overthrown in a coup d'etat in March 2003 more than 50.000 people fled to Chad, across the northern border. From the beginning of the refugee crisis, UNHCR has been present in the area to house and protect the refugees.

Following a renewed influx of large numbers of refugees in autumn 2005, UNHCR adopted a new strategy of ‘integration’ for their newest camp ‘Gondje’. ‘Integration’ aims for a joint use of camp facilities, such as schools and clinics, by the refugee population as well as by the local Chadian population. It is meant to bring benefits to the underdeveloped region of southern Chad. On the other hand, this strategy can also lead to a permanent resettlement of the refugees from CAR in Chad. Based on recent fieldwork in the area and in the camp of ‘Gondje’, this paper traces the strategy of ‘integration’ through a number of narratives as well as spatial analyses, puts it into a context of the planning strategies of refugee camps followed by UNHCR, and speculates on the effects and repercussions of this strategy. As emergency situations and the field of developmental work are becoming the areas within which architects are increasingly practicing, the article also sheds light on the responsibilities and the dilemmas the profession faces when operating in these humanitarian contexts.

This paper aims to investigate the potential of the “image-idea” of a “circular economy” for re-thinking property in law: In particular, to develop a strategy for making visible “alternative property practices” of community ownership across the subject areas of business and property law, to enhance the visibility of models of community ownership and interrogate their potential.

Case study research was undertaken into three public houses to investigate the ways in which the orthodoxies of property and ownership in the academy are challenged by evidence of “alternative property practices” in the community.

Using this approach renders visible tensions between the logics of economic value and social asset, carried in processes of abstraction and materiality, and mediated within the field of property by the development of techniques for holding property as title and benefit. It reveals the ways in which “property” as idea, practice and technique is used by people seeking to disrupt or defend against the economic logic of profit and investment. It raises questions concerning how property and law is imaged in the academy and it introduces one way of using an image-idea to open new perspectives and potential.

These implications emerge: the partiality of orthodox accounts of property; the importance of thinking property in terms of life-cycle and logics ecologies, field and techniques; how an model-theory derived from one discipline can be repurposed, in a second life, in an other discipline as an “image-idea” to refresh the host discipline; the significance of investigating “community assets” within and for property law and the need for more research into “alternative property practices” and the importance of case studies.

An enhanced knowledge of the development and potential of “community assets” within the academy, and of the potential to promote and support “alternative property practices” with the requisite legal skills and techniques – alongside a consideration of the limits of formal law in terms of policy expectations.

The research is of value to community activists in thinking how law can be used to support community development in terms of holding community assets; and the limitations of formal law which then requires an embedded approach considering how the development of practices and narratives can support community initiatives in relation to property held for community benefit.

There has been very little coverage of “community assets” within legal research, especially moving across business and property as subject areas, and no coverage on public houses taken into community ownership. This paper combines an introduction to the relevant legal forms with a consideration of the use of them in practice: considering, in particular, how practices and narratives deployed by and within the community think and present “property” as a means by which to counter the economic logic of profit. All this is made possible through the use of case-studies made visible by the utilization of the image-idea of the circular economy – used here not as a model-theory, but rather as an aid to opening thinking into new territories accessed through new perspectives.

Restructuring a business process (BP) model may enhance the BP performance and improve its understandability. So-far proposed restructuring methods use either refactoring which focuses on structural aspects, social network discovery which uses semantic information to guide the affiliation process during its analysis, or social network rediscovery which uses structural information to identify clusters of actors according to their relationships. The purpose of this paper is to propose a hybrid method that exploits both the semantic and structural aspects of a BP model.

The proposed method first generates a social network from the BP model. Second, it applies hierarchical clustering to determine the performers’ partitions; this step uses the social context which specifies features related to performers, and two new distances that account for semantic and structural information. Finally, it applies a set of behavioral and organizational restructuring rules adapted from the graph optimization domain; each rule uses the identified performers’ partitions and the business context to reduce particular quality metrics.

The efficiency of the proposed method is illustrated through well-established complexity metrics. The illustration is made through the development of a tool that fully supports the proposed method and proposes a strategy for the application of the restructuring rules.

The proposed method has the merit of combining the semantic and structural aspects of a Business Process Modeling Notation model to identify restructuring operations whose ordered application reduces the complexity of the initial model.

The challenge of truckload routing is increased in complexity by the introduction of stochastic demand. Typically, this demand is generalized to follow a Poisson distribution. In this chapter, we cluster the demand data using data mining techniques to establish the more acceptable distribution to predict demand. We then examine this stochastic truckload demand using an econometric discrete choice model known as a count data model. Using actual truckload demand data and data from the bureau of transportation statistics, we perform count data regressions. Two outcomes are produced from every regression run, the predicted demand between every origin and destination, and the likelihood that that demand will occur. The two allow us to generate an expected value forecast of truckload demand as input to a truckload routing formulation. The negative binomial distribution produces an improved forecast over the Poisson distribution.

To determine the influence of the development of clustering of the national business environment on the level of digital competitiveness.

The research was performed using the following methods: statistical analysis, correlation analysis and comparative analysis.

We study the influence of the development of clustering of the national business environment on the level of digital competitiveness. It is revealed that the studied developed countries (Singapore, Denmark and Switzerland) demonstrate a high level of clustering of business, which is assessed through the use of the indicator ‘State of сluster development’, and a high level of digital competitiveness. The considered developing countries (Peru, Mexico and the Philippines) have medium values of the above variables. Only Peru was able to use a highly effective mechanism of clustering, which influenced the digitalisation of sectors of the economy, which have business clusters. We also describe the competitive advantages of the development of cluster entrepreneurial structures, which ensure their economic and market success.

The scientific novelty of the results obtained is due to the elaboration on the specifics of the influence of the cluster strategy of business integration on the level of national digital competitiveness.

For autonomous vehicles, trajectory prediction of surrounding vehicles is beneficial to improving the situational awareness of dynamic and stochastic traffic environments, which is a crucial and indispensable element to realize highly automated driving.

In this paper, the overall framework consists of two parts: first, a novel driver characteristic and intention estimation (DCIE) model is built to indicate the higher-level information of the vehicle using its low-level motion variables; then, according to the estimation results of the DCIE model, a classified Gaussian process model is established for probabilistic vehicle trajectory prediction under different motion patterns.

The whole method is later applied and analyzed in the highway lane-change scenarios with the parameters of models learned from the public naturalistic driving data set. Compared with other traditional methods, the performance of this proposed approach is proved superior, demonstrated by the higher accuracy in the long prediction horizon and a more reasonable description of uncertainty.

This hierarchical approach is proposed to make trajectory prediction accurately both in the short term and long term, which can also deal with the uncertainties caused by the perception system or indeterminate vehicle behaviors.