A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

Stigmergy-based modeling to discover urban activity patterns from positioning data

Positioning data offer a remarkable source of information to analyze crowds urban dynamics. However, discovering urban activity patterns from the emergent behavior of crowds involves complex system modeling. An alternative approach is to adopt computational techniques belonging to the emergent paradigm, which enables self-organization of data and allows adaptive analysis. Specifically, our approach is based on stigmergy. By using stigmergy each sample position is associated with a digital pheromone deposit, which progressively evaporates and aggregates with other deposits according to their spatiotemporal proximity. Based on this principle, we exploit positioning data to identify high density areas (hotspots) and characterize their activity over time. This characterization allows the comparison of dynamics occurring in different days, providing a similarity measure exploitable by clustering techniques. Thus, we cluster days according to their activity behavior, discovering unexpected urban activity patterns. As a case study, we analyze taxi traces in New York City during 2015

Technological troubleshooting based on sentence embedding with deep transformers

AbstractIn nowadays manufacturing, each technical assistance operation is digitally tracked. This results in a huge amount of textual data that can be exploited as a knowledge base to improve these operations. For instance, an ongoing problem can be addressed by retrieving potential solutions among the ones used to cope with similar problems during past operations. To be effective, most of the approaches for semantic textual similarity need to be supported by a structured semantic context (e.g. industry-specific ontology), resulting in high development and management costs. We overcome this limitation with a textual similarity approach featuring three functional modules. The data preparation module provides punctuation and stop-words removal, and word lemmatization. The pre-processed sentences undergo the sentence embedding module, based on Sentence-BERT (Bidirectional Encoder Representations from Transformers) and aimed at transforming the sentences into fixed-length vectors. Their cosine similarity is processed by the scoring module to match the expected similarity between the two original sentences. Finally, this similarity measure is employed to retrieve the most suitable recorded solutions for the ongoing problem. The effectiveness of the proposed approach is tested (i) against a state-of-the-art competitor and two well-known textual similarity approaches, and (ii) with two case studies, i.e. private company technical assistance reports and a benchmark dataset for semantic textual similarity. With respect to the state-of-the-art, the proposed approach results in comparable retrieval performance and significantly lower management cost: 30-min questionnaires are sufficient to obtain the semantic context knowledge to be injected into our textual search engine

An adaptive stigmergy-based system for evaluating technological indicator dynamics in the context of smart specialization

Regional innovation is more and more considered an important enabler of welfare. It is no coincidence that the European Commission has started looking at regional peculiarities and dynamics, in order to focus Research and Innovation Strategies for Smart Specialization towards effective investment policies. In this context, this work aims to support policy makers in the analysis of innovation-relevant trends. We exploit a European database of the regional patent application to determine the dynamics of a set of technological innovation indicators. For this purpose, we design and develop a software system for assessing unfolding trends in such indicators. In contrast with conventional knowledge-based design, our approach is biologically-inspired and based on self-organization of information. This means that a functional structure, called track, appears and stays spontaneous at runtime when local dynamism in data occurs. A further prototyping of tracks allows a better distinction of the critical phenomena during unfolding events, with a better assessment of the progressing levels. The proposed mechanism works if structural parameters are correctly tuned for the given historical context. Determining such correct parameters is not a simple task since different indicators may have different dynamics. For this purpose, we adopt an adaptation mechanism based on differential evolution. The study includes the problem statement and its characterization in the literature, as well as the proposed solving approach, experimental setting and results

Enhancing biologically inspired swarm behavior: metaheuristics to foster the optimization of UAVs coordination in target search

Recent miniaturization in Unmanned Aerial Vehicles (UAVs) technology encourages the use of many small UAVs for search missions in unknown environments, provided that the autonomous and adaptive coordination logic can be e ective. In this research field, biologically inspired metaheuristics have been proposed to mimics swarms, flocks, and other coordination schemas. The design and management of such systems is a research challenge when considering (i) combination and optimization of multiple metaheuristics and (ii) enhancements of biologically inspired metaheuristic through technological advances. In this paper the swarm coordination of UAVs employed in target search is based on ocking and stigmergy, to provide robust formation control and dynamic environmental information sharing, respectively. The design of both metaheuristics takes into account UAVs equipment, and the coordination logic is adapted to the mission by means of a di erential evolutionary algorithm. This algorithm optimizes the aggregated structural parameters of all metaheuristics to allow the most efficient coordination with respect to the mission environment. Some possible enhancements of stigmergy are studied by simulating target search tasks on synthetic and real-world scenarios

An emergent strategy for characterizing urban hotspot dynamics via GPS data

The increasing volume of urban human mobility data arises unprecedented opportunities to monitor and understand city dynamics. Identifying events which do not conform to the expected patterns can enhance the awareness of decision makers for a variety of purposes, such as the management of social events or extreme weather situations [1]. For this purpose GPS-equipped vehicles provide huge amount of reliable data about urban dynamics, exhibiting correlation with human activities, events and city structure [2]. For example, in [3] the impact of a social event is evaluated by analyzing taxi traces data. Here, the authors model typical passenger flow in an area, in order to compute the probability that an event happens. Then, the event impact is measured by analyzing abnormal traffic flows in the area via Discrete Fourier Transform. In [4] GPS trajectories are mapped through an Interactive Voting-based Map Matching Algorithm. This mapping is used for off-line characterization of normal drivers’ behavior and real-time anomalies detection. Furthermore, the cause of the anomalies is found exploiting social network data. In [5] the authors employ a Multiscale Principal Component Analysis to analyze Taxi GPS data in order to detect traffic anomalies. The most of the methods in the literature can be grouped into four categories: distance-based, cluster-based, classification-based, and statistics-based [6]. Typically, due to the complexity of this kind of data, the modeling and comparison of their dynamics over time are hard to manage and parametrize [7]. In this paper, we present an innovative technique aimed to handle such complexity, providing a study of urban hotspot dynamics

Detecting urban road congestion via parametric adaptation of position-based stigmergy

Urban traffic management requires congestion detection. Traffic shape changes over time and location in which it is observed. Moreover it depends on roads, lines and crossroads arrangement. In addition, each congestion event has its own peculiarities (e.g. duration, extension, flow). Therefore, to give correct responses any detection model needs some kind of parametric adjustment. In this paper, we present an adaptive biologically-inspired technique for swarm aggregation of on-vehicle GPS devices positions, able to detect traffic congestion. The aggregation principle of the position samples is based on a digital mark, released at each sample in a digital space mapping the physical one, and evaporated over time. Consequently, marks aggregation occurs and stays spontaneously while many stationary vehicles are crowded into a road. In order to identify actually relevant traffic events, marks aggregation has to be correctly configured. This is achieved by tuning the mark’s structural parameters. Considering that each urban area has a specific traffic flow and density, determining a proper set of parameters is not trivial. Here, we approach the issue using different differential evolution variants, showing their impact on performance

Using an autoencoder in the design of an anomaly detector for smart manufacturing

According to the smart manufacturing paradigm, the analysis of assets’ time series with a machine learning approach can effectively prevent unplanned production downtimes by detecting assets’ anomalous operational conditions. To support smart manufacturing operators with no data science background, we propose an anomaly detection approach based on deep learning and aimed at providing a manageable machine learning pipeline and easy to interpret outcome. To do so we combine (i) an autoencoder, a deep neural network able to produce an anomaly score for each provided time series, and (ii) a discriminator based on a general heuristics, to automatically discern anomalies from regular instances. We prove the convenience of the proposed approach by comparing its performances against isolation forest with different case studies addressing industrial laundry assets’ power consumption and bearing vibrations

Assessing Refugees' Integration via Spatio-temporal Similarities of Mobility and Calling Behaviors

In Turkey the increasing tension, due to the presence of 3.4 million Syrian refugees, demands the formulation of effective integration policies. Moreover, their design requires tools aimed at understanding the integration of refugees despite the complexity of this phenomenon. In this work, we propose a set of metrics aimed at providing insights and assessing the integration of Syrians refugees, by analyzing a real-world Call Details Records (CDRs) dataset including calls from refugees and locals in Turkey throughout 2017. Specifically, we exploit the similarity between refugees’ and locals’ spatial and temporal behaviors, in terms of communication and mobility in order to assess integration dynamics. Together with the already known methods for data analysis, we use a novel computational approach to analyze spatio-temporal patterns: Computational Stigmergy, a bio-inspired scalar and temporal aggregation of samples. Computational Stigmergy associates each sample to a virtual pheromone deposit (mark). Marks in spatiotemporal proximity are aggregated into functional structures called trails, which summarize the spatiotemporal patterns in data and allows computing the similarity between different patterns. According to our results, collective mobility and behavioral similarity with locals have great potential as measures of integration, since they are: (i) correlated with the amount of interaction with locals; (ii) an effective proxy for refugee's economic capacity, thus refugee's potential employment; and (iii) able to capture events that may disrupt the integration phenomena, such as social tensions

Design and simulation of the emergent behavior of small drones swarming for distributed target localization

A swarm of autonomous drones with self-coordination and environment adaptation can offer a robust, scalable and flexible manner to localize objects in an unexplored, dangerous or unstructured environment. We design a novel coordination algorithm combining three biologically inspired processes: stigmergy, flocking and evolution. Stigmergy, a form of coordination exhibited by social insects, is exploited to attract drones in areas with potential targets. Flocking enables efficient cooperation between flock mates upon target detection, while keeping an effective scan. The two mechanisms can interoperate if their structural parameters are correctly tuned for a given scenario. Differential evolution adapts the swarm coordination according to environmental conditions. The performance of the proposed algorithm is examined with synthetic and real-world scenarios