The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity

Due to the complexity of the traffic flow dynamics in urban road networks, most quantitative descriptions of city traffic so far are based on computer simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation approach, which facilitates a simple simulation of congestion spreading in cities. First, we show that a quantization of the macroscopic turning flows into units of single vehicles is necessary to obtain realistic fluctuations in the traffic variables, and how this can be implemented in a fluid-dynamic model. Then, we propose a new method to simulate destination flows without the requirement of individual route assignments. Combining both methods allows us to study a variety of different simulation scenarios. These reveal fundamental relationships between the average flow, the average density, and the variability of the vehicle densities. Considering the inhomogeneity of traffic as an independent variable can eliminate the scattering of congested flow measurements. The variability also turns out to be a key variable of urban traffic performance. Our results can be explained through the number of full links of the road network, and approximated by a simple analytical formula

Operation Regimes and Slower-is-Faster-Effect in the Control of Traffic Intersections

The efficiency of traffic flows in urban areas is known to crucially depend on signal operation. Here, elements of signal control are discussed, based on the minimization of overall travel times or vehicle queues. Interestingly, we find different operation regimes, some of which involve a "slower-is-faster effect", where a delayed switching reduces the average travel times. These operation regimes characterize different ways of organizing traffic flows in urban road networks. Besides the optimize-one-phase approach, we discuss the procedure and advantages of optimizing multiple phases as well. To improve the service of vehicle platoons and support the self-organization of "green waves", it is proposed to consider the price of stopping newly arriving vehicles.Comment: For related work see http://www.helbing.or

Exploiting citation networks for large-scale author name disambiguation

We present a novel algorithm and validation method for disambiguating author names in very large bibliographic data sets and apply it to the full Web of Science (WoS) citation index. Our algorithm relies only upon the author and citation graphs available for the whole period covered by the WoS. A pair-wise publication similarity metric, which is based on common co-authors, self-citations, shared references and citations, is established to perform a two-step agglomerative clustering that first connects individual papers and then merges similar clusters. This parameterized model is optimized using an h-index based recall measure, favoring the correct assignment of well-cited publications, and a name-initials-based precision using WoS metadata and cross-referenced Google Scholar profiles. Despite the use of limited metadata, we reach a recall of 87% and a precision of 88% with a preference for researchers with high h-index values. 47 million articles of WoS can be disambiguated on a single machine in less than a day. We develop an h-index distribution model, confirming that the prediction is in excellent agreement with the empirical data, and yielding insight into the utility of the h-index in real academic ranking scenarios.Comment: 14 pages, 5 figure

Smart food waste management : embedded machine learning vs cloud based solutions

In Switzerland, 2.8 million tons of food are lost or wasted across all stages of food production - every year. This equates to approximately 330 kg of food waste per person. By analysing and classifying discarded food with a smart waste analysis system combined with machine learning, valuable insights can be gained and the amount of wasted food can be significantly reduced. In this paper, we present how we have developed an embedded system which helps to solve this task. The embedded system operates in a decentralized manner: It captures an image every time food is thrown into a bin. The discarded food is identified and classified with machine learning algorithms. This provides a detailed insight into the structure of food waste for customers, e.g. restaurants or canteens. We implemented the machine learning algorithm directly on the embedded systems control unit. We found that running machine learning directly on embedded devices has many advantages compared to running them in the cloud: We saved significant amounts of cloud storage and reduced power consumption by up to a factor 100. In addition, privacy was increased and required bandwidth reduced because only the machine learning results are forwarded to the cloud, not the full data

Optimal Pricing Strategy for Wireless Social Community Networks

The increasing number of mobile applications fuels the demand for affordable and ubiquitous wireless access. The traditional wireless network technologies such as EV-DO or WiMAX provide this service but require a huge upfront investment in infrastructure and spectrum. On the contrary, as they do not have to face such an investment, social community operators rely on subscribers who constitute a community of users. The pricing strategy of the provided wireless access is an open problem for this new generation of wireless access providers. In this paper, using both analytical and simulation approaches, we study the problem comprised of modeling user subscription and mobility behavior and of coverage evolution with the objective of finding optimal subscription fees. We compute optimal prices for wireless social community networks with both static and semi-dynamic pricing. Coping with an incomplete knowledge about users, we calculate the best static price and prove that optimal fair pricing is the optimal semi-dynamic pricing for social community operators in monopoly situations. Moreover, we have developed a simulator to verify optimal prices of social community operators with complete and incomplete knowledge. Our simulation results show that the optimal fair pricing strategy significantly improves the cumulative payoff of social community operators

How citation boosts promote scientific paradigm shifts and Nobel Prizes

Nobel Prizes are commonly seen to be among the most prestigious achievements of our times. Based on mining several million citations, we quantitatively analyze the processes driving paradigm shifts in science. We find that groundbreaking discoveries of Nobel Prize Laureates and other famous scientists are not only acknowledged by many citations of their landmark papers. Surprisingly, they also boost the citation rates of their previous publications. Given that innovations must outcompete the rich-gets-richer effect for scientific citations, it turns out that they can make their way only through citation cascades. A quantitative analysis reveals how and why they happen. Science appears to behave like a self-organized critical system, in which citation cascades of all sizes occur, from continuous scientific progress all the way up to scientific revolutions, which change the way we see our world. Measuring the "boosting effect" of landmark papers, our analysis reveals how new ideas and new players can make their way and finally triumph in a world dominated by established paradigms. The underlying "boost factor" is also useful to discover scientific breakthroughs and talents much earlier than through classical citation analysis, which by now has become a widespread method to measure scientific excellence, influencing scientific careers and the distribution of research funds. Our findings reveal patterns of collective social behavior, which are also interesting from an attention economics perspective. Understanding the origin of scientific authority may therefore ultimately help to explain, how social influence comes about and why the value of goods depends so strongly on the attention they attract.Comment: 6 pages, 6 figure

Predicting Scholars' Scientific Impact

We tested the underlying assumption that citation counts are reliable predictors of future success, analyzing complete citation data on the careers of ~150,000 scientists. Our results show that i) among all citation indicators, the annual citations at the time of prediction is the best predictor of future citations, ii) future citations of a scientist’s published papers can be predicted accurately (r^2=0.80 for a 1-year prediction, P<0.001) but iii) future citations of future work are hardly predictable.ISSN:1932-620

Explained variance of future citations.

<p>Future citations of published papers (bottom) and of future papers in , , and subsequent years (marked with paper selection time-windows in top ) for to years after the time of prediction were estimated. Explained variance by annual citations () in black; Extra explained variance by including the remaining indicators in red.</p

Future citations of published papers (Model and ) and future papers (Model , , and ) at the time of prediction as estimated by the annual citations at the time of prediction.

<p>Future citations of published papers (Model and ) and future papers (Model , , and ) at the time of prediction as estimated by the annual citations at the time of prediction.</p