Ecological User Equilibrium in Traffic Management (TM)?

With increasing environmental sustainability awareness significant attention on ecological traffic management (eco-TM) has come into the focus of researchers and practitioners. While different approaches have been applied to reach minimal pollutant production, the classic user equilibrium calculation with the pollutant production as travel costs instead of using travel times remains in the center of attention. However, the validity of such a direct transformation to find a user equilibrium is questionable. In this paper, a simplified analytical approach to examine the above aforementioned validity has been carried out, followed by a simulation approach to verify the results of the analytical approach. The result shows that the pollutant production function violates the usual assumption of a monotonous function (typically, emission has a minimum at travel speeds around 60 km/h). It also indicates that the respective algorithms to compute the user equilibrium must deal with the fact, that the equilibrium solution is not unique and is dependent on the initial solution. This means that substantial modifications to the algorithms that compute the user equilibrium have to be discussed since they do not work as intended when pollutant production is used as travel costs, especially in a transportation system with mixed speeds that cover a range around the minimum emission speed

ShaRP: Shape-Regularized Multidimensional Projections

Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics.Comment: To appear in EuroVA Workshop 202

ShaRP: Shape-Regularized Multidimensional Projections

Projections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature — i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique — ShaRP — that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics

Multivariate Time Series Retrieval with Symbolic Aggregate Approximation, Regular Expression, and Query Expansion

We present SAXRegEx, a method for pattern search in multivariate time series in the presence of various distortions, such as duration variation, warping, and time delay between signals. For example, in the automotive industry, calibration engineers spontaneously search for event-induced patterns in fresh measurements under time pressure. Current methods do not sufficiently address duration (horizontal along the time axis) scaling and inter-track time delay. One reason is that it can be overwhelmingly complex to consider scaling and warping jointly and analyze temporal dynamics and attribute interrelation simultaneously. SAXRegEx meets this challenge with a novel symbolic representation modeling adapted to handle time series with multiple tracks. We employ methods from text retrieval, i.e., regular expression matching, to perform a pattern retrieval and develop a novel query expansion algorithm to deal flexibly with pattern distortions. Experiments show the effectiveness of our approach, especially in the presence of such distortions, and its efficiency surpassing the state-of-the-art methods. While we design the method primarily for automotive data, it is well transferable to other domains

Simulating the impact of privately owned automated vehicles within the region Test Bed Lower Saxony, Germany

Automated and connected vehicles are assumed to have a major impact on road safety, traffic flow, energy consumption, greenhouse gas emissions, as well as on future mobility. This paper aims to analyze, the impact of privately owned automated vehicles on travel behavior in the region covering the Test Bed Lower Saxony in Germany. The main focus is laid on the evaluation of long-distance trips in the entire study area as well as on commuter journeys to and from the city of Brunswick. An agent-based demand model in conjunction with a traffic flow model was used to simulate four scenarios with different penetration rates of fully automated vehicles. The results show a major shift in the mode share, an increasing of the daily mileage, and reduced travel time of the motorized individual transport, as well as minor changes in travel distance and total traffic volume