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Register a new userEnvironmental Impact of Bundling Transport Deliveries Using SUMO: Analysis of a cooperative approach in Austria
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Added by
Cristina Olaverri-Monreal
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Urban Traffic is recognized as one of the major CO2 contributors that puts a high burden on the environment. Different attempts have been made for reducing the impacts ranging from traffic management actions to shared-vehicle concepts to simply reducing the number of vehicles on the streets. By relying on cooperative approaches between different logistics companies, such as sharing and pooling resources for bundling deliveries in the same zone, an increased environmental benefit can be attained. To quantify this benefit we compare the CO2 emissions, fuel consumption and total delivery time resulting from deliveries performed by one cargo truck with two trailers versus by two single-trailer cargo trucks under real conditions in a simulation scenario in the city of Linz in Austria. Results showed a fuel consumption and CO2 emissions reduction of 28% and 34% respectively in the scenario in which resources were bundled in one single truck.
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Speed and cost of logistics are two major concerns to on-line shoppers, but they generally conflict with each other in nature. To alleviate the contradiction, we propose to exploit existing taxis that are transporting passengers on the street to relay packages collaboratively, which can simultaneously lower the cost and accelerate the speed. Specifically, we propose a probabilistic framework containing two phases called CrowdExpress for the on-time package express deliveries. In the first phase, we mine the historical taxi GPS trajectory data offline to build the package transport network. In the second phase, we develop an online adaptive taxi scheduling algorithm to find the path with the maximum arriving-on-time probability on-the-fly upon real- time requests, and direct the package routing accordingly. Finally, we evaluate the system using the real-world taxi data generated by over 19,000 taxis in a month in the city of New York, US. Results show that around 9,500 packages can be delivered successfully on time per day with the success rate over 94%, moreover, the average computation time is within 25 milliseconds.
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With the improvement of the pattern recognition and feature extraction of Deep Neural Networks (DPNNs), image-based design and optimization have been widely used in multidisciplinary researches. Recently, a Reconstructive Neural Network (ReConNN) has been proposed to obtain an image-based model from an analysis-based model [1, 2], and a steady-state heat transfer of a heat sink has been successfully reconstructed. Commonly, this method is suitable to handle stable-state problems. However, it has difficulties handling nonlinear transient impact problems, due to the bottlenecks of the Deep Neural Network (DPNN). For example, nonlinear transient problems make it difficult for the Generative Adversarial Network (GAN) to generate various reasonable images. Therefore, in this study, an improved ReConNN method is proposed to address the mentioned weaknesses. Time-dependent ordered images can be generated. Furthermore, the improved method is successfully applied in impact simulation case and engineering experiment. Through the experiments, comparisons and analyses, the improved method is demonstrated to outperform the former one in terms of its accuracy, efficiency and costs.
The proper determination of soil moisture on different scales is important for applications in a variety of fields. We aim to develop a high-level soil moisture product with high temporal and spatial resolution by assimilating the multilayer soil moisture product SCATSAR-SWI (Scatterometer Synthetic Aperture Radar Soil Water Index) into the surface model SURFEX. In addition, we probe the impact of the findings on the Numerical Weather Prediction (NWP) in Austria. The data assimilation system consists of the NWP model AROME and the SURFEX Offline Data Assimilation, which provide atmospheric forcing and soil moisture fields as mutual input. To address the known sensitivity of the employed simplified Extended Kalman Filter to the specification of errors, we compute the observation error variances of the SCATSAR-SWI locally using Triple Collocation Analysis and implement them into the assimilation system. The verification of the forecasted 2 m temperature and relative humidity against measurements of Austrian weather stations shows that the actual impact of the local error approach on the atmospheric forecast is slightly positive to neutral compared to the standard error approach, depending on the time of the year. The direct verification of the soil moisture analysis against a gridded water balance product reveals a degradation of the unbiased root mean square error for small observation errors.
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