Prospects of the Activity-Based Modelling Approach: A Review of Sweden’s Transport Model- SAMPERS

The rapid changes in global development scenarios, such as technological advancements, lifestyle decisions and climate change, call for updated transport models to test micro-level policy decisions. This paper explores the advances in activity-based transport modelling in simulating travel demand in urban scenarios, focusing on Sweden’s National Transport model. Sampers is used for impact analysis, investment calculations for traffic simulations, transport policy implementation evaluations, and accessibility and impact analysis of extensive changes in land use and transport systems in cities and regions of Sweden. This research systematically compares individual components, sub-models, and algorithms and discusses integrations with cutting-edge agent-based models. Furthermore, recent research and projects for Sampers are investigated, highlighting its advantages over current models, potential gaps and limitations, and long-term development prospects. The study concludes by cross-referencing Sampers’ global developments and regional needs to assess its long-term development prospects

A study of vortex ring generation by a circular disc

A vortex ring is a region where the fluid mostly spins around an imaginary axis line that forms a closed loop. It is a fundamental phenomenon for the fluid passing by an object. In general, there are two methods associated with the axisymmetric vortex generation: fluid discharge from an orifice or a nozzle, and disc start-up instantly. Recent study by Yang (2012) showed that the different mechanisms of vortex generation could lead to a similar formation process and a universal principle of the optimal vortex formation could exist. Present work is mainly based on a numerical simulation study of disc vortex ring formation. A commercial Computational Fluid Dynamics solver is employed to carry out the simulation. The simulation parameters are selected the same as those of Yang’s (2012) experimental study. The model is built with fluid passing by a 30mm diameter and 2mm thickness disc in a large computational domain. The simulation results are validated with experimental data. By studying the Iso-surface, representative values, i.e. size of both vortex ring and vortex ring core, circulation and kinetic energy during the formation phases of the vortex ring are investigated. Comparison and analyses between the numerical simulation and the experimental data will be given in detail

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

A spatio-temporal deep learning model for short-term bike-sharing demand prediction

Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems

Experimental realization of chiral Landau levels in two-dimensional Dirac cone systems with inhomogeneous effective mass

Chiral zeroth Landau levels are topologically protected bulk states that give rise to chiral anomaly. Previous discussions on such chiral Landau levels are based on three-dimensional Weyl degeneracies. Their realizations using two-dimensional Dirac point systems, being more promising for future applications, were never reported before. Here we propose a theoretical and experimental scheme for realizing chiral Landau levels in a photonic system. By introducing an inhomogeneous effective mass through breaking local parity inversion symmetries, the zeroth-order chiral Landau levels with one-way propagation characteristics are experimentally observed. In addition, the robust transport of the chiral zeroth mode against defects in the system is experimentally tested. Our system provides a new pathway for the realization of chiral Landau levels in two-dimensional Dirac systems, and may potentially be applied in device designs utilizing the transport robustness

On Efficient Reinforcement Learning for Full-length Game of StarCraft II

StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon. In this work, we investigate a set of RL techniques for the full-length game of StarCraft II. We investigate a hierarchical RL approach involving extracted macro-actions and a hierarchical architecture of neural networks. We investigate a curriculum transfer training procedure and train the agent on a single machine with 4 GPUs and 48 CPU threads. On a 64x64 map and using restrictive units, we achieve a win rate of 99% against the level-1 built-in AI. Through the curriculum transfer learning algorithm and a mixture of combat models, we achieve a 93% win rate against the most difficult non-cheating level built-in AI (level-7). In this extended version of the paper, we improve our architecture to train the agent against the cheating level AIs and achieve the win rate against the level-8, level-9, and level-10 AIs as 96%, 97%, and 94%, respectively. Our codes are at https://github.com/liuruoze/HierNet-SC2. To provide a baseline referring the AlphaStar for our work as well as the research and open-source community, we reproduce a scaled-down version of it, mini-AlphaStar (mAS). The latest version of mAS is 1.07, which can be trained on the raw action space which has 564 actions. It is designed to run training on a single common machine, by making the hyper-parameters adjustable. We then compare our work with mAS using the same resources and show that our method is more effective. The codes of mini-AlphaStar are at https://github.com/liuruoze/mini-AlphaStar. We hope our study could shed some light on the future research of efficient reinforcement learning on SC2 and other large-scale games.Comment: 48 pages,21 figure