THE EFFECTS OF EXCHANGE RATE CHANGES ON THE CO-MOVEMENT OF EQUITY MARKETS

This paper analyzes the co-movement of the US equity market and 10 markets in Asia and Oceania (i.e., referred to as domestic markets). We find that the daily returns of the ten emerging markets are significantly correlated with the performance of US market in the previous trading day. Also, we analyze the contemporaneous change in the US/domestic market exchange rate, and how it affects this co-movement. We find that the correlation between the US market and domestic markets is positively related to the net-trade balance that exists between these countries. Countries that tend to net-export to the US are affected more positively by the strengthening of the US dollar compared to the domestic currency

從中日勳章互授看清末兩國關係的劇變和維繫

関西大

Designing Customised Bus Routes for Urban Commuters with the Existence of Multimodal Network – A Bi-Level Programming Approach

Customised bus (CB) is a cutting-edge mean of transportation and has been implemented worldwide. To support the spread of the CB system, methodologies for CB network design have been conducted. However, a majority of them cannot be adopted directly for multi-modal transportation environment. In this paper, we proposed a bi-level programming model to fill this gap. The upper-level problem is to maximise the usage of the CB system with the limitation of operation constraints. Meanwhile, the lower-level problem is to capture the traveller’s choice by minimising traveller’s generalised cost during travel. A solving procedure via genetic algorithm is further proposed and validated via the metro data at Shanghai. The results indicated that the proposed CB route network would attract nearly 5,000 users during morning peak period under the given metro transaction data. We further studied the features of the selected routes and found that the CB network mainly served residence to commercial or industrial parks travellers and would provide travel service with fewer stops, and higher travel efficiency by travelling through expressway

Spatiotemporal Learning of Multivehicle Interaction Patterns in Lane-Change Scenarios

Interpretation of common-yet-challenging interaction scenarios can benefit well-founded decisions for autonomous vehicles. Previous research achieved this using their prior knowledge of specific scenarios with predefined models, limiting their adaptive capabilities. This paper describes a Bayesian nonparametric approach that leverages continuous (i.e., Gaussian processes) and discrete (i.e., Dirichlet processes) stochastic processes to reveal underlying interaction patterns of the ego vehicle with other nearby vehicles. Our model relaxes dependency on the number of surrounding vehicles by developing an acceleration-sensitive velocity field based on Gaussian processes. The experiment results demonstrate that the velocity field can represent the spatial interactions between the ego vehicle and its surroundings. Then, a discrete Bayesian nonparametric model, integrating Dirichlet processes and hidden Markov models, is developed to learn the interaction patterns over the temporal space by segmenting and clustering the sequential interaction data into interpretable granular patterns automatically. We then evaluate our approach in the highway lane-change scenarios using the highD dataset collected from real-world settings. Results demonstrate that our proposed Bayesian nonparametric approach provides an insight into the complicated lane-change interactions of the ego vehicle with multiple surrounding traffic participants based on the interpretable interaction patterns and their transition properties in temporal relationships. Our proposed approach sheds light on efficiently analyzing other kinds of multi-agent interactions, such as vehicle-pedestrian interactions. View the demos via https://youtu.be/z_vf9UHtdAM.Comment: for the supplements, see https://chengyuan-zhang.github.io/Multivehicle-Interaction

Statistical‐based approach for driving style recognition using Bayesian probability with kernel density estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166283/1/itr2bf00581.pd