Asian Transport Studies最新文献

The objective of this study is to utilize natural language processing technologies to examine data gathered from Twitter related to logistics in Japan during the COVID-19 pandemic. The Bidirectional Encoder Representations from Transformers (BERT) machine learning model is utilized to assess the sentiment of the content. The findings suggest a positive outlook on logistics during time frame analyzed. This research has four key implications: (1) the sentiment towards the term "logistics" is generally positive as per our analysis; (2) there is a trend of increasing interest in logistics in western Japan in 2022; (3) social media can be utilized as a tool to address the challenges faced by the logistics industry; and (4) our research highlights the potential of using social media data to provide a more timely and comprehensive analysis of logistics and transportation trends.

In this study, we analyzed and compared the topology-based vulnerability of several railway networks to plan a preventive strategy using two vulnerability criteria, i.e., global efficiency- and algebraic connectivity-based vulnerabilities. We studied the feasibility of the latter as an alternative method. Furthermore, we compared the results and correlations of these vulnerability criteria with each other and the edge betweenness centrality to analyze the relationships between all three indicators that consider the most critical sections of networks. Our case study was obtained by combining three main railway networks in Kyushu, Japan, which have suspended operations in the past because of several natural disasters. These results proffer solutions in identifying the most important sections among stations from multiple viewpoints to manage protection priorities and conduct routine inspections or repairs.

We discuss the possibilities to distinguish different types of tourists based on Wi-Fi sensor data. The data are obtained from 20 sensors employed in Higashiyama, Kyoto, which is an area highly frequented by tourists. We describe tourist-tours as a sequence of sensors at which they are observed. Based on these records a clustering approach is chosen where we select as clustering variables, among others, the degree of detours and the length of time they are observed. We find that we can distinguish groups of tourists that are visiting a number of sightseeing spots in a short time from others who walk through the area more leisurely and are likely enjoying souvenir shops and restaurants. For the main tourist clusters than a Recursive Logit approach is applied to model their route-choice based on path length and attractions en-route. We find that the estimated parameters reflect these group characteristics.

The present study investigates the behavioural impact of COVID-19 on commuter's mobility. For this purpose, an online questionnaire survey was prepared, which was circulated in Delhi, India. More than 200 respondents participated in the survey. The information regarding travel patterns, working, mental health, and psychological stress are obtained for pre, during and post-COVID-19 periods. The results highlight that a significant number of commuters stated to shift their choice of commuting from shared modes to private modes of transport in the post-COVID-19 period. About 28.7% of persons who change their modes are likely to use it for a more extended period. Due to lockdown, 65% of people who are working/studying from home expressed to resume offline mode after COVID-19. About 80% of the persons will have anxiety and tension, which is likely to affect their driving behaviour (e.g., rash driving, distraction, insecurity).

The continuous spread of coronavirus disease 2019 (COVID-19) has had a substantial impact on China's domestic airline networks. It is important for airlines to identify key airports and airport roles in future network design. In this paper, a k-core algorithm is used to decompose the network layers during different periods of COVID-19 to investigate the network structure and the airport role change. By considering both airport degree and route traffic, network characteristics are analyzed, and the key airports are determined based on network evaluation. The results show that the airline network is robust due to its mixed hub-and-spoke network structure, which is basically dominated by direct flights between airports. However, different operation patterns should be implemented based on airport roles. It is not advisable for airlines to pursue network connectivity at the cost of a low passenger load factor.

Because road surface snow conditions are mainly monitored by road patrols, if road surface conditions can be estimated based on meteorological conditions and traffic volume, winter road management can be performed more efficiently. Therefore, this study focuses on estimating road surface snow conditions. The relationship between weather conditions, traffic volume, and road surface conditions was analyzed, and a road surface condition estimation model was constructed using random forest. In addition, because there is a relationship between road surface conditions and tire noise, we estimated the road surface condition by adding tire noise to the weather and traffic volume. As a result, we constructed a model for estimating the road surface condition from the weather and traffic volume, with an accuracy of approximately 95%. The accuracy was slightly lower when the tire noise was added.

Urban mobility is likely to undergo significant changes due to the ongoing COVID-19 pandemic and future potential outbreaks. This study examines the influence of the COVID-19 pandemic on the operational services of old-style LAMAT (i.e., Remork) drivers. The pandemic's influence was investigated using several analytical approaches—including mean comparison and regression analyses—which used data collected from an interview survey with 186 Remork drivers in Phnom Penh from December 25–28, 2020. The results indicated a negative influence of the COVID-19 pandemic on the livelihoods of Remork drivers; that is, the drivers' monthly income has substantially declined since the pandemic began (p < 0.01). The impacted drivers also expressed their difficulties during the pandemic and requested various forms of public support. This study provides different perspectives on analytical techniques to examine the possible influence of COVID-19 on society and offers guidance for relevant urban transport policies in response to the current pandemic and the new normal.

Automotive aftermarket operations cover a wide scope from aftersales car maintenance to the last stage of car's lifecycle, including all activities in aftermarket parts and services. Logistics operation planning in automotive aftermarket suffers from demand uncertainty and inaccurate forecasting issues. These issues lead to not only inappropriate inventory level, but also poor performance of order fulfilment and customer satisfaction. Therefore, this study explores the important antecedents of order fulfilment using decision making trial and evaluation laboratory (DEMATEL) based on thirteen criteria. An automotive aftermarket company handling business in Southeast Asia is employed as the empirical case. The methodology can determine the critical factors in the logistics operations of automotive aftermarket considering interdependency among factors involved in the proposed system. According to the analytical results, the company should improve accuracy of demand planning, supply chain lead time and stock availability superiorly.

Conventional transportation policies for railroads have primarily focused on minimizing the negative utility, such as shortening the travel time and reducing congestion. However, with the recent introduction of trains with extra fares for greater comfort and changes in work styles, there is an increasing need to focus on the positive utility of travel itself. Moreover, advances in machine learning and artificial intelligence research have facilitated highly accurate and objective analysis from vast amounts of data. The purpose of this research is to construct a new transportation choice model using inverse reinforcement learning, which is a machine learning method, and to quantify the positive utility of commuter railroads. The results of a comparison of the proposed model with conventional methods indicate the advantages and disadvantages of the model. Further, a transportation choice model for railroads was created to understand the tendency of each selected train type.

Electric Vehicles being at a nascent stage in India, albeit at a slower growth rate, faces a significant barrier of economic compatibility. For countries like India, the economic aspect plays a vital role in the widespread of Electric Vehicles. Concentrating on the cost aspect, this paper contemplates the economic compatibility of battery electric vehicles (BEVs) with conventional IC Engine Vehicles (ICEVs), considering a paring vehicle approach and analysing the case study of India. As less research is carried out for India, we aim to develop an extensive total cost of ownership (TCO) model. Furthermore, the Sensitivity analysis presents the effect on TCO by considering parameters as incentives, reduction in battery prices, finance interest rates and Annual Kilometre Travelled (AKT) &EV + PV integration. For the base case, a much abysmal performance is observed for BEVs. While accounting for various Sensitivity analysis parameters, some BEVs reach cost parity while some become more economical than ICEVs.