Asian Transport Studies最新文献

This research aims to comprehend commuting experiences in rapidly growing metropolis from the perspective of accessibility, especially subjective accessibility, to better understand barriers and opportunities to using public transport. We examined Jakarta as an exemplar city experiencing an ever-increasing car dependency despite proactive policies and projects for modal shift. A qualitative descriptive method is used to investigate the nuances of individual experiences across mode and incomes. In-depth analysis of 110 questionnaires indicated that the public transport users perceived a higher-level accessibility due to convenience, affordability, and no congestion, while private transport users had low-level perception. The stereotypes of public transport appeared to nullify the benefits of public transport. Furthermore, most individuals had complicated attitudes about a certain mode, and positive feeling was not necessarily related to mode choice. For “access-for-all”, considering objective and subjective measures holistically is essential for interrogating critical issues of transport system and optimizing positive travel experiences.

The rapid urbanisation in cities, and its associated complexities demand that sophisticated decision support tools such as the LUTI models be employed to assist the balanced and sustainable development of transport and land use. It is evident from literature studies that the majority of LUTI models need extensive data, making them expensive and time-consuming, which will be a challenge for developing countries. On the other hand, with the advent of information and communication technology, the availability of high frequency (HF) data is increasing which can be collected at disaggregated level data frequently at little or no cost. The main focus of this research is trying to bridge the gap that exists between research with this type of data and it subsequent application in real context. On the basis of this, the paper focuses on (1) summarizing existing LUTI models and their corresponding data requirements; (2) explaining the sources of HF data in LUTI modelling; and (3) discussing the applications and challenges in implementation of such data in LUTI modelling. This review identifies the recent development of technology and availability of HFD can fill the gap of data availability for LUTI which has been discussed by a great number of authors in literature. Furthermore this kind of novel source of data can increase the potential of LUTI model particularly in developing countries, where land use and transport patterns are changing rapidly and where traditional forms of data are expensive to collect.

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.

The COVID-19 pandemic has radically altered how the world uses public transportation. Our study investigates the significant decrease in travel demand for public transport in Bangkok, Thailand due to the fear of contracting COVID-19, government public health measures aimed at mitigating risk, and remote and online behaviors adopted by the public. We examined the determinants of changes in travel frequency for all trip purposes among public transport users in Bangkok, Thailand during the COVID-19 pandemic using the within-between random effect model. These included online activities and behaviors, individual perceptions of COVID-19 risks, and sociodemographic and socioeconomic factors. Our findings revealed that demand for public transportation will likely return as COVID-19 becomes endemic and that work-from-home measures, remote learning, and food delivery services could substantially reduce individual travel demand. These hold important lessons for policymakers in balancing public health measures with the evolving supply and demand needs of Bangkok's transportation infrastructure.

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).

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.

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.

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.

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.

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.