Research in Transportation Business and Management最新文献

The use of autonomous delivery robots (ADRs) in last-mile delivery, as an emerging innovation, could have drastic implications for businesses and customers. This study adapts and extends the UTAUT2 (Unified Theory of Acceptance and Use of Technology) model to investigate the determinants of acceptance for autonomous food delivery robots. We examine the impacts of privacy and security concerns on the relationship between social influence and adoption behaviour. In addition, the relationship between environmental friendliness and behavioural intention toward adoption is examined. These factors are fairly unexplored in previous studies in the context of food ADRs. In doing so, the study collected 590 online responses from potential customers using a random sampling technique. The results showed that both moderation effects of privacy concerns and security concerns are supported and both variables deteriorated the social influence of adoption. In addition, the negative relationship between contactless delivery and the adoption of ADRs was confirmed in this study. Furthermore, the results confirmed the positive impact of environmental friendliness on the adoption of food ADRs. The findings offer valuable insights into the effective deployment strategies of ADRs in food delivery.

Transportation is a critical contributor to carbon emissions, with road transportation playing a dominant role due to its dense network and versatility. However, the overreliance on road transportation has led to congestion, impacting reliability. As international trade grows, the demand for sustainable logistics practices intensifies. Intermodal transportation systems have emerged as a promising solution, harnessing different modes to reduce emissions and environmental impact while optimizing costs. It is important to underscore the significance of mode combinations in achieving environmental goals, aligning with the broader concept of environmental sustainability that encompasses economic and social dimensions. This article contributes to this evolving landscape by presenting a bi-objective intermodal transportation problem focusing on carbon emission reduction. Leveraging machine learning algorithms, including multiple linear regression, support vector regression, decision tree, and random forest, we predict transportation-based CO₂ emissions, offering environmentally friendly logistics plans. Our research responds to the call for green intermodal transportation, addresses financial incentives, emphasizes profit maximization, and reflects the growing influence of government policies. This paper outlines our methodology, presents a real-world case study, and offers computational results, underscoring the significance of sustainable intermodal transportation in the context of global climate goals and government initiatives.

This paper explores factors that affected the passenger load factors (PLF) in post-pandemic international flights from Taiwan to leading travel destinations, including Asia (Japan, China, Vietnam, South Korea, Thailand, and Hong Kong) and the Americas (the United States and Canada). Monthly data for outbound flights from 43 airlines were collected between January 2022 and May 2023. A hybrid model combining latent class clustering analysis with multinomial logit models was developed to identify unobserved heterogeneity in variables affecting the PLF. In addition to investigating the impact of factors such as flight frequencies, flight length, aircraft sizes, airline types, alliance members, jet fuel prices, seasonality, travel restrictions, and public health events, findings also revealed other significant impacts on the PLF including arranging connecting flights for transfer at the airport, offering promotional fares, and policy of free baggage allowances using the piece concept. Furthermore, a comparison of flight operations in pre- and post-pandemic found changes in adjusting operating strategies, including legacy airlines using narrow-body aircraft for short-haul flights and offering airfare promotions on long-haul flights are more likely to increase the PLF in the post-COVID-19 recovery phase.

This paper constructs a comprehensive evaluation system for urban low-carbon passenger transportation development based on the VPOSR model. First, the game theory combination G1 and CRITIC-entropy weight methods are used to obtain the indicator weights. Secondly, the TOPSIS method was combined to calculate and get the comprehensive value index for 2007–2021 and select the data of Tianjin from 2017 to 2021 to analyze the composite value index of the guideline level. Finally, the obstacle factors hindering the invention of low-carbon transportation in different periods are identified, and Tianjin is taken as an example for analysis and corresponding countermeasures. The results show that Tianjin's urban low-carbon passenger transportation structure improved significantly from 2007 to 2021, and the comprehensive value index increased from 0.357 to 0.558, but still at the intermediate level. The number of new energy vehicles is a key influencing factor for optimizing urban passenger transport structure in Tianjin.

Maritime clusters play a crucial role in the economic development of countries, encompassing seaports, maritime businesses, universities, and government agencies. South Asia, although lacking established maritime clusters, exhibits potential for the emergence of such clusters in its seaports. Evaluating the competitiveness of these emerging maritime port clusters is vital as it directly impacts their performance and future potential as world-class maritime clusters. However, no previous attempts have been made to measure the competitiveness of these emerging port clusters. This pioneering study addresses this gap by assessing the level of competitiveness in the emerging South Asian maritime port clusters. The research develops a competitive index using the Multi-Attribute Utility Theory (MAUT) approach, identifying key factors that enhance cluster competitiveness and evaluating the competence of each port cluster across these factors. The findings indicate that Jawaharlal Nehru Port Trust (JNPT) near Mumbai ranks as the top-indexed port cluster, followed by the cluster of Colombo and Male. This study provides a comprehensive assessment tool, identifies strengths and weaknesses, and provides actionable policy implications to enhance the competitiveness of maritime port clusters in South Asia and serves as a foundation for future research and policymaking in this field.

The rapid development of the railway transport network and the improvement of transport accessibility (TA) have the potential to facilitate the regional tourism industries and tourism economic connection (TEC). Quantifying the mutual interaction between TA and TEC is crucial for promoting coordinated and sustainable development. Our study focused on 41 cities in the Yangtze River Delta (YRD) region. By utilizing train schedule data from the 12306.cn website and statistics for 2010 and 2019, the weighted average travel time for each city was calculated to represent the regional railway TA. The modified gravity model incorporating average travel distances was then introduced to estimate the intensity of TEC between cities. Finally, a coupling coordination model was constructed to quantify the coupling correlation degree (CCD) between railway TA and TEC. The comparison results for 2019 and 2010 revealed an improved TA and its overall equilibrium in the YRD region, with a “core-periphery” pattern. The trend towards a spatially networked intensity of regional TEC became noticeable, with key railways as the axes. The CCD between regional railway TA and TEC showed an overall upward trend, where about 87% of cities improved at least one CCD grade. The regional spatial imbalances decreased but still existed, where southern and eastern cities were more coordinated. The reliability of proposed methods was also confirmed by correlations with socioeconomic indicators and population flow data. This study can provide fundamental support for the coordinated development of regional tourism and transport system, and integrated development strategy of the YRD region.

Over the past 35 years, the development of high-speed rail (HSR) in Japan has markedly enhanced market access and altered spatial distributional dynamics, thereby transforming the socioeconomic landscape in both urban and rural areas. This study delves into the impact of improved market access on regional disparities, with a particular focus on the shifts in population and economic activity between urban centers and their surrounding peripheral regions. We examine three key hypotheses: (1) HSR influences population dynamics, potentially leading to population decline in underdeveloped areas; (2) it affects demographic patterns, particularly in rural areas; and (3) it contributes to economic disparities, enhancing urban prosperity potentially at the expense of rural regions. Our analysis reveals pronounced effects of HSR: urban areas, exemplified by Tokyo, have seen substantial socioeconomic gains including a 26.10% income rise and an 18.34% population increase. Conversely, rural regions exhibit declining economic indicators with income dropping by 6.32% and population falling by 6.83%, accompanied by aging demographics. These trends, further illuminated by night-time light (NTL) data, underscore the uneven impact of HSR. Simulations suggest the continuation of these patterns with future HSR expansions. The study highlights the need for strategic policy measures to address the growing spatial disparities induced by HSR, advocating equitable infrastructure development and sustainable urban planning.

The issue of road safety is a growing global concern, and evaluating performance in this area is crucial for the development of effective safety policies and the allocation of safety resources. Amidst the input-output perspective of road safety risks, this study endeavors to assess the performance of provincial road safety in China from 2012 to 2021. To achieve this goal, the research employs data envelopment analysis (DEA) and the DEA-window approach, grounded in the framework of variable/constant returns to scale (VRS/CRS) models. The results reveal that Shanghai and Tibet consistently achieved efficient scores using both the DEA-VRS and DEA-CRS methods throughout the past decade. There is a small difference of 0.028 and 0.025 between the average scores obtained from the DEA and DEA-window analysis using the VRS and CRS models, respectively. Shanghai, Tibet, and Beijing demonstrate exceptional performance in both the DEA-CRS-window and DEA-VRS-window analysis, while North China, Southwest China, Central China, and South China exhibits the significant variations in performance ranking. This study enhances the understanding of the current road safety status in China and offers valuable insights for policymakers to formulate targeted measures, aiming to continuously improve road safety performance across various provinces through benchmarking management

The tariff landscape within Switzerland's public transport network is characterized by limited options: the flat rate (Generalabonnement), designed to accommodate frequent customers, and the pay-per-use model (Halbtax), tailored for those who use the network infrequently. However, there is a gap in provision for medium-frequency customers, who lack an intermediary tariff option. Our study employs a combination of conjoint analysis and market simulations to assess the potential appeal of a hypothetical novel three-part tariff structure, referred to as the bonus tariff. The paper first examines customers tariff selection, probing whether choices are influenced by biases favoring either the pay-per-use or flat-rate options. Further, we calculate the significance of various tariff attributes and identify sociodemographic variables predictive of tariff preference. Attributes such as the pre-pay credit and bonus levels exhibit the highest part-worth utilities. The analysis shows that the bonus tariff would capture a significant market share. A market simulation underscores the sensitivity of the business case to factors including bonus levels and the proportion of customers transitioning from flat-rate subscriptions. The overarching outcome suggests a positive impact on revenue with the introduction of the bonus tariff. The conclusion delves into the reasons for these findings and outlines implications for tariff management strategies.

Covid-19 has been a major shock for European aviation. After the general relaxation of state aid rules by the European Commission, Member States stepped in to support airlines, airports and other stakeholders in aviation. However, distribution of state resources raised significant doubts from competitors, as well as academic literature, with regard to the conditions of the aids and their effect on competition. A two-round Delphi study was employed in order to investigate the opinion of experts in European aviation, with the participation of thirty experts from airlines, airports, handling agents, government authorities and academia. The results show an overall appraisal of the European framework and its flexibility, but also desire for a greater degree of control, complaints over bureaucracy and procedural delays, as well as concern on the effects of state aid on competition in the internal aviation market.