Transportation Research Part D-transport and Environment最新文献

With global warming, the Arctic shipping route has become an important alternative when traditional routes face disruption risk. This study sets up a multistage game-theoretical model to investigate the decision problem of shipping supply chain with the traditional route’s disruption risk, Arctic route option, and emission allowance. The paper finds that depending on some important factors (such as market potential, route distance, emission allowance, shipping disruption risk, etc.), the shipping strategy based on traditional route (STR) and that based on Arctic route (SAR) may either be mutually exclusive (i.e., only STR or SAR is selected) or coexist. Furthermore, the disruption risk may lead to two effects, the competition effect (i.e., decreasing the procurement quantity via the traditional route) and the dependence effect (i.e., increasing the procurement quantity via the traditional route). The emission allowance may either weaken or strengthen the dependence effect, but always strengthens the competition effect. A case study is conducted to verify the model and results. This study theoretically enriches the literature on shipping management and Arctic route management, and provide decision-making solutions for shipping supply chains.

The success of electric vehicle (EV) adoption greatly depends on spatial deployment of charging infrastructure. However, existing literature often overlooks the spatial cross-side network effect (SCNE) of infrastructure on EV adoption. Using the quarterly panel data of Tesla in the United States (US), this study reveals that EV adoption in one state would increase by 5.75% and 1.09% if the charging accessibility increases by 1% in this state and its neighboring states respectively. Furthermore, such effect is heterogeneous between Tesla’s superchargers (SC) and destination charging (DC) stations, in that only accessibility to SC stations can significantly drive vehicle adoption across neighboring states. Moreover, the SCNE of DC stations on vehicle adoption can be amplified if the neighboring states are positioned more centrally in the US interstate highway network. We offer managerial and policy implications for businesses and governments to promote the adoption of EVs by developing the spatially deployed infrastructure.

Three types of intermodals are defined: bus-centric, metro-centric, and hybrid, each representing combinations of bus, metro, and a mix of metro and bus with other travel modes for a trip, respectively. Using the household survey from Nanjing, China, comprising 162,191 trips, we applied the multiple models to reveal the nonlinear effects of socio-demographic and travel-related attributes on intermodal travel choices. Results show that bus-centric intermodal choice accounts for 65.82% of the total among the three types. The optimal model, random forest, indicates the relative importance of travel-related, individual, and household attributes, contributing 46.28%, 31.14%, and 22.59% respectively. Non-public transit travel time demonstrates an inverted V-shaped association with bus-centric intermodal choice, with a peak at around 5 min. Older individuals prefer bus-centric intermodal, while younger lean towards metro-centric and hybrid intermodal. Compared to car ownership and motorcycle ownership, bike ownership and E-bike ownership exhibit relatively high impact on intermodal travel choices.

Quantifying the utilization of wildlife crossing structures (CSs) requires considering the local population background. This study assessed the effectiveness of 22 CSs based on the abundance of two ungulates (khulan and goitered gazelle) in northwestern China and analyzed variables influencing their usage such as openness, water source, and human activities in drylands. Results showed that there were 7 and 13 CSs with higher performance indices (PRi) for khulan and goitered gazelle, respectively. Khulan favors CSs near water sources and with large openness, while goitered gazelle prefers structures that include ponding and also feature large openness. Factors like distance to the CSs of the railway, predators, and human activities affect the utilization of CSs by goitered gazelle. This study emphasized the importance of designing and managing effective wildlife CSs by focusing on openness and water resources. It is recommended that CSs can be spatially consistent in multiple transportation lines run parallel.

Shipping companies face the trilemma of investing in cheaper but more polluting conventional vessels, in more expensive but eco-friendly ones or postponing their investment. We quantify the price premium paid for eco-friendly vessels and investigate its determinants. The results indicate that eco vessels trade at an average premium of 25% compared to their conventional counterparts, while the corresponding income premia are between 9% and 15%. Our findings further suggest that the price premium depends on the market conditions, with income premia, past price premia, and fleet supply being strong drivers, while fuel costs and market liquidity have a less important effect. The magnitude and significance of these drivers vary based on the market state and segment. Overall, the paper documents the adverse effect that the current technological and regulatory uncertainty has on vessel investment and highlights the need for further policy intervention to reduce market uncertainty and encourage green investment.

Commuters are exposed to significantly high pollution levels at traffic intersections. This study utilized the generalized Pareto distribution to model commuters’ exposure to extreme particulate matter (PM) levels across 36 traffic intersections in Varanasi, India. A Bayesian hierarchical framework was deployed to account for the seasonal variation. The monthly return levels for extreme PM_2.5 (PM₁₀) were 589 (1127), 474 (961), and 429 (902) µg/m³ during winter, spring, and summer, respectively. The extreme PM_2.5 and PM₁₀ concentrations exceeded the NAAQS severe category for all three seasons. There is a 0.72 % (1.47 %) chance that during winter, PM_2.5 (PM₁₀) levels would exceed that of the Delhi smog event (PM_2.5: 585 µg/m³, PM₁₀: 989 µg/m³). These findings raise concerns about public health and the environment, particularly in winter. The results would guide policymakers in enforcing stringent measures to reduce extreme exposures at traffic intersections in densely populated cities.

Simulation studies suggest that Shared Automated Vehicles (SAVs) could reduce the total vehicle kilometres travelled (VKT) thanks to efficiently pooling multiple users in one vehicle. However, mode choice studies indicate that SAVs would attract mostly public transport users, leading to an increase in VKT. This paper is among the first to combine these operational and behavioural expectations and the first to do so analytically. In our theoretical set-up, travellers choose between car, public transport, and SAVs, depending on their individual valuation of private travel and other attributes of each mode. We find that the introduction of SAVs lead to a VKT change in public-transport-oriented cities ranging from a small decrease to a large increase, where the latter is true for plausible parameter settings and hence is a cautionary point for SAV-introduction policies. Conversely, SAVs would attract only few travellers in private-transport-oriented cities and therefore would not significantly impact VKT.

The study explores the capability of various machine learning (ML) models in maritime accident risk prediction. Data from 1981 to 2021 from the Norwegian Maritime Authorities (NMA) was analysed together with the data of 51 different weather-related variables, which were collected from Visual Crossing for each accident recorded in the NMA dataset. The findings reveal an increased predictive ability of ML models when relevant weather data is introduced. The results show that the Light Gradient Boosted Trees with Early Stopping perform the best, with a five-fold cross validation accuracy of 70.23% when weather data was included, compared to 64.86% without. Furthermore, the study revealed that the leading weather variables for accident prediction are wind, sea level pressure, visibility, and moon phase. The most effective multi-classification ML algorithm can be deployed for improving maritime safety resilience through vulnerability assessment and preparedness.

Under the looming climate crisis, public concerns have been raised regarding the environmental impacts of private jet flights. Electric aircraft are currently considered one of the most encouraging technological keys to significantly reducing the environmental impacts of aviation. This research aims to study the replacement of currently used business jets with fully electric aircraft. This study is based on actual flight data of a European business carrier. The carbon dioxide emissions reduction potential for different aircraft ranges was estimated. Obtained results showed that with fully electric aircraft the studied business carrier could significantly reduce its annual emissions by up to 93%. On a per-passenger basis, the emissions could decrease from 6,787 g of carbon dioxide equivalent per passenger kilometre to 449 g. However, as electric aircraft will operate at a slower speed than business jets, flight times would increase by 10–41 min, depending on the distance.

Business model innovation is imperative for electric vehicles (EVs) firms to maintain a competitive edge and the transition towards transportation electrification. However, numerous socio-technical barriers are likely to impede this innovation process. This study aims to assess the risks of various barriers through a novel fuzzy multiple-criteria decision-making (MCDM)-based failure mode and effects analysis (FMEA) model. To accomplish this, a panel of experts was convened to identify potential barriers, prioritizing risk criteria by interval-valued intuitionistic fuzzy sets (IVIFSs) and analytic hierarchy process, and utilizing the IVIF-improved grey relational projection for evaluating and ranking barriers. The model underscores the importance of considering the unique characteristics of EV business model innovation, and a hierarchical system of risk criteria for evaluating barriers is formulated based on the traditional risk factors of FMEA. The findings corroborate that ‘blocked network nodes’, ‘unprofitable revenue models’, and ‘inadequate market segmentation’ are the foremost barriers in China.