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With the emergence of Level 4 automated vehicles, it is necessary to investigate the impact of these vehicles on mode choice. Previous studies have looked at the potential benefits and drawbacks of automated vehicles, but there has been little research done on how these vehicles will impact individuals’ travel behaviors. This paper proposes a needs-based approach to study the impact of Level 4 automated vehicles on mode choice. The approach takes into consideration the travel needs of different individuals and their willingness to adopt new technologies. Through a stated preference survey in China, the data on travel preferences and the perceived safety levels of automated vehicles can be collected. Then, a model is built to simulate the adoption of Level 4 automated vehicles and estimate the mode split for different scenarios. The results indicated that private AV modes are preferred, and business and nonwork trips may be the targeted market for all AV modes. Overall, value of automation increases with income for private modes, with large variance. Furthermore, Pro-AV attitude has a positive effect on value of automation, especially for self-owned AV and AV subscription. Accordingly, the needs-based approach demonstrates a promising method to study the impact of new technologies on travel behaviors and provides insights for policy makers to promote more sustainable transportation systems.

Freeway merging zones are critical for freeway operations and management due to potential crashes arising from complex vehicle merging behaviours. This paper investigates the application of a spatio-temporal deep learning model to infer crash risks in the zones. We first introduce a crash risk index based on Time-To-Collision and vehicle merging patterns. An innovation is that the developed spatio-temporal transformer model can analyze the evolving risk index. This model effectively captures dynamic risk features through a multi-head attention mechanism within its spatio-temporal learning components. Numerical experiments on nine inference tasks with varying spatial resolutions show improved performance of the model with lower resolution. Moreover, the ST-Transformer model is benchmarked against three advanced deep learning models, which consistently demonstrates its superiority in capturing spatio-temporal dependence in risk sequences. This investigation significantly contributes to a richer understanding of proactive traffic safety, providing valuable insights for advanced freeway management and driver assistance systems. [ABSTRACT FROM AUTHOR]

The global shipping industry still frequently faces the problems of low profitability and excessive capacity despite the claimed benefits of capacity sharing between alliance members. This paper investigates capacity sharing within shipping alliance members and its impacts on social welfare under stochastic demand. The dynamic game models constructed in this study examine four scenarios based on whether the alliance members share their surplus capacities and whether their pricing is flexible (their prices are determined before or after the demand realization). The results suggest that flexible pricing always harms the social welfare while capacity sharing improves the social welfare. Without capacity sharing, the government's subsidy or tax is needed when the carriers' service substitute degree is low or high, respectively. If the carriers agree with capacity sharing, the tax is needed under flexible pricing while neither subsidy nor tax is needed under the inflexible pricing. This paper provides explanations on the low profits of the shipping lines with their pricing and alliance strategies. Moreover, the policy implications identified in this study can provide support for governments to make their regulation on carriers. [ABSTRACT FROM AUTHOR]

Transition to electrified transit vehicles has attracted a great public attention to achieve a greener public transport service. This work develops a methodology for multi-type electric buses (EBs) accommodating spatio-temporally imbalanced passenger demand to improve significantly the operating efficiency. However, a new complexity of this multi-type EB scheme in contrast to conventional diesel buses occurs because multi-type EBs are characterized by different capacities, limited driving ranges, decisions on recharging time and/or locations and high initial investment costs. This work proposes a new, integrated timetabling and vehicle scheduling problem with shifting departure time to attain an even-load timetable using different types of EBs at a route's max-load stop, considering the use of fast/opportunity charging strategy. A genetic algorithm associated with right shifting of departure time has been developed to solve the resulting formulation, which is shown to be an NP-hard problem. A numerical example is used to illustrate the developed methodology, and a case study based on a scenario in the city of Dandong, China shows that the scheme of combining multiple vehicle types for a bus route not only can reduce the total cost but also bring out greater benefits than the single vehicle-type operation. From the operator viewpoint, it reduces passenger load surplus cost by approximately 11.2% for small Type A and 14.8% for large Type B. Moreover, the value of leftover pax unit cost has a significant effect on the selection of vehicle types, but has little effect on the number of trips or departures. This work shows that the higher the leftover pax unit cost is, the higher the number of large vehicle types is. [ABSTRACT FROM AUTHOR]

This paper focuses on the analysis of the COVID-19 effects on passenger shipping in Danish waters as an example and aims to analyse the differences in passenger vessel activities and emissions before and after the COVID-19 outbreak. Two sets of Automatic Identification System (AIS) data for the passenger ships sailing in Danish waters associated with the whole year respectively for 2020 and 2019 are used for a comprehensive evaluation of the passenger shipping activities in the region by means of the analysis of variance and bottom-up emission models. A comparison of those results based on the two datasets shows that the COVID-19 pandemic has a major impact on cruise ships, with a significant reduction in the number of ships, average speed, and average draught. In contrast, the pandemic has a smaller impact on ferry-pax only and ferry-ro pax vessels. The effects can also be seen from the fact that, after the COVID-19 outbreak, SOx emissions from cruise ships, ferry-pax only and ferry-ro pax vessels were reduced by 50.71%, 0.51% and 0.82%, respectively. This investigation provides an important reference for policy makers in the marine environment sector. [ABSTRACT FROM AUTHOR]