Conference Agenda

Purpose:

Offshore wind energy is a key component in achieving the decarbonization goals set by the German government. These goals are 40 GW of installed offshore wind energy capacity by 2035 and 70 GW by 2045 in German waters. In order to reach these ambitious goals, the levelized cost of energy must be reduced. Logistics and thus the associated costs represent a major challenge especially during the O&M phase due to the distances and harsh wind and wave conditions.

The Operation & Maintenance (O&M) phase accounts for about 25% of the levelized cost of energy and is one of the levers to reduce the costs of offshore wind. Standard O&M agreements are set up for an initial period of 5 to 10 years during which the original equipment manufacturer (OEM) is responsible for O&M. After this period the owner of an offshore wind farm has to decide whether to extend this contract, do the O&M itself or contract another company to do the O&M.

For external third-party providers of O&M it is thus difficult to decide what prices they should offer for the new contract since the real O&M processes are only known to the OEM. A method to mitigate this disadvantage by analyzing historical AIS (Automatic Identification System) data will be proposed in this paper.

Methodology:

In this paper a method was developed to analyze historical AIS data and weather data to identify O&M processes of an offshore wind farm including the movements of the vessels and the duration of the visits to each turbine using Python. AIS data transmits every vessel’s position, speed, and course over ground at certain intervals based on said speed, which can allow the routes of O&M vessels to be traced.

Findings:

The findings are that this method can be used to analyze the O&M processes ex post and thus can give external entities valuable insights into the inner workings of O&M processes in offshore wind farms. It is possible to see which vessels were used and for how long they were at the wind turbines and thus determine the O&M processes.

Originality:

The use of AIS data is a new field in offshore wind logistics and has mainly been used to identify collision risks in the past but not to identify O&M processes and thus provide a basis for improving them. The paper is original because it analyzes the AIS data and uses it as a basis for future business decisions.

Inland waterway transport on the Rhine corridor forms a critical component of European freight logistics, moving high volumes of bulk cargo under increasingly variable operating conditions. Traffic along the corridor is frequently disrupted by hydrological extremes, infrastructure failures, meteorological events, and operational incidents. However, the relationship between disruption frequency and actual traffic impact remains insufficiently researched. This gap is significant, as infrastructure investment and resilience planning are often based on event-frequency statistics, implicitly assuming that the most frequent disruptions also cause the greatest impacts.Recent studies have examined this issue using Notices to Skippers data from the EURIS portal and negative binomial regression to estimate the effects of short- and long-term events on daily vessel passages at lock systems along the Rhine, Main, and Danube. Related AIS data-based research has addressed vessel emissions, typhoon-induced network resilience, and Rhine water-level forecasting. However, these studies do not integrate disruption taxonomies with corridor-wide AIS-derived traffic measurements.Existing analyses have two main limitations: they assess impacts only at locks rather than across the full navigable corridor, overlook threshold effects and interactions between concurrent disruptions. Consequently, the relative contribution of each disruption category to traffic impacts on the Rhine remains unclear. Hence, a disruption attribution framework that links classified disruption events with corridor-wide traffic performance indicators is proposed. Disruptions are grouped into hydrological, meteorological, infrastructural, and operational categories and represented through time- and location-specific features. Traffic conditions are measured continuously along river segments using AIS and Notice to Skippers data, enabling the estimation of speed deviations and accumulated delays. A data-driven modelling approach is used subsequently to estimate segment-level traffic impacts and attribute these impacts to individual disruption categories. This enables a transparent assessment of how different disruption types contribute to corridor-wide traffic delays. The final results quantify the contribution of different disruption categories to traffic delays and reveal that disruption frequency does not necessarily correspond to traffic impact, suggesting that frequency-based monitoring may misrepresent risk priorities. The resulting category-level attribution weights may provide waterway authorities, port operators, and policymakers with a transparent, data-driven basis for prioritizing infrastructure investment, maintenance scheduling, and targeted resilience measures.

Despite Thailand’s substantial investments in rail infrastructure over the past decade, the freight modal shift from road to rail remains severely limited. As of 2024, rail freight accounts for less than 2% of total cargo volume, while road transport continues to dominate nearly 80% of the national logistics system. This Chronic imbalance drive persistent logistics inefficiencies, traffic congestion, rapid infrastructure deterioration, severe environmental externalities, and inflated supply chain costs. Traditional modal shift research relies on static econometrics or optimization models, such as discrete choice logit / probit or optimization models, that fail to capture the endogenous feedback mechanisms, infrastructure degradation cycles, and stakeholder behavioral delays inherent to transport systems. Consequently, these static approaches are unable to demonstrate the long-term, dynamic evaluation of freight transport systems, risking suboptimal policy recommendations.

This study develops a System Dynamics framework to integrate the complex, time-dependent interactions between road and rail freight transport. The proposed model evaluates modal choice behavior, generalized transport costs, operational capacity, congestion, infrastructure investment deterioration within a unified dynamic system driven by policy feedback loops. Notably, the framework incorporates perception delays through perceived generalized costs to reflect the gradual behavioral shift of freight operators and shippers

The framework was initially calibrated using data from the strategic Northeastern–Eastern Thailand freight corridor that connects inland production hubs to Laem Chabang Port. The model simulates the long-term interactions among freight demand, traffic congestion, infrastructure performance, rail utilization, terminal handling delays, and maintenance cycles. In particular, the study emphasizes the joint role of externalities internalization policies (such as truck enforcement and road pricing measures) and rail infrastructure enhancements (including capacity expansion, terminal development, and service quality upgrades). This framework enables a rigorous analysis of policy coordination, execution timing and latent capacity bottlenecks.

Preliminary simulation results demonstrate that rail infrastructure investment alone is insufficient to induce a substantial modal shift if road transport externalities remain underpriced. Conversely, road enforcement policies alone increase overall logistics costs if rail networks lack the capacity to absorb the shifting freight demand. Effective modal shift emerges from coordinated policy combinations implemented under appropriate timing conditions. Furthermore, the model reveals highly dynamic system behaviors, including nonlinear modal transitions, supply-chain spill-back effects from rail capacity shortage, balancing feedback from terminal congestion, and long-term reductions in road deterioration and congestion. Ultimately, this research introduces an integrated dynamic policy-feedback framework that unifies modal competition, infrastructure degradation, externalities, and transport system performance within a single endogenous system. The proposed framework serves as a robust policy simulation tool for evaluating sustainable freight transport strategies in developing economies.