19. A Transportation Planning Decision Support System

The system-level responses to the regulation require structuring and modeling decisions and scenarios involving multiple actors. To establish a joint problem statement, the SPADE methodology was used. SPADE is a soft systems engineering approach valuable in handling complex problems in multi-actor environments (Aspen, Haskins, and Fet 2018; Haskins 2008). The methodology was applied to identify stakeholders (S), problems (P), and alternative strategies (A) to synthesize a decision analytical structure (D) for further modeling and evaluation (E). The stakeholder analysis helped classify key actors to include in the subsequent problem formulation. These included cruise companies, port authorities, transport companies, tourist operators and politicians. The problem formulation helped structure strategic responses to the regulation and identify uncertainties, scenarios, and key performance indicators to use in the subsequent modeling and analysis.

Next, a transportation system simulation model was established. As both road and sea traffic would be affected by the zero-emission resolution and respond interactively to alternative strategic actions taken, two separate models were developed and connected to assess the overall dynamic system response. The land traffic model handled all transport on road links in the area and included a module to specify cruise characteristics. The sea traffic model handled cruise ship activity and local ferry transport. The combined model made it possible to estimate sustainability performance metrics such as air emissions and traffic congestion. More elaborate information about transport models and parameters may be found in Díez-Gutiérrez and Babri (2020, 2022), Johansen (2021) and Johansen, et al. (2021).

In order to estimate air emissions in a holistic simulation model, several components were developed using tools across levels 1–3 in the CapSEM toolbox. Firstly, models were developed to predict operational responses (level 3) to various perturbations in the transportation system. This entailed addressing traveler preferences and impacts on e.g. route choice (Díez-Gutiérrez and Babri 2022). On this level, models to derive energy consumption for various operational patterns in road and sea traffic were also established (Aspen, Johansen and Babri 2020). Life cycle inventory data was used to establish emission factors (Level 2) from alternative fuels in sea traffic (Winnes and Fridell, 2010). Lastly, process models (Level 1) to derive emissions for various operational profiles, fuels, and abatement technologies were created (Aspen et al., 2020, Johansen 2021, Johansen et al. 2021).

The four-level approach enabled holistic and comprehensive analyses and evaluation of strategic policy responses for key transportation actors. The comprehensive study included assessment criteria across all sustainability dimensions, but for simplicity, a truncated illustration of the decision support system and associated tools deployed is shown in Fig. 19.1.

The application of systems engineering (Level 4) provided several problem structuring elements, such as definition of system boundaries, key stakeholders, a specified set of strategies, scenarios, and performance evaluation criteria. Figure 19.2 shows the study area with the strategic transport responses to the new regulation from the multi-stakeholder group consulted. Through their engagement, five main strategies were defined to explore transportation patterns for visitors to Geiranger based on alternative cruise traffic routing. For all strategies, cruise ship emissions were calculated based on a configuration of marine gas oil with exhaust gas cleaning technology installed (SCR). Strategy 1 (S1) was to work toward business as usual (BAU) which represents the current situation where cruise ships call to port in Geiranger. This would require delayed enforcement or reversal of the parliament regulation. Strategy 2 (S2) was to work towards a dispensation for zero-emission sailing in a “blue corridor” within the world heritage area. Strategy 3 (S3) was to develop a new cruise port outside the world heritage area in the Stranda village. Strategy 4 (S4) was to take no action and make cruise ships call to nearby ports outside the world heritage area, while strategy 5 (S5) was to route cruise ships outside the entire area, visiting other sites than the ports within the study area. Within all scenarios, various combinations of land traffic (bus) and zero-emission vessels to bring visitors to various sites in the Geiranger village were also computed. To account for uncertainties, scenarios for high, medium and low visitor volumes were also used when assessing strategies.

The map structure has boundaries and strategies for cruise traffic links and road traffic links. It has S 1 to S 5 ports. The strategy and ports are business as usual, Geiranger, Blue corridor, Hellesylt, new port, Stranda, nearby ports, Alesund, Nordfjordeid, and olden or loen, no cruise, outside the study area.

By deploying models at all CapSEM Levels, it was possible to assess the total transportation system performance across all strategies and scenarios. Figure 19.3 shows selected results from the broader sustainability impact assessment, displaying the CO₂ and NO_x emissions for a medium scenario where a bus roundtrip is assumed for visitors traveling between alternative ports and the Geiranger village. The figure illustrates that potential cruise-generated bus traffic would only contribute to a small portion of the total transport-related air emissions in the study area. This insight was critical as several stakeholders were concerned about problem shifting through the transferal of emissions from sea to land traffic. The analysis showed that emissions from road traffic were negligible compared to emissions from cruise ships and that cruise port location was of a greater importance.

2 bar graphs illustrate the C O 2 emissions in tons and N O x emissions in kilograms from the cruise, cruise-generated land traffic, and regular land traffic under the B A U, blue corridor, new harbor, nearby harbors, and no cruise strategies. Cruise M G O plus S C R generates the maximum C O 2 and N O x emissions, followed by cruise-generated land traffic and regular land traffic.

Another critical parameter of concern was the potential for traffic congestion on road links in the study area due to cruise traffic rerouting. As cruise-generated bus traffic would increase significantly following strategies 1–4, an estimation of reduced speed compared to the respective speed limit was performed for each road segment under a maximum traffic scenario. The results in Fig. 19.4 show that the Blue Corridor strategy generated the highest level of congestion on road links in the study area. For this strategy, it was evident that local transport between cruise port and the village had to be accommodated partially or wholly by sea transport compliant with the regulatory zero-emission requirement.

A map layout depicts the road congestion caused by cruise-generated bus transport across B A U, blue corridor, new harbor, and nearby harbor strategies.

In this chapter, the CapSEM toolbox has been explored and applied for developing a decision support system in the transport sector. The explicit formulation and combination of models within the four-level CapSEM structure proved useful in addressing transportation system sustainability issues in the case study.

From a modeling and analysis perspective, the CapSEM Levels helped organize a model breakdown structure in the DSS. The sustainability performance of the total transportation system depends on elements at all CapSEM levels: Physical input-output processes in energy conversion, techno-economic processes in product systems, the operational behavior of transport system actors and material, and strategic policy and planning processes at the system level. At the same time, models were designed to let information propagate through the layers facilitating increasingly complex inferences about the sustainability performance of transportation measures. This was convenient as it helped manage and exploit multiple domains and logics necessary to support transportation planning. It also made it easier to compartmentalize critical factors and assumptions in the model structure and keep track of key parameters and their sensitivities.

From the viewpoint of stakeholder engagement and interaction, the approach also facilitated a clear and transparent dialogue between analysts and various decision-makers on the data, assumptions, and reasoning at each system level. This is important to ensure stakeholder comprehension, judgment, and utilization of information and knowledge in transportation planning processes.

This case provides a simple illustration of how the CapSEM Model may be applied in the transport sector. While the chapter only focused on assessing air emissions from various responses to environmental regulation, several other sustainability aspects could and should be explored utilizing the CapSEM Model and associated tools. This includes other environmental impacts, such as land use and ecological impacts, as well as social and economic aspects influenced by various transportation system planning strategies.