Modeling dynamic traffic processes requires detailed traveler-level demand unavailable in aggregated national forecasting models. We present a kernel-based approach for generating synthetic disaggregate travel demand using travel survey data. The method links survey respondents to agents in a synthetic population through similarity kernels over socio-demographics, schedule characteristics, and activity–location feasibility, and embeds these in a Metropolis–Hastings sampling framework to generate complete daily plans. The approach provides a modular way to combine heterogeneous information sources and to transfer observed behaviour to new populations without requiring exact matches between individuals. We demonstrate the approach on a stylized grid-world and on real data, where survey records are mapped to a national synthetic population. Results show that the method reproduces key marginal travel patterns, preserves individual variability, and avoids collapse toward modal behaviour, illustrating its potential as a demand synthesis and data-fusion tool in dynamic traffic modelling. 

Individual-level travel surveys provide valuable but incomplete information about travel demand. They typically cover only a subset of the population and lack temporal and spatial detail. A synthetic population provides a complete representation of residents in a region with socio-demographic characteristics but often without mobility behavior. This work aims to extend the information in a travel survey to the full synthetic population, thereby generating consistent, individual-level mobility patterns at scale. These disaggregated travel schedules are useful for many applications, including the creation of physically consistent dynamic origin–destination (OD) matrices. This problem has direct policy relevance. Transport planning in Sweden relies on demand forecasts from the national model Sampers, which produces static OD matrices. Such matrices ignore within[1]day variations in travel and therefore cannot adequately capture time-dependent congestion phenomena, such as the build-up and dissipation of queues during peak periods. The existing dynamic network model Dynameq can represent these processes, but only if provided with time[1]dependent demand. By disaggregating survey data to a synthetic population, we provide a pathway to generate the necessary dynamic demand representations.

In this paper we propose a model for joint ferry network design and scheduling in archipelago public transport systems. The model uses a multi-trip vehicle routing approach with inbound-outbound and returning trip structures where traffic requirements are implemented as window requirements. A case study is performed scheduling a full day of traffic in the southern archipelago outside of Gothenburg, Sweden. Results indicate that this approach can capture the many different stopping patterns of a line seen in real systems and produce realistic time-tables. Computational performance is shown to be highly dependent on the number of window constraints included.

In near coastal environments, nautical charts provide crucial information for navigation and routing both in real-time operations and during planning stages. The cost of data collection as well as capacity constraints in the processing pipeline make reliable bathymetric information in such areas sparse. Prioritization rules can help guide the efforts to where information is the most valuable. AIS data provide accounts of real ship movements, indicating both desirable paths and minimum depths. We propose a statistical model for combining sparse bathymetric soundings with AIS observations for improved prediction of depths for generation of feasible transportation corridors. The method relies on viewing AIS draughts as censored observations of the true depth. A case-study is performed for the southern archipelago of Gothenburg using the program R-INLA. The non-stationarity caused by having boundaries with known (zero) depth and holes (land) in the domain is handled through discretization. Varying amounts of AIS data, ranging from none to 1824 observations, are used in the experiments. Results show predicted depths within the range of data values, and that inclusion of AIS data serve to push the field down to ensure that traverseable areas are predicted as such revealing corridors in narrow passages where bathymetric soundings are lacking. 

Archipelagos are rural regions characterized by their distinct geo-graphic layouts with many separated island communities. Water bodies hinder access to community services and make a well-functioning ferry-based public transport system the only feasible means of mass transportation. Both between islands and to connect to the mainland. Maritime environments provide great routing flexibility. However, outdated or imprecise nautical charts can hinder the safe use of such possibilities in commercial traffic. 

This thesis develops methods for strategic planning of archipelago transportation systems, enabling the valuation of novel fairways. Contributions are made to the fields of applied optimization and statistical modeling of spatial data considering the distinct features of archipelago environments. 

To improve the inference of fairways, a data fusion model for combining traditional bathymetric data and ship trajectory data from the automatic identification system (AIS) for depth inference is developed. It utilizes probabilistic modeling and survival analysis for spatial interpolation of the two qualitatively different sources of information. To handle the large amounts of automatic identification system (AIS) data and the non-stationarity cause by holes (islands) in the model region, a discretized mesh approach is used. 

In the second step an optimization model for joint network design and scheduling of ferry public transport, with the capability of evaluating currently unavailable fairways with opening costs. In this study, characteristic features of archipelago ferry networks are identified. These features are then expressed using a modified vehicle routing model with multiple trips. Results show that factors such as traffic requirements, operating costs and fleet composition has a large impact on the value of a fairway. 

In summary, the thesis develops methods for inferring novel fairways using the alternate source of information provided by ship trajectory data. To evaluate such fairways, an optimization model for assessing their scheduling value in ferry based public transport systems is developed. 

Skärgårdar utgör landsbygdsområden kännetecknade av sina särskilda geografiska förutsättningar. Vatten separerar ösamhällen och utgör fysiska begränsningar som påverkar tillgången till samhällstjänster. De gör ett väl fungerande vattenburet kollektivtrafiksystem centralt för att möjliggöra god tillgänglighet både mellan öarna och till fastlandet för alla invånare. Marina miljöer erbjuder vidare stor flexibilitet i ruttplaneringen. Däremot kan föråldrade eller oprecisa sjökort hindra säker användning av sådana möjligheter i kommersiell trafik.

Denna avhandling utvecklar metoder för att förbättra den strategiska planering av skärgårdars transportsystem och möjliggöra värdering av nya farleder. Bidrag görs inom tillämpad optimering och rumslig statistisk modellering som tar hänsyn till de särskilda egenskaperna skärgårdsmiljöer besitter.

För att förbättra skattningen av farleder utvecklas en datafusionsmodell som kombinerar traditionella djupdata med fartygstrajektorier från Automatic Identification System (AIS). Probabilistisk modellering och överlevnadsanalys används för rumslig interpolering av de två kvalitativt olika informationskällorna. För att hantera de stora datamängderna i AIS-datat och icke-stationäriteten som introduceras av hål (öar) i studieområdet används en diskretiserad modelleringsansats.

I det andra steget utvecklas en optimeringsmodell för kombinerad nätverksdesign och schemaläggning av färjetrafiken. Modellen, som baseras på en modifierad ruttplaneringsmodell med flera resor, inkluderar många av de karakteristiska drag för skärgårdstrafik som identifierats och erbjuder möjlighet att utvärdera för närvarande otillgängliga korridorer med fasta öppningskostnader. Resultaten visar att faktorer såsom trafikeringskrav, driftskostnader och fordonsflottans sammansättning bör tas i beaktande vid värdering av nya farleder.

Sammanfattningsvis pekar avhandlingen på hur förbättrad djupinformation kan hjälpa till att härleda möjliga transportkorridorer som inte finns med i sjökorten. Den presenterar vidare en metod för inferens av sådana farleder baserad på faktiska fartygstrajektorier och utvecklar i nästa steg en optimeringsmodell som i ett helhetsgrepp bedömer deras värde i kollektivtrafiksystemet.