This paper applies recursive logit (RL) to model activity-trip chaining behaviour. We present a comparison between two approaches to applying the RL model in this context. In the first ‘sequential’ approach, agents form a trip chain by making a sequence of joint choices of activity location (i.e. trip destination) and travel mode, ending the chain by choosing to return home. The second ‘dynamic’ approach adds a time variable. Its agents form a full-day activity/travel schedule by making a sequence of choices either to continue the current activity for a fixed timestep or make a joint choice of new activity location and travel mode. We estimate parameters for both models using data from a Stockholm travel survey and validate model simulations against observed data. The models reproduce patterns of observed behaviour beyond their estimated parameters, including different types of trip chains and the spatial distribution of activities. While the dynamic model is advantageous in its ability to predict agent schedules, reflect time-varying travel conditions and endogenously represent space–time constraints, it does not surpass the simpler sequential model on mutual areas of trip chaining behaviour. We conclude that the RL model is well-suited to model trip chaining behaviour, and that the simpler sequential approach may be appropriate for many modelling purposes.

The City of Stockholm is conducting a scenario planning exercise to explore where potential future office development should be planned: closer to the city centre as in the status quo, in peripheral hubs on the outskirts of the city, or dispersed throughout multiple neighbourhoods. To support this exercise, this paper models these three scenarios using a nested work location and dynamic activity-based scheduling model. Our model predicts that high-income individuals have the highest consumer welfare benefits and are over-represented as workers in all scenarios. Developing more central office space will likely reinforce existing geographical patterns of income inequality in Stockholm; developing peripheral or dispersed office space, especially in the south of the city, will challenge these patterns. However, the model also illustrates a tension between the goals of equity and the environment. By taking advantage of existing transit infrastructure and congestion patterns, more central office development will result in lower vehicle kilometers travelled and lower car mode share for commuting than more peripheral or dispersed development.

Travel demand analysis is one of the core constituents of transportation studies. Therequired insight to maintain and develop a sustainable transportation system, in additionto learning from previous research globally and locally, is generated from studyingthe effects of previous policies, investigating future possibilities and potential outcomes,and describing the current situation. The objective of the thesis is to use urbanmodeling and decision support methods to contribute to the knowledge that improvesdecision making for a sustainable society.

In this thesis, three of the papers focus on implementation and application of adynamic model of movement for prediction and forecast of workplace demand andaccessibility, and for a trip chaining problem. This framework formulates movementthrough a Markov chain and solves it by using the Bellman equation which by theassumption of IID Gumbel error terms turns into a recursive logit, which reflects dynamicand directional nature of time in modeling movement. This approach is usedfor modeling a workplace choice model and accessibility to work (Paper 1), that isapplied for workplace allocation in a scenario planning framework for urban developmentgrowth with a 2040 forecasted synthetic population (Paper 3), and a dynamictrip chaining model with flexible number of trips in the chain (Paper 4). The workplacelocation choice model is unique as it connects the land use and transport modelsin a framework that is consistent with random utility maximization approach whilerespecting forward-looking behavior of individuals and the dynamic and directionalnature of time. This approach allows for evaluations of counterfactual scenarios inPaper 1 and in future workplace growth in Paper 3 for understanding the implicationsof workplace allocations under different urban planning scenarios in terms of travelbehavior, with implied social segregation issues, workplace demand and distributionof welfare. When applied in a trip chaining context (Paper 4), the methodology allowsfor flexibility in number of trips in the chain, with the implication of creating the linkbetween trip chaining problem and the marginal utility of time and the marginal rateof substituition not only for different trip purposes, e.g. work and leisure, but also forany other variables in the model, even in the absence of scheduling, which is novel inthe literature of trip chaining and time valuation.

A fundamental aspect of the methodology that is used in this thesis revolves aroundthe utilization of Multivariate Extreme Value (MEV) models. We explore the intricaciesof these models in detail in Paper 2, and take one step against the conventionby using a replicated nest in a multinomial setting. We discuss the empirical implicationof retrieving exchangeability, when it is compromised. This approach gives areminder to respect the complexity of error term in MEV models, while benefitingfrom the generality of its definition.

Travel time and costs are among the factors that impact transportation and land useinteractions. In Paper 5, we address this interaction by exploiting a natural experimentto investigate green vehicle owners’ responses to phasing out of their time-varyingtoll exemption in Stockholm through comparison of data in 2012 and 2013, whichwere before and after this policy was implemented. The results show a significantdrop in the total number of green vehicles that crossed the toll stations in 2013, anda significant shift to off-peak crossing time in the toll stations in 2013 compared to2012.

Analys av reseefterfrågan utgör en central komponent i transportstudier. Nödvändigkunskap för att utveckla och upprätthålla ett hållbart transportsystem, tillsammansmed lärdomar från tidigare forskning både globalt och lokalt, erhålls genom att studeraeffekterna av tidigare politik, undersöka framtida möjligheter och potentiella resultatutifrån en empirisk beskrivning av den nuvarande situationen. Målet med dennaavhandling är att använda stadsmodellering och beslutsstödsmetoder för att bidra tillkunskap som förbättrar beslutsfattandet för ett hållbart samhälle.

Tre av artiklarna i denna avhandling fokuserar på implementering och tillämpningav en dynamisk modell av mobilitet för prognostisering av efterfrågan och tillgänglighetpå till arbetsplatser, respektive kedjeresande. Denna ram formulerar mobiliteti ett et Markov-kedja-problem, vilken löses genom Bellman-ekvationen. Genom antagandetom IID Gumbel-feltermar kan modellen formuleras som en rekursiv logit,vilket återspeglar den dynamiska och riktade naturen av tid. Denna metod användsför att modellera en arbetsplatsvalmodell och tillgänglighet till arbete (Artikel 1), somtillämpas för arbetsplatsallokering i en scenarioplaneringsram för stadsutveckling meden syntetisk befolkning prognostiserad för 2040 (Artikel 3) samt en dynamisk modellför kedjerörelse med ett flexibelt antal resor i kedjan (Artikel 4). Arbetsplatsvalmodellenär unik eftersom den kopplar samman markanvändnings- och transportmodelleri en ram som är konsistent med den slumpmässiga nytto-maximeringsansatsen, samtidigtsom den respekterar individers framåtblickande beteende och tidens dynamiskaoch riktade natur. Denna ansats möjliggör utvärderingar av kontra-faktiska scenarieri Artikel 1 och i framtida arbetsplatsutveckling i Artikel 3 för att förstå konsekvensernaav arbetsplatsallokeringar under olika stadsplaneringsscenarier avseenderesebeteende, med implicerade frågor kring social segregation, arbetsplatsefterfråganoch fördelning av välfärd. I en kontext för kedjeresande (Artikel 4) erbjuder modellenflexibilitet när det gäller antalet resor i kedjan, vilket möjliggör skapandet av enlänkmellan kedjeresande, den marginella nyttan av tid och den marginella substitutionskvoten,inte bara för olika ändamål med resan, t.ex. arbete och fritid, utan ocksåför andra variabler i modellen , även utan schemaläggning, vilket är ett bidrag inomlitteraturen om kedjeresande och tidsvärdering.

En grundläggande aspekt av den metod som används i denna avhandling kretsarkring användningen av multivariata extremvärdesmodeller (MEV). I Artikel 2 utforskarvi dessa modeller mer i detalj och prövar en replikerad nästling, vilket intetillhör praxis inom området. Vi diskuterar detta förfaringssätt utifrån modellteoretiskutbytbarhet (exhcangeability). Denna ansats påminner om att hantera komplexitetenhos felstrukturen i MEV-modeller, samtidigt som man kan dra nytta av dess generalitet.

Restid och kostnader är bland de faktorer som påverkar samspel mellan transportoch markanvändning. I Artikel 5 behandlar vi denna interaktion genom att användaett naturexperiment för att undersöka ägare av gröna fordon och deras svar på avvecklingenav deras tidsvarierande undantag från trängselskatten i Stockholm genom jämförelseav data från 2012 och 2013, dvs. före och efter att denna policy implementerades.Resultaten visar en betydande minskning av det totala antalet gröna fordon sompasserade trängselstationerna 2013, samt en betydande övergång till icke-rusningstidför passage genom trängselstationerna 2013 jämfört med 2012.

Stockholm established time-varying congestion pricing in 2007, and adopted a toll exemption as a temporary incentive for green vehicles (GVs) that ended in 2012. We examine the behavioral effects of phasing out the exemption by studying the change in cordon crossing events for GV morning commuters between May 2012 and May 2013, with a random sample of conventional vehicles (CVs) as control. The results suggest i) a significant drop in the total number of crossings; ii) a slight shift towards later journeys in the morning; and iii) a reduction in the ratio of peak-toll period crossings to other ones.

To better understand the interactions between physical built environment conditions and one’s well-being, we created a passive data collector for travellers and made the first step towards an explanatory model based on psychophysiological relations. By measuring biometric information from select trial participants we showed how different controlled factors are affecting the heart rate of the participants. A regression model with the impact factors such as speed, location, time and activity (accelerometer data) reveals how the factors relate to each other and how they correlate with the recorded individual’s heart rates throughout the observed period. For examples, the results show that the increase in movement speed is not linearly correlated with the heart rate. One’s heart rate would increase significantly when the individual reaches brisk walking and running speed, but not before nor after. Early morning and early evening time slots were the time where the observed individuals have the highest heart rates, which may correlate to individuals’ commute activities. Heart rates at the office would be lower than at home, which might correlate to more physical activities in the household. 

Travel surveys can uncover information regarding travel behaviour, needs, and more. Collected information is utilised to make choices when reorganising or planning built environments. Over the years, methods for conducting travel surveys have changed from interviews and forms to automated travel diaries in order to monitor trips made by travellers. With the fast progression of technological advancements, new possibilities for operationalising such travel diaries can be implemented, changing from utilising mobile to wearable devices. Wearable devices are often equipped with sensors which collect continuous biometric data from sources that are not reachable from standard mobile devices. Data collected through wearable devices range from heart rate and blood pressure to temperature and perspiration. This advancement opens new possible layers of information in the collection of travel data. Such biometric data can be used to derive psychophysiological conditions related to cognitive load, which can uncover in-depth knowledge regarding stress and emotions. This paper aims to explore the possibilities of data analysis on the data collected through a software combining travel survey data, such as position and time, with heartrate, to gain knowledge of the implications of such data. The knowledge about the implications of spatial configurations can be used to create more accessible environments.

We present an approach to simulating individuals' daily trip and activity chains which treats these as the emergent behaviour of a dynamic decision problem. In our framework, agents in a Markov Decision Process try to maximize the total reward of their path through the state space. As agents observe the immediate reward of transitions and the expected utility of the resulting states, their actions are modelled using a recursive logit model. We present three recursive logit model specifications for trip chaining: destination choice only, mode and destination choice, and a fully dynamic model which adds activity scheduling. We estimate parameters for these models using data from a Stockholm travel survey and validate the models against observed data in simulations. The models produce trip chains that reproduce observed behaviour including the distribution of activities per tour and trip chain patterns. The dynamic model reproduces observed trip chain timing and produces reasonable variability in behaviour for agents with different home and work locations. The dynamic model is advantageous in its ability to predict dynamic trip chaining behaviour, reflect time-varying travel conditions and endogenously represent space-time constraints.