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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 (Pap, 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 kont, QC 20231218

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., QC 20231218

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., QC 20220921

QC 20231229

QC 20231229

QC 20231229

The utilisation of travel surveys can uncover layers of information regarding travel behaviour, travel needs, and more. The collected information is utilised to make strategic planning choices when reorganising or planning new built environments. Over the years, the methods for conducting travel surveys have changed from manual interviews and paper forms to automated travel diaries which are monitoring the trips made by the survey participants. With the fast progression of technological advancements, new possibilities for operationalising said types of automated travel diaries can be changed from utilising mobile devices to wearable devices. Wearable devices are often equipped with sensors which can collect continuous biometric data from sources which are not reachable from standard mobile devices such as smartphones. The biometric data that can be collected through wearable devices ranging from heart rate and blood pressure to temperature and perspiration, given the proper sensors. This advancement opens for 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 more in-depth knowledge regarding stress and emotions, given the right variables and sample rate. This paper aims to explore the possibilities in terms of data analysis on a data set collected through a software combining traditional travel survey data, such as position and time, with biometric data, in this case; heartrate, to gain knowledge of the implications of such collected data. The knowledge about the implications of spatial configurations can be used in the planning phase of new areas, in order to create more accessible environments, as the information could be used to make neutral, or even encouraging, environments for travellers., This work is currently not under review but is intended to be submitted to the “International Conference on Transport Survey Methods” once the call for submissions is opened.QCR 20191022

The utilisation of travel surveys can uncover layers of information regarding travel behaviour, travel needs, and more. The collected information is utilised to make strategic planning choices when reorganising or planning new built environments. Over the years, the methods for conducting travel surveys have changed from manual interviews and paper forms to automated travel diaries which are monitoring the trips made by the survey participants. With the fast progression of technological advancements, new possibilities for operationalising said types of automated travel diaries can be changed from utilising mobile devices to wearable devices. Wearable devices are often equipped with sensors which can collect continuous biometric data from sources which are not reachable from standard mobile devices such as smartphones. The biometric data that can be collected through wearable devices ranging from heart rate and blood pressure to temperature and perspiration, given the proper sensors. This advancement opens for 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 more in-depth knowledge regarding stress and emotions, given the right variables and sample rate. This paper aims to explore the possibilities in terms of data analysis on a data set collected through a software combining traditional travel survey data, such as position and time, with biometric data, in this case; heartrate, to gain knowledge of the implications of such collected data. The knowledge about the implications of spatial configurations can be used in the planning phase of new areas, in order to create more accessible environments, as the information could be used to make neutral, or even encouraging, environments for travellers., This work is currently not under review but is intended to be submitted to the “International Conference on Transport Survey Methods” once the call for submissions is opened.QCR 20191022

The utilisation of travel surveys can uncover layers of information regarding travel behaviour, travel needs, and more. The collected information is utilised to make strategic planning choices when reorganising or planning new built environments. Over the years, the methods for conducting travel surveys have changed from manual interviews and paper forms to automated travel diaries which are monitoring the trips made by the survey participants. With the fast progression of technological advancements, new possibilities for operationalising said types of automated travel diaries can be changed from utilising mobile devices to wearable devices. Wearable devices are often equipped with sensors which can collect continuous biometric data from sources which are not reachable from standard mobile devices such as smartphones. The biometric data that can be collected through wearable devices ranging from heart rate and blood pressure to temperature and perspiration, given the proper sensors. This advancement opens for 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 more in-depth knowledge regarding stress and emotions, given the right variables and sample rate. This paper aims to explore the possibilities in terms of data analysis on a data set collected through a software combining traditional travel survey data, such as position and time, with biometric data, in this case; heartrate, to gain knowledge of the implications of such collected data. The knowledge about the implications of spatial configurations can be used in the planning phase of new areas, in order to create more accessible environments, as the information could be used to make neutral, or even encouraging, environments for travellers., This work is currently not under review but is intended to be submitted to the “International Conference on Transport Survey Methods” once the call for submissions is opened.QCR 20191022

The utilisation of travel surveys can uncover layers of information regarding travel behaviour, travel needs, and more. The collected information is utilised to make strategic planning choices when reorganising or planning new built environments. Over the years, the methods for conducting travel surveys have changed from manual interviews and paper forms to automated travel diaries which are monitoring the trips made by the survey participants. With the fast progression of technological advancements, new possibilities for operationalising said types of automated travel diaries can be changed from utilising mobile devices to wearable devices. Wearable devices are often equipped with sensors which can collect continuous biometric data from sources which are not reachable from standard mobile devices such as smartphones. The biometric data that can be collected through wearable devices ranging from heart rate and blood pressure to temperature and perspiration, given the proper sensors. This advancement opens for 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 more in-depth knowledge regarding stress and emotions, given the right variables and sample rate. This paper aims to explore the possibilities in terms of data analysis on a data set collected through a software combining traditional travel survey data, such as position and time, with biometric data, in this case; heartrate, to gain knowledge of the implications of such collected data. The knowledge about the implications of spatial configurations can be used in the planning phase of new areas, in order to create more accessible environments, as the information could be used to make neutral, or even encouraging, environments for travellers., This work is currently not under review but is intended to be submitted to the “International Conference on Transport Survey Methods” once the call for submissions is opened.QCR 20191022

The utilisation of travel surveys can uncover layers of information regarding travel behaviour, travel needs, and more. The collected information is utilised to make strategic planning choices when reorganising or planning new built environments. Over the years, the methods for conducting travel surveys have changed from manual interviews and paper forms to automated travel diaries which are monitoring the trips made by the survey participants. With the fast progression of technological advancements, new possibilities for operationalising said types of automated travel diaries can be changed from utilising mobile devices to wearable devices. Wearable devices are often equipped with sensors which can collect continuous biometric data from sources which are not reachable from standard mobile devices such as smartphones. The biometric data that can be collected through wearable devices ranging from heart rate and blood pressure to temperature and perspiration, given the proper sensors. This advancement opens for 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 more in-depth knowledge regarding stress and emotions, given the right variables and sample rate. This paper aims to explore the possibilities in terms of data analysis on a data set collected through a software combining traditional travel survey data, such as position and time, with biometric data, in this case; heartrate, to gain knowledge of the implications of such collected data. The knowledge about the implications of spatial configurations can be used in the planning phase of new areas, in order to create more accessible environments, as the information could be used to make neutral, or even encouraging, environments for travellers., This work is currently not under review but is intended to be submitted to the “International Conference on Transport Survey Methods” once the call for submissions is opened.QCR 20191022