Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

Reliable forecasting models are needed to achieve the climate related goals in the face of increasing transport demand. Such models can predict the long-term behavioural response to policy interventions, including infrastructure investments, and thus provide valuable pre-dictions for decision makers. Contemporary forecasting models are mainly based on national travel surveys. Unfortunately, the response rates of such surveys have steadily declined, implying that the respondents become less representative of the whole population. A particular weakness is that it is likely that respondents with a high valuation of time are less willing to respond to surveys (because they have less time available for such), and therefore there is a high chance that they are underrepresented among the respondents. The valuation of time plays an important role for the cost benefit analyses of public policies including transport investments, and there is no reliable way of controlling for this uneven sampling of time preferences. Fortunately, there is simultaneously an increase in the number of signals sent between mobile phones and network antennae, and research has now reached the point where it is possible to determine not only the travel destination but also the travel mode based on mobile phone network antennae connections. The aim of this thesis is to investigate if and how mobile phone network data can be used to estimate transportation mode choice demand models that can be used for forecasting and planning. Key challenges with using this data source in the context of mode choice models are identified and met. The identified challenges include uncertainty in the choice variable, the difficulty to distinguish car and bus trips, and the lack of information about the trip purpose. In the first paper we propose three possible model formulations and analyse how the uncertainty in the choice outcome variable would play a role in the different model formulations. We also conclude that it is indeed possible to estimate mode choice demand models based on mobile phone network data, with good results in terms of behavioural interpretability and significance. In the second paper we estimate models using a nested logit structure to account for the difficulty in separating bus and car, and a latent class model specification to meet the challenge of having an unknown trip purpose. / <p><strong>Funding agencies:</strong> The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research program Transportekonomi at The Swedish Transport Administration.</p>

The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior changes and unusual consumption events. I propose a new reciprocity index to measure the level of reciprocity between users and their communication partners. The experimental results show that this approach is effective. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and marketing. In my future work I plan to analyze and study the social network dynamics and evolution.