In order to support efficient transportation planning decisions, household travel survey data with high levels of accuracy are essential. Due to a number of issues associated with conventional household travel surveys, including high cost, low response rate, trip misreporting, and respondents’ self-reporting bias, government and private agencies are desperately searching for alternative data collection methods. Recent advancements in smart phones and Global Positioning System (GPS) technologies present new opportunities to track travelers’ trips. Considering the high penetration rate of smartphones, it seems reasonable to use smartphone data as a reliable source of individual travel diary. Many studies have applied GPS-Based data in planning and demand analysis but mobile phone GPS data has not received much attention. The Google Location History (GLH) data provide an opportunity to explore the potential of these data. This research presents a study using GLH data, including the data processing algorithm in deriving travel information and the potential applications in understanding travel patterns. The main goal of this study is to explore the potential of using cell phone GPS data to advance the understanding in mobility and travel behavior. The objectives of the study include: a) assessing the technical feasibility of using smartphones in transportation planning as a substitute of traditional household survey b) develop algorithms and procedures to derive travel information from smartphones; and c) identify applications in mobility and travel behavior studies that could take advantage of these smartphones GPS data, which would not have been possible with conventional data collection methods. This research aims to demonstrate how accurate travel information can be collected and analyzed with lower cost using smartphone GPS data and what analysis applications can be made possible with this new data source. Moreover, the framework developed in this study can provide valuable insights for others who are interested in using cell phone data. GLH data are obtained from 45 participants in a two-month period for the study. The results show great promise of using GLH data as a supplement or complement to conventional travel diary data. It shows that GLH provides sufficient high resolution data that can be used to study people’s movement without respondent burden, and potentially it can be applied to a large scale study easily. The developed algorithms in this study work well with the data. This study supports that transportation data can be collected with smartphones less expensively and more accurately than by traditional household travel survey. These data provide the opportunity to facilitate the investigation of various issues, such as less frequent long-distance travel, hourly variations in travel behavior, and daily variations in travel behavior.

This thesis is developed upon inquires on urban spatial structure of Southeast Asian extended metropolitan region (EMR) and its impacts on travel. Literature suggests that while efforts in promoting transport sustainability in the developed world have included policy measures involving urban spatial structure and its physical features as a consequence of the understanding on strong link between land use and transport, there has been lack of understandings on the spatial structure in major cities in Southeast Asia. Exploratory spatial data analysis (ESDA) is adopted for identification of important components of the spatial structure of employment in the Jakarta Metropolitan Area (JMA). The approach has been specifically designed in order to extract clusters as suggested in the Southeast Asian EMR concept. It is found that the spatial structure of employment in the JMA consists of the following major components: the urban core of Jakarta; the single dominant and expanded regional CBD within the urban core of Jakarta; manufacturing corridors that are largely follow toll roads radiating out of the urban core; local government regions that in general have not been developed into substantial sub-centres; desakota areas overlapping the manufacturing corridors and the agricultural areas; and portions of agricultural areas in the outer parts of Bekasi, Bogor and Tangerang regencies. The result shows that spatial structure of JMA conforms to the Southeast Asian EMR concept rather than the monocentric, polycentric or sprawl patterns debated for the case of developed cities. Commuting impacts of the identified spatial structure of employment and its physical features are investigated using the desireline analysis, home-to-work trip pattern comparisons (ANOVA) by the employment clusters, and ordinary linear regression and logistic regression models. It is found that the spatial structure identified and its physical features have significant associations to variations in the pattern of commuting across the region. The physical features of the employment spatial structure identified include important policy sensitive variables such as job density, job to household ratio, land use diversity and job accessibility. Policy implications of the findings are developed and centred on recommending both the spatial structure of employment and its physical characteristics that promote more sustainable transport in the JMA.

Mental map is considered as an individual’s mental representation of his/her spatial cognition. People learn from the environment and add information to their personal mental map. It becomes important when we try to understand the relationships between one’s travel decision processes and their choice sets. The aim of this paper is to study the relationship between individuals’ activity travel patterns and their mental map by exploring people’s spatial cognition, their activity space and related factors. Two-week travel diary and mental maps were collected for the same 57 individuals in Stockholm. Respondents were asked to report their recent trip information in the travel diary and draw their familiar areas in specified maps. The specified maps, to some extent, reflect respondents’ mental maps by transferring this abstract concept from one’s mind to a visual representation. The derived mental maps were manually drawn and transferred from graph to ASCII code in ArcGIS. The visited activity locations on where people travelled during the observed period were used to construct one’s activity space. The key determinants that construct these activity space and mental map will be investigated. Marginal effect of each key variable will be calculated to understand the magnitude of influence of each variable into the spatial distribution of the given individual’s activity space and mental map. The result shows that individual’s activity space is not necessarily within individual mental map. Both activity space and mental map are correlated with individual’s travel pattern factors. Mental map has positive influence to the formation of activity space. The inference of marginal effect is useful for urban planning, promoting transport policies and analyzing the effect of transport infrastructure since it can help to locate the places that constitute individual’s activity space and mental map areas.

Human behaviour produces massive greenhouse gas emissions, which trigger climate change and more unpredictable weather conditions. The fluctuation of daily weather corresponds to variations of everyday travel behaviour. This influence, although is less noticeable, can have a strong impact on the transport system. Specifically, the climate in Sweden is becoming warmer in the recent 10 years. However, it is largely unknown to what extent the change of travel behaviour would respond to the changing weather. Understanding these issues would help analysts and policy makers incorporate local weather and climate within our policy design and infrastructure management. The thesis contains eight papers exploring the weather and climate impacts on individual travel behaviour, each addressing a subset of this topic. Paper I explores the weather impact on individual’s mode choice decisions. In paper II and III, individual’s daily activity time, number of trips/trip chains, travel time and mode shares are jointly modelled. The results highlight the importance of modelling activity-travel variables for different trip purposes respectively. Paper IV develops a namely nested multivariate Tobit model to model activity time allocation trade-offs. In paper V, the roles of weather on trip chaining complexity is explored. A thermal index is introduced to better approximate the effects of the thermal environment. In paper VI, the role of subjective weather perception is investigated. Results confirm that individuals with different socio-demographics would have different subjective weather perception even given similar weather conditions. Paper VII derives the marginal effects of weather variables on transport CO2 emissions. The findings show more CO2 emissions due to the warmer climate in the future. Paper VIII summaries the existing findings in relations between weather variability and travel behaviour, and critically assesses the methodological issues in previous studies. / <p>QC 20160516</p> / Understanding the complexity of changes of travelers’ activity-travel choices and related transport CO2 emissions due to the variation of weather and climate in Sweden (Centre för Transport Studie, projekt kod: 446) / Understanding the impacts of weather and climate change on travel behavior (Centre för Transport Studie, projekt kod: 291)

Kina är idag en av världens ledande ekonomier och placerar sig på andra plats gällande landets BNP. Under de senaste åren har tillväxten av Kinas turistnäring ökat i en snabb takt. En av de bidragande faktorerna bakom detta är att regler och gränser har luckrats upp och ger kineserna möjlighet att besöka allt fler destinationer världen över. Från år 2000 till 2012 har Kinas utrikesturism ökat från 10 miljoner resor till 83 miljoner. Med sin stadiga årliga befolkningstillväxt beräknar UNWTO, United Nations World Tourism Organization, att 100 miljoner kineser kommer att resa redan innan år 2015. Frågan är om, och hur Sverige ska satsa på att attrahera det ökade antalet resande kineser. / China is currently one of the world's leading economies and is ranked in second place in terms of GDP. In recent years, the growth of China's outbound tourism has increased rapidly. Contributing factors behind the increase are the new rules and regulations regarding outbound tourism, which gives the Chinese people the opportunity to visit more destinations worldwide. From the year of 2000 to 2012, China's outbound tourism has increased from 10 million travels to 83 million. With China’s steady annual population growth UNWTO, the United Nations World Tourism Organization, estimates that 100 million Chinese will be traveling before the year of 2015. The question is whether, and how, Sweden should attract the growing numbers of Chinese travelers

O principal objetivo deste trabalho é analisar a aplicabilidade de um minerador de dados para obter relações entre padrões de viagens encadeadas e características sócio-econômicas de viajantes urbanos. Para representar as viagens encadeadas, as viagens correspondentes a cada indivíduo do banco de dados foram codificadas em termos de seqüência de letras que indicam uma ordem cronológica em que atividades são desenvolvidas. O minerador de dados utilizado neste trabalho é árvore de decisão e classificação, uma ferramenta de análise disponível no software S-Plus. A análise foi baseada na pesquisa origem-destino realizada pelo Metrô-SP na região metropolitana de São Paulo, por meio de entrevistas domiciliares, em 1987. Um dos importantes resultados é que indivíduos que têm atributos sócio-econômicos e de viagens similares não se comportam de maneira similar; pelo contrário, eles fazem diferentes padrões de viagens encadeadas, as quais podem ser descritas em termos de probabilidade ou freqüência associada a cada padrão. Portanto, o minerador de dados deve possuir a habilidade para representar essa distribuição. A consistência do resultado foi analisada comparando-os com alguns resultados encontrados na literatura referente a análise de viagem baseada em atividades. A principal conclusão é que árvore de decisão e classificação aplicada a dados individuais, contendo encadeamento de viagem codificado e atributos socioeconômicos e de viagem, permite extrair conhecimento e informações ocultas que ajudam a compreender o comportamento de viagem de viajantes urbanos. / The main aim of this work is to analyze the applicability of a data miner for obtaining relationships between trip-chaining patterns and urban trip-makers socioeconomic characteristics. In order to represent the trip-chains, trips corresponding to each individual in the data set were coded in terms of letters indicating a chronological order in which activities are performed. Data miner applied in this work is decision and classification tree, an analysis tool available in S-Plus software package. The analysis was based on the origin-destination home-interview survey carried out by Metrô-SP in São Paulo metropolitan area. One of the important findings is that individuals having similar socieconomic and trip attributes do not behave in a similar way; on the contrary, they make different trip-chaining patterns, which may be described in term of probability or frequency associated to each pattern. Therefore, the data miner should have ability to represent that distribution. The consistency of results was analyzed by comparing them with some results found in literature related to activity-based travel analysis. The main conclusion is that decision and classification tree applied to individual data, containing coded trip-chaining and socioeconomic and trip attributes, allows extracting hidden knowledge and information that help to understand the travel behaviour of urban trip-makers.

O principal objetivo deste trabalho é analisar a aplicabilidade de um minerador de dados para obter relações entre padrões de viagens encadeadas e características sócio-econômicas de viajantes urbanos. Para representar as viagens encadeadas, as viagens correspondentes a cada indivíduo do banco de dados foram codificadas em termos de seqüência de letras que indicam uma ordem cronológica em que atividades são desenvolvidas. O minerador de dados utilizado neste trabalho é árvore de decisão e classificação, uma ferramenta de análise disponível no software S-Plus. A análise foi baseada na pesquisa origem-destino realizada pelo Metrô-SP na região metropolitana de São Paulo, por meio de entrevistas domiciliares, em 1987. Um dos importantes resultados é que indivíduos que têm atributos sócio-econômicos e de viagens similares não se comportam de maneira similar; pelo contrário, eles fazem diferentes padrões de viagens encadeadas, as quais podem ser descritas em termos de probabilidade ou freqüência associada a cada padrão. Portanto, o minerador de dados deve possuir a habilidade para representar essa distribuição. A consistência do resultado foi analisada comparando-os com alguns resultados encontrados na literatura referente a análise de viagem baseada em atividades. A principal conclusão é que árvore de decisão e classificação aplicada a dados individuais, contendo encadeamento de viagem codificado e atributos socioeconômicos e de viagem, permite extrair conhecimento e informações ocultas que ajudam a compreender o comportamento de viagem de viajantes urbanos. / The main aim of this work is to analyze the applicability of a data miner for obtaining relationships between trip-chaining patterns and urban trip-makers socioeconomic characteristics. In order to represent the trip-chains, trips corresponding to each individual in the data set were coded in terms of letters indicating a chronological order in which activities are performed. Data miner applied in this work is decision and classification tree, an analysis tool available in S-Plus software package. The analysis was based on the origin-destination home-interview survey carried out by Metrô-SP in São Paulo metropolitan area. One of the important findings is that individuals having similar socieconomic and trip attributes do not behave in a similar way; on the contrary, they make different trip-chaining patterns, which may be described in term of probability or frequency associated to each pattern. Therefore, the data miner should have ability to represent that distribution. The consistency of results was analyzed by comparing them with some results found in literature related to activity-based travel analysis. The main conclusion is that decision and classification tree applied to individual data, containing coded trip-chaining and socioeconomic and trip attributes, allows extracting hidden knowledge and information that help to understand the travel behaviour of urban trip-makers.

When a new transport service is introduced, people have to learn and familiarize themselves with the new service before they decide to adopt it. These processes are developed over time, thus produce dynamics in individuals’ behavioural responses towards the service. This affects the demand of the new service, thus affect revenues. Available studies have examined the factors influencing these responses from microeconomic perspectives. The influence of the theory-based subjective factors has not been examined empirically. Understanding these would assist transport and urban planners to design a better marketing strategy to increase the market share of the new service. A change in seasons affect individuals’ activity-travel decisions, thus produce dynamics in activitytravel patterns in different seasons. Individuals’ constraints, in a form of mandatory activities (working/studying), are influencing individuals’ decisions to participate in day-to-day nonmandatory activities (leisure and routine activities). The interdependency between travel demand, time allocation and mode choice that considers interactions between mandatory and non-mandatory activities, in different seasons is less explored. Understanding these would assist transport planners and operators to manage travel demand strategies across different seasons of the year and provide better transportation systems for all individuals. This thesis includes five papers. Paper I explores individuals’ characteristics of the quick-response and the adopters of the new public transport (PT) service and examines the temporal effects. Paper II investigates the subjective factors influencing a quick-response to the new PT service by proposing a modified attitude-behaviour framework. Paper III and IV analyse the effects of seasonal variations and individuals’ constraints on their day-to-day activity-travel decisions and patterns. Paper V analyses the attrition and fatigue in the two-week travel diary panel survey instrument. / <p>QC 20170323</p>

Indian cities have been witnessing rapid transformation due to the synergistic effect of industrialisation, flourishing-economy, motorisation, population explosion, and migration. The alarming increase in travel demand as an after effect of the transformation, and the scarcity in transport infrastructures have exacerbated urban transport issues such as congestion, pollution, and inequity. Due to the escalating cost of transport infrastructure and the scarcity of resources such as space, there has been an increasing interest in promoting sustainable transportation policy measures for the optimum use of existing resources. Such policy measures mostly target the activitytravel behaviour of individuals to bring about desired changes in the transport sector. However, the responses of individuals to most of the measures are complex or unknown. The current ‘commute trip-based’ aggregate travel demand analysis strategy followed in most of the Indian cities is inadequate for providing basic inputs to understand the activity-travel behaviour of individuals under such policy interventions. Furthermore, the current analysis strategy also ignores the activitytravel behaviour of non-workers – who include homemakers, unemployed, and retired individuals – whose inclusion to transportation planning is relevant when the proposed policies are mostly ‘citizen-centric’. Analysis of activity-travel behaviour of non-workers provide important inputs to transportation planning as their activity-travel behaviour, and responses to transportation policies are different from that of workers. However, case studies exploring the activity-travel behaviour of non-workers from Indian cities are very limited. Appraising the practical importance of this subject, the current research undertakes a comprehensive analysis of the activity-travel behaviour of non-workers from a developing country’s context. To fulfil the goal, a series of empirical analysis are conducted on a primary activity-travel weekday survey data collected from Bangalore city. The analysis provides insightful findings and interpretations consistent with a developing country’s perspective. The day-planner format of time use diary, which was observed to have satisfactory performances in developed countries, is apparently have inferior performances in a developing country’s context. Further, the face-to-face method of survey administration is observed to have higher operating and economic efficiencies compared to the drop-off and pick-up method. The comprehensive analysis of activity-travel behaviour of non-workers indicate that comparing with their counterparts in the developed world (e.g. the U.S.), non-workers in Bangalore city are observed to have lower activity participation level (in terms of time allocation and number of stops), higher dependency on walking, lower trip chaining tendency, and a distinct time-of-day preference for departing to activity locations. On the other hand, the analysis shows similarities (mode use and trip chaining) and differences (time allocation and departure time choice) with the findings of the case studies from the developing world (e.g. China). Activity-travel behaviour of non-workers belonging to low-income households is characterised by lower activity participation level, higher dependency on sustainable transport modes, and lower trip chaining propensity, compared to other two income groups (middle and high-income groups). The research also suggests that built environment measures have their highest impacts on non-workers’ travel decisions related to shopping. Finally, the joint analysis of activity participation and travel behaviour of non-workers indicate that in-home maintenance activity duration drives the time allocation and travel behaviour of non-workers, and non-workers trade in-home discretionary activity duration with travel time. The joint analysis also shows that the time spent on children’s and elders’ activity is an important time allocation of its own. Keywords: Activity-travel behaviour, Non-worker, Time Use, Income Groups, India