In motion –a traffic program for Nacka is the result of a survey of the traffic system and transportation situation in Nacka and suggests possible development opportunities. A new traffic strategy will be developed in the municipality of Nacka the coming years, which is the reason for this study: to map the current situation, identify strengths and weaknesses, needs and opportunities. This program suggests possible development opportunities and gives an historical overview of the transportation system, the current situation and an analysis of different transportation modes. A mapping of other municipalities’ traffic strategies or programs have been done and included into the program as well as a prognosis for the coming years in the municipality. The work has identified goals and contains a suggestion for an action plan. The suggestions can serve as a basis for further development to fulfill the set goals in the comprehensive plan, and also more specific in the transportation plan, to get a better interlinked and efficient transportation system in Nacka.

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This study examined the factors related to travel mode choices in the Dallas-Fort Worth Metropolitan Area. Changes in population, life style and economy of the Dallas-Fort Worth region over the last few decades demand a careful re-examination of travel demand tools and methods. The purpose of the study was to provide an understanding of transportation modal choice in the region. Those demographic variables best predicting the choices were identified. The Home Interview Survey, a set of disaggregate data from the 19 84 North Central Texas Council of Governments (NCTCOG) Regional Travel Survey, was analyzed using logistic regression. The major findings of the research indicate that about 97 percent of the travelers in the study area used private cars and 3 percent used public transit. Household income and cars-vans were significant explanatory variables. The impact of household income and number of car-vans available upon an individual's decision for travel mode choice were very important. The number of car-vans available in the household, and age of respondents were significant predictors in travel mode. Household members with incomes of $30,000 to $39,000 and those with incomes of at least $50,000 tended to use more private cars than did other income groups. Also, household members with incomes below $9,000 used more public transportation. People reporting a lower preference for cars were younger than 26 years or older than 55 years of age.

L'activité physique a un impact important sur la santé populationnelle, et les comportements de transport constituent une partie substantielle de l'activité physique totale. Ce travail de thèse a pour objectif d'améliorer les mesures de l'activité physique liées au transport et d'utiliser ces nouvelles mesures dans des études de cas empirique sur l'activité physique liée au transport des adultes âgés de 35 à 83 ans résidant en Ile-de-France. Méthodes: Des données GPS et d'accéléromètre ont été collectée dans le cadre de " RECORD étude GPS " pour 236 participants. L'Enquête Globale Transport a recueilli des données sur une population de 21332 participants sur une période d'un jour. Les méthodes statistiques utilisées incluent Random Forests, des régressions binomiales négatives; et des systèmes d'information géographique. Résultats Les unités de temps plus courtes ont donné lieu à des estimations d'activité physique beaucoup plus importantes. Nous avons observé 18,9 min T-APMV par jour en moyenne dans cet échantillon représentatif de l'Ile-de -France. Les participants ayant un niveau d'éducation plus élevé ont plus de T-APMV que les participants moins instruits. Les personnes ayant un revenu du ménage plus élevé ont moins T-APMV par jour. Conclusion Ce travail renforce les recommandations de la littérature d'une harmonisation plus poussée des indicateurs de l'activité physique basés sur l'accéléromètre. Des interventions concernant les modes de transport peuvent avoir un effet important sur l'activité physique. / Background Physical activity has an important impact on various health outcomes, and transport accounts for a substantial part of total physical activity. This PhD work aimed to improve measures of transport-related physical activity and to report empirical findings on the transport-related physical activity of adults aged 35 to 83 years living in Ile-de-France. Methods The RECORD GPS Study collected GPS and accelerometer data for 236 participants over a 7-day period, resulting in the observation of 7425 trips. The Enquête Globale Transport) collected data over one day, resulting in the observation of 82084 trips for 21332 participants. The methods used include random forest prediction models, geographical information systems, and negative binomial regressions. Results Shorter epochs (time units) resulted in considerably larger estimates of moderate-to-vigorous physical activity MVPA. This finding supports calls from the literature for further harmonization of accelerometer-based indicators of physical activity. We observed an average 18.9 minutes of daily T-MVPA (95% confidence interval: 18.6; 19.2 minutes). Participants with a higher level of education did more T-MVPA than their less educated counterparts. In contrast, people with a higher household income did less T-MVPA per day. Conclusion This PhD work was the first study to combine a very detailed dataset - including GPS, accelerometer, and mobility behaviour data - and a large-scale transport survey. Transport interventions could have an important impact on physical activity for this population.

Thanks to the development in recent years, the placement of miniaturized sensors such as accelerometers, gyroscopes, magnetometers, global positioning system receivers (GPS), microphones or others to commercially sold smartphones is increasing. Use of these sensors (which are to be found in the smartphone) for human activity recognition with health care improvement in mind is a discussed theme. Advantages of the use of smartphone for human movement monitoring lies in the fact that it is a device that the person measured carries with them and there are no additional costs. The disadvantages are a limited storage and battery. Therefore, only accelerometer, gyroscope, magnetometer, and microphone were chosen because their combination achieves best results. GPS sensor was excluded for its lack of reliability in sampling and for being energy demanding. Features were computed from the measured data and used for learning of the classification model. The highest accuracy was achieved with the use of a machine learning method called Random Forest. The main goal of this work was to create an algorithm for transportation mode recognition using signals sensed by a smartphone. The created algorithm succeeds in classification of walk, car, bus, tram, train, and bike in 97.4 % with 20 % holdout validation. When tested on a new set of data from the tenth volunteer, the resulting accuracy counted as average form classification recall for each transportation mode reached 90.49 %.

Encouraging a more sustainable commuter mode shift and improving urban transportation systems have the potential to reduce anthropogenic greenhouse gas emissions (GHGs), a major contributor to climate change. Replacing some single-occupancy vehicle (SOV) trips with alternative modes of transportation, such as public transit, walking, or bicycling, represents one approach to begin reducing transportation-related emissions. Collectively, these shifts in transportation patterns would help to reduce the negative social, economic, and environmental costs associated with high rates of personal vehicle use. Employer-provided benefits programs have the potential to influence commuter behavior by making sustainable, alternative commuting choices a more convenient and economically feasible option. In addition, the implementation of these programs can have broader benefits such as helping to achieve municipal and regional sustainability goals and improving community members’ physical health and quality of life. This study applies qualitative and quantitative analysis to investigate employee commuting behavior in response to employer-provided benefits in Kendall Square, a neighborhood in Cambridge, Massachusetts. The employee and employer survey data analyzed in this research was originally collected by TransAction, a transportation consulting firm in the Greater Boston region. To comply with Cambridge’s Parking and Transportation Demand Management (PTDM) Ordinance, TransAction works closely with companies to coordinate and manage onsite commuter services programs and prepare PTDM Annual Reports. The primary objectives of this research are: 1) to introduce the multi-dimensional benefits of re-envisioning the existing transportation networks; 2) to determine the influence of employer-provided commuting benefits on employee commute mode choice; 2) to provide guidance for employers interested in promoting a more sustainable employee commute mode split; and 3) to present the broader implications and applications of this research for employers, municipalities, and coordinating agencies interested in reducing SOV commuting trips and promoting the use of more sustainable, alternative modes of transportation. Overall, the findings from this research illustrate that while employers have the ability to promote more sustainable transportation choices among their employees, the complexity of the transportation network (and its interdependencies) requires collaboration among all stakeholders to initiate widespread, comprehensive changes.

Energy and environmental impacts are two factors that will influence urban region composition in the near future. One emerging issue is the effect on water usage resulting from changes in regional or urban transportation trends. With many regions experiencing stresses on water availability, transportation planners and users need to combine information on transportation-related water consumption for any region and assess potential impacts on local water resources from the expansion of alternative transportation modes. This thesis will focus on use-phase water consumption factors for multiple vehicle modes, energy and fuel pathways, roads, and vehicle infrastructure for a given transportation network. While there are studies examining life cycle impacts for energy generation and vehicle usage, few repeatable models exist for assessing overall water consumption across several transportation modes within urban regions. As such, the question is: is it possible to develop a traceable decision support model that combines and assesses water consumption from transportation modes and related mobility infrastructure for a given mobility network? Based on this, an object-oriented system model of transportation elements was developed using the Systems Modeling Language (SysML) and Model-Based Systems Engineering principles to compare water consumption across vehicle modes for assessing the resiliency of existing infrastructure and water resources. To demonstrate the intent of this model, daily network usage water consumption will be analyzed for current and alternative network scenarios projected by policies regarding the expansion of alternative energy. The model is expected to show variations in water consumption due to fluctuations in energy pathways, market shares, and driving conditions, from which the model should help determine the feasibility of expanding alterative vehicles and fuels in these networks. While spatially explicit data is limited compared to the national averages that are used as model inputs, the analytical framework within this model closely follows that of existing assessments and the reusable nature of SysML model elements allows for the future expansion of additional transportation modes and infrastructure as well as other environmental analyses.