Travel to outdoor recreational spaces belongs to a general class of research questions for understanding destination and travel mode choices. In travel demand modelling, discrete choice models (DCMs) have been applied to understand and predict a wide range of choices, such as how people choose among alternative destinations for jobs, homes, shopping, personal services etc. Surprisingly, DCMs have rarely been used to understand and model travel to outdoor recreational spaces. In the current literature for modelling travel to outdoor recreational spaces, the established models are Negative Binomial Regression (NBR) models, such as what was used in the UK NEA studies. However, these NBR models were developed to assess the effects of travel to outdoor recreational spaces at a national level, and they are not intended for assessing choices of individual sites. One reason for this is, as identified by previous studies, is that compared with the DCMs, the NBR models have certain limits on estimating people's choice behaviours. There is, therefore, no existing model that can represent and predict how people choose to travel to outdoor recreational spaces. Given the importance of outdoor recreational activities to urban land use planning and public health, this is a clear gap in the field. The aim of this study is to develop a new travel demand model capable of representing and predicting travel to individual outdoor recreational sites. This is achieved by answering four main research questions: First, how to build the new model for outdoor recreational travel? Secondly, is the estimation accurate enough? Thirdly, to what extent can the new model be transferred to destinations outside the case study area? And, finally, how can city planners and designers use this new method? The new model draws upon ideas from random utility theory that underlies the conventional travel demand models to represent trip generation, trip distribution and mode choice. This research follows the standard modelling procedure: data collection and preliminary analysis, model calibration, model validation and model application. The data are collated from a wide range of sources that, importantly for model transferability, cover all areas in England. The new model has been calibrated for a case study area which spanned 14 selected districts in the North-West region. Validation of the new model is based on estimating the numbers of trips to two outdoor recreational sites (Wigg Island and Wigan Flashes) and to nine English National Parks where data on visitor trips exist. In the final stage of the research, the new model is applied to estimate the changes that would arise from planning and design interventions in existing (Wigg Island and Moore Nature Reserve) and proposed (Arpley Country Park) sites. At the end of this process, it is possible to show that the new model can predict the number of trips to individual destinations and that the model can be transferred to other outdoor recreation sites. Furthermore, the new model presented here is capable of predicting the changes in the volume and catchment of visits to an existing green space after land use planning or urban ecological interventions. This is a completely new theoretical model that is focused on understanding and quantifying the travel choices to outdoor recreation sites, which can inform decision makers by forecasting changes in outdoor recreational travel demand, according to different planning scenarios.

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Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

Understanding how households make decisions with regards to their vehicle fleet based on their demographics, socio-economic status and travel patterns is critical for managing the financial, economic, social and environmental health of cities. Vehicle fleets therefore form a component of the Integrated Land Use, Transportation and Environment (ILUTE) modelling system under development at the University of Toronto. ILUTE is a year-by-year agent-based microsimulation model of demographics, land use and economic patterns, vehicle fleet decisions and travel choices in the Greater Toronto and Hamilton Area. This thesis extends previous work that modelled the quantity, class and vintage of vehicles in ILUTE households. This revised model offers three key improvements: transaction decisions are made sensitive to travel patterns, fuel costs are better represented, and vehicle purchases are considered in the context of the overall household budgeting. Results are promising, but further model validation is required. Potential extensions of the research are discussed.

With reliable models to forecast travel demand, traffic planners and decision-makers can be assisted in choosing the best solutions to obtain traffic performance goals. Practitioners have traditionally been relying on infrequent, costly and respondent pressurized travel surveys as their main source of data for these models. The drawbacks of the data collection method highlight a need to search for alternative sources of data used for the purpose. One such source is public transport “tap-in” data. This thesis executed a case study with the target of improving the travel demand model of Norrköping via public transport data. An algorithm that estimates the alighting station of travellers was applied to a data set provided by the public transport operator of the city. By allocating the OD-demand from stations to the traffic analysis zones used in the model a straightforward integration method using the tap-in estimate as a reference matrix could be used. The target with the method was to redistribute the demand in such a way that the public transport demand approached the tap-in estimate but that the total demand for all modes for the OD-pair remained unchanged. The results gave some indication that the integration of tap-in data improved the model performance from the perspective of public transports. In a regression analysis comparing the number of entries per station the integration of tap-in data increased the correlation coefficient from 0,845 to 0,864. Further was the performance for other transport modes seemingly not worsened by the integration of tap-in data. Finding an allocation procedure that was generic but still accurate proved complex. Further were drawbacks with the integration procedure highlighted where the method executed affected the results of the model, not its behaviour. The consequence of this is that, though the model might be an accurate representation of the current state of traffic, it is difficult to execute the same procedure when investigating future states. Still, the thesis stressed some of the potential for public transport data in modelling contexts, where the role of the data, given the procedure executed, still is of complementary character to travel surveys.

Shared modes of transport, including car sharing, have been pointed out as one way of reducing private car use, contributing to an efficient transportation system that fulfills societal and environmental goals.Previous studies show that a share of car sharing users sells or refrains from acquire a new vehicle, when entering car sharing. Also, on average, car sharing has been shown to reduce Vehicle Kilometers Traveled (VKT) by car among the users.This study is conducted in three parts. First, a literature review of the effects of car sharing on travel behavior and car ownership is presented. Second, an implementation of car sharing in an existing transport model is described and the estimated effects are analyzed in relation to the findings in the literature study. In the final part, the car sharing module is reformulated to model a station-based car sharing system, where the distances to car sharing vehicles are used to distribute the effect of car sharing on car ownership spatially.This work contributes to the field by connecting the results from previous research about car sharing with practical transport modelling. The model of the station-based car sharing system is a useful tool for planners when considering the placement of car sharing stations. Also, this study provides an updated literature review covering findings of the effects of car sharing on travel behaviour and car ownership.Keywords: car sharing, station-based car sharing, travel demand modelling, vehicle ownership modelling, four-step model

Transport is one of the basic needs of a functioning society. Unfortunately, transport also pollutes our cities and release greenhouses gases. Driverless technology is a technology predicted to disrupt the future transport system, and perhaps change how we travel from private cars to shared vehicles. This study focuses on the aspect of privately owned versus shared driverless vehicles, to create more knowledge of how the future transport system will look. A utility-based demand model is used to find the demand for private and shared transport when driverless vehicles are available. The utility of different transport options is estimated by looking at earlier studies about the performance of driverless cars, driverless buses and shared driverless taxis, which is used as input for the utility model. The results indicate that driverless technology will not be a catalyst that makes transport go from private to shared. While driverless buses can improve public transport, and shared driverless taxis outcompete current taxis, driverless technology will also improve private vehicles. The results in this study imply that the sustainability improvements earlier reports have predicted with a high use of shared driverless transportation might not materialise unless efforts are done to increase use of shared transportation. / Transport är ett av de grundläggande behoven för ett välfungerande samhälle. På samma gång släpper transporter ut både växthusgaser och skadliga partiklar. Självkörande teknik är något som förväntas revolutionera framtidens transportsystem, förhoppningen är att de ska förändra hur folk reser från privata bilar till delade transporter. Denna studie fokuserar på den förhoppningen. Kommer framtidens transporter ske i privata självkörande fordon eller delade självkörande fordon och vad i sin tur betyder det för framtidens transportsystem? Med en nyttobaserad efterfråge- och bilinnehavsmodell modelleras efterfrågan av självkörande delade taxis, självkörande bussar och självkörande privatbilar. Resultaten indikerar att självkörande teknik inte nödvändigtvis kommer vara en katalysator som får människor att sluta äga och använda privatbilar. Självkörande bussar kan göra kollektivtrafiken bättre, och självkörande delade taxibilar kommer troligtvis användas mer än dagens taxis. Men självkörande privatbilar kommer också ha många fördelar, och de som äger dem kommer dessutom troligtvis köra längre sträckor än dagens bilister. Resultatet av denna rapport indikerar därför att de stora förväntningarna som finns på självkörande teknik gällande delade transporter kan vara felaktiga, om inte andra åtgärder också görs för att öka delning. Att delningen inte ökar gör också att de hållbarhetsförbättringar som vissa tidigare rapporter förutspått inte nödvändigtvis kommer ske.