Metropolitan planning organizations (MPOs) have become increasingly interested in incorporating land use patterns and design ideas into transportation problems. Many design ideas under the umbrella of the New Urbanism; yet in practice they hardly get fully implemented in the standard transportation planning procedures. This research intends to contribute to the continuing debate on land use pattern-travel connection by adding further empirical evidence from the Austin, TX region. Also, it demonstrates ways to integrate land use patterns in transportation demand analysis. The study identifies 42 mixed use districts (MXD) in the Austin region and analyzes the following aspects of travel behavior in MXDs and non-MXDs: production trip rates, frequency of produced trips, network trip length, internal rate of capture, and person-miles of travel (PMT). The study contributes to transportation planning and policy making in Central Texas by providing local empirical evidence on urban form-travel connection. The study’s method and process can be of interest to a broad audience in academia and practice. / text

Activity-based travel demand models are generally considered superior to their trip-based counterparts, as activity-based models (ABMs) explicitly model individuals in contrast to the aggregate nature of trip-based models. There have been a number of comparisons between trip- and activity-based models, but these comparisons focus almost exclusively on the technical ability of the two model types, while not considering the practical benefits an ABM may or may not have to a transportation agency. This research performs a more holistic comparison between trip- and activity-based models, focused specifically on the practical differences between model types, both in terms of usability and capability for complex analysis. We use the existing Wasatch Front model as a representative trip-based model, and an ActivitySim implementation in the same area as a representative ABM. We create three hypothetical scenarios in both models: a change in land use, an improvement to commuter rail service, and an increase in remote work. We discuss the process of creating each scenario in both models, and perform several example analyses with each scenario and model. We find that many commonly-cited reasons for the lack of ABM adoption may not be as applicable as previously thought. ABMs are often considered more complicated than trip-based models, requiring more data and computational resources. While ABMs do require more input data, we found that in our case the complexity of the model and the computational resources required were similar between model types. Additionally, the ABM allows for much more intuitive and straightforward interpretation of results.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

Microsimulation is becoming more popular in transportation research. The purpose of this research is to explore the potential of microsimulation by integrating an existing activity-based travel demand model with an agent-based traffic simulation model. Differences in model precisions from the two models are resolved through a series of data conversions, and the models are able to form an iterative process similar to previous modelling frameworks. The resulting model is then used for emission modelling where the traditional average-speed model is improved by exploiting agent-based traffic simulation results. Results from emission modelling have demonstrated the advantages of the microsimulation approach over conventional methodologies that rely heavily on temporal or spatial aggregation.

Regional growth caused the emergence of traffic congestion and pollution in the past few decades, which have started to affect small urban areas. These problems are not only related to transportation system design but also to land use planning. There has been growing recognition that the relationship between land use and transportation needs to be understood and analyzed in a consistent and systematic way. Integrated urban models have recently been introduced and implemented in several metropolitan areas to systematically examine the relationship between land use and transportation. The general consensus in the field of integrated urban models is that each model has its own limitations and assumptions because they are each designed for different application purposes. This dissertation proposes a new type of methodology to integrate the regression-based land use model and the combined trip distribution-assignment transportation model that can be applied to both metropolitan areas and small urban areas. The proposed integrated land use and transportation model framework has three components: the regression-based land use model, the combined trip distributionassignment transportation model, and the interaction between these two models. The combined trip distribution-assignment model framework provides the platform to simultaneously integrate the transportation model with the land use model. The land use model is developed using an easy-to-implement method in terms of correlation and regression analysis. The interaction between the land use model and the transportation model is examined by two model frameworks: feedback model framework and simultaneous model framework. The feedback model framework solves the land use model and the transportation model iteratively. The simultaneous model framework brings the land use model and the transportation models into one optimization program after introducing the used path set. Both the feedback model and the simultaneous model can be solved to estimate link flow, origin-destination (OD) trips, and household distribution with the results satisfying network equilibrium conditions. The proposed integrated model framework has an “affordable and easy-toimplement” land use model; it can be performed in small urban areas with limited resources. The model applications show that using the proposed integrated model framework can help decision-makers and planners in preparing for the future of their communities.

In recent years, various researches are under progress to enhance the quality of the travel survey. These researches were mainly performed with the aid of GPS technology. Initially the researches were mainly focused on the vehicle travel mode due to the availability of GPS technology in vehicle. But, nowadays due to the accessible of GPS devices for personal uses, researchers have diverted their focus on personal mobility in all travel modes. This master’s thesis aimed at developing a mechanism to extract one type of travel survey information particularly travel mode from collected GPS dataset. The available GPS dataset is collected for travel modes of walk, bike, car, and public transport travel modes such as bus, train and subway. The developed procedure consists of two stages where the first is the dividing the track trips into trips and further the trips into segments by means of a segmentation process. The segmentation process is based on an assumption that a traveler switches from one transportation mode to the other. Thus, the trips are divided into walking and non walking segments. The second phase comprises a procedure to develop a classification model to infer the separated segments with travel modes of walk, bike, bus, car, train and subway. In order to develop the classification model, a supervised classification method has been used where decision tree algorithm is adopted. The highest obtained prediction accuracy of the classification system is walk travel mode with 75.86%. In addition, the travel modes of bike and bus have shown the lowest prediction accuracy. Moreover, the developed system has showed remarkable results that could be used as baseline for further similar researches.

Travel demand models are a useful tool to assess transportation projects. Within travel demand, long-distance trips represent a significant amount of the total vehicle-kilometers travelled, in contrast to commuting trips. Consequently, they pay a relevant role in the economic, social and environmental impacts of transportation. This paper describes the development of a microscopic long-distance travel demand model for the Province of Ontario (Canada) and analyzes the sensitivity to the implementation of a new high speed rail corridor. Trip generation, destination choice and mode choice models were developed for this research. Multinomial logit models were estimated and calibrated using the Travel Survey for Residents in Canada (TSRC). It was complemented with location-based social network data from Foursquare, improving the description of activities and diverse land uses at the destinations. Level of service of the transit network was defined by downloading trip time, frequency and fare using the planning service Rome2rio. New scenarios were generated to simulate the impacts of a new high speed rail corridor by varying rail travel times, frequencies and fares of the rail services. As a result, a significant increase of rail modal shares was measured, directly proportional to speed and frequency and inversely proportional to price.