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This project advances the current understanding of intraurban rail passengers and their travel experiences to help rail industry leaders tailor policy approaches to fit specific, relevant segments of their target population. Using a Q-sorting technique and cluster analysis, preliminary research identified five perspectives occurring in a small sample of rail passengers who varied in their frequency and location of rail travel as well as certain sociodemographic characteristics. Revealed perspectives (named to capture the gist of their content) included “Rail travel is about the destination, not the journey”; “Despite challenges, public transport is still the best option”; “Rail travel is fine”; “Rail travel? So far, so good”; and “Bad taste for rail travel.” This paper discusses each of the perspectives in detail and considers them in relation to tailored policy implications. An overarching finding from this study is that improving railway travel access requires attention to physical, psychological, financial, and social facets of accessibility. For example, designing waiting areas to be more socially functional and comfortable has the potential to increase ridership by addressing social forms of access, decreasing perceived wait times, and making time at the station feel like time well spent. Even at this preliminary stage, the Q-sorting technique promises to provide a valuable, holistic, albeit fine-grained, analysis of passenger attitudes and experiences that will assist industry efforts in increasing ridership.

Traffic safety studies need more than what the current micro-simulation models can provide, as they presume that all drivers exhibit safe behaviors. Therefore, existing micro-simulation models are inadequate to evaluate the safety impacts of managed motorway systems such as Variable Speed Limits. All microscopic traffic simulation packages include a core car-following model. This paper highlights the limitations of the existing car-following models to emulate driver behaviour for safety study purposes. It also compares the capabilities of the mainstream car-following models, modelling driver behaviour with precise parameters such as headways and time-to-collisions. The comparison evaluates the robustness of each car-following model for safety metric reproductions. A new car-following model, based on the personal space concept and fish school model is proposed to simulate more accurate traffic metrics. This new model is capable of reflecting changes in the headway distribution after imposing the speed limit from variable speed limit (VSL) systems. This model can also emulate different traffic states and can be easily calibrated. These research findings facilitate assessing and predicting intelligent transportation systems effects on motorways, using microscopic simulation.