analysis of ongoing empirical data collection and recommendations for future

Analysis of ongoing empirical data collection and

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analysis of ongoing empirical data collection and recommendations for future empirical research. 2. The Model We built our agent-based model using Netlogo [16], representing commuter agents choosing a mode that minimizes the disutility of their trip, with the variables comprising their utility function being monetary cost, time, and safety. In this section we describe the model’s agents and landscape, their attributes, and the decision rules by which they interact and change. We model our agents and environment after four neighborhoods in the Chicago Metropolitan Region with access to a train station of the Chicago Transit Authority (CTA) system. The neighborhoods represent combinations of two important factors in mode choice: land-use mix and density, which is also strongly associated with the transit type and level of service provided, and income. The neighborhoods are ( Figure 1 ): (1) Evanston (higher income, higher-density mixed use) at the Davis station, (2) Skokie (higher income, lower-density single use) at the Dempster-Skokie station, (3) Cicero
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International Journal of Transportation Vol.4, No.1 (2016) Copyright 2016 SERSC 5 (lower income, lower-density single use) at the 54 th /Cermak station, and (4) Pilsen (lower income, higher-density mixed use) at the Damen station. Figure 1. Location of the Four Chicago Neighborhoods: Evanston, Skokie, Cicero and Pilsen. CTA Train Network in Color Our model represents six modes: (1) walk to train, (2) bike to destination, (3) bike to train, (4) bus to destination, (5) drive to destination, and (6) shuttle to train. We excluded other modes ( e.g. , carpooling or driving to station) because they would increase the computational complexity of the model without adding insight into our question of how shuttles would support transit and encourage commuters to shift from driving all the way to work. Other modes are also variations on the six we implemented; in the case of carpooling, for example, driving all the way to work would be a close approximation. All modes reflect frequencies and commute times for travel to work and school in the Chicago “Loop” during morning rush hour. The model is informed by the literature and publicly available data on commuter behavior, land use, and transit service, and commuters’ preferences for different aspects of transportation (summarized in Table 1, and detailed in each section below). A parallel effort to survey commuters in our four target neighborhoods has been completed. The next stage of the project is to inform our model with response
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International Journal of Transportation Vol.4, No.1 (2016) 6 Copyright 2016 SERSC data to test the robustness of the insights we derive here and to provide more specific policy recommendations. At this stage of our research, the model provides estimates of impacts under the best circumstances that can be generalized to a range of conditions, and generates questions to address with a future data-based version of our model.
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  • Fall '14
  • loop, International Journal of Transportation

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