Current Research
Modal Shift Optimized Transit Route Design
Increasing modal shift from auto to transit is a desirable objective for achieving better system-w
ide mobility. Automobile users might consider shifting to transit if they have an affordable and a good quality service available. Given the constrained fleet and crew resources of transit companies due to the costly transit operations, it is essential to optimize various transit network elements and maximize the quality of the offered service in order to realize the maximum transit ridership. Transit route design is concerned with determining various design elements, such as service frequency and fleet size. In addition, as the first points of contact between passengers and transit service, transit stop locations and spacing are crucial design elements in terms of ensuring maximum service coverage and reasonable accessibility. The route design process currently depends on the experience of the planner, in which a set of service standards and guidelines are followed to specify the minimum acceptable level of service. That is succeeded by generating and examining a number of design scenarios based on different combinations of design elements in order to select the best alternative. However, current design approaches are criticized for yielding suboptimal designs in terms of maximizing modal shift from auto users toward transit. Another limitation is being unable to capture the effect of the proposed design on the existing demand along other transit routes and whether the attracted demand is resulting from a mode shift or a transit route shift.
This research propose
s a modelling framework for generating optimal transit route designs that maximize demand attraction. The framework builds upon and extends the powerful capabilities of the existing microsimulation learning-based approach for transit assignment (MILATRAS), to tackle the route design problem. MILATRAS currently models transit assignment given a fixed set of transit routes and transit demand. The proposed work will add to MILATRAS a modal shift module to enable evaluating the impact of transit investments that usually target auto drivers. Modal shift barriers such as attitudes and habit formation will be captured in the model by specifying a threshold or inertia against shifting between modes. The proposed approach to transit route design problem is divided into two main stages. Firstly, tools will be developed to generate a set of competing candidate route designs based on shortest path algorithms, service guidelines and constraints regarding several design aspects such as minimum stop spacing and maximum route length. Secondly, an optimization tool will be developed to select the optimum transit route alignment and design characteristics, considering demand variability among both modes and routes.
Such approach is more desirable for transit network planning than the previous approaches in terms of its practical realism for real-world applications. Further, it will not only optimize the overall cost for both passengers and the operator, but also the objective function will incorporate the desired level of modal shift between both competing modes. Furthermore, the interaction between the level of service of the transit route and the variability of demand will be captured.
“The more people who use transit, the better the ride for the driving public;
public transit affects everyone!”
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