Figure 4 - uploaded by Willem Klumpenhouwer
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Transit agencies struggle daily with the randomness experienced by vehicles as they traverse their routes. Since reliability is one of the most important factors in determining the quality of a transit system, it is important that agencies use effective and data-informed strategies to combat this randomness as much as possible. One commonly used st...
Citations
... Route 3 southbound in 2015, with labelled time points(Klumpenhouwer, 2018) ...
... Preliminary results from this application were developed by and presented inKlumpenhouwer (2018) ...
Representing the randomness that is inherent in transportation networks is of key importance for planners, schedulers, analysts, and users. While many detailed stochastic-sensitive simulation models of transportation networks exist, they are often costly, closed, and extremely data intensive, requiring significant investment from researchers and agencies to develop and analyze. To provide a higher-order understanding of the reliability impacts of new infrastructure or operational policies, new flexible data analysis tools and modelling methods are needed. This research presents an adaptable mathematical framework for modelling transportation networks using Markov chains, and presents a new open source tool, MCRoute, which allows users to quickly prototype and analyze stochastic networks using data or theoretical distributions. Applications of the model and the tool are demonstrated in three case studies: (i) the potential impact of infrastructure improvements at a bottleneck on a railway corridor, (ii) the simulation of bus schedule adherence and bunching occurrences under various holding control scenarios, and (iii) the analysis of path-based reliability of users on a multi-stage transit journey across various modes.
... Practically, it may be that a transit agency has a set slack time at stops regardless of the optimal value, or that the value of τ i can be optimized separately using a separate technique such as the one developed by Wirasinghe (1993). Klumpenhouwer (2018) found that slack time adjustment is secondary in its effect on reliability compared with time point placement, and that sensitivity to slack time exists mainly for slack times in the range of 0 to 6 minutes. ...
... We present here an improved heuristic algorithm developed by Klumpenhouwer (2018) utilizing the following key assumptions and strategies: ...
... In Section 7, current practice is considered to use a slack time of three minutes while the heuristic algorithm allows slack time to vary between 0 and 6 minutes. This range was chosen based on the analysis of slack time sensitivity done by Klumpenhouwer (2018). ...
For a scheduled bus route adopting the holding control strategy, determining the optimal number and location of time points is considered a long-standing but elusive problem. In this paper, we take a new approach to the problem by developing a Markov Chain model to accurately capture the stochastic nature of a bus as it moves along a route in mixed traffic. Transition matrices are created using theoretical distributions of travel time calibrated with stop-to-stop travel time and dwell time data. The approach captures analytically the bus behavior while still allowing the model to be informed by the unique characteristics of the route, including travel time between stops and passenger demand. This stochastic process model mimics the physical phenomenon of Brownian motion, and it is found that the compounding nature of randomness leads to greater unreliability as the route progresses. Theoretical analysis of routes allows us to demonstrate where problem points may exist on the route and can point to locations where reliability improvements may be more effective. We develop a cost function to capture the values of time of passengers including waiting time due to early and late buses, and lost time at time points. We include operating cost capturing the increased cost of travel time caused by added control, and the improved overtime costs resulting from more consistent service. Using data from automated vehicle location (AVL) and automated passenger count (APC) systems, an operational route in Calgary, Canada is optimized using the developed model and cost function. A heuristic optimization algorithm is developed to consider high-cost stops iteratively which improves the cost function compared with existing configurations and with fewer time points.
