PANDA: a software tool for improved train dispatching with focus on passenger flows

Abstract

We introduce the decision support tool PANDA (Passenger Aware Novel Dispatching Assistance). Our web-based tool is designed to provide train dispatchers with detailed real-time information about the current passenger flow and the multidimensional impact of waiting decisions in case of train delays. After presenting the algorithmic background and PANDA's main features, we show how it can be utilized in a typical use case scenario for train dispatchers. Besides its practical value for train dispatchers, the framework can be used to systematically study scientific questions. Exemplarily, we use our software to experimentally analyse the influence of waiting decisions on realistic passenger flows of Deutsche Bahn. In a first experiment, we evaluate PANDA's potential benefit for passengers. Our findings indicate that a remarkable reduction in total delay might be possible in comparison to current practice. In two additional experiments, we investigate the timing aspect of waiting decisions. Our observations suggest that the timing of waiting decisions is of crucial importance and that a carefully implemented early rerouting strategy has a significant potential to reduce resulting delays of passengers.

A review on railway delay management

“…The existing literature often distinguishes between a macroscopic and a microscopic view to differentiate between DM and rescheduling. Sometimes it is mentioned that DM models get their information for the decision-making process in an online or offline manner, see, e.g., Schmidt (2013), Rückert et al (2017). However, not all information statuses are covered with that, i.e., stochastic models are not considered.…”

“…In a numerical study the different objectives and dispatching decisions are varied and it turns out that the interaction of simulation and optimization leads to decreased passenger disutility. Rückert et al (2017) introduce PANDA, a web-based decision support tool for dispatchers. PANDA reflects real-time information on passenger flows and evaluates the effects of wait-depart decisions in the network.…”

“…The other third consists of models with incomplete information, usually solved with heuristics. Stochastic Berger et al (2011a, b), Biederbick and Suhl (2007), Ginkel and Schöbel (2007), Kliewer and Suhl (2011), König and Schön (2020), Lemnian et al (2016), Rückert et al (2017), Schachtebeck and Schöbel (2010), Schöbel (2001Schöbel ( , 2009, Schön and König (2018), Suhl et al (2001) Greece Bender et al (2013) Japan Kanai et al (2011) Netherlands Corman et al (2012Corman et al ( , 2017, , Dollevoet et al (2012Dollevoet et al ( , 2015 1 3…”

Dynamic Passenger Assignment for Major Railway Disruptions Considering Information Interventions

“…In general, frequency-based models are suitable for such transit systems where the operations are so frequent that passengers can be assumed to board the first train when waiting at a station. While in railway systems where the operation frequency is relatively low, schedule-based models are commonly used, like Binder et al (2017a) and Rückert et al (2017).…”

“…Kanai et al (2011), Dollevoet et al (2012), Sato et al (2013), and Corman et al (2017) consider the alternative choices that passengers might have, where the capacities of vehicles are assumed to be infinite. While most papers consider the train delays as known input to the optimization, Rückert et al (2017) observe the train delays in real time, and predict the passenger flows due to any possible wait-depart decisions to help the dispatchers make informed decisions. In these papers, train orders can be changed, but no trains are delayed significantly or cancelled/short-turned, which however take place during disruptions.…”