@misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Analysing and visualising bike-sharing demand with outliers}, series = {Discover Data}, journal = {Discover Data}, number = {1}, issn = {2731-6955}, doi = {10.1007/s44248-023-00001-z}, abstract = {Bike-sharing is a popular component of sustainable urban mobility. It requires anticipatory planning, e.g. of station locations and inventory, to balance expected demand and capacity. However, external factors such as extreme weather or glitches in public transport, can cause demand to deviate from baseline levels. Identifying such outliers keeps historic data reliable and improves forecasts. In this paper we show how outliers can be identified by clustering stations and applying a functional depth analysis. We apply our analysis techniques to the Washington D.C. Capital Bikeshare data set as the running example throughout the paper, but our methodology is general by design. Furthermore, we offer an array of meaningful visualisations to communicate findings and highlight patterns in demand. Last but not least, we formulate managerial recommendations on how to use both the demand forecast and the identified outliers in the bike-sharing planning process.}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Analysing and visualising bike sharing demand with outliers}, series = {arXiv}, journal = {arXiv}, doi = {10.48550/arXiv.2204.06112}, pages = {1 -- 45}, abstract = {Bike-sharing is a popular component of sustainable urban mobility. It requires anticipatory planning, e.g. of terminal locations and inventory, to balance expected demand and capacity. However, external factors such as extreme weather or glitches in public transport, can cause demand to deviate from baseline levels. Identifying such outliers keeps historic data reliable and improves forecasts. In this paper we show how outliers can be identified by clustering terminals and applying a functional depth analysis. We apply our analysis techniques to the Washington D.C. Capital Bikeshare data set as the running example throughout the paper, but our methodology is general by design. Furthermore, we offer an array of meaningful visualisations to communicate findings and highlight patterns in demand. Last but not least, we formulate managerial recommendations on how to use both the demand forecast and the identified outliers in the bike-sharing planning process.}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Identifying and responding to outlier demand in revenue management}, series = {European Journal of Operational Research}, volume = {293}, journal = {European Journal of Operational Research}, number = {3}, doi = {10.1016/j.ejor.2021.01.002}, pages = {1015 -- 1030}, abstract = {Revenue management strongly relies on accurate forecasts. Thus, when extraordinary events cause outlier demand, revenue management systems need to recognise this and adapt both forecast and controls. Many passenger transport service providers, such as railways and airlines, control the sale of tickets through revenue management. State-of-the-art systems in these industries rely on analyst expertise to identify outlier demand both online (within the booking horizon) and offline (in hindsight). So far, little research focuses on automating and evaluating the detection of outlier demand in this context. To remedy this, we propose a novel approach, which detects outliers using functional data analysis in combination with time series extrapolation. We evaluate the approach in a simulation framework, which generates outliers by varying the demand model. The results show that functional outlier detection yields better detection rates than alternative approaches for both online and offline analyses. Depending on the category of outliers, extrapolation further increases online detection performance. We also apply the procedure to a set of empirical data to demonstrate its practical implications. By evaluating the full feedback-driven system of forecast and optimisation, we generate insight on the asymmetric effects of positive and negative demand outliers. We show that identifying instances of outlier demand and adjusting the forecast in a timely fashion substantially increases revenue compared to what is earned when ignoring outliers.}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Detecting outlier demand in railway networks}, series = {5th AIRO Young Workshop at Universit{\`a} degli Studi di Napoli Federico II, Italy}, volume = {5}, journal = {5th AIRO Young Workshop at Universit{\`a} degli Studi di Napoli Federico II, Italy}, pages = {31}, abstract = {Transport service providers, such as airlines and railways, often use revenue manage-ment to control offers and demand in mobility networks. Such systems rely on accu-rate demand forecasts as input for the underlying optimisation models. When changes in the market place cause demand to deviate from the expected values, revenue man-agement controls no longer fit for the resulting outliers. Analysts can intervene if they deem the demand forecast to be inaccurate. However, existing research on judgemen-tal forecasting highlights fallibility and bias when human decision makers are not sys-tematically supported in such tasks. This motivates the need for automated alerts to highlight outliers and thereby support analysts. Network effects complicate the problem of detecting outlier demand in practice. Pas-sengers often book travel itineraries that stretch across multiple legs of a network. Thereby, they requests products that require multiple resources - seats on several legs. In the rail and long-distance coach industries, the large number of possible itiner-aries makes it likely for outlier demand to affect multiple legs. At the same time, outli-er demand from a single itinerary may be difficult to recognise as it mixes with regular demand on individual legs. To support outlier detection in transport networks, we pre-sent a method to aggregates outlier detection across highly correlated legs. We propose to use the results of this analysis to construct a ranked alert list that can support ana-lyst decision making. We show that by aggregating we are able to improve detection performance compared to considering each leg in isolation.}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Detecting outlying demand in multi-leg bookings for transportation networks}, series = {31st European Conference on Operational Research (EURO) Proceedings}, volume = {31}, journal = {31st European Conference on Operational Research (EURO) Proceedings}, pages = {28}, abstract = {Network effects complicate transport demand forecasting in general and outlier detection in particular. For example, a sudden increase in demand for a specific destination will not only affect the legs arriving at that destination, but also connected legs nearby in the network. Network effects are particularly strong when service providers, such as railway or coach companies, offer many multi-leg itineraries. In such situations, automated alerts can help analysts to adjust demand forecasts and enable reliable planning. In this presentation, we outline a novel two-step method for automatically detecting outlying demand from transportation network book� ings. The first step clusters network legs according to the observed booking patterns. The second step identifies outliers within each cluster to create a ranked alert list of affected legs. We illustrate the method using empirical data obtained from Deutsche Bahn. In addition, we present a detailed simulation study that quantifies the improvement from the clustering step and implications of ranking to measure the criticality of the outliers. Our results show that the proposed approach outperforms independently analysing each leg, especially in highly connected networks where most passengers book multi-leg itineraries.}, language = {en} } @misc{RennieCleophasSykulskietal., author = {Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam and Dost, Florian}, title = {Outlier detection in network revenue management}, series = {OR Spectrum}, journal = {OR Spectrum}, issn = {0171-6468}, doi = {10.1007/s00291-023-00714-2}, abstract = {This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn.}, language = {en} }