Studying Solutions of the p-Median Problem for the Location of Public Bike Stations

The use of bicycles as a means of transport is becoming more and more popular today, especially in urban areas, to avoid the disadvantages of individual car traffic. In fact, city managers react to this trend and actively promote the use of bicycles by providing a network of bicycles for public use and stations where they can be stored. Establishing such a network involves the task of finding best locations for stations, which is, however, not a trivial task. In this work, we examine models to determine the best location of bike stations so that citizens will travel the shortest distance possible to one of them. Based on real data from the city of Malaga, we formulate our problem as a p-median problem and solve it with a variable neighborhood search algorithm that was automatically configured with irace. We compare the locations proposed by the algorithm with the real ones used currently by the city council. We also study where new locations should be placed if the network grows.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research was partially funded by the University of Málaga, Andalucı́a Tech, the Spanish MINECO and FEDER projects: TIN2014- 57341-R, TIN2016-81766-REDT, and TIN2017-88213-R. C. Cintrano is supported by a FPI grant (BES-2015-074805) from Spanish MINECO

An approach to modeling bike-sharing systems based on spatial equity concept

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SOLUTION TO FLEET SIZE OF DOCKLESS BIKE-SHARING STATION BASED ON MATRIX ANALYSIS

Aiming at the problems of the lack of reasonable judgment of fleet size and non-optimization of rebalancing for dockless bike-sharing station, based on the usage characteristics of dockless bike-sharing, this paper demonstrates that the Markov chain is suitable for the analysis of the fleet size of station. It is concluded that dockless bike-sharing Markov chain probability limit state (steady-state) only exists and is independent of the initial probability distribution. On that basis, this paper analyses the difficulty of the transition probability matrix parameter statistics and the power method of the bike-sharing Markov chain, and constructs the transition probability sparse matrix in order to reduce computational complexity. Since the sparse matrices may be reducible, the rank-one updating method is used to construct the transition probability random prime matrix to meet the requirements of steady-state size calculation. An iterative method for solving the steady-state probability is therefore given and the convergence speed of the method is analysed. In order to improve the practicability of the algorithm, the paper further analyses the construction methods of the initial values of the dockless bike-sharing and the transition probability matrices at different time periods in a day. Finally, the algorithm is verified with practical and simulation data. The results of the algorithm can be used as a baseline reference data to dynamically optimize the fleet size of dockless bike-sharing station operated by bike-sharing companies for strengthening standardized management

Let’s shuffle: Facility Optimal Location for Stations within Bicycle Sharing Systems in the City of Buenos Aires after the pandemic

People’s habits have changed after the pandemic and cycling around the city of Buenos Aires is no exception. This thesis leverages literature on Capacitated Facility Location Problems (CFLP) to build an optimal bike-sharing network to minimize the total system’s cost. The objective is to decide which stations should be left open to meet projected demand in the worst-possible cases, ensuring that users do not have to walk more than a predefined distance to the facility that is closest to them. Results suggest that there is an excess of stations in the downtown area and idle capacity that could be relocated in peripheral areas, reflected by a positive load factor increase of 2x after the optimization is done. The solution shows that up to 70% of total costs could be saved after using our optimization model, by closing down facilities while meeting demand. While total cost is estimated as the budget that needs to be invested to ramp up the system from scratch, it is a useful metric that shows us how the network could be optimized taking away stations from overcrowded areas without losing any of the current demand. All of these bike-sharing facilities could be relocated to areas that have a low-density of bikes, improving access to the cycling system in the city of Buenos Aires

Faster Multi-Modal Route Planning With Bike Sharing Using ULTRA

We study multi-modal route planning in a network comprised of schedule-based public transportation, unrestricted walking, and cycling with bikes available from bike sharing stations. So far this problem has only been considered for scenarios with at most one bike sharing operator, for which MCR is the best known algorithm [Delling et al., 2013]. However, for practical applications, algorithms should be able to distinguish between bike sharing stations of multiple competing bike sharing operators. Furthermore, MCR has recently been outperformed by ULTRA for multi-modal route planning scenarios without bike sharing [Baum et al., 2019]. In this paper, we present two approaches for modeling multi-modal transportation networks with multiple bike sharing operators: The operator-dependent model requires explicit handling of bike sharing stations within the algorithm, which we demonstrate with an adapted version of MCR. In the operator-expanded model, all relevant information is encoded within an expanded network. This allows for applying any multi-modal public transit algorithm without modification, which we show for ULTRA. We proceed by describing an additional preprocessing step called operator pruning, which can be used to accelerate both approaches. We conclude our work with an extensive experimental evaluation on the networks of London, Switzerland, and Germany. Our experiments show that the new preprocessing technique accelerates both approaches significantly, with the fastest algorithm (ULTRA-RAPTOR with operator pruning) being more than an order of magnitude faster than the basic MCR approach. Moreover, the ULTRA preprocessing step also benefits from operator pruning, as its running time is reduced by a factor of 14 to 20

A Spatiotemporal Functional Model for Bike-Sharing Systems -- An Example based on the City of Helsinki

Understanding the usage patterns for bike-sharing systems is essential in terms of supporting and enhancing operational planning for such schemes. Studies have demonstrated how factors such as weather conditions influence the number of bikes that should be available at bike-sharing stations at certain times during the day. However, the influences of these factors usually vary over the course of a day, and if there is good temporal resolution, there could also be significant effects only for some hours/minutes (rush hours, the hours when shops are open, and so forth). Thus, in this paper, an analysis of Helsinki's bike-sharing data from 2017 is conducted that considers full temporal and spatial resolutions. Moreover, the data are available at a very high frequency. Hence, the station hire data is analysed in a spatiotemporal functional setting, where the number of bikes at a station is defined as a continuous function of the time of day. For this completely novel approach, we apply a functional spatiotemporal hierarchical model to investigate the effect of environmental factors and the magnitude of the spatial and temporal dependence. Challenges in computational complexity are faced using a bootstrapping approach. The results show the necessity of splitting the bike-sharing stations into two clusters based on the similarity of their spatiotemporal functional observations in order to model the station hire data of Helsinki's bike-sharing system effectively. The estimated functional influences of the proposed factors are different for the two clusters. Moreover, the estimated parameters reveal high random effects in the data that are not explained by the mean of the process. In this random-effects model, the temporal autoregressive parameter dominates the spatial dependence.Comment: 28 pages, 11 figures, submitted to journa

Mining large-scale human mobility data for long-term crime prediction

Traditional crime prediction models based on census data are limited, as they fail to capture the complexity and dynamics of human activity. With the rise of ubiquitous computing, there is the opportunity to improve such models with data that make for better proxies of human presence in cities. In this paper, we leverage large human mobility data to craft an extensive set of features for crime prediction, as informed by theories in criminology and urban studies. We employ averaging and boosting ensemble techniques from machine learning, to investigate their power in predicting yearly counts for different types of crimes occurring in New York City at census tract level. Our study shows that spatial and spatio-temporal features derived from Foursquare venues and checkins, subway rides, and taxi rides, improve the baseline models relying on census and POI data. The proposed models achieve absolute R^2 metrics of up to 65% (on a geographical out-of-sample test set) and up to 89% (on a temporal out-of-sample test set). This proves that, next to the residential population of an area, the ambient population there is strongly predictive of the area's crime levels. We deep-dive into the main crime categories, and find that the predictive gain of the human dynamics features varies across crime types: such features bring the biggest boost in case of grand larcenies, whereas assaults are already well predicted by the census features. Furthermore, we identify and discuss top predictive features for the main crime categories. These results offer valuable insights for those responsible for urban policy or law enforcement

A spatio-temporal deep learning model for short-term bike-sharing demand prediction

Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems