Designing Optimal Routes for Cycle-tourists

AbstractBicycles are becoming an increasingly popular mean of transport. Being healthy and affordable, they provide a sustainable alternative way of movement, for both leisure and work commuting. In both cases demand increases when bike devoted tracks are available. Providing bike trails that connect touristic spots is a cheap way of increasing the appeal and promoting the development of those regions featuring beautiful landscapes, strong cultural traditions, and historical monuments within a small area. This is the case of the Trebon region, South Bohemia, whose local administrators face the problem of optimally investing scarce resources to set up a network of cycle-dedicated tracks, exploiting existing trails or by reconstruction works, turning gravel roads or unsurfaced forest tracks into paved bike trails. As a first step, we address the design of a single route, modeled as a path on a directed graph between two given nodes, maximizing a utility function related to the attractiveness of the path. Attractiveness depends on several features, such as a service facility, a restaurant serving typical food, an historical village, or a scenic landscape to be enjoyed along the way. Two kinds of resource constraints bound the solution. A path maximum duration, which depends on how many times each arc is traversed, and a maximum budget for setting up the infrastructure, which depends on which arcs are selected. Since a cyclist may be willing to traverse an edge more than once - think, for example, of a detour from the main way to be travelled back and forth to reach a point of interest – cycles can be part of the route. The attractiveness function is concave and decreases after reaching its maximum at a few traversals. Such features make the problem new and challenging. We present an integer linear programming model and validate it by an experimental campaign on realistic data for the Trebon region

Using Bikeshare Datasets to Improve Urban Cycling Experience and Research Urban Cycling Behaviour

With access to public and shared transport systems becoming increasingly digitized, transaction datasets of unprecedented size as well as temporal and spatial precision are automatically generated (Blythe and Bryan 2007; Bagchi and White 2005; Pelletier et al. 2011). Data collected through smartcard payment methods are perhaps the largest and most obvious example. Although introduced for the purpose of improving payment processes, such data provide a detailed view of demand on a transport system, the potential for service improvements to be suggested (Ferrari et al. 2014) and an opportunity for studying individual traveller behaviour (Agard et al. 2006; Morency et al. 2006; Lathia et al. 2013). A substantial benefit of such data over more traditional data collection methods is that a complete and total record of usage for every smartcard customer is automatically generated (Bagchi and White 2005). Problems associated with sampling and recall bias, which make actively collected travel surveys somewhat difficult to administer, are avoided. The two most obvious disadvantages, at least for travel behaviour research, are that those individuals using smartcard technology may not be representative of the total population using that system or navigating a city more generally; and that variables such as individual trip purpose can only be inferred since they are not recorded directly

SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE

It is nowadays widely accepted that solving traffic congestion from the demand side is more important and more feasible than offering more capacity or facilities for transportation. Following a brief overview of evolution of the concept of Travel Demand Management (TDM), there is a discussion on the TDM foundations that include demand-side strategies, traveler choice and application settings and the new dimensions that ATDM (Active forms of Transportation and Demand Management) bring to TDM, i.e. active management and integrative management. Subsequently, the authors provide a short review of the state-of-the-art TDM focusing on relevant literature published since 2000. Next, we highlight five TDM topics that are currently hot: traffic congestion pricing, public transit and bicycles, travel behavior, travel plans and methodology. The paper closes with some concluding remarks

Fluid and Diffusion Limits for Bike Sharing Systems

Bike sharing systems have rapidly developed around the world, and they are served as a promising strategy to improve urban traffic congestion and to decrease polluting gas emissions. So far performance analysis of bike sharing systems always exists many difficulties and challenges under some more general factors. In this paper, a more general large-scale bike sharing system is discussed by means of heavy traffic approximation of multiclass closed queueing networks with non-exponential factors. Based on this, the fluid scaled equations and the diffusion scaled equations are established by means of the numbers of bikes both at the stations and on the roads, respectively. Furthermore, the scaling processes for the numbers of bikes both at the stations and on the roads are proved to converge in distribution to a semimartingale reflecting Brownian motion (SRBM) in a $N^{2}$-dimensional box, and also the fluid and diffusion limit theorems are obtained. Furthermore, performance analysis of the bike sharing system is provided. Thus the results and methodology of this paper provide new highlight in the study of more general large-scale bike sharing systems.Comment: 34 pages, 1 figure

The Impact of Bike-Sharing Services on Local Business

Despite the potential of bike-sharing services to influence the local economy, the impact of these services on local businesses has been overlooked. Hence, this research aims to examine how a bike-sharing program influences the success of local businesses. We analyzed sales data of 2,928 local restaurants recorded from February to August 2019, along with bike rentals made near the restaurants. Our findings show that the number of bike rentals around a restaurant is positively associated with its sales. Further, the impact of bike rentals intensifies when there are more subway passengers around the restaurants but weakens with more bus passengers. Moreover, non-franchise and less-popular restaurants benefit more from bike-sharing programs. Our findings offer significant contributions to the existing literature by theorizing the value of sharing economy in the local economy. Practically, our results provide strategic implications for policymakers, bike-sharing service providers, and local businesses to effectively use sharing economy programs

Diseño de una red de bicicletas públicas en la ciudad de Montevideo – Uruguay

Considerando la situación a la cual ha llegado la contaminación atmosférica y la congestión vehicular en las grandes ciudades, se han impulsado medidas para promover modos de transporte sostenible, como las bicicletas públicas, buscando una alternativa a la utilización de vehículos motorizados. Los sistemas públicos de bicicletas han incrementado su popularidad como sistemas de transporte sostenibles en los últimos años en muchos países del mundo. Dentro de los elementos más importantes para su implementación, se encuentra la localización óptima de las estaciones de recogida y devolución de bicicletas. En este estudio se desarrollan dos modelos de localización-asignación para resolver el problema de diseño de una red de bicicletas públicas. Se logra determinar el número de estaciones que son necesarias, la ubicación de las estaciones y la flota total de bicicletas del sistema, considerando restricciones de presupuesto. En un caso maximizando la demanda y en el otro priorizando la selección de la capacidad de las estaciones según la demanda. Los modelos se plantean para la cuidad de Montevideo, Uruguay y se resuelven computacionalmente utilizado lenguaje de programación Python y software de optimización Gurobi®.Considering the global situation related to air pollution and vehicular congestion, many countries are promoting sustainable modes of transport, as an alternative to motorized vehicles, such as bike-sharing . Bike-sharing systems are increasing their popularity as sustainable transportation systems in recent years. One of the most important elements for its implementation is the optimal location of the stations. This work presents two different location-allocation models to solve the design problem of the bike-sharing system. The models determines the require number of stations, the location of the stations and the total fleet of bicycles in the system, considering budget constraints. In one case the objective function is to maximize the demand and in the other, the suitable selection of different types of stations according to demand. The models are applied to Montevideo, Uruguay and are computationally solved using Python programming language and Gurobi® optimization software.Universidad de Sevilla. Máster en Organización Industrial y Gestión de Empresa

Dynamic Redeployment to Counter Congestion or Starvation in Vehicle Sharing Systems

Extensive usage of private vehicles has led to increased traffic congestion, carbon emissions, and usage of non-renewable resources. These concerns have led to the wide adoption of vehicle sharing (ex: bike sharing, car sharing) systems in many cities of the world. In vehicle-sharing systems, base stations (ex: docking stations for bikes) are strategically placed throughout a city and each of the base stations contain a pre-determined num-ber of vehicles at the beginning of each day. Due to the stochastic and individualistic movement of customers, there is typically either congestion (more than required) or starvation (fewer than required) of vehicles at cer-tain base stations. As demonstrated in our experimental results, this happens often and can cause a significant loss in demand. We propose to dynamically redeploy idle vehicles using carriers so as to minimize lost de-mand or alternatively maximize revenue for the vehicle sharing company. To that end, we contribute an opti-mization formulation to jointly address the redeploy-ment (of vehicles) and routing (of carriers) problems and provide two approaches that rely on decomposabil-ity and abstraction of problem domains to reduce the computation time significantly. Finally, we demonstrate the utility of our approaches on two real world data sets of bike-sharing companies.

On the Simultaneous Computation of Target Inventories and Intervals for Bimodal Bike-Sharing Systems

ABSTRACT: The emerging demand for electric bicycles in recent years has prompted several Bike-Sharing Systems around the world to adapt their service to a new wave of commuters. Many of these systems have incorporated electric bikes into their network while still maintaining the use of regular mechanical bicycles. However, the presence of two types of bikes in a Bike-Sharing network may impact how rebalancing operations should be conducted in the system. Regular and electric bikes may exhibit distinct demand patterns throughout the day, which can hinder efficient planning of such operations. In this paper, we propose a new model that provides rebalancing recommendations based on the demand prediction for each type of bike. Additionally, we simulate the performance of our model under different scenarios, considering commuters’ varying inclination to substitute their preferred bike with one of a different type. Our empirical experiments indicate the potential of our model to improve user satisfaction, reducing the total lost demand by approximately 10%, while reducing the lost demand for electric bikes by around 30%, on average, when compared to the existing rebalancing strategy used by the real-world Bike-Sharing System under study. Remarkably, this was accomplished while maintaining an almost identical average hourly count of rebalancing operations

Service Region Design for Urban Electric Vehicle Sharing Systems

Emerging collaborative consumption business models have shown promise in terms of both generating business opportunities and enhancing the efficient use of resources. In the transportation domain, car sharing models are being adopted on a mass scale in major metropolitan areas worldwide. This mode of servicized mobility bridges the resource efficiency of public transit and the flexibility of personal transportation. Beyond the significant potential to reduce car ownership, car sharing shows promise in supporting the adoption of fuel- efficient vehicles, such as electric vehicles (EVs), due to these vehicles special cost structure with high purchase but low operating costs. Recently, key players in the car sharing business, such as Autolib, Car2Go and DriveNow, have begun to employ EVs in an operations model that accommodates one-way trips. On the one hand (and particularly in free-floating car sharing), the one-way model results in significant improvements in coverage of travel needs and therefore in adoption potential compared with the conventional round-trip-only model (advocated by ZipCar, for example). On the other hand, this model poses tremendous planning and operational challenges. In this work, we study the planning problem faced by service providers in designing a geographical service region in which to operate the service. This decision entails trade-offs between maximizing customer catchment by covering travel needs and controlling fleet operations costs. We develop a mathematical programming model that incorporates details of both customer adoption behavior and fleet management (including EV repositioning and charging) under imbalanced travel patterns. To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. Mathematically, the problem can be approximated by a mixed integer second-order cone program, which is computationally tractable with practical scale data. Applying this approach to the case of Car2Go’s service with real operations data, we address a number of planning questions and suggest that there is potential for the future development of this service