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Combatting the mismatch: Modeling bike-sharing rental and return machine learning classification forecast in Seoul, South Korea
Abstract
Bike-sharing is rapidly gaining popularity due to health, transportation, and recreational benefits. As more people use bike-sharing, the burden of reallocating bikes will increase because of the mismatch between outgoing and incoming bikes. Optimizing truck routes, incentivizing users, and crowdsourcing are common suggestions to mitigate rebalancing issues. This research aims to provide a procedure to adjust landscape conditions as an alternative strategy. Comprehensive landscape metrics are quantified by FRAGSTATS analysis. Using public bike-sharing data in Seoul, South Korea, we analyzed spatial and temporal mismatch characteristics. Hot spot analysis was conducted to identify hot and cold spots of bike-sharing use in two scenarios: outgoing and incoming trips. This was used to generate tree-based binary ensemble machine learning classification models. Shapley Additive exPlanations (SHAP) values were calculated between hot and cold spots to understand how landscape characteristics and other determinants affect the mismatch. Our results suggest that climate and bike-sharing related factors significantly affect bike-sharing use. Transportation land use and landscape characteristics like the magnitude of biodiversity, contiguity, shape, area, and edge significantly contribute to labeling. The findings of this study can help bike-sharing operators better navigate their bike-sharing services.
... Based on this idea, we suggest adding or removing some electronic fence parking points according to bicycle-sharing usage to meet the public s demand for borrowing and returning bicycle-sharing [39]. Moreover, by rewarding citizens who park their shared bicycles properly and penalizing those who engage in improper parking, we can encourage citizens to develop a civilized parking habit and improve the order and convenience of the shared bicycle system [40]. By adjusting the layout and quantity of electronic fence parking areas and implementing hybrid incentive programs, we can address the inconvenience of public bicycle borrowing and returning. ...
... Based on this idea, we suggest adding or removing some electronic fence parking points according to bicycle-sharing usage to meet the public's demand for borrowing and returning bicycle-sharing [39]. Moreover, by rewarding citizens who park their shared bicycles properly and penalizing those who engage in improper parking, we can encourage citizens to develop a civilized parking habit and improve the order and convenience of the shared bicycle system [40]. By adjusting the layout and quantity of electronic fence parking areas and implementing hybrid incentive programs, we can address the inconvenience of public bicycle borrowing and returning. ...
Bicycle-sharing is experiencing rapidly as a low-carbon transport mode of travel, with the advantages of low cost and sustainability. Bicycle-sharing operators use electronic fence parking points to manage bicycle-sharing, but it is too time-consuming and impractical to manage them individually. Therefore, it is necessary to cluster the electronic fence parking points and implement regionalized management. This study uses a Mean-shift clustering algorithm to cluster the electronic fence parking points on Xiamen Island, construct a bicycle-sharing dispatching station, and divide the management area. Singular value decomposition is employed to analyze the travel demand patterns of bicycle-sharing and travel characteristics. In addition, we constructed regression models to explore the relationship between the urban built environment and bicycle-sharing trips during the morning and evening peak hours. The study results show that: (1) The 73 dispatching stations constructed cover 86.72% of the bicycle-sharing data, with a good clustering effect. (2) The travel demand for bicycle-sharing shows multiple patterns and different characteristics in different spatial and temporal distributions, which are influenced by land use. (3) There are spatial and temporal differences in the impact of the urban built environment on bicycle-sharing trips, especially residential and enterprise poi densities have opposite effects on shared bicycle-sharing during morning and evening peak hours. The research results of this paper can serve in the planning of bicycle-sharing dispatch stations and the differentiated management and dispatch of bicycle-sharing, which can reduce operating costs and promote the development of sustainable urban transport.
... In this section, we discuss the board applicability of the open-space ratio functions. We also discuss how to [24,25], green coverage ratio [26,27], public open-space ratio [28,29], space ratio [30], park ratio [31,32], water surface ratio [33,34], river ratio [35,36], and road ratio [37,38]. In recent years, residents have been concerned about walkability to open space, and many studies analyze walkability. ...
An open-space ratio is often used as a first basic metric to examine the distribution of open space in urbanized areas. Originally, the open-space ratio was defined as the ratio of the area of open space (unbuilt area) to the area of its building site. In recent years, residents have become more concerned with the open-space ratios in the broader neighborhoods of their individual buildings than with their own building sites. To address this concern, this paper proposes a method for dealing with the open-space ratio in the variable -meter buffer zone around each building, called the open-space ratio function, and implements it using standard GIS operators. The function and its implemented analytical tool can answer the following questions. First, this function shows how the ratio varies with respect to the bandwidth to discuss the modifiable area unit problem. Second, as the ratio changes, the function shows in which bandwidth zone the ratio is the highest, indicating the best open-space environment zone. Third, in the pairwise comparison for housing selection, the function shows in which bandwidth zone a specific house is better than another. Fourth, the function shows in which bandwidth zone the variance among all buildings in a region is the greatest. Fifth, in this zone, buildings are clustered in terms of open-space ratio. The resulting clusters are the most distinct. Sixth, to examine the open-space ratio around a clump of buildings (such as a housing complex), the function shows how to obtain clumps. Seventh, it is shown how the open-space function provides a wide range of applicability without changing the mathematical formulation. Finally, this paper shows how to implement the function in a simple computational method using operators and a processing modeler provided by the standard GIS without additional software.
... Their results showed no significant difference between the accuracy achieved by the models tested. Similarly, Choi et al. [41] used data from Seoul, Korea and found small differences in the performance of Decision Tree (DT), Random Forest (RF), and Extra Tree (ET) models. Xu et al. [42] proved that LSTM algorithm has provided better results for Nanjing (China). ...
Public Bicycle Sharing Systems (BSS) have spread in many cities for the last decade. The need of analysis tools to predict the behavior or estimate balancing needs has fostered a wide set of approaches that consider many variables. Often, these approaches use a single scenario to evaluate their algorithms, and little is known about the applicability of such algorithms in BSS of different sizes. In this paper, we evaluate the performance of widely known prediction algorithms for three sized scenarios: a small system, with around 20 docking stations, a medium-sized one, with 400+ docking stations, and a large one, with more than 1500 stations. The results show that Prophet and Random Forest are the prediction algorithms with more consistent results, and that small systems often have not enough data for the algorithms to perform a solid work.
... Notably, a pronounced rental increase occurs at 8 a.m. and 6 p.m. These times correspond to standard commute hours, suggesting these periods significantly use the BSS [40,41]. Furthermore, the data reveals a noticeable increase in rentals from 7 p.m. to 12 a.m., outside typical commute hours. ...
Bike-sharing is a powerful solution to urban challenges (e.g., expanding bike communities, lowering transportation costs, alleviating traffic congestion, reducing emissions, and enhancing health). Accurately predicting bike-sharing demand not only ensures the system meets community needs but also optimizes resource allocation, reduces operational costs, and enhances the user experience, thereby increasing the system's sustainability and city-wide benefits. However, prediction is complicated in low-computing environments with insufficient data due to privacy regulations or policy constraints. This study proposes an online learning-based two-stage forecasting model based on a low-computing environment with insufficient data for robust, fast multistep-ahead prediction for bike-sharing demand in Seoul. The model was applied with exploratory data analysis (EDA) to the Seoul Bike-sharing Demand dataset, split into insufficient training and sufficient testing sets. First, we generated prediction values for the random forest, extreme gradient boosting, and Cubist methods in training and testing. Second, we used the Ranger package trained with external factors and prediction values using time-series cross-validation for multistep-ahead prediction 1 h to 1 day later. We compared the model performance with that of 23 machine and deep learning models to verify its superiority. Using interpretability methods and EDA, we reported the relationships between external factors and bike-sharing demand.
The coronavirus pandemic may provoke an increase on our overreliance on private car usage due to a permanent loss of confidence on public transport (PT), threatening current decarbonization efforts of the transport sector. Thus, alternative modes like bike sharing systems (BSS) must be considered.
In this study, through conducting 16 semi-structured interviews and by employing thematic analysis, we explore the users' perceptions of using Lisbon's BSS during this pandemic. Our findings show that the observed decrease on BSS usage during the COVID-19 lockdowns was mostly due to mandatory teleworking than to a perceived infection risk. Even during the height of the pandemic, users still turned to BSS to fulfil their essential trip needs. Users considered bike sharing to have a lower infection risk comparatively to PT, with some users joining BSS during the pandemic to specifically avoid using PT. Furthermore, users associate riding a shared bicycle with a pleasant activity that reduces their travel times and costs, while also providing health and environmental benefits. Consequently, bike sharing contributes to the resilience of transport systems by providing its users with a transport alternative perceived to have a low infection risk, ensuring their mobility needs during disruptive events.
Findings from this research provide evidence that support policies, such as, expanding BSS coverage areas, optimizing rebalancing operations, introducing shared e-bikes, and implementing segregated cycling lanes alongside BSS. These policies may be particularly effective at increasing the competitiveness of BSS as an alternative mode during disruptive public health crises and beyond.
Introduction
The sudden COVID-19 pandemic poses a fresh and tough challenge to bike sharing systems (BSS). With this epidemic as a shock event, this paper aspires to shed light on the phenomenon of changing demand and usage regularity in New York City’s BSS under the epidemic environment, spanning a period of 18 months.
Methods
Technically, BSS’s normal performance and the timely responses to the outbreak could be conceptualized as having four different stages. One provides a comparative analysis of bike sharing spatial-temporal mobility patterns and connectivity of the bike sharing usage network, before and during the public health crisis with a macroscopic perspective. Also, a multivariate investigation of user and trip characteristics on BSS is conducted to uncover the difference in the frequency of outdoor and sojourn time between various user communities.
Results
Due to the impact of the outbreak, BSS registered severe ridership drops, yet it quickly recovered to the pre-pandemic levels within months. The decline of bike sharing usage was felt throughout all the areas during the outbreak. However, there were places where BSS ridership actually increased, particularly in the areas near supermarkets, parks and hospitals. The less densely connected network of the bike sharing usage has also resulted in a reduction in users’ destination heterogeneity. This study also finds evidence of the significant gender, age and cycling pattern gaps in response to potential risk.
Conclusions
Investigating the dynamics of bike sharing usage will help to comprehend how the serious pandemic caused by COVID-19 impacts people’s daily mobility. Practically, this work hopes to provide insights into adapting this unprecedented pandemic so as to respond to similar events in the future.
COVID-19 has dramatically impacted urban mobility, of which public transport (PT) has been particularly affected. With PT ridership plummeting due to infection fears and many people returning to work, there is a danger of a steep rise in car use that would exacerbate environmental and health problems. Therefore, other modes such as bike sharing should be considered as potential alternatives during the coronavirus pandemic.
This study focuses on assessing how coronavirus has impacted bike sharing by implementing a travel behaviour survey to the users of GIRA, the bike sharing system (BSS) of Lisbon. While the coronavirus has led some to decrease the frequency of use or quit the system, other users have increased the usage or joined GIRA during the pandemic. Furthermore, most users who have quit or decreased the usage of GIRA justify their decision not so much on avoiding the risk of infection (although for some it is an important reason) but on having stopped commuting due to COVID-19. The survey has also revealed substantial changes not only on the usage patterns of GIRA users but also on their relationship with other modes of transport. While before the pandemic, most respondents were shifting from PT to GIRA, that percentage has declined, with an increase on the share of users replacing walking, private car, and personal cycling. Moreover, the motivations for using bike sharing related with avoiding PT and maintaining a social distance during the trip have gained more relevance. Concurrently, the perceived safety of using PT has drastically declined, and while the perceived safety of using GIRA has also decreased it was in a much smaller scale.
Policy insights can be derived from this research on how bike sharing can contribute to a more sustainable and resilient urban transport system. During infectious public health crises such as COVID-19, BSS can be a viable transport alternative, not only providing the population with an affordable mode of transport where social distancing can be maintained in most of the trip but also mitigating a modal shift from PT to the private car.
Dockless bike sharing plays an important role in residents’ daily travel, traffic congestion, and air pollution. Recently, urban greenness has been proven to be associated with bike sharing usage around metro stations using a global model. However, their spatial associations and bike sharing usage on public holidays have seldom been explored in previous studies. In this study, urban greenness was obtained objectively using eye-level greenness with street-view images by deep learning segmentation and overhead view greenness from the normalized difference vegetation index (NDVI). Geographically weighted regression (GWR) was applied to fill the research gap by exploring the spatially varying association between dockless bike sharing usage on weekdays, weekends, and holidays, and urban greenness indicators as well as other built environment factors. The results showed that eye-level greenness was positively associated with bike sharing usage on weekdays, weekends, and holidays. Overhead-view greenness was found to be negatively related to bike usage on weekends and holidays, and insignificant on weekdays. Therefore, to promote bike sharing usage and build a cycling-friendly environment, the study suggests that the relevant urban planner should pay more attention to eye-level greenness exposure along secondary roads rather than the NDVI. Most importantly, planning implications varying across the study area during different days were proposed based on GWR results. For example, the improvement of eye-level greenness might effectively promote bike usage in northeastern and southern Futian districts and western Nanshan on weekdays. It also helps promote bike usage in Futian and Luohu districts on weekends, and in southern Futian and southeastern Nanshan districts on holidays.
The Covid-19 pandemic has abruptly changed well-established mobility patterns, as the need for social distancing and lockdown orders have driven citizens to reduce their movements and avoid crowded mass transit. In this context, we look at the case of New York City's bike sharing system, one of the largest in the world, to gain insights on the socio-economic variables behind urban mobility during a pandemic. We exploit several sources of Smart City data to analyze the relationship between bike sharing, public transport, and other modes of transportation, deriving interesting insights for future urban planning, both city-wide and at the neighborhood level. The New York City case study shows some of the most important trends during the lockdown, and the combination between mobility and socio-economic data can be used to understand the consequences of the pandemic on different communities, as well as the future directions of expansion and management of the bike sharing system and urban infrastructure.
Cities globally are grappling with the negative externalities of car travel and are therefore striving to move towards a more sustainable urban transportation system. The introduction and popularity of new personal transport modes, such as e-scooters and electric bicycles, could potentially accelerate this transition as they become more commonplace and are accepted into regulatory frameworks. The integration of these new modes and vehicles into public transport systems, for example, could enhance accessibility and lead to potential modal shifts away from private car use. In order to assess the potential for change that micromobility holds, it is key to study these new modes in the context of access and egress trips to and from public transport.
This paper presents an extensive systematic literature review of studies that focus specifically on the integration of micromobility and public transport systems and is, to the knowledge of the authors, the first review focusing on this specific aspect of micromobility. This paper offers an understanding of how this topic has been studied to date, which factors and aspects have been considered and analysed, which causalities have been identified in the research, in addition to identifying gaps in the literature and providing guidance for future research on this topic. Furthermore, this paper provides a comprehensive collection and critical discussion of suggestions and recommendations included in the literature which are analysed in this study, aimed at improving and further promoting the effective integration of micromobility and public transport services.
Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.
The full societal impact COVID-19 pandemic is laid bare in urban mobility patterns. This research explored the recently published data on the operation of subway and bike share systems (BSS) during the COVID-19 outbreak in New York city, providing evidence on its impact over urban transport systems, but also on how its different components can work in conjunction. The BSS has proved to be more resilient than the subway system, with a less significant ridership drop (71% vs 90% ridership drop and 50% decrease on the ridership ratio) and an increase on its trips' average duration (from 13 min to 19 min per trip). Moreover, the study found evidence of a modal transfer from some subway users to the bike sharing system. The first effects of the free BSS programs aimed at essential service workers were also evaluated. BSS can improve the resilience of urban transport systems to disruptive events. Overall, this paper offers clues on how bike sharing, and cycling in general, can support the transition to a post-coronavirus society.
Bike sharing is increasingly attracting more riders in cities around the world for its benefits regarding the urban environment and public health. The public bike sharing program of Seoul, South Korea, first launched in October 2015, is now widely spread around the city and serves more than 27,000 riders daily. However, concerns are being raised as rising air pollution levels in Seoul, represented by particulate matter (PM) levels, in recent years may negatively discourage citizens from using bike sharing. This study investigates the impact of PM10 and PM2.5 levels on bike sharing use in Seoul and seeks to identify any seasonal differences. A series of negative binomial regression models, which take into account control variables like weather conditions and calendar events, are adopted to empirically measure the impacts. Results show that the PM levels yield statistically significant negative impacts (p < 0.01) on bike sharing use throughout the year. The impacts are particularly stronger in winter and spring, when the PM levels are higher. Findings suggest that PM levels may operate as driving factors for bike sharing use in addition to meteorological conditions like temperature, humidity, and precipitation.
Rental bike sharing is an urban mobility model that is affordable and ecofriendly. The public bike sharing model is widely used in several cities across the world over the past decade. Because bike use is rising constantly, understanding the system demand in prediction is important to boost the operating system readiness. This article presents a prediction model to meet user demands and efficient operations for rental bikes using Random Forest (RF), which is a homogeneous ensemble method. The approach is carried out in Seoul, South Korea to predict the hourly use of rental bikes. RF is compared with Support Vector Machine with Radial Basis Function Kernel, k‐nearest neighbor and Classification and Regression Trees to verify RF supremacy in rental bike demand prediction. Performance Index measures the efficiency of RF compared to the other predictive models. Also, the variable importance analysis is performed to assess the most important characteristics during different seasons by creating a predictive model using RF for each season. The results show that the influence of variables changes depending on the seasons that suggest different operating conditions. RF models trained with yearly and seasonwise models show that bike sharing demand can be further improved by considering seasonal change.
This paper investigated the travel patterns of 1.7 million shared E-scooter trips from April 2018 to February 2019 in Austin, TX. There were more than 6000 active E-scooters in operation each month, generating over 150,000 trips and covered approximately 117,000 miles. During this period, the average travel distance and operation time of E-scooter trips were 0.77 miles and 7.55 min, respectively. We further identified two E-scooter usage hotspots in the city (Downtown Austin and the University of Texas campus). The spatial analysis showed that more trips originated from Downtown Austin than were completed, while the opposite was true for the UT campus. We also investigated the relationship between the number of E-scooter trips and the surrounding environments. The results show that areas with higher population density and more residents with higher education were correlated with more E-scooter trips. A shorter distance to the city center, the presence of transit stations, better street connectivity, and more compact land use were also associated with increased E scooter usage in Austin, TX. Surprisingly, the proportion of young residents within a neighborhood was negatively correlated with E-scooter usage.
This research paper presents a rule-based regression predictive model for bike sharing demand prediction. In recent days, Pubic rental bike sharing is becoming popular because of is increased comfortableness and environmental sustainability. Data used include Seoul Bike and Capital Bikeshare program data. Both data have weather data associated with it for each hour. For both the dataset, five statistical models were trained with optimized hyperparameters using a repeated cross validation approach and testing set is used for evaluation: (a) CUBIST (b) Regularized Random Forest (c) Classification and Regression Trees (d) K Nearest Neighbour (e) Conditional Inference Tree. Multiple evaluation indices such as R², Root Mean Squared Error, Mean Absolute Error and Coefficient of Variation were used to measure the prediction performance of the regression models. The results show that the rule-based model CUBIST was able to explain about 95 and 89% of the Variance (R²) in the testing set of Seoul Bike data and Capital Bikeshare program data respectively. An analysis with variable importance was carried to analyse the most significant variables for all the models developed with the two datasets considered. The variable importance results have shown that Temperature and Hour of the day are the most influential variables in the hourly rental bike demand prediction.
Recently, there has been an increase in bike-sharing programs on university campuses. However, there is scarce literature on the campus characteristics that are most favorable for these programs. This study evaluates the impacts of campus characteristics, temporal factors, and weather events on daily bike-sharing trips within U.S. university campuses. The study used data collected between 2014 and 2018 from 177 stations located within 25 campuses. With an average of 38 trips per day, a high variability in the number of daily trips was observed, with a maximum of 708 and a minimum of zero. Both traditional Negative Binomial (NB) and Mixed Effect Negative Binomial (MENB) models were applied, and their performances were compared based on a set of criteria. The MENB performed better than NB. The MENB model results revealed that among campus related factors, number of faculty/staffs, number of full-time students, private universities, campus size, and Latino and Asian student dominance are positively associated with an increase in trips per day, whereas number of part-time students, distance from campus to central business district, and students' ages are negatively associated. The temporal factors depicted that weekends, public holidays, time between semesters, and summer semester were associated with decreases in number of daily trips, while years in operation and fall semesters were positively associated with daily ridership. Severe weather events resulted in less bike-sharing, and average daily temperature was positively associated with number of trips. Campus bike-sharing planners can utilize these findings for estimating daily bike-sharing ridership on campuses.
Shared bikes have become popular traveling tools in our daily life. The successful operation of bike sharing systems (BSS) can greatly promote energy saving in a city. In BSS, stations becoming empty or full is the main cause of customers failing to rent or return bikes. Some truck-based rebalancing strategies are proposed to solve this problem. However, there are still challenges around the relocation of bikes. The truck operating costs also need to be considered. In this paper, we propose a customer-oriented rebalancing strategy to solve this problem. In our strategy, two algorithms are proposed to ensure the whole system is balanced for as long as possible. The first algorithm calculates the optimal state of each station through the one-dimensional Random Walk Process with two absorption walls. Based on the derived optimal state of each station, the second algorithm recommends the station that has the largest difference between its current state and its optimal state to the customer. In addition, a simulation system of shared bikes based on the historical records of Bay Area Bikeshare is built to evaluate the performance of our proposed rebalancing strategy. The simulation results indicate that the proposed strategy is able to effectively decrease the imbalance in the system and increase the system's performance compared with the truck-based methods.
Bike-sharing is a new low-carbon and environment-friendly mode of public transport based on the “sharing economy”. Since 2017, the bike-sharing market has boomed in China’s major cities. Bikes equipped with GPS transmitters are docked along sidewalks that can be easily accessed through smartphone apps. However, this new form of transport has also led to problems, such as illegal parking, vandalism, and theft, each of which presents a major administrative challenge. Further, imbalances in user demand and bike availability need to be overcome to ensure a convenient, flexible service for customers. Hence, predicting a cyclist’s destination could be of great importance to shared-bike operators. In this paper, we propose an innovative deep learning model to predict the most probable destination for each user. The model, called destination prediction network based on spatiotemporal data (DPNst), comprises three steps. First, the data is preprocessed and a pool of likely candidate destinations is generated based on frequent item mining. This candidate set is then used to build the DPNst model: a long short-term memory network learns the user’s behavior; a convolutional neural network learns the spatial relationships between the origin and the candidate destinations; and a fully connected neural network learns the external features. In the final step, DPNst dynamically aggregates the output of the three neural networks based on the given data and generates the predictions. In a series of experiments on real-world stationless bike-sharing data, DPNst returned an F1 score of 42.71% and demonstrated better performance overall than the compared baselines.
Dockless bike-sharing is becoming popular all over the world, and short-term spatiotemporal distribution forecasting on system state has been further enlarged due to its dynamic spatiotemporal characteristics. We employ a deep learning approach, named the convolutional long short-term memory network (conv-LSTM), to address the spatial dependences and temporal dependences. The spatiotemporal variables including number of bicycles in area, distribution uniformity, usage distribution, and time of day as a spatiotemporal sequence in which both the input and the prediction target are spatiotemporal 3D tensors within one end-to-end learning architecture. Experiments show that conv-LSTM outperforms LSTM on capturing spatiotemporal correlations.
Bike-sharing services are flourishing in Smart Cities worldwide. They provide a low-cost and environment-friendly transportation alternative and help reduce traffic congestion. However, these new services are still under development, and several challenges need to be solved. A major problem is the management of rebalancing trucks in order to ensure that bikes and stalls in the docking stations are always available when needed, despite the fluctuations in the service demand. In this work, we propose a dynamic rebalancing strategy that exploits historical data to predict the network conditions and promptly act in case of necessity. We use Birth-Death Processes to model the stations' occupancy and decide when to redistribute bikes, and graph theory to select the rebalancing path and the stations involved. We validate the proposed framework on the data provided by New York City's bike-sharing system. The numerical simulations show that a dynamic strategy able to adapt to the fluctuating nature of the network outperforms rebalancing schemes based on a static schedule.
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
It is important to understand both what a classification metric expresses and what it hides.
Cities promote strong bicycle networks to support and encourage bicycle commuting. However, the application of network science to bicycle facilities is not very well studied. Previous work has found relationships between the amount of bicycle infrastructure in a city and aggregate bicycle ridership, and between microscopic network structure and individual tripmaking patterns. This study fills the missing link between these two bodies of literature by developing a standard methodology for measuring bicycle facility network quality at the macroscopic level and testing its association with bicycle commuting. Bicycle infrastructure maps were collected for 74 Unites States cities and systematically analyzed to evaluate their network structure. Linear regression models revealed that connectivity and directness are important factors in predicting bicycle commuting after controlling for demographic variables and the size of the city. These findings provide a framework for transportation planners and policymakers to evaluate their local bicycle facility networks and set regional priorities that support nonmotorized travel behavior, and for continued research on the structure and quality of bicycle infrastructure and behavior.
Bicycle usage can be affected by colder weather, precipitation, and excessive heat. The research presented here analyzes the effect of weather on the use of the Washington, DC, bikeshare system, exploiting a dataset of all trips made on the system. Hourly weather data, including temperature, rainfall, snow, wind, fog, and humidity levels are linked to hourly usage data. Statistical models linking both number of users and duration of use are estimated. Further, we evaluate trips from bikeshare stations within one quarter mile of Metro (subway) stations at times when Metro is operating. This allows us to determine whether Metro serves as a back-up option when weather conditions are unfavorable for bicycling. Results show that cold temperatures, rain, and high humidity levels reduce both the likelihood of using bikeshare and the duration of trips. Trips taken from bikeshare stations proximate to Metro stations are affected more by rain than trips not proximate to Metro stations and less likely when it is dark. This information is useful for understanding bicycling behavior and also for those planning bikeshare systems in other cities.
The influence of landscape factors on nutrient fluxes is highly variable,
depending on which fluxes dominate and vary in the subject catchment.
Watersheds where the input is the most variable source, soil qualities, the
proportion of certain land uses, proximity to the water body and runoff determine
nutrient transport. In catchments, where variation in chemical and physical
conduits and barriers determines the flows, the factors of the landscape pattern
also explain the differences in nutrient losses.
Nitrogen, apart from other nutrients, is better determined by factors of
agriculture and the qualities of the soil. Due to the relevance of anaerobic
conditions in denitrification, one of the specifically important factors is water
regime. It has been proposed that the greatest variance among nitrogen losses
occurs in small catchments (
Foreword by Richard T.T. Forman, Harvard University (Extract)
(…)
The gaping lacuna in wise planning is not more knowledge, but rather the scarcity of accessible, informative and (especially) compelling syntheses and handbooks. The authors, from ecology, landscape architecture, and land-use planning, are experts in spatial pattern and landscape metrics, and have blended their expertise into this handy, readable book. Hopefully analogous collaborations will proliferate, with the land around us being the big beneficiary.
The authors highlight the importance of absorbing landscape ecology principles and then applying them in spatial planning. That has been, and is, exactly the growing success story in strengthening forestry (e.g., water- shed management), biological conservation (rare species protection), transportation (road ecology), and wildlife management.
Landscape metrics, the measures of spatial pattern, are indicators of many human and natural conditions on land, from built-area patterns and built-system flows to the big three habitat issues: loss, degradation, and fragmentation. The pages ahead lucidly portray landscape metrics and their application, effectively sorting a large number in the literature down to a core set of ten. Both opportunities and caveats are explained. For the first time this scientific area is made available and readable.
Landscape structure is a key indicator of how the land works for people and for nature. Thus, changing landscape pattern emerges as a convenient “handle” for planners, and for each of us, to improve the planet. (…) In effect, landscape metrics represent a “pre-handle,” or primer, for the planners’ handle for change. (…)
The pages in your hand reveal a rich array of insights and an important big-picture perspective. Planners and ecologists, and indeed all who think about changing the land, will be enriched by the exploration ahead.
One of the problems in bicycle sharing systems design is the estimation of the potential demand to the service, especially in countries where this type of systems is not yet implemented. The main objective of this methodology is to relate the demand of bike-sharing systems with external characteristics that affects the bicycle usage in order to obtain its territorial distribution. Due to the limited information available in Portugal this paper will focus on the determination of demand based on the experience of other countries. The method is applied to a middle size Portuguese city, Coimbra.
In this paper, we have compared the classification results of two models i.e. Random Forest and the J48 for classifying twenty versatile datasets. We took 20 data sets available from UCI repository [1] containing instances varying from 148 to 20000. We compared the classification results obtained from methods i.e. Random Forest and Decision Tree (J48). The classification parameters consist of correctly classified instances, incorrectly classified instances, F-Measure, Precision, Accuracy and Recall. We discussed the pros and cons of using these models for large and small data sets. The classification results show that Random Forest gives better results for the same number of attributes and large data sets i.e. with greater number of instances, while J48 is handy with small data sets (less number of instances). The results from breast cancer data set depicts that when the number of instances increased from 286 to 699, the percentage of correctly classified instances increased from 69.23% to 96.13% for Random Forest i.e. for dataset with same number of attributes but having more instances, the Random Forest accuracy increased.
The COVID-19 pandemic and social distancing restrictions have had a significant impact on urban mobility. As micro mobility offers less contact with other people, docked or dockless e-scooters and bike-sharing have emerged as alternative urban mobility solutions. However, little empirical research has been conducted to investigate how COVID-19 might affect micro mobility usage, especially in a major Asian city. This research aims to study how COVID-19 and other related factors have affected bike-sharing ridership in Seoul, South Korea. Using detailed urban telecommunication data, this study explored the spatial-temporal patterns of a docked bike-sharing system in Seoul. Stepwise negative binomial panel regressions were conducted to find out how COVID-19 and various built environments might affect bike-sharing ridership in the city. Our results showed that open space areas and green infrastructure had statistically significant positive impacts on bike-sharing usage. Compared to registered population factors, real-time telecommunication floating population had a significant positive relationship with both bike trip count and trip duration. The model showed that telecommunication floating population has a significant positive impact on bike-sharing trip counts and trip duration. These findings could offer useful guidelines for emerging shared mobility planning during and after the COVID-19 pandemic.
Bike sharing systems have been recently adopted by a growing number of cities as a new means of transportation offering citizens a flexible, fast and green alternative for mobility. Users can pick up or drop off the bicycles at a station of their choice without prior notice or time planning. This increased flexibility comes with the challenge of unpredictable and fluctuating demand as well as irregular flow patterns of the bikes. As a result, these systems can incur imbalance problems such as the unavailability of bikes or parking docks at stations. In this light, operators deploy fleets of vehicles which re-distribute the bikes in order to guarantee a desirable service level. Can we engage the users themselves to solve the imbalance problem in bike sharing systems? In this paper, we address this question and present a crowdsourcing mechanism that incentivizes the users in the bike repositioning process by providing them with alternate choices to pick or return bikes in exchange for monetary incentives. We design the complete architecture of the incentives system which employs optimal pricing policies using the approach of regret minimization in online learning. We investigate the incentive compatibility of our mechanism and extensively evaluate it through simulations based on data collected via a survey study. Finally, we deployed the proposed system through a smartphone app among users of a large scale bike sharing system operated by a public transport company, and we provide results from this experimental deployment. To our knowledge, this is the first dynamic incentives system for bikes re-distribution ever deployed in a real-world bike sharing system.
The COVID-19 pandemic breaking out at the end of 2019 has seriously impacted urban human mobility and poses great challenges for traffic management and urban planning. An understanding of this influence from multiple perspectives is urgently needed. In this study, we propose a multiscale geospatial network framework for the analysis of bike-sharing data, aiming to provide a new perspective for the exploration of the pandemic impact on urban human mobility. More specifically, we organize the bike-sharing data into a network representation, and divide the network into a three-scale structure, ranging from the whole bike system at the macroscale, to the network community at the mesoscale and then to the bicycle station at the microscale. The spatiotemporal analysis of bike-sharing data at each scale is combined with visualization methods for an intuitive understanding of the patterns. We select New York City, one of the most seriously influenced city by the pandemic, as the study area, and used Citi Bike bike-sharing data from January to April in 2019 and 2020 in this area for the investigation. The analysis results show that with the development of the pandemic, the riding flow and its spatiotemporal distribution pattern changed significantly, which had a series of effects on the use and management of bikes in the city. These findings may provide useful references during the pandemic for various stakeholders, e.g., citizens for their travel planning, bike-sharing companies for bicycle dispatching and bicycle disinfection management, and governments for traffic management.
The coronavirus disease 2019 (COVID-19) pandemic has had a rapid and significant effect on human mobility because of the travel restriction to slow the spread of the infectious disease. However, the impact caused by COVID-19 is not the same for all modes of transportation. In previous studies, public transport has shown the greatest decline compared with other modes, and bike-sharing systems have been less affected by COVID-19 than public transport. This study aims to investigate the impact of COVID-19 on bike-sharing systems in detail over a longer period than previous studies to determine the changes in the ridership and usage patterns of bike-sharing systems depending on the circumstances related to COVID-19. This study found that bike rentals for leisure purposes rather than for means of transportation have increased during the COVID-19 pandemic, which tends to be more distinct during outbreaks. Moreover, it was also shown that the status of COVID-19 and the strong social distancing affected bike rentals, and the effects of some factors related to bike-sharing ridership on bike rentals have significantly changed because of the change in the mobility patterns.
Studies on bike-share programs have dramatically increased during the past decades. While numerous studies have examined various factors affecting bike-share demand at the station-level, few attempts have been made to understand bike-share ridership at the origin-destination (OD) level due to technical difficulties. The objective of this study is to examine whether existing public transit characteristics affect bike-share ridership at OD-level. We combined three datasets: (1) bike-share ridership data, (2) land-use and bike-transit infrastructure, and (3) bike-transit route characteristics between OD pairs of bike stations. Zero-inflated negative binomial (ZINB) regression models were used for the analysis. Our results showed that the travel distance between OD bike stations, land-use compositions, and the existence of bike-friendly infrastructures were significant factors determining bike-share ridership at the OD-level. In particular, a longer duration of public transit trips than bike-share, and more transit transfers, were associated with bike-share ridership. Further, this study showed that bike-share and public transit might compete with or promote each other, even within the city. The study's findings suggest that the relative efficiency of bike-share compared to public transit is highly associated with bike-share demand and help to increase the utility of bike-share system in response to several limitations of existing public transit networks.
Bike sharing can leverage its physical distancing advantages for responding to the COVID-19 pandemic, but system management and communication are essential to support healthy transportation. This study addresses the need to understand the range of bike share systems’ responses to the pandemic by reviewing bike share system cases in the United States and reports survey responses from bike share users in San Antonio (TX). Five out of eleven bike share systems communicated their responses to the pandemic online at the time of review. 43% of survey respondents who were unemployed due to the pandemic reported increasing use of the bike share system, whereas 36% of employed respondents decreased ridership. Most respondents were unaware of the bike share operator’s steps to control the spread of COVID-19 for users. Moderate-frequency riders (1-2 times per month) may increase bike sharing the most after Coronavirus restrictions are lifted, from 22% of respondents to 34%. Based on our findings, we suggest bike share operators should expand communication efforts about policies and actions to support community health, explore how to serve unemployed and low-income communities best, and prepare for the equitable expansion of ridership following the pandemic.
The generalized bike-sharing rebalancing problem (BRP) entails driving a fleet of capacitated vehicles to rebalance bicycles among bike-sharing system stations at a minimum cost. To solve this NP-hard problem, we present a highly effective memetic algorithm that combines (i) a randomized greedy construction method for initial solution generation, (ii) a route-copy-based crossover operator for solution recombination, and (iii) an effective evolutionary local search for solution improvement integrating an adaptive randomized mutation procedure. Computational experiments on real-world benchmark instances indicate a remarkable performance of the proposed approach with an improvement in the best-known results (new upper bounds) in more than 46% of the cases. In terms of the computational efficiency, the proposed algorithm shows to be nearly two to six times faster when compared to the existing state-of-the-art heuristics. In addition to the generalized BRP, the algorithm can be easily adapted to solve the one-commodity pickup-and-delivery vehicle routing problem with distance constraints, as well as the multi-commodity many-to-many vehicle routing problem with simultaneous pickup and delivery.
Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes. A Data mining technique is employed for overcoming the hurdles for the prediction of hourly rental bike demand. This paper discusses the models for hourly rental bike demand prediction. Data used include weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information. The paper also explores an filtering of features approach to eliminate the parameters which are not predictive and ranks the features based on its prediction performance. Five Statistical regression models were trained with their best hyperparameters using repeated cross-validation and the performance is evaluated using a testing set: (a) Linear Regression (b) Gradient Boosting Machine (c) Support Vector Machine (Radial Basis Function Kernel) (d) Boosted Trees, and (e) Extreme Gradient Boosting Trees. When all the predictors are employed, the best model Gradient Boosting Machine can give the best and highest R² value of 0.96 in the training set and 0.92 in the test set. Furthermore, several analyzes are carried out in Gradient Boosting Machine with different combinations of predictors to identify the most significant predictors and the relationships between them.
This paper aimed to address the need for a comprehensive review on the factors affecting bike-sharing demand to bridge the gaps by deepening the knowledge on weather, built environment and land use, public transportation, station level, socio-demographic effects, temporal factors, and safety. This article evaluates recent studies on station-based bike sharing in literature and seeks answers to two main research questions: First, how do the weather conditions, built environment and land use, public transportation, socio-demographic attributes, temporal factors, and safety affect the bike-sharing trip demand? Second, what are the most commonly used factors in literature affecting trip demand? For this purpose, an overview of the factors affecting trip demands has been established to evaluate the performance of Bike-Share Programs(BSPs) comprehensively. The results can provide reliable estimate for planners or decision-makers in understanding the key factors contributing to bike-sharing demand. The information obtained from this overview can also be a guideline for BSP planners, policymakers and researchers to improve the efficiency of BSPs.
As a green travel mode, bike-sharing system is becoming more and more important in urban traffic. Due to the uneven distribution of users’ demand in time and space, researchers focus on the rebalancing problems, but few of them study the dynamic time series characteristics of demand changes which is helpful to understand the user’s behavior mechanism and optimize the management strategy. Firstly, the demand fluctuation “mode” is defined by using statistical physics method, and the directed-weighted networks of the demand variation of Station Based and Free Floating Bike-sharing systems in different periods are established, the cumulative time rule of the new nodes is also given. Evolutionary differences in node strength and its distribution, average path length and Betweenness of two networks are compared, the characteristics of key modes and the transformation relationship between modes are revealed. Finally, a network similarity measure function is constructed to quantify the dependence of demand fluctuation between two systems. This paper not only describes the demand fluctuation of different bike-sharing systems in more details than other method, but also lays a foundation for green travel demand prediction. This can help operators or governments rebalance the uneven demand in time, which can make the supply more effectively match the demand.
In this paper we deal with the stochastic version of a problem arising in the context of self-service bike sharing systems, which aims at determining minimum cost routes for a fleet of homogeneous vehicles in order to redistribute bikes among stations. The Bike sharing Rebalancing Problem with Stochastic Demands is a variant of the one-commodity many-to-many pickup and delivery vehicle routing problem where demands at each station are represented by random variables, with associated probability distributions, that depend on stochastic scenarios. We develop stochastic programming models that are solved using different approaches, in particular, the L-Shaped and branch-and-cut. Moreover, we also propose heuristic algorithms based on an innovative use of positive and negative correlations among stations’ stochastic demands, as well as an efficient strategy for checking feasibility. The proposed solution approaches are evaluated and compared by means of extensive computational experiments on newly realistic benchmark instances.
Problem, research strategy, and findings: Planners increasingly involve stakeholders in co-producing vital planning information by crowdsourcing data using online map-based commenting platforms. Few studies, however, investigate the role and impact of such online platforms on planning outcomes. We evaluate the impact of participant input via a public participation geographic information system (PPGIS), a platform to suggest the placement of new bike share stations in New York City (NY) and Chicago (IL). We conducted 2 analyses to evaluate how close planners built new bike share stations to those suggested on PPGIS platforms. According to our proximity analysis, only a small percentage of built stations were within 100 feet (30 m) of suggested stations, but our geospatial analysis showed a substantial clustering of suggested and built stations in both cities that was not likely due to random distribution. We found that the PPGIS platforms have great promise for creating genuine co-production of planning knowledge and insights and that system planners did take account of the suggestions offered online. We did not, however, interview planners in either system, and both cities may be atypical, as is bike share planning; moreover, multiple factors influence where bike stations can be located, so not all suggested stations could be built.
Takeaway for practice: Planners can use PPGIS and similar platforms to help stakeholders learn by doing and to increase their own local knowledge to improve planning outcomes. Planners should work to develop better online participatory systems and to allow stakeholders to provide more and better data, continuing to evaluate PPGIS efforts to improve the transparency and legitimacy of online public involvement processes.
This paper investigates cyclist route choices using global positioning system (GPS) data collected from 750 bicycles in Hamilton, Ontario's bike share system – SoBi (Social Bicycles) Hamilton. A dataset containing 161,426 GPS trajectories describing observed routes of cyclists using SoBi bikes over a 12-month period (April 1, 2015 to March 31, 2016) is used for analysis. This study groups trips by origin-destination hub pairs and uses a GIS (geographic information system)-based map-matching algorithm to generate routes along with attributes such as length, number of intersections, number of turns, and unique road segments. Unique routes and their use frequencies are extracted from all the hub-to-hub trips using a GIS-based link signature extraction tool developed for this research. The most popular routes between hubs taken by cyclists are then identified as dominant routes and their attributes are compared to those of corresponding shortest path routes derived by minimizing distance traveled. The comparison finds significant differences in multiple attributes, and demonstrates that dominant routes are significantly longer than their shortest distance counterparts, suggesting that cyclists are willing to detour for routes characterized by positive features such as bicycle facilities and low traffic volumes. Detouring does, however, come at a cost – increases in number of turns and number of intersections. This research not only enhances our understanding of cyclist route preferences within a bike share system, it also presents a GIS-based approach for identifying potential locations for future bike facilities based on such preferences.
–Several benefits have contributed to the increasing popularity of bike-share systems in cities around the world. In addition to traffic congestion, environmental concerns are also compelling cities to seek more sustainable modes of transportation. A key factor in the efficacy of bike-share networks is the location of bike stations in relation to potential related criteria. Therefore, site suitability analysis for bike-share stations using quantitative methods is essential. This study attempted to evaluate the current status of bike-share stations in Karsiyaka, Izmir, and to locate future station sites by comparing them to existing stations. To do so, different multi-criteria decision-making methods were combined with a geographic information system (GIS) to address twelve conflicting criteria. Specifically, the analytic hierarchy process was applied to obtain criteria weights, and multi-objective optimization by ratio analysis was used to evaluate current and potential alternatives. Our study demonstrates the superiority of the suggested locations compared to the existing stations.
There is a growing literature on the changing travel patterns in the United States. The changes are largely driven by the emerging shared-mobility services and the travel behavior of the younger generations. This study builds on an investigation of Millennials’, Gen Xers’ and Baby Boomers’ bike sharing ridership in New York City. This study examines station-level bike share use focusing on whether and how the effects of land-use and built environment vary across different population segments. Using New York’s Citi Bike system data, we develop zero-inflated negative binominal models to estimate hourly trip productions at stations for five age cohorts: younger Millennials (born 1995 to 2000), mid Millennials (1989 to 1994), older Millennials (born 1979 to 1988), Generation Xers (born 1965 to 1978), and Baby Boomers (born 1946 to 1964). Consistent with the literature, our results suggest that weather related variables, land-use and built environment characteristics have significant effects on the overall bike sharing usage. Our findings also reveal variations across age cohorts. For example, intersection density is positively related to younger Millennials’ bike share trip production. However, this factor is not statistically significant for other age groups. Our findings provide valuable insights for planners and policy-makers, and set the basis for improving the understanding of cohort differences in bike sharing demand.
Public bicycle systems are widely spread across many cities worldwide. ‘Tashu’, a public bicycle sharing system in Daejeon, was installed in 2009 and it is one of the well-established public bike-sharing systems in South Korea. Previous studies in the literature found that in general, bicycling is affected by weather conditions and temporal characteristics. However, the degrees of impacts or the signs of effects may be different depending on the stations. Therefore, this study investigated the different effects of weather conditions and temporal characteristics according to the characteristics of the stations at the station level analysis in addition to the system level analysis. For the cost-effective station level analysis, clustering analysis was utilized to find out the groups of the stations with the similar properties. Moreover, temperature humidity index (THI) and the indicator variable of heatwaves were introduced to consider the interaction between temperature and humidity and measure the influence of high temperature, which has been rarely considered. In the system level analysis, the results showed that the selected factors have the different influence over the different time periods within a day. Especially, scorching heat and non-working days differently affect the demand for public bikes by hours. Also, it was observed that high temperature over 30 °C reduces the bicycle usage, which revealed the necessity of taking into account not only severe colds but also heatwaves in the prediction of the demand. By clustering analysis, the stations were partitioned into the three clusters. One cluster shows the strong peak in the morning while two others have peaks in the evening. The effects of weather conditions and non-working days on the demand for public bicycles were different depending on the clusters, which seemed to be related to the main purposes of bike usage in the clusters.
This paper analyzes the socio-demographic profile and travel behavior of the “Velo’v” bikesharing scheme annual members in Lyon (France). This scheme started in 2005 and has now around 350 stations and 4500 bikes in operation, with more than 50,000 annual members. By the means of an Internet-based survey more than 3000 respondents were described by their detailed socio-demographic profile, their travel means and habits, a one-day activity-travel diary and additionally a seven days activity-travel diary filled by around 700 volunteers. By this way the survey covers all travel modes and day-to-day variations in travel behavior beyond the sole use of shared bike. We analyze with a discrete choice model the socio-demographic and spatial factors affecting the probability of being an annual member of the Velo’v scheme. Then we compare with descriptive statistics their daily travel behavior involving as well bike sharing as other traditional
modes to the travel behavior of the general population as given with the latest Household Travel Survey available in the Lyon area (2015). The majority of Velo’v annual members are male, younger and hold higher social positions when compared with the Lyon’s general population. An individual higher social position and the residential proximity to stations have both separate and positive effects on the probability of being an annual member of the service. Velo’v members are not captive from public transport, a majority of them have access to a car and they are fully multimodal in their day-to-day travel behavior. Velo’v bikes are used by them for any activity, not necessarily every day, like any other travel mode. The multimodal behavior of Velo’v members shows that Velo’v supply fits especially a demand not satisfied when the public transport station is too distant from home.
Public Bicycle Sharing Programs (PBSPs) have become a prominent feature across city spaces worldwide. In less than a decade, PBSPs have grown from a small number of European cities to include five continents and in excess of 200 schemes. Despite the rapid rise of this new transport opportunity, there has been limited research on the underlying dynamics of these schemes, arguably reflecting a lack of detailed data available to researchers. The current paper redresses the observed deficit using trip level data from Brisbane’s ‘CityCycle’, the largest PBSP in Australia. These data provide an opportunity to investigate the spatio-temporal dynamics of a large PBSP system, specifically the effects of weather and calendar events on the geographic and temporal patterning of public bicycle use.
Employing novel spatial analytical techniques we explore the impact of site specific weather conditions and calendar events on the spatio-temporal dynamics of the case study PBSP. We conclude by highlighting how the results from such analyses may form part of an evidence base for policy makers, providing insights into ‘best practice’ and potentially informing future PBSP expansions to further enhance uptake of this non-motorised urban transport mode.
Although ecological connectivity conservation in urban areas has recently been recognized as an important issue, less is known about its relationship to urban form and landscape pattern. This study investigates how urban morphology influences regional ecosystem pattern and landscape connectivity. Two metropolitan landscapes, Phoenix, AZ, USA, and Izmir, Turkey, were compared, both of which are fast-growing regions in their national context. A wide range of variables were considered for identifying natural and urban properties. The natural characteristics include typology of urban ecosystems, urban to natural cover ratio, dominant habitat type, urban biodiversity, landscape context, and connectivity conservation efforts. Urban parameters examine urban form, urban extent, urban cover proportion, growth rate, populations, urban gradient, major drivers of urbanization, urban density, and mode/approach of urban development. Twelve landscape metrics were measured and compared across the natural patches. Results show that there is little difference in landscape connectivity in the rural zones of Phoenix and Izmir, although Phoenix has slightly higher connectivity values. The connectivity variance in urbanized areas, however, is significantly dependent on the region. For example, Phoenix urban zones have substantially lower connectivity than either urban or suburban zones in Izmir. Findings demonstrate that small and compact urban settlements with more dense populations are more likely to conserve landscape connectivity compared to multiple-concentric but amalgamated urban form spreading all over the landscape (aka urban sprawl).
This report describes a program, FRAGSTATS, developed to quantify landscape structure. Two separate versions of FRAGSTATS exist: one for vector images and one for raster images. In this report, each metric calculated by GRAGSTATS is described in terms of its ecological application and limitations. Example landscapes are included, and a discussion is provided of each metric as it relates to the sample landscapes. -from Authors