Florida Bay Science Program: a synthesis of research on Florida Bay

This report documents the progress made toward the objectives established in the Strategic Plan revised in 1997 for the agencies cooperating in the program. These objectives are expressed as five questions that organized the research on the Florida Bay ecosystem: Ecosystem History What was the Florida Bay ecosystem like 50, 100, and 150 years ago? Question 1—Physical Processes How and at what rates do storms, changing freshwater flows, sea level rise, and local evaporation and precipitation influence circulation and salinity patterns within Florida Bay and exchange between the bay and adjacent waters? Question 2—Nutrient Dynamics What is the relative importance of the influx of external nutrients and of internal nutrient cycling in determining the nutrient budget for Florida Bay? What mechanisms control the sources and sinks of the bay’s nutrients? Question 3—Plankton Blooms What regulates the onset, persistence, and fate of planktonic algal blooms in Florida Bay? Question 4—Seagrass Ecology What are the causes and mechanisms for the observed changes in the seagrass community of Florida Bay? What is the effect of changing salinity, light, and nutrient regimes on these communities? Question 5—Higher Trophic Levels What is the relationship between environmental and habitat change and the recruitment, growth, and survivorship of animals in Florida Bay? Each question examines different characteristics of the Florida Bay ecosystem and the relation of these to the geomorphological setting of the bay and to processes linking the bay with adjacent systems and driving change.This report also examines the additional question of what changes have occurred in Florida Bay over the past 150 years

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management

improving parking availability prediction in smart cities with iot and ensemble based model

Abstract Smart cities are part of the ongoing advances in technology to provide a better life quality to its inhabitants. Urban mobility is one of the most important components of smart cities. Due to the growing number of vehicles in these cities, urban traffic congestion is becoming more common. In addition, finding places to park even in car parks is not easy for drivers who run in circles. Studies have shown that drivers looking for parking spaces contribute up to 30% to traffic congestion. In this context, it is necessary to predict the spaces available to drivers in parking lots where they want to park. We propose in this paper a new system that integrates the IoT and a predictive model based on ensemble methods to optimize the prediction of the availability of parking spaces in smart parking. The tests that we carried out on the Birmingham parking data set allowed to reach a Mean Absolute Error (MAE) of 0.06% on average with the algorithm of Bagging Regression (BR). This results have thus improved the best existing performance by over 6.6% while dramatically reducing system complexity

Performance evaluation of stochastic systems with dedicated delivery bays and general on-street parking

As freight deliveries in cities increase due to retail fragmentation and e-commerce, parking is becoming a more and more relevant part of transportation. In fact, many freight vehicles in cities spend more time parked than they are moving. Moreover, part of the public parking space is shared with passenger vehicles, especially cars. Both arrival processes and parking and delivery processes are stochastic in nature. In order to develop a framework for analysis, we propose a queueing model for an urban parking system consisting of delivery bays and general on-street parking spaces. Freight vehicles may park both in the dedicated bays and in general on-street parking, while passenger vehicles only make use of general on-street parking. Our model allows us to create parsimonious insights into the behavior of a delivery bay parking stretch as part of a limited length of curbside. We are able to find explicit expressions for the relevant performance measures, and formally prove a number of monotonicity results. We further conduct a series of numerical experiments to show more intricate properties that cannot be shown analytically. The model helps us shed light onto the effects of allocating scarce urban curb space to dedicated unloading bays at the expense of general on-street parking. In particular, we show that allocating more space to dedicated delivery bays can also make passenger cars better off

Exploration of Older Adults’ Travel Behavior and Their Transportation Barriers

Both the number of older adults and their proportion of the population are increasing rapidly in the United States. By 2040, about 20.7% of the U.S. population will be 65 and older (Harrison & Ragland, 2003a). These dramatic changes in the composition of the population will bring new challenges to the provision of transportation services. This is because the travel patterns and needs of older adults are likely to become more complicated. A growing number of people will find it increasingly difficult to meet their transportation needs. As the life expectancy of older adults is likely to continue to increase, a greater number of older people will face mobility issues alone (Alsnih & Hensher, 2003). Researchers widely agree that the aging population in the U.S. relies heavily on cars (as drivers or passengers) because they are convenient, flexible, and allow them to live independently and participate in normal daily activities (Haustein, 2012; Rosenbloom, 2005). However, dispersed land use patterns in the United States, the growing number of older adults living in suburban areas, and the current transportation infrastructure in the country make the use of a car a necessity rather than an option for a large proportion of older adults. However, as they age, their physical and mental health deteriorates, making driving dangerous for them. Therefore, it is of great importance to understand the transportation problems of older adults and provide them with reliable and acceptable alternative modes of transportation to help them meet their transportation needs. The study presented here aims to examine the transportation problems of older adults living in urban and suburban areas, make policy recommendations, and identify effective strategies to help them meet their mobility needs. To this end, the study used a mixed-method approach to identify the factors that influence older adults\u27 travel behavior and the issues they face when walking, biking, and using transit. In-depth, one-on-one surveys were conducted in three counties in southeastern Wisconsin with 178 English-speaking older adults aged 65 and older living independently in institutionalized senior housing (i.e., subsidized housing and retirement communities) and in noninstitutionalized buildings. The first main chapter of the thesis (Chapter 4) examines the factors that influence older adults\u27 mode choice for grocery shopping and aims to predict older adults\u27 travel behavior for going to the grocery store. A quantitative analysis involving statistical and machine learning techniques was conducted with older adults who traveled to the grocery store by car, carpool, walking, or public transit (N=153). The results of the study show that household car ownership and having a valid driver\u27s license are the most important factors influencing travel mode choice by older adults. However, age group (65-74 or 75+) and physical disability were not significant factors influencing older adults\u27 choice of transportation mode for grocery shopping. The second main chapter of this study (Chapter 5) examines the reasons why older adults who hold a valid driver\u27s license intend to renew their license when it expires (yes), or whether they do not intend to do so or are hesitant (no/not sure). Using a mixed-method approach including binomial logit regression and qualitative analysis, 116 older adults were surveyed. Results suggest that being 75 years of age and older, having a physical disability, and having a lower level of education (high school and below) negatively influence older adults\u27 decision to renew their driver\u27s license. Older adults who drive frequently and indicate that they would like to be able to drive to destinations easily are more likely to renew their driver\u27s license after it expires. The third main chapter of this thesis (Chapter 6) aims to examine the barriers and challenges older adults face when using modes of transportation other than the personal automobile, such as walking, bicycling, public transit, and ride-hailing. A qualitative content analysis of the 103 open-ended responses was used to fit the results into an ecological model. The study recommends four main actions to help policymakers and city governments overcome these barriers: (1) implement transportation education and outreach programs, (2) improve accessibility to services and facilities through land use policies, (3) improve transportation infrastructure and services, and (4) help for-profit and nonprofit organizations organize informal groups to walk, bike, or carpool together. This thesis has important implications for policy makers and urban practitioners to meet the transportation needs of older adults. Improving transportation infrastructure and providing older adults with reliable and high-standard non-automobile transportation alternatives, managing future land use dynamics and investing in sustainable land use patterns, and coordinating with organizations to support social networks (such as informal clubs and local groups) that help older adults meet their travel needs are among some of these important implications

Increasing Transit Ridership: Lessons from the Most Successful Transit Systems in the 1990s, MTI Report-01-22

This study systematically examines recent trends in public transit ridership in the U.S. during the 1990s. Specifically, this analysis focuses on agencies that increased ridership during the latter half of the decade. While transit ridership increased steadily by 13 percent nationwide between 1995 and 1999, not all systems experienced ridership growth equally. While some agencies increased ridership dramatically, some did so only minimally, and still others lost riders. What sets these agencies apart from each other? What explains the uneven growth in ridership

The sources, impact and management of car park runoff pollution: a review

Traffic emissions contribute significantly to the build-up of diffuse pollution loads on urban surfaces with their subsequent mobilisation and direct discharge posing problems for receiving water quality. This review focuses on the impact and mitigation of solids, metals, nutrients and organic pollutants in the runoff deriving from car parks. Variabilities in the discharged pollutant levels and in the potentials for pollutant mitigation complicate an impact assessment of car park runoff. The different available stormwater best management practices and proprietary devices are reported to be capable of reductions of between 20% and almost 100% for both suspended solids and a range of metals. This review contributes to prioritising the treatment options which can achieve the appropriate pollutant reductions whilst conforming to the site requirements of a typical car park. By applying different treatment scenarios to the runoff from a hypothetical car park, it is shown that optimal performance, in terms of ecological benefits for the receiving water, can be achieved using a treatment train incorporating permeable paving and bioretention systems. The review identifies existing research gaps and emphasises the pertinent management practices as well as design issues which are relevant to the mitigation of car park pollution

ICT for Sustainable Last-Mile Logistics: Data, People and Parcels

In this paper we present a vision of how ICT can be leveraged to help combat the impact on pollution, congestion and carbon emissions contributed by the parcel delivery sector. This is timely given annual growth in parcel deliveries, especially same-day deliveries, and the need to inform initiatives to clean up our cities such as the sales ban on new petrol and diesel vehicles in the UK by 2040. Our insights are informed by research on parcel logistics in Central London, leveraging a data set of parcel manifests spanning 6 months. To understand the impact of growing e-commerce trends on parcel deliveries we provide a mixed methods case study leveraging data-driven analysis and qualitative fieldwork to demonstrate how ICT can uncover the impact of parcel deliveries on delivery drivers and their delivery rounds during seasonal deliveries (or “the silly season”). We finish by discussing key opportunities for intervention and further research in ICT4S and co-created Smart Cities, connecting our findings with existing research and data as a call to the ICT4S community to help tackle the growth in carbon emissions, pollution and congestion linked to parcel deliveries