Evaluating tramway infrastructure on biodiversity and ecosystem services

Tramways in urban areas for mass transit has been suggested to have a lower environmental footprint than roads. However, studies on the impact of tramways and the surrounding infrastructure on biodiversity is extremely rare despite the potential ecological effects associated with this anthropogenic feature. Surprisingly, we found fewer than 10 papers published on tramway-wildlife interactions, which is significantly lower (vs dozens of thousands) than that of other transportation methods. As tramways and stations may be managed sustainably by planting short vegetation on the track and roofs of tramway stations, they may be good examples of land-sharing policies in green urban planning, improving both biodiversity and people's well-being. The potential environmental benefits of green practices for commercially available tramways should be strictly tested and applied, especially in the context of the growing popularity of tramway systems worldwide.

A curated data resource to support safe carbon dioxide transport-route planning

Supporting the national target of net-zero greenhouse gas emissions in the United States by 2050, the Bipartisan Infrastructure Law (BIL) authorized investments into carbon capture and storage (CCS), highlighting the need for the safe and sustainable transport of carbon dioxide (CO2). Curated to support CO2 pipeline route planning optimization and assess existing energy transport corridors, the CCS Pipeline Route Planning Database is a compilation of 47 publicly available, authoritative geospatial data resources, spanning the contiguous U.S., and some including Alaska and Hawaii. Key considerations were identified following comprehensive literature review, which included state legislation, known pipeline stressors, and energy, environmental, and social justice (EJSJ) considerations. Data layers were sorted into relevant categories (i.e., natural hazards, boundaries) and assigned preliminary weights representing potential social, environmental, and economic costs associated with routing pipelines. Version one of the CCS Pipeline Route Planning Database, made available on the Energy Data eXchange® (EDX), contains categorized vector features representing protected areas, public and energy infrastructure, EJSJ factors, potential risks, federal and state regulations and legislation, and natural features, along with associated metadata. This paper provides details on individual layers, methods used to identify data needs, acquire, and process the disparate data, as well as planned enhancements to future versions of this database.

Impact of transportation infrastructure and urbanization on environmental pollution: evidence from novel wavelet quantile correlation approach

In terms of achieving sustainable development goals (SDGs), the developing economies are facing many issues, and one of the key issues is environmental degradation. Being a developing economy, Pakistan is also experiencing thought-provoking impacts of global warming and still far away from the ideal track of sustainable development. For addressing environment-related issue and achieving the targets of SDGs, a policy-level reorientation might be necessary. In this view, this study investigates the impact of economic growth, transport infrastructure, urbanization, financial development, and renewable energy consumption on CO2 emissions by using the data of Pakistan during 1990-2020. For this purpose, we use novel wavelet quantile correlation approach. The empirical results of wavelet quantile correlation approach demonstrate that economic growth, transport infrastructure, urbanization, and financial development are responsible for environmental pollution. Whereas, result also claims that renewable energy consumption is a useful tool for reducing environmental pollution in Pakistan. Moreover, the results of FMOLS approach show that 1% increase in economic growth, transportation infrastructure, urbanization, and financial development increases CO2 emissions by 0.240, 0.010, 0.478, and 0.102%, respectively. However, 1% increase in renewable energy usage reduces CO2 emission by 1.083%. Based on the empirical outcomes, this study proposes comprehensive policy framework for achieving the targets of SDG 7 (clean energy), SDG 8 (economic growth), SDG 11 (sustainable cities and communities), and SDG 13 (climate action).

Rapid behavioral responses of endangered tigers to major roads during COVID-19 lockdown

Roads pose a major, and growing, challenge for the conservation of endangered species. However, very little is known about how endangered species behaviorally respond to roads and what that means for road mitigation strategies. We used the nation-wide lockdown in Nepal during the COVID-19 pandemic as a natural experiment to investigate how dramatic reductions in traffic volume along the national highway affected movements of two GPS-collared tigers (Panthera tigris)-a globally endangered species. This work is the first systematic research on tigers in Nepal using radiotelemetry or GPS tracking data since the 1980s. We found that the highway more strongly constrained the space use and habitat selection of the male in Parsa National Park than the female in Bardia National Park. Over the entire study period, the female on average crossed 10 times more often per week than the male, and when he was near the highway, he was over 11 times more probable to not cross it than to cross during the day. However, we also found that the cessation of traffic during the pandemic lockdown relaxed tiger avoidance of roads and made the highway more permeable for both animals. They were 2-3 times more probable to cross the highway during the lockdown than before the lockdown. In the month following the lockdown, the space use area of the male tiger tripled in size (160-550 km²), whereas the female's shrunk to half its previous size (33-15 km²). These divergent patterns likely reflect differences between the two parks in their highway traffic volumes and regulations as well as ecological conditions. Our results provide clear evidence that vehicle traffic on major roads impede tiger movements, but also that tigers can respond quickly to reductions in human pressures. We conclude by identifying various actions to mitigate road impacts on tigers and other endangered species.

Where do knowledge-intensive firms locate in Germany?-An explanatory framework using exponential random graph modeling

This paper analyzes how positional and relational data in 186 regions of Germany influence the location choices of knowledge-based firms. Where firms locate depends on specific local and interconnected resources, which are unevenly distributed in space. This paper presents an innovative way to study such firm location decisions through network analysis that relates exponential random graph modeling (ERGM) to the interlocking network model (INM). By combining attribute and relational data into a comprehensive dataset, we capture both the spatial point characteristics and the relationships between locations. Our approach departs from the general description of individual location decisions in cities and puts extensive networks of knowledge-intensive firms at the center of inquiry. This method can therefore be used to investigate the individual importance of accessibility and supra-local connectivity in firm networks. We use attributional data for transport (rail, air), universities, and population, each on a functional regional level; we use relational data for travel time (rail, road, air) and frequency of relations (rail, air) between two regions. The 186 functional regions are assigned to a three-level grade of urbanization, while knowledge-intensive economic activities are grouped into four knowledge bases. This research is vital to understand further the network structure under which firms choose locations. The results indicate that spatial features, such as the population of or universities in a region, seem to be favorable but also reveal distinct differences, i.e., the proximity to transport infrastructure and different valuations for accessibility for each knowledge base.

Data set for French historical light aids to navigation (F-LAN) covering the period 1775-1929

F-LAN is a geospatial data set that documents hundreds of coastal lights that guided ships around France from medieval times to 1929. F-LAN provides visibility range for individual lights. The authors collected all data from scholarly literature, historical coastal navigational charts, and official lighthouse surveys. F-LAN allows users to track the provision of coastal lighting over time. It complements the existing LAN dataset for England and Wales. Thanks to F-LAN it is now possible to map and analyse light coverage for France over two centuries. It can be used to analyse coastal routing, infrastructure investment and the relationship between accidents and the provision of light in any given area.

Air pollution emission effects of changes in transport supply: the case of Bogotá, Colombia

Transportation policy and planning decisions, including decisions on new infrastructure and public transport improvements, affect local and global environmental conditions. This work studies the effect of increased road capacity on traffic-related emissions in Bogotá using a tool that couples a transportation model with emission factors from COPERT IV. We followed a parametric approach varying transport supply and demand, comparing three scenarios: a baseline scenario that represents the transportation system in Bogota in 2015; scenario 1 incorporates five highway capacity-enhancement projects in Bogotá and assumes insensitive travel demand; scenario 2 incorporates the new highway projects but assumes a demand increase of 13% in vehicle trips with private cars. Results include daily and annual values of traffic-related emissions of five air pollutant criteria: CO, NO_x, PM₁₀, SO₂, and VOC for the baseline scenario, scenario 1, and scenario 2. We found a reduction in emissions after adding highway capacity and assuming inelastic demand (scenario 1). Scenario 1 results in a 15% reduction in PM₁₀ emissions and a 10% reduction in NO_x emissions. In contrast, results for scenario 2 suggest increased emissions for all air pollutant criteria (e.g., VOC and CO emissions increase by 21% and 22% compared with the baseline scenario). Therefore, new traffic demand would eliminate the emission savings observed in scenario 1 and could potentially further degrade air quality in Bogotá. While an exact estimate of induced demand that may result from highway expansion in Bogotá is not available, this analysis highlights that such projects could lead to an increase in emissions unless there is a combined effort to managing demand of private vehicle trips.

Assessing flood probability for transportation infrastructure based on catchment characteristics, sediment connectivity and remotely sensed soil moisture

Flooding may damage important transportation infrastructures, such as roads, railways and bridges, which need to be well planned and designed to be able to withstand current and possible future climate-driven increases in flood frequencies and magnitudes. This study develops a novel approach to predictive statistical modelling of the probability of flooding at major road-stream intersection sites, where water, sediment and debris can accumulate and cause failure of drainage facilities and associated road damages. Two areas in south-west Sweden, affected by severe floods in August 2014, are used in representative case studies for this development. A set of physical catchment-descriptors (PCDs), characterizing key aspects of topography, morphology, soil type, land use, hydrology (precipitation and soil moisture) and sediment connectivity in the water- and sediment-contributing catchments, are used for the predictive flood modelling. A main novel contribution to such modelling is to integrate the spatiotemporal characteristics of remotely-sensed soil moisture in indices of sediment connectivity (IC), thereby also allowing for investigation of the role of soil moisture in the flood probability for different road-stream intersections. The results suggest five categories of PCDs as especially important for flood probability quantification and identification of particularly flood-prone intersections along roads (railways, etc.) These include: channel slope at the road-stream intersection and average elevation, soil properties (mainly percentage of till), land use cover (mainly percentage of urban areas), and a sediment connectivity index that considers soil moisture in addition to morphology over the catchment.

Simulation of the hydraulic performance of highway filter drains through laboratory models and stormwater management tools

Road drainage is one of the most relevant assets in transport infrastructure due to its inherent influence on traffic management and road safety. Highway filter drains (HFDs), also known as "French Drains", are the main drainage system currently in use in the UK, throughout 7000 km of its strategic road network. Despite being a widespread technique across the whole country, little research has been completed on their design considerations and their subsequent impact on their hydraulic performance, representing a gap in the field. Laboratory experiments have been proven to be a reliable indicator for the simulation of the hydraulic performance of stormwater best management practices (BMPs). In addition to this, stormwater management tools (SMT) have been preferentially chosen as a design tool for BMPs by practitioners from all over the world. In this context, this research aims to investigate the hydraulic performance of HFDs by comparing the results from laboratory simulation and two widely used SMT such as the US EPA's stormwater management model (SWMM) and MicroDrainage®. Statistical analyses were applied to a series of rainfall scenarios simulated, showing a high level of accuracy between the results obtained in laboratory and using SMT as indicated by the high and low values of the Nash-Sutcliffe and R ² coefficients and root-mean-square error (RMSE) reached, which validated the usefulness of SMT to determine the hydraulic performance of HFDs.

Predicting and communicating flood risk of transport infrastructure based on watershed characteristics

This research aims to identify and communicate water-related vulnerabilities in transport infrastructure, specifically flood risk of road/rail-stream intersections, based on watershed characteristics. This was done using flooding in Värmland and Västra Götaland, Sweden in August 2014 as case studies on which risk models are built. Three different statistical modelling approaches were considered: a partial least square regression, a binomial logistic regression, and artificial neural networks. Using the results of the different modelling approaches together in an ensemble makes it possible to cross-validate their results. To help visualize this and provide a tool for communication with stakeholders (e.g., the Swedish Transport Administration - Trafikverket), a flood 'thermometer' indicating the level of flooding risk at a given point was developed. This tool improved stakeholder interaction and helped highlight the need for better data collection in order to increase the accuracy and generalizability of modelling approaches.

Sociospatial patterning of the use of new transport infrastructure: Walking, cycling and bus travel on the Cambridgeshire guided busway

BACKGROUND

New transport infrastructure may help promote active travel, thereby contributing to increasing overall physical activity and population health gain. In 2011 a guided busway with a path for walking and cycling was opened in Cambridgeshire, UK. This paper investigates the predictors of walking, cycling and bus use on the busway.

METHODS

Cross-sectional analyses of the final questionnaire wave (2012) of the Commuting and Health in Cambridge cohort study following the opening of the busway. Participants were 453 adult commuters who had not moved home or workplace. Busway use was self-reported and proximity calculated using GIS. Separate multivariable logistic regression models were used to assess predictors of walking, cycling and bus use on the busway.

RESULTS

Exposure to the intervention (proximity: the negative square root of the distance from home to busway in kilometres) increased the odds of use for cycling (OR 2.18; 95% CI 1.58 to 3.00), bus travel (OR 1.53, 95% CI 1.15 to 2.02) and walking (OR 1.34; 95% CI 1.05 to 1.70). The effect of exposure was strengthened in towns for bus use, and in towns and villages for walking, compared with urban areas. Men were more likely than women to have cycled on the busway, whereas individual socioeconomic characteristics did not predict bus use or walking.

CONCLUSION

New high-quality transport infrastructure attracts users, determined by geographical exposure and spatial contextual factors such as settlement size and availability of parking at work. Future longitudinal analyses will determine effects on overall travel and physical activity behaviour change.