Seasonal variation of riverine nutrient inputs in the northern Bay of Biscay (France), and patterns of marine phytoplankton response

Seasonal variations in nutrient inputs are described for the main rivers (Loire and Vilaine) flowing into the northern Bay of Biscay. The river plumes are high in N/P ratio in late winter and spring, but not in the inner plume during the summer. Conservative behavior results in most nutrients entering the estuary and eventually reaching the coastal zone. Temporal and spatial aspects of phytoplankton growth and nutrient uptake in the northern Bay of Biscay distinguish the central area of salinity 34 from the plume area. The first diatom bloom appears offshore in late winter, at the edge of the river plumes, taking advantage of haline stratification and anticyclonic “weather windows.” In spring, when the central area of the northern shelf is phosphorus-limited, small cells predominate in the phytoplankton community and compete with bacteria for both mineral and organic phosphorus. At that period, river plumes are less extensive than in winter, but local nutrient enrichment at the river mouth allows diatom growth. In summer, phytoplankton become nitrogen-limited in the river plumes; the central area of the shelf is occupied by small forms of phytoplankton, which are located on the thermocline and use predominantly regenerated nutrients.     

Statistical modelling for ship propulsion efficiency

This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks and Gaussian processes (GP). The data presented is a publicly available full-scale data set, with a whole range of features sampled over a period of 2 months. We further discuss interpretations of the operational data in relation to the underlying physical system.     

Testing electric vehicle demand in ‘hybrid households’ using a reflexive survey

The debate over electric vehicles (EVs) pivots largely on issues of market demand: will consumers purchase a vehicle that provides substantially less driving range, yet can be refueled at home, than an otherwise comparable gasoline vehicle? Also, what role do other unique attributes of EVs play in the purchase decision? Most previous studies find that limited driving range is a serious market barrier; many of those same studies ignore or under-value other novel attributes. To probe these future consumer decision processes deeply and robustly, we first devised and conducted detailed, interactive and experiment-oriented interviews. Then, incorporating what we learned, we designed an innovative mail survey and administered it to 454 multi-car households in California. The four-stage mail survey included a video of EV use and recharging and other informational material, completion of a 3-day trip diary and map of activity locations, and vehicle choice experiments. In addition to propulsion systems, respondents made choices of body styles, driving ranges, and other features. We formalized and tested what we call the hybrid household hypothesis: households who choose EVs will be purposefully diversifying their vehicle holdings to achieve the unique advantages of different propulsion systems. The hypothesis is supported, given the assumptions in our experimental design. In fact, a significantly larger number of EVs are chosen than the minimum number that would support our hypothesis. We find that purchases of battery-powered EVs by hybrid households would account for between 7 and 18% of annual light duty vehicle sales in California. EVs sold to fleets and other households would be in addition to those identified by this study.     

High downforce race car vertical dynamics: aerodynamic index

Race car performance is strongly affected by aerodynamics. Due to downforce generated by the vehicle floor (i.e. diffuser), vehicle ride heights are key parameters to improve performance, and the coupling of aerodynamics and suspension is one of the key points of race car setting. This work focuses on the suspension and aerodynamic coupling from the vertical dynamics point of view. Besides road holding performance, for race cars, aerodynamic performance and stability are major factors. Downforce decreases laptime (the main performance target) but pitch instability is a non-desired effect that can happen in high downforce race cars. A new vertical dynamic performance index is proposed through the use of simulation to improve aerodynamic performance and understand the pitch instability phenomenon. This new index uses all relevant vehicle nonlinearities related to vertical dynamics and can handle a specific track profile and vehicle speed range, allowing the analysis be conducted according to a circuit specification. A previously validated Formula 3 car model was used as an example.     

Incorporating crowding into the San Francisco activity-based travel model

Information produced by travel demand models plays a large role decision making in many metropolitan areas, and San Francisco is no exception. Being a transit first city, one of the most common uses for San Francisco??s travel model SF-CHAMP is to analyze transit demand under various circumstances. SF-CHAMP v 4.1 (Harold) is able to capture the effects of several aspects of transit provision including headways, stop placement, and travel time. However, unlike how auto level of service in a user equilibrium traffic assignment is responsive to roadway capacity, SF-CHAMP Harold is unable to capture any benefit related to capacity expansion, crowding??s effect on travel time nor or any of the real-life true capacity limitations. The failure to represent these elements of transit travel has led to significant discrepancies between model estimates and actual ridership. Additionally it does not allow decision-makers to test the effects of policies or investments that increase the capacity of a given transit service. This paper presents the framework adopted into a more recent version of SF-CHAMP (Fury) to represent transit capacity and crowding within the constraints of our current modeling software.     

Nudging people towards more sustainable residential choice decisions: an intervention based on focalism and visualization

Transportation - There have been numerous behavior change studies focused on sustainable travel mode choices. In this study we focused on the residential choices that in turn influence travel...     

Effects of road network on vegetation pattern in Xishuangbanna, Yunnan Province, Southwest China

The paper presents the results of a case study carried out in Xishuangbanna, Southwest China, to examine the relationship between vegetation dynamics and road network extension. We find that lower level roads had more effects on vegetation patches. Minor roads, including Class IV and Class V roads, contributed most to regional landscape fragmentation while high level roads had larger effect on vegetation dynamics in road effect zone. Further, road classes have close relationships with village distribution, topography fluctuation and landscape fragmentation. Topography, however had no direct relationship with vegetation pattern.     

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

     

Crowdsourced data for bicycling research and practice

ABSTRACT

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.     

Speed limit laws in America: The role of geography, mobility and ideology

Speed limits had been centralized at the federal level since 1974, until decisions were devolved to the states in 1995. However, the centralization debate has reemerged in recent years. This paper conducts the first econometric analysis of the determinants of speed limit laws and State reactions after the repeal. By using mobility, geographic and political variables, our results suggest that geography - which reflects private mobility needs and social preferences -, is one of the main factors influencing speed limit laws, together with political ideology. Furthermore, we identify the presence of regional and time diffusion effects. By presenting first evidence on policy determinants, we provide a better understanding of the formulation of the heterogeneity of speed limits in US and offer implications for the debate on centralization and decentralization of transport policy.