On the Existence of Optimal Level of Women’s Intelligence in Men’s Perception: Evidence from a Speed Dating Experiment

We study gender differences in preferences for mate characteristics such as perceived physical attractiveness and intelligence using data from a speed dating experiment. We have observed that women give greater weight to perceived physical attractiveness than intelligence in their mating decisions. Probability of women’s positive speed dating decision rises with men’s perceived physical attractiveness (in this case we observe increasing marginal effects) and intelligence (with diminishing marginal effects). Marginal rate of substitution of men’s perceived physical attractiveness for intelligence is the highest for low levels of men’s perceived intelligence and the lowest for high values of men’s perceived intelligence. Men also give greater weight to perceived physical attractiveness than intelligence in their mating choices. Probability of men’s positive decision rises with women’s perceived physical attractiveness (in this case we observe diminishing marginal effects). The relationship between probability of men’s positive decision and women’s perceived intelligence is non-monotonic. The optimal level of women’s intelligence in men’s perception exists. This optimal value rises with women’s perceived physical attractiveness

Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail. Document type: Articl

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Impact of Component Size on Plug-In Hybrid Vehicle Energy Consumption Using Global Optimization

Plug-in hybrid electric vehicles are a promising alternative to gas-only vehicles and offer the potential to greatly reduce fuel use in transportation. Their potential energy consumption is highly linked to the size of components. This study focuses on the impact of the electric system energy and power on control and energy consumption. Based on a parallel pre-transmission architecture, several vehicles were modeled, with an all-electric range from 5 to 40 miles on the UDDS, to illustrate various levels of available electric energy. Five others vehicles were created, with various levels of power and same battery energy. The vehicles were then simulated under optimal control on multiple combinations of cycle and distance by using a global optimization algorithm. The global optimization algorithm, based on the Bellman principle, ensures a fair comparison between different vehicles by making each vehicle operate at its maximal potential. The results from each optimization are thoroughly analyzed to highlight control patterns. The potential minimal fuel consumption that can be achieved by each of them is presented. The results can also be used to find the potential minimal greenhouse gases emissions

Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail

Energy Consumption Prediction of a Vehicle along a User-Specified Real-World Trip

Standard cycles provide an easy way to evaluate the energy consumption of vehicles, but it is the energy consumption that occurs on real-world trips that really matters to the driver and, to a larger extent, society. This study shows how digital maps and vehicle simulation tools can be used to estimate energy consumption on a real-world trip. The user (1) selects a trip in the mapping service ADAS-RP (Advanced Driver Assistance Systems Research Platform), (2) defines a vehicle model in the vehicle powertrain simulation tool Autonomie, and (3) runs and analyzes the simulation in that same tool. For each section of the trip, ADAS-RP provides various information that can include speed limits, historic data on traffic pattern speeds, the slopes of the routes, and the positions of stop signs and traffic lights. The first stage of processing this information is to schedule the stops and to create an intermediate speed target that takes those stops into account. The final driver demand speed includes transitions – accelerations and decelerations – between sections with different intermediate speed targets, or around stops. The ADAS-RP/Autonomie process is then used to compute the energy consumption of a hybrid electric vehicle and a conventional vehicle on 10 trips defined across the United States and Germany. The hybrid vehicle is more fuel efficient, especially on congested routes and routes with downhill slopes, the effect of which is analyzed in further detail. Document type: Articl