A parallelized micro-simulation platform for population and mobility behavior. Application to Belgium.

In this book we aim at developing an agent-based micro-simulation framework for (large) population evolution and mobility behaviour. More specifically we focus on the agents generation and the traffic simulation parts of the platform, and its application to Belgium. Hence we firstly develop a synthetic population generator whose main characteristics are its sample-free nature, its ability to cope with moderate data inconsistencies and different levels of aggregation. We then generate the traffic demand forecasting with a stochastic and flexible activity-based model relying on weak data requirements. Finally, a traffic simulation is completed by considering the assignment of the generated demand on the road network. We give the initial developments of a strategic agent-based alternative to the conventional simulation-based dynamic traffic assignment models

Special Libraries, April 1938

Volume 29, Issue 4https://scholarworks.sjsu.edu/sla_sl_1938/1003/thumbnail.jp

Urine is an important nitrogen source for plants irrespective of vegetation composition in an Arctic tundra:insights from a 15N-enriched urea tracer experiment

1. Mammalian herbivores can strongly influence nitrogen (N) cycling and herbivore urine could be a central component of the N cycle in grazed ecosystems. Despite its potential role for ecosystem productivity and functioning, the fate of N derived from urine has rarely been investigated in grazed ecosystems. 2. This study explored the fate of 15N-enriched urea in tundra sites that have been either lightly or intensively grazed by reindeer for more than 50 years. We followed the fate of the 15N applied to the plant canopy, at 2 weeks and 1 year after tracer addition, in the different ecosystem N pools. 3. 15N-urea was rapidly incorporated in cryptogams and in aboveground parts of vascular plants, while the soil microbial pool and plant roots sequestered only a marginal proportion. Further, the litter layer constituted a large sink for the 15N-urea, at least in the short term, indicating a high biological activity in the litter layer and high immobilization in the first phases of organic matter decomposition. 4. Mosses and lichens still constituted the largest sink for the 15N-urea 1 year after tracer addition at both levels of grazing intensity demonstrating their large ability to capture and retain N from urine. Despite large fundamental differences in their traits, deciduous and evergreen shrubs were just as efficient as graminoids in taking up the 15N-urea. The total recovery of 15N-urea was lower in the intensively grazed sites, suggesting that reindeer reduce ecosystem N retention. 5. Synthesis The rapid incorporation of the applied 15N-urea indicates that arctic plants can take advantage of a pulse of incoming N from urine. In addition, δ 15N values of all taxa in the heavily grazed sites converged towards the δ 15N values for urine, bringing further evidence that urine is an important N source for plants in grazed tundra ecosystems

An adaptive agent-based approach to traffic simulation

The aim of this work is to present the initial exploration of a behavioural Dynamic Traffic Assignment model, particularly suitable to be used and implemented in agent-based micro-simulations. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time spent. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents\u27 strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large networks. Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non-strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against MATSim and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results

Robustness of artificial neural network and discrete choice modelling in presence of unobserved variables

peer reviewe