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We use a simple traffic micro-simulation model based on queueing dynamics as introduced by Gawron [IJMPC, 9(3):393, 1998] in order to simulate traffic in Portland/Oregon. Links have a flow capacity, that is, they do not release more vehicles per second than is possible according to their capacity. This leads to queue built-up if demand exceeds capacity. Links also have a storage capacity, which means that once a link is full, vehicles that want to enter the link need to wait. This leads to queue spill-back through the network. The model is compatible with route-plan-based approaches such as TRANSIMS, where each vehicle attempts to follow its pre-computed path. Yet, both the data requirements and the computational requirements are considerably lower than for the full TRANSIMS microsimulation. Indeed, the model uses standard emme/2 network data, and runs about eight times faster than real time with more than 100 000 vehicles simultaneously in the simulation on a single Pentium-type CPU.

We derive the model's fundamental diagrams and explain it. The simulation is used to simulate traffic on the emme/2 network of the Portland (Oregon) metropolitan region (20 000 links). Demand is generated by a simplified home-to-work destination assignment which generates about half a million trips for the morning peak. Route assignment is done by iterative feedback between micro-simulation and router. An iterative solution of the route assignment for the above problem can be achieved within about half a day of computing time on a desktop workstation. We compare results with field data and with results of traditional assignment runs by the Portland Metropolitan Planning Organization.

Thus, with a model such as this one, it is possible to use a dynamic, activities-based approach to transportation simulation (such as in TRANSIMS) with affordable data and hardware. This should enable systematic research about the coupling of demand generation, route assignment, and micro-simulation output.

Online-to-offline (O2O) on-demand services require one-hour delivery and the demands vary substantially within one day. The capacity plans in the O2O industry evolve into three main modes: (i) in-house drivers only; (ii) full-time and part-time crowd-sourcing drivers; (iii) a mix of in-house and crowd-sourcing drivers. For current capacity plans, two issues remain unclear for both academia and industry. First, what is the optimal staffing decision when considering the behaviors of crowd-sourcing drivers. Second, how to choose from different capacity plans to match different operation strategies and market environments. To address these questions, we build an M/M/n queueing model to optimize the staffing decision with the aim of minimizing the total operation costs. Incentive mechanisms for both customers and crowd-sourcing drivers are crafted to improve their loyalty towards the O2O platform, in order to better manage capacity. Moreover, we apply a real dataset from one of the largest O2O platforms in China to verify our model. Our analyses show that adding flexibility — capacity-type flexibility and agent flexibility — to the O2O on-demand logistics system can help control costs and maintain a high service level. Furthermore, conditions in which different capacity plans match with different operation strategies and market environments are proposed.

This article is devoted to applications of fuzzy set theory, possibility theory and evidence theory in civil engineering, presenting current work of a group or researchers at the University of Innsbruck. We argue that these methods are well suited for analyzing and processing the parameter uncertainties arising in soil mechanics and construction management. We address two specific applications here: finite element computations in foundation engineering and a queueing model in earth work.