22,389 research outputs found
The Spatial Variability of Vehicle Densities as Determinant of Urban Network Capacity
Due to the complexity of the traffic flow dynamics in urban road networks,
most quantitative descriptions of city traffic so far are based on computer
simulations. This contribution pursues a macroscopic (fluid-dynamic) simulation
approach, which facilitates a simple simulation of congestion spreading in
cities. First, we show that a quantization of the macroscopic turning flows
into units of single vehicles is necessary to obtain realistic fluctuations in
the traffic variables, and how this can be implemented in a fluid-dynamic
model. Then, we propose a new method to simulate destination flows without the
requirement of individual route assignments. Combining both methods allows us
to study a variety of different simulation scenarios. These reveal fundamental
relationships between the average flow, the average density, and the
variability of the vehicle densities. Considering the inhomogeneity of traffic
as an independent variable can eliminate the scattering of congested flow
measurements. The variability also turns out to be a key variable of urban
traffic performance. Our results can be explained through the number of full
links of the road network, and approximated by a simple analytical formula
On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms
We study the interaction between a fleet of electric, self-driving vehicles
servicing on-demand transportation requests (referred to as Autonomous
Mobility-on-Demand, or AMoD, system) and the electric power network. We propose
a model that captures the coupling between the two systems stemming from the
vehicles' charging requirements and captures time-varying customer demand and
power generation costs, road congestion, battery depreciation, and power
transmission and distribution constraints. We then leverage the model to
jointly optimize the operation of both systems. We devise an algorithmic
procedure to losslessly reduce the problem size by bundling customer requests,
allowing it to be efficiently solved by off-the-shelf linear programming
solvers. Next, we show that the socially optimal solution to the joint problem
can be enforced as a general equilibrium, and we provide a dual decomposition
algorithm that allows self-interested agents to compute the market clearing
prices without sharing private information. We assess the performance of the
mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact
on the Texas power network. Lack of coordination between the AMoD system and
the power network can cause a 4.4% increase in the price of electricity in
Dallas-Fort Worth; conversely, coordination between the AMoD system and the
power network could reduce electricity expenditure compared to the case where
no cars are present (despite the increased demand for electricity) and yield
savings of up $147M/year. Finally, we provide a receding-horizon implementation
and assess its performance with agent-based simulations. Collectively, the
results of this paper provide a first-of-a-kind characterization of the
interaction between electric-powered AMoD systems and the power network, and
shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and
Systems XIV and accepted by TCNS. In Version 4, the body of the paper is
largely rewritten for clarity and consistency, and new numerical simulations
are presented. All source code is available (MIT) at
https://dx.doi.org/10.5281/zenodo.324165
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