5,157 research outputs found
Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms
This paper considers the problem of routing and rebalancing a shared fleet of
autonomous (i.e., self-driving) vehicles providing on-demand mobility within a
capacitated transportation network, where congestion might disrupt throughput.
We model the problem within a network flow framework and show that under
relatively mild assumptions the rebalancing vehicles, if properly coordinated,
do not lead to an increase in congestion (in stark contrast to common belief).
From an algorithmic standpoint, such theoretical insight suggests that the
problem of routing customers and rebalancing vehicles can be decoupled, which
leads to a computationally-efficient routing and rebalancing algorithm for the
autonomous vehicles. Numerical experiments and case studies corroborate our
theoretical insights and show that the proposed algorithm outperforms
state-of-the-art point-to-point methods by avoiding excess congestion on the
road. Collectively, this paper provides a rigorous approach to the problem of
congestion-aware, system-wide coordination of autonomously driving vehicles,
and to the characterization of the sustainability of such robotic systems.Comment: 11 pages, 3 figures. Presented at Robotics: Science and Systems (RSS)
2016. Version 2 is the extended version of the final submission included in
the conference proceedings. The title of the initial submission was modified
in deference to RSS's double-blind submission process: in this version, the
title matches the published pape
Optimizing the Deployment of Electric Vehicle Charging Stations Using Pervasive Mobility Data
With recent advances in battery technology and the resulting decrease in the
charging times, public charging stations are becoming a viable option for
Electric Vehicle (EV) drivers. Concurrently, wide-spread use of
location-tracking devices in mobile phones and wearable devices makes it
possible to track individual-level human movements to an unprecedented spatial
and temporal grain. Motivated by these developments, we propose a novel
methodology to perform data-driven optimization of EV charging stations
location. We formulate the problem as a discrete optimization problem on a
geographical grid, with the objective of covering the entire demand region
while minimizing a measure of drivers' discomfort. Since optimally solving the
problem is computationally infeasible, we present computationally efficient,
near-optimal solutions based on greedy and genetic algorithms. We then apply
the proposed methodology to optimize EV charging stations location in the city
of Boston, starting from a massive cellular phone data sets covering 1 million
users over 4 months. Results show that genetic algorithm based optimization
provides the best solutions in terms of drivers' discomfort and the number of
charging stations required, which are both reduced about 10 percent as compared
to a randomized solution. We further investigate robustness of the proposed
data-driven methodology, showing that, building upon well-known regularity of
aggregate human mobility patterns, the near-optimal solution computed using
single day movements preserves its properties also in later months. When
collectively considered, the results presented in this paper clearly indicate
the potential of data-driven approaches for optimally locating public charging
facilities at the urban scale.Comment: 11 pages, 8 figures, submitte
Surrogate-based toll optimization in a large-scale heterogeneously congested network
Toll optimization in a large-scale dynamic traffic network is typically
characterized by an expensive-to-evaluate objective function. In this paper, we
propose two toll level problems (TLPs) integrated with a large-scale
simulation-based dynamic traffic assignment (DTA) model of Melbourne,
Australia. The first TLP aims to control the pricing zone (PZ) through a
time-varying joint distance and delay toll (JDDT) such that the network
fundamental diagram (NFD) of the PZ does not enter the congested regime. The
second TLP is built upon the first TLP by further considering the minimization
of the heterogeneity of congestion distribution in the PZ. To solve the two
TLPs, a computationally efficient surrogate-based optimization method, i.e.,
regressing kriging (RK) with expected improvement (EI) sampling, is applied to
approximate the simulation input-output mapping, which can balance well between
local exploitation and global exploration. Results show that the two optimal
TLP solutions reduce the average travel time in the PZ (entire network) by
29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity of
congestion distribution achieves higher network flows in the PZ and a lower
average travel time or a larger total travel time saving in the entire network.Comment: 16 pages, 7 figure
Scalable and Congestion-aware Routing for Autonomous Mobility-on-Demand via Frank-Wolfe Optimization
We consider the problem of vehicle routing for Autonomous Mobility-on-Demand
(AMoD) systems, wherein a fleet of self-driving vehicles provides on-demand
mobility in a given environment. Specifically, the task it to compute routes
for the vehicles (both customer-carrying and empty travelling) so that travel
demand is fulfilled and operational cost is minimized. The routing process must
account for congestion effects affecting travel times, as modeled via a
volume-delay function (VDF). Route planning with VDF constraints is notoriously
challenging, as such constraints compound the combinatorial complexity of the
routing optimization process. Thus, current solutions for AMoD routing resort
to relaxations of the congestion constraints, thereby trading optimality with
computational efficiency. In this paper, we present the first
computationally-efficient approach for AMoD routing where VDF constraints are
explicitly accounted for. We demonstrate that our approach is faster by at
least one order of magnitude with respect to the state of the art, while
providing higher quality solutions. From a methodological standpoint, the key
technical insight is to establish a mathematical reduction of the AMoD routing
problem to the classical traffic assignment problem (a related vehicle-routing
problem where empty traveling vehicles are not present). Such a reduction
allows us to extend powerful algorithmic tools for traffic assignment, which
combine the classic Frank-Wolfe algorithm with modern techniques for
pathfinding, to the AMoD routing problem. We provide strong theoretical
guarantees for our approach in terms of near-optimality of the returned
solution
Applications of sensitivity analysis for probit stochastic network equilibrium
Network equilibrium models are widely used by traffic practitioners to aid them in making decisions concerning the operation and management of traffic networks. The common practice is to test a prescribed range of hypothetical changes or policy measures through adjustments to the input data, namely the trip demands, the arc performance (travel time) functions, and policy variables such as tolls or signal timings. Relatively little use is, however, made of the full implicit relationship between model inputs and outputs inherent in these models. By exploiting the representation of such models as an equivalent optimisation problem, classical results on the sensitivity analysis of non-linear programs may be applied, to produce linear relationships between input data perturbations and model outputs. We specifically focus on recent results relating to the probit Stochastic User Equilibrium (PSUE) model, which has the advantage of greater behavioural realism and flexibility relative to the conventional Wardrop user equilibrium and logit SUE models. The paper goes on to explore four applications of these sensitivity expressions in gaining insight into the operation of road traffic networks. These applications are namely: identification of sensitive, ‘critical’ parameters; computation of approximate, re-equilibrated solutions following a change (post-optimisation); robustness analysis of model forecasts to input data errors, in the form of confidence interval estimation; and the solution of problems of the bi-level, optimal network design variety. Finally, numerical experiments applying these methods are reported
A context-based geoprocessing framework for optimizing meetup location of multiple moving objects along road networks
Given different types of constraints on human life, people must make
decisions that satisfy social activity needs. Minimizing costs (i.e., distance,
time, or money) associated with travel plays an important role in perceived and
realized social quality of life. Identifying optimal interaction locations on
road networks when there are multiple moving objects (MMO) with space-time
constraints remains a challenge. In this research, we formalize the problem of
finding dynamic ideal interaction locations for MMO as a spatial optimization
model and introduce a context-based geoprocessing heuristic framework to
address this problem. As a proof of concept, a case study involving
identification of a meetup location for multiple people under traffic
conditions is used to validate the proposed geoprocessing framework. Five
heuristic methods with regard to efficient shortest-path search space have been
tested. We find that the R* tree-based algorithm performs the best with high
quality solutions and low computation time. This framework is implemented in a
GIS environment to facilitate integration with external geographic contextual
information, e.g., temporary road barriers, points of interest (POI), and
real-time traffic information, when dynamically searching for ideal meetup
sites. The proposed method can be applied in trip planning, carpooling
services, collaborative interaction, and logistics management.Comment: 34 pages, 8 figure
Surrogate-assisted cooperative signal optimization for large-scale traffic networks
Reasonable setting of traffic signals can be very helpful in alleviating
congestion in urban traffic networks. Meta-heuristic optimization algorithms
have proved themselves to be able to find high-quality signal timing plans.
However, they generally suffer from performance deterioration when solving
large-scale traffic signal optimization problems due to the huge search space
and limited computational budget. Directing against this issue, this study
proposes a surrogate-assisted cooperative signal optimization (SCSO) method.
Different from existing methods that directly deal with the entire traffic
network, SCSO first decomposes it into a set of tractable sub-networks, and
then achieves signal setting by cooperatively optimizing these sub-networks
with a surrogate-assisted optimizer. The decomposition operation significantly
narrows the search space of the whole traffic network, and the
surrogate-assisted optimizer greatly lowers the computational burden by
reducing the number of expensive traffic simulations. By taking Newman fast
algorithm, radial basis function and a modified estimation of distribution
algorithm as decomposer, surrogate model and optimizer, respectively, this
study develops a concrete SCSO algorithm. To evaluate its effectiveness and
efficiency, a large-scale traffic network involving crossroads and T-junctions
is generated based on a real traffic network. Comparison with several existing
meta-heuristic algorithms specially designed for traffic signal optimization
demonstrates the superiority of SCSO in reducing the average delay time of
vehicles
Dynamic Pricing in Shared Mobility on Demand Service
We consider a profit maximization problem in an urban mobility on-demand
service, of which the operator owns a fleet, provides both exclusive and shared
trip services, and dynamically determines prices of offers. With knowledge of
the traveler preference and the distribution of future trip requests, the
operator wants to find the pricing strategy that optimizes the total operating
profit of multiple trips during a specific period, namely, a day in this paper.
This problem is first formulated and analyzed within the dynamic programming
framework, where a general approach combining parametric rollout policy and
stochastic optimization is proposed. A discrete-choice-based price optimization
model is then used for the request level optimal decision problem and leads to
a practical and computationally tractable algorithm for the problem.
Our algorithm is evaluated with a simulated experiment in the urban traffic
network in Langfang, China, and it is shown to generate considerably higher
profit than naive strategies. Further analysis shows that this method also
leads to higher congestion level and lower service capacity in the urban
traffic system, which highlights a need for policy interventions that balance
the private profit making and the system level optimality
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
Air traffic control is a real-time safety-critical decision making process in
highly dynamic and stochastic environments. In today's aviation practice, a
human air traffic controller monitors and directs many aircraft flying through
its designated airspace sector. With the fast growing air traffic complexity in
traditional (commercial airliners) and low-altitude (drones and eVTOL aircraft)
airspace, an autonomous air traffic control system is needed to accommodate
high density air traffic and ensure safe separation between aircraft. We
propose a deep multi-agent reinforcement learning framework that is able to
identify and resolve conflicts between aircraft in a high-density, stochastic,
and dynamic en-route sector with multiple intersections and merging points. The
proposed framework utilizes an actor-critic model, A2C that incorporates the
loss function from Proximal Policy Optimization (PPO) to help stabilize the
learning process. In addition we use a centralized learning, decentralized
execution scheme where one neural network is learned and shared by all agents
in the environment. We show that our framework is both scalable and efficient
for large number of incoming aircraft to achieve extremely high traffic
throughput with safety guarantee. We evaluate our model via extensive
simulations in the BlueSky environment. Results show that our framework is able
to resolve 99.97% and 100% of all conflicts both at intersections and merging
points, respectively, in extreme high-density air traffic scenarios.Comment: 10 page
Gramian-Based Optimization for the Analysis and Control of Traffic Networks
This paper proposes a simplified version of classical models for urban
transportation networks, and studies the problem of controlling intersections
with the goal of optimizing network-wide congestion. Differently from
traditional approaches to control traffic signaling, a simplified framework
allows for a more tractable analysis of the network overall dynamics, and
enables the design of critical parameters while considering network-wide
measures of efficiency. Motivated by the increasing availability of real-time
high-resolution traffic data, we cast an optimization problem that formalizes
the goal of minimizing the overall network congestion by optimally controlling
the durations of green lights at intersections. Our formulation allows us to
relate congestion objectives with the problem of optimizing a metric of
controllability of an associated dynamical network. We then provide a technique
to efficiently solve the optimization by parallelizing the computation among a
group of distributed agents. Lastly, we assess the benefits of the proposed
modeling and optimization framework through microscopic simulations on typical
traffic commute scenarios for the area of Manhattan. The optimization framework
proposed in this study is made available online on a Sumo microscopic simulator
based interface [1]
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