11,137 research outputs found
Planning of integrated mobility-on-demand and urban transit networks
We envision a multimodal transportation system where Mobility-on-Demand (MoD)
service is used to serve the first mile and last mile of transit trips. For
this purpose, the current research formulates an optimization model for
designing an integrated MoD and urban transit system. The proposed model is a
mixed-integer non-linear programming model that captures the strategic behavior
of passengers in a multimodal network through a passenger assignment model. It
determines which transit routes to operate, the frequency of the operating
routes, the fleet size of vehicles required in each transportation analysis
zone to serve the demand, and the passenger flow on both road and transit
networks. A Benders decomposition approach with several enhancements is
proposed to solve the given optimization program. Computational experiments are
presented for the Sioux Falls multimodal network. The results show a
significant improvement in the congestion in the city center with the
introduction and optimization of an integrated transportation system. The
proposed design allocates more vehicles to the outskirt zones in the network
(to serve the first mile and last mile of transit trips) and more frequency to
the transit routes in the city center. The integrated system significantly
improves the share of transit passengers and their level of service in
comparison to the base optimized transit system. The sensitivity analysis of
the bus and vehicle fleet shows that increasing the number of buses has more
impact on improving the level of service of passengers compared to increasing
the number of MoD vehicles. Finally, we provide managerial insights for
deploying such multimodal service.Comment: 39 pages, 6 figure
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, in prep. for journal submission. In V3, we add a proof that the
socially-optimal solution can be enforced as a general equilibrium, a
privacy-preserving distributed optimization algorithm, a description of the
receding-horizon implementation and additional numerical results, and proofs
of all theorem
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
Regulating TNCs: Should Uber and Lyft Set Their Own Rules?
We evaluate the impact of three proposed regulations of transportation
network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for
drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip
congestion tax. The impact is assessed using a queuing theoretic equilibrium
model which incorporates the stochastic dynamics of the app-based ride-hailing
matching platform, the ride prices and driver wages established by the
platform, and the incentives of passengers and drivers. We show that a floor
placed under driver earnings pushes the ride-hailing platform to hire more
drivers and offer more rides, at the same time that passengers enjoy faster
rides and lower total cost, while platform rents are reduced. Contrary to
standard economic theory, enforcing a minimum wage for drivers benefits both
drivers and passengers, and promotes the efficiency of the entire system. This
surprising outcome holds for almost all model parameters, and it occurs because
the wage floors curbs TNC labor market power. In contrast to a wage floor,
imposing a cap on the number of vehicles hurts drivers, because the platform
reaps all the benefits of limiting supply. The congestion tax has the expected
impact: fares increase, wages and platform revenue decrease. We also construct
variants of the model to briefly discuss platform subsidy, platform
competition, and autonomous vehicles
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