9,692 research outputs found
Online Predictive Optimization Framework for Stochastic Demand-Responsive Transit Services
This study develops an online predictive optimization framework for
dynamically operating a transit service in an area of crowd movements. The
proposed framework integrates demand prediction and supply optimization to
periodically redesign the service routes based on recently observed demand. To
predict demand for the service, we use Quantile Regression to estimate the
marginal distribution of movement counts between each pair of serviced
locations. The framework then combines these marginals into a joint demand
distribution by constructing a Gaussian copula, which captures the structure of
correlation between the marginals. For supply optimization, we devise a linear
programming model, which simultaneously determines the route structure and the
service frequency according to the predicted demand. Importantly, our framework
both preserves the uncertainty structure of future demand and leverages this
for robust route optimization, while keeping both components decoupled. We
evaluate our framework using a real-world case study of autonomous mobility in
a university campus in Denmark. The results show that our framework often
obtains the ground truth optimal solution, and can outperform conventional
methods for route optimization, which do not leverage full predictive
distributions.Comment: 34 pages, 12 figures, 5 table
A dynamic ridesharing dispatch and idle vehicle repositioning strategy with integrated transit transfers
We propose a ridesharing strategy with integrated transit in which a private
on-demand mobility service operator may drop off a passenger directly
door-to-door, commit to dropping them at a transit station or picking up from a
transit station, or to both pickup and drop off at two different stations with
different vehicles. We study the effectiveness of online solution algorithms
for this proposed strategy. Queueing-theoretic vehicle dispatch and idle
vehicle relocation algorithms are customized for the problem. Several
experiments are conducted first with a synthetic instance to design and test
the effectiveness of this integrated solution method, the influence of
different model parameters, and measure the benefit of such cooperation.
Results suggest that rideshare vehicle travel time can drop by 40-60%
consistently while passenger journey times can be reduced by 50-60% when demand
is high. A case study of Long Island commuters to New York City (NYC) suggests
having the proposed operating strategy can substantially cut user journey times
and operating costs by up to 54% and 60% each for a range of 10-30 taxis
initiated per zone. This result shows that there are settings where such
service is highly warranted
Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach
The integrated development of city clusters has given rise to an increasing
demand for intercity travel. Intercity ride-pooling service exhibits
considerable potential in upgrading traditional intercity bus services by
implementing demand-responsive enhancements. Nevertheless, its online
operations suffer the inherent complexities due to the coupling of vehicle
resource allocation among cities and pooled-ride vehicle routing. To tackle
these challenges, this study proposes a two-level framework designed to
facilitate online fleet management. Specifically, a novel multi-agent feudal
reinforcement learning model is proposed at the upper level of the framework to
cooperatively assign idle vehicles to different intercity lines, while the
lower level updates the routes of vehicles using an adaptive large neighborhood
search heuristic. Numerical studies based on the realistic dataset of Xiamen
and its surrounding cities in China show that the proposed framework
effectively mitigates the supply and demand imbalances, and achieves
significant improvement in both the average daily system profit and order
fulfillment ratio
A survey of urban drive-by sensing: An optimization perspective
Pervasive and mobile sensing is an integral part of smart transport and smart
city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is
gaining popularity in both academic research and field practice. The DS
paradigm has an inherent transport component, as the spatial-temporal
distribution of the sensors are closely related to the mobility patterns of
their hosts, which may include third-party (e.g. taxis, buses) or for-hire
(e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is
therefore essential to understand, assess and optimize the sensing power of
vehicle fleets under a wide range of urban sensing scenarios. To this end, this
paper offers an optimization-oriented summary of recent literature by
presenting a four-step discussion, namely (1) quantifying the sensing quality
(objective); (2) assessing the sensing power of various fleets (strategic); (3)
sensor deployment (strategic/tactical); and (4) vehicle maneuvers
(tactical/operational). By compiling research findings and practical insights
in this way, this review article not only highlights the optimization aspect of
drive-by sensing, but also serves as a practical guide for configuring and
deploying vehicle-based urban sensing systems.Comment: 24 pages, 3 figures, 4 table
The Role of Intelligent Transportation Systems and Artificial Intelligence in Energy Efficiency and Emission Reduction
Despite the technological advancements in the transportation sector, the
industry continues to grapple with increasing energy consumption and vehicular
emissions, which intensify environmental degradation and climate change. The
inefficient management of traffic flow, the underutilization of transport
network interconnectivity, and the limited implementation of artificial
intelligence (AI)-driven predictive models pose significant challenges to
achieving energy efficiency and emission reduction. Thus, there is a timely and
critical need for an integrated, sophisticated approach that leverages
intelligent transportation systems (ITSs) and AI for energy conservation and
emission reduction. In this paper, we explore the role of ITSs and AI in future
enhanced energy and emission reduction (EER). More specifically, we discuss the
impact of sensors at different levels of ITS on improving EER. We also
investigate the potential networking connections in ITSs and provide an
illustration of how they improve EER. Finally, we discuss potential AI services
for improved EER in the future. The findings discussed in this paper will
contribute to the ongoing discussion about the vital role of ITSs and AI
applications in addressing the challenges associated with achieving energy
savings and emission reductions in the transportation sector. Additionally, it
will provide insights for policymakers and industry professionals to enable
them to develop policies and implementation plans for the integration of ITSs
and AI technologies in the transportation sector.Comment: 25 pages, 4 figure
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