2,617 research outputs found
Uncertain demand prediction for guaranteed automated vehicle fleet performance
Mobility-on-demand (MoD) services offer a convenient and efficient transportation option, using technology to replace traditional modes. However, the flexibility of MoD services also presents challenges in controlling the system. One of the major issues is supply-demand imbalance, caused by uneven stochastic travel demand. To address this, it is crucial to predict the network behavior and proactively adapt to future travel demand.In this thesis, we present a stochastic model predictive controller (SMPC) that accounts for uncertainties in travel demand predictions. Our method make use of Gaussian Process Regression (GPR) to estimate passenger travel demand and predict time patterns with uncertainty bounds. The SMPC integrates these demand predictions into a receding horizon MoD optimization and uses a probabilistic constraining method with a user-defined confidence interval to guarantee constraint satisfaction. This result in a Chance Constrained Model Predictive Control (CCMPC) solution. Our approach has two benefits: incorporating travel demand uncertainty into the MoD optimization and the ability to relax the solution into a simpler Mixed-Integer Linear Program (MILP). Our simulation results demonstrate that this method reduces median customer wait time by 4% compared to using only the mean prediction from GPR. By adjusting the confidence bound, near-optimal performance can be achieved
The Merits of Sharing a Ride
The culture of sharing instead of ownership is sharply increasing in
individuals behaviors. Particularly in transportation, concepts of sharing a
ride in either carpooling or ridesharing have been recently adopted. An
efficient optimization approach to match passengers in real-time is the core of
any ridesharing system. In this paper, we model ridesharing as an online
matching problem on general graphs such that passengers do not drive private
cars and use shared taxis. We propose an optimization algorithm to solve it.
The outlined algorithm calculates the optimal waiting time when a passenger
arrives. This leads to a matching with minimal overall overheads while
maximizing the number of partnerships. To evaluate the behavior of our
algorithm, we used NYC taxi real-life data set. Results represent a substantial
reduction in overall overheads
Inferring transportation modes from GPS trajectories using a convolutional neural network
Identifying the distribution of users' transportation modes is an essential
part of travel demand analysis and transportation planning. With the advent of
ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach
for inferring commuters' mobility mode(s) is to leverage their GPS
trajectories. A majority of studies have proposed mode inference models based
on hand-crafted features and traditional machine learning algorithms. However,
manual features engender some major drawbacks including vulnerability to
traffic and environmental conditions as well as possessing human's bias in
creating efficient features. One way to overcome these issues is by utilizing
Convolutional Neural Network (CNN) schemes that are capable of automatically
driving high-level features from the raw input. Accordingly, in this paper, we
take advantage of CNN architectures so as to predict travel modes based on only
raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving,
and train. Our key contribution is designing the layout of the CNN's input
layer in such a way that not only is adaptable with the CNN schemes but
represents fundamental motion characteristics of a moving object including
speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the
quality of GPS logs through several data preprocessing steps. Using the clean
input layer, a variety of CNN configurations are evaluated to achieve the best
CNN architecture. The highest accuracy of 84.8% has been achieved through the
ensemble of the best CNN configuration. In this research, we contrast our
methodology with traditional machine learning algorithms as well as the seminal
and most related studies to demonstrate the superiority of our framework.Comment: 12 pages, 3 figures, 7 tables, Transportation Research Part C:
Emerging Technologie
Heuristics and policies for online pickup and delivery problems
Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of
Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to
be accommodated. The rm has a eet of almost two hundred vehicles of various types,
mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function.
The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies
to be implemented that are based on this rule ranking, and helps dispatchers to manage
the vehicles of the eet. It also provides results for the human resources required each
single day and within the di erent periods of the day. We complement the quite promising
results obtained with a discussion on future additions and improvements such as channel
eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the
Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro
Heuristics and policies for online pickup and delivery problems
Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of
Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to
be accommodated. The rm has a eet of almost two hundred vehicles of various types,
mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function.
The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies
to be implemented that are based on this rule ranking, and helps dispatchers to manage
the vehicles of the eet. It also provides results for the human resources required each
single day and within the di erent periods of the day. We complement the quite promising
results obtained with a discussion on future additions and improvements such as channel
eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the
Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro
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
Traffic at the Edge of Chaos
We use a very simple description of human driving behavior to simulate
traffic. The regime of maximum vehicle flow in a closed system shows
near-critical behavior, and as a result a sharp decrease of the predictability
of travel time. Since Advanced Traffic Management Systems (ATMSs) tend to drive
larger parts of the transportation system towards this regime of maximum flow,
we argue that in consequence the traffic system as a whole will be driven
closer to criticality, thus making predictions much harder. A simulation of a
simplified transportation network supports our argument.Comment: Postscript version including most of the figures available from
http://studguppy.tsasa.lanl.gov/research_team/. Paper has been published in
Brooks RA, Maes P, Artifical Life IV: ..., MIT Press, 199
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