88 research outputs found
A multi-functional simulation platform for on-demand ride service operations
On-demand ride services or ride-sourcing services have been experiencing fast
development in the past decade. Various mathematical models and optimization
algorithms have been developed to help ride-sourcing platforms design
operational strategies with higher efficiency. However, due to cost and
reliability issues (implementing an immature algorithm for real operations may
result in system turbulence), it is commonly infeasible to validate these
models and train/test these optimization algorithms within real-world ride
sourcing platforms. Acting as a useful test bed, a simulation platform for
ride-sourcing systems will be very important to conduct algorithm
training/testing or model validation through trails and errors. While previous
studies have established a variety of simulators for their own tasks, it lacks
a fair and public platform for comparing the models or algorithms proposed by
different researchers. In addition, the existing simulators still face many
challenges, ranging from their closeness to real environments of ride-sourcing
systems, to the completeness of different tasks they can implement. To address
the challenges, we propose a novel multi-functional and open-sourced simulation
platform for ride-sourcing systems, which can simulate the behaviors and
movements of various agents on a real transportation network. It provides a few
accessible portals for users to train and test various optimization algorithms,
especially reinforcement learning algorithms, for a variety of tasks, including
on-demand matching, idle vehicle repositioning, and dynamic pricing. In
addition, it can be used to test how well the theoretical models approximate
the simulated outcomes. Evaluated on real-world data based experiments, the
simulator is demonstrated to be an efficient and effective test bed for various
tasks related to on-demand ride service operations
Quantifying the benefits of vehicle pooling with shareability networks
Taxi services are a vital part of urban transportation, and a considerable
contributor to traffic congestion and air pollution causing substantial adverse
effects on human health. Sharing taxi trips is a possible way of reducing the
negative impact of taxi services on cities, but this comes at the expense of
passenger discomfort quantifiable in terms of a longer travel time. Due to
computational challenges, taxi sharing has traditionally been approached on
small scales, such as within airport perimeters, or with dynamical ad-hoc
heuristics. However, a mathematical framework for the systematic understanding
of the tradeoff between collective benefits of sharing and individual passenger
discomfort is lacking. Here we introduce the notion of shareability network
which allows us to model the collective benefits of sharing as a function of
passenger inconvenience, and to efficiently compute optimal sharing strategies
on massive datasets. We apply this framework to a dataset of millions of taxi
trips taken in New York City, showing that with increasing but still relatively
low passenger discomfort, cumulative trip length can be cut by 40% or more.
This benefit comes with reductions in service cost, emissions, and with split
fares, hinting towards a wide passenger acceptance of such a shared service.
Simulation of a realistic online system demonstrates the feasibility of a
shareable taxi service in New York City. Shareability as a function of trip
density saturates fast, suggesting effectiveness of the taxi sharing system
also in cities with much sparser taxi fleets or when willingness to share is
low.Comment: Main text: 6 pages, 3 figures, SI: 24 page
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
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Modeling and Optimizing Routing Decisions for Travelers and On-demand Service Providers
This thesis investigates the dynamic routing decisions for individual travelers and on-demand service providers (e.g., regular taxis, Uber, Lyft, etc).
For individual travelers, this thesis models and predicts route choice at two time-scales: the day-to-day and within-day. For day-to-day route choice, methodological development and empirical evidences are presented to understand the roles of learning, inertia and real-time travel information on route choices in a highly disrupted network based on data from a laboratory competitive route choice game. The learning of routing policies instead of simple paths is modeled when real-time travel information is available, where a routing policy is defined as a contingency plan that maps realized traffic conditions to path choices. Using data from a competitive laboratory experiment, prediction performance is then measured in terms of both one-step and full trajectory predictions. For within day route choice, a recursive logit model is formulated in a stochastic time-dependent (STD) network without sampling any choice sets. A decomposition algorithm is then proposed so that the model can be estimated in reasonable time. Estimation and prediction results of the proposed model are presented using a data set collected from a subnetwork of Stockholm, Sweden.
Taxis and ride-sourcing vehicles play an important role in providing on-demand mobility in an urban transportation system. Unlike individual travelers, they do not have a clear destination when there\u27s no passenger on board. The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP) problem to maximize long-term profit over the full working period. Two approaches are proposed to solve the problem. One is the model-based approach where a model of the state transitions of the environment is obtained from queuing-theory based passenger arrival and competing taxi distribution processes. An enhanced value iteration for solving the MDP problem is then proposed making use of efficient matrix operations. The other is the model-free Reinforcement Learning (RL) approach, which learns the best policy directly from observed trajectory data. Both approaches are implemented and tested in a mega city transportation network with reasonable running time, and a systematic comparison of the two approaches is also provided
Recent Advances in Graph-based Machine Learning for Applications in Smart Urban Transportation Systems
The Intelligent Transportation System (ITS) is an important part of modern
transportation infrastructure, employing a combination of communication
technology, information processing and control systems to manage transportation
networks. This integration of various components such as roads, vehicles, and
communication systems, is expected to improve efficiency and safety by
providing better information, services, and coordination of transportation
modes. In recent years, graph-based machine learning has become an increasingly
important research focus in the field of ITS aiming at the development of
complex, data-driven solutions to address various ITS-related challenges. This
chapter presents background information on the key technical challenges for ITS
design, along with a review of research methods ranging from classic
statistical approaches to modern machine learning and deep learning-based
approaches. Specifically, we provide an in-depth review of graph-based machine
learning methods, including basic concepts of graphs, graph data
representation, graph neural network architectures and their relation to ITS
applications. Additionally, two case studies of graph-based ITS applications
proposed in our recent work are presented in detail to demonstrate the
potential of graph-based machine learning in the ITS domain
A greedy approach for increased vehicle utilization in ridesharing networks
In recent years, ridesharing platforms have become a prominent mode of
transportation for the residents of urban areas. As a fundamental problem,
route recommendation for these platforms is vital for their sustenance. The
works done in this direction have recommended routes with higher passenger
demand. Despite the existing works, statistics have suggested that these
services cause increased greenhouse emissions compared to private vehicles as
they roam around in search of riders. This analysis provides finer details
regarding the functionality of ridesharing systems and it reveals that in the
face of their boom, they have not utilized the vehicle capacity efficiently. We
propose to overcome the above limitations and recommend routes that will fetch
multiple passengers simultaneously which will result in increased vehicle
utilization and thereby decrease the effect of these systems on the
environment. As route recommendation is NP-hard, we propose a k-hop-based
sliding window approximation algorithm that reduces the search space from
entire road network to a window. We further demonstrate that maximizing
expected demand is submodular and greedy algorithms can be used to optimize our
objective function within a window. We evaluate our proposed model on
real-world datasets and experimental results demonstrate superior performance
by our proposed model
Automated and electrified ride-hailing fleet: opportunities and management optimisation
This thesis explores key aspects and problems of technological innovations in the context of ride-hailing systems, shedding light on their profound implications for the industry.
Chapter 2 introduces a centralised matching approach that integrates the EV charge scheduling problem into the optimisation framework of ride-hailing systems. The objective represents three-fold benefits: direct financial gains, service quality and system efficiency, and fleet profitability. Moreover, the chapter addresses the practical scenario where human drivers may reject charging assignments lacking personal incentives, leading to a driver compliance behavioural model and a corresponding incentivisation scheme.
Chapter 3 introduces a macroscopic model underpinning demand-supply dynamics within mixed-fleet ride-hailing markets. Employing a model predictive control (MPC) framework, it optimises control variables to maximise operators' profits through dynamic trip fares for AVs and HVs, and the active AV fleet size. The study accounts for human driver work patterns and different exit behaviours. Leveraging historical data and real-time inputs, a comprehensive simulation testbed substantiates the efficacy of the proposed strategy in maximising operator profits while mitigating trip cancellations.
Chapter 4 introduces a decentralised cooperative cruising approach for a-taxi fleet as an essential contingency plan during complete communication breakdowns. It quantifies road centralities using PageRank, serving as a measure for long-term passenger encounter likelihoods. This metric informs both cruising route planning and network partitioning for effective destination selection. Comparative analyses against benchmark strategies reveal significant enhancements in service performance across various fleet sizes.
The research contributes comprehensive methodologies and insights, paving the way for more efficient, sustainable, and adaptable transportation systems
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