178 research outputs found
A Data-driven Resilience Framework of Directionality Configuration based on Topological Credentials in Road Networks
Roadway reconfiguration is a crucial aspect of transportation planning,
aiming to enhance traffic flow, reduce congestion, and improve overall road
network performance with existing infrastructure and resources. This paper
presents a novel roadway reconfiguration technique by integrating optimization
based Brute Force search approach and decision support framework to rank
various roadway configurations for better performance. The proposed framework
incorporates a multi-criteria decision analysis (MCDA) approach, combining
input from generated scenarios during the optimization process. By utilizing
data from optimization, the model identifies total betweenness centrality
(TBC), system travel time (STT), and total link traffic flow (TLTF) as the most
influential decision variables. The developed framework leverages graph theory
to model the transportation network topology and apply network science metrics
as well as stochastic user equilibrium traffic assignment to assess the impact
of each roadway configuration on the overall network performance. To rank the
roadway configurations, the framework employs machine learning algorithms, such
as ridge regression, to determine the optimal weights for each criterion (i.e.,
TBC, STT, TLTF). Moreover, the network-based analysis ensures that the selected
configurations not only optimize individual roadway segments but also enhance
system-level efficiency, which is particularly helpful as the increasing
frequency and intensity of natural disasters and other disruptive events
underscore the critical need for resilient transportation networks. By
integrating multi-criteria decision analysis, machine learning, and network
science metrics, the proposed framework would enable transportation planners to
make informed and data-driven decisions, leading to more sustainable,
efficient, and resilient roadway configurations.Comment: 103rd Transportation Research Board (TRB) Annual Meetin
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
Performance Evaluation of Public Transport Networks and Its Optimal Strategies Under Uncertainty
The study introduces a novel framework to enhance public transportation performance in uncertain situations, incorporating multi-aspiration-level goal programming and Monte Carlo simulation to manage uncertainty. The process involves creating a public transport criteria matrix using an analytic hierarchy process and optimizing the network based on weight results. Three Australian case studies are used to validate the proposed methodology
Multimodal statewide freight transportation modeling process
http://www.worldcat.org/oclc/3927718
Fairness-Based Transportation Resilience for Communities under Tsunami Hazard
Abstract: Natural disasters such as tsunamis have catastrophic impacts on the functionality and resilience of transportation networks in impacted areas, and they can damage coastal regions hundreds of kilometers away from the earthquake that caused them, resulting in a significant number of casualties. As a result, the ultimate goal of this study was to develop a fair-based evacuation model under tsunami hazards. The proposed fairness-based evacuation model used in this study aimed to give evacuees equal access to emergency facility centers and assembly areas, reducing the number of casualties and assessing the capability of providing the evacuees' needs
Transport visions network - Report 7 - Freight and logistics
This is the seventh in a series of reports to be produced by the Transport Visions Network. The Network is a novel venture to project the views of young professionals into the debate concerning the future of transport and its role in society. It is comprised of individuals who are aged 35 or under from universities, consultancies and public authorities both in the UK and overseas.This report offers a range of solutions to problems associated with goods movement. It begins with a consideration of the current and expected future context for the operation of freight and logistics in the UK. It considers present policy approaches to the problems of goods movement before introducing the Network's own ideas and visions which are developed under three different scenarios for the future of society: Going Global; Nation State; and Local Living
Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review
Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems
Modeling Framework and Solution Methodologies for On-Demand Mobility Services With Ridesharing and Transfer Options
The growing complexity of the urban travel pattern and its related traffic congestion, along with the extensive usage of mobile phones, invigorated On-Demand Mobility Services (ODMS) and opened the door to the emergence of Transportation Network Companies (TNC). By adopting the shared economy paradigm, TNCs enable private car owners to provide transportation services to passengers by providing user-friendly mobile phone applications that efficiently match passengers to service providers. Considering the high level of flexibility, convenience, and reliability of ODMS, compared to those offered by traditional public transportation systems, many metropolitan areas in the United States and abroad have reported rapid growth of such services. This dissertation presents a modeling framework to study the operation of on-demand mobility services (ODMS) in urban areas. The framework can analyze the operation of ODMS while representing emerging services such as ridesharing and transfer. The problem is formulated as a mixed-integer program and an efficient decomposition-based methodology is developed for its solution. This solution methodology aims at solving the offline version of the problem, in which the passengers’ demand is assumed to be known ii for the entire planning horizon. The presented approach adopts a modified column generation algorithm, which integrates iterative decomposition and network augmentation techniques to analyze networks with moderate size. Besides, a novel methodology for integrated ride-matching and vehicle routing for dynamic (online) ODMS with ridesharing and transfer options is developed to solve the problem in real-time. The methodology adopts a hybrid heuristic approach, which enables solving large problem instances in near real-time, where the passengers’ demand is not known a priori. The heuristic allows to (1) promptly respond to individual ride requests and (2) periodically re-evaluate the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The outcomes of experiments considering hypothetical and real-world networks are presented. The results show that the modified column generation approach provides a good quality solution in less computation time than the CPLEX solver. Additionally, the heuristic approach can provide an efficient solution for large networks while satisfying the real-time execution requirements. Additionally, investigation of the results of the experiments shows that increasing the number of passengers willing to rideshare and/or transfer increases the general performance of ODMS by increasing the number of served passengers and associated revenue and reducing the number of needed vehicles
Robust transportation network design under user equilibrium
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2007.Includes bibliographical references (p. 59-63).We address the problem of designing a transportation network in the presence of demand uncertainty, multiple origin-destination pairs and a budget constraint for the overall construction cost, under the behavioral assumption that travelers optimize their own travel costs (i.e., the "user-equilibrium" condition). Under deterministic demand, we propose an exact integer optimization approach that leads to a quadratic objective, linear constraints optimization problem. As a result, the problem is efficiently solvable via commercial software, when the costs are linear functions of traffic flows. We then use an iterative algorithm to address the case of nonlinear cost functions. While the problem is intractable under probabilistic assumptions on demand uncertainty, we extend the previous model and propose an iterative algorithm using a robust optimization approach that models demand uncertainty. We finally report extensive numerical results to illustrate that our approach leads to tractable solutions for large scale networks.by Yun Lu.S.M
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