Integrating Supply and Demand Perspectives for a Large-Scale Simulation of Shared Autonomous Vehicles

2020 ◽  
Vol 2674 (7) ◽  
pp. 181-192 ◽  
Author(s):  
Krishna Murthy Gurumurthy ◽  
Felipe de Souza ◽  
Annesha Enam ◽  
Joshua Auld
Keyword(s):  
Autonomous Vehicles ◽  
High Performance ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Network ◽  
Supply And Demand ◽  
Transportation Systems ◽  
Point Of View ◽  
Computational Time ◽  
Vehicle Assignment

Transportation Network Companies (TNCs) have been steadily increasing the share of total trips in metropolitan areas across the world. Micro-modeling TNC operation is essential for large-scale transportation systems simulation. In this study, an agent-based approach for analyzing supply and demand aspects of ride-sourcing operation is done using POLARIS, a high-performance simulation tool. On the demand side, a mode-choice model for the agent and a vehicle-ownership model that informs this choice are developed. On the supply side, TNC vehicle-assignment strategies, pick-up and drop-off operations, and vehicle repositioning are modeled with congestion feedback, an outcome of the mesoscopic traffic simulation. Two case studies of Bloomington and Chicago in Illinois are used to study the framework’s computational speed for large-scale operations and the effect of TNC fleets on a region’s congestion patterns. Simulation results show that a zone-based vehicle-assignment strategy scales better than relying on matching closest vehicles to requests. For large regions like Chicago, large fleets are seen to be detrimental to congestion, especially in a future in which more travelers will use TNCs. From an operational point of view, an efficient relocation strategy is critical for large regions with concentrated demand, but not regulating repositioning can worsen empty travel and, consequently, congestion. The TNC simulation framework developed in this study is of special interest to cities and regions, since it can be used to model both demand and supply aspects for large regions at scale, and in reasonably low computational time.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Calibration of MATSim in the context of natural hazards in Belgium

2016 ◽  
Author(s):  
Mario Cools ◽  
Ismaïl Saadi ◽  
Ahmed Mustafa ◽  
Jacques Teller
Keyword(s):  
Travel Behavior ◽  
Large Scale ◽  
Travel Demand ◽  
Scale Effects ◽  
Transportation Network ◽  
Transportation Systems ◽  
Generation Process ◽  
Traffic Pattern ◽  
Synthetic Population ◽  
River Floods

In Belgium, river floods are among the most frequent natural disasters and they may cause important changes on travel demand. In this regard, we propose to set up a large scale scenario using MATSim for guarantying an accurate assessment of the river floods impact on the transportation systems. In terms of inputs, agent-based models require a base year population. In this context, a synthetic population with a respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into the dynamic traffic assignment simulator, i.e. MATSim. With respect to data, households travel surveys are the main inputs for synthesizing the populations. Besides, a steady-state inundation map is integrated within MATSim for simulating river floods. To our knowledge, very few studies have focused on how river floods affect transportation systems. In this regard, this research will undoubtedly provide new insights in term of methodology and traffic pattern analysis under disruptions, especially with regard to spatial scale effects. The results indicate that at the municipality level, it is possible to capture the effects of disruptions on travel behavior. In this context, further disaggregation is needed in future studies for identifying to what extent results are sensitive to disaggregation. In addition, results also suggest that the target sub-population exposed to flood risk should be isolated from the rest of the travel demand to reach have more sensitive effects.DOI: http://dx.doi.org/10.4995/CIT2016.2016.4098

scholarly journals Real-time Analysis of City Scale Transportation Networks in New Orleans Metropolitan Area using an Agent Based Model Approach

2019 ◽  
Vol 271 ◽  
pp. 06007
Author(s):  
Millard McElwee ◽  
Bingyu Zhao ◽  
Kenichi Soga
Keyword(s):  
New Orleans ◽  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Regional Scale ◽  
Agent Based Model ◽  
Agent Based ◽  
Real Time Analysis

The primary focus of this research is to develop and implement an agent-based model (ABM) to analyze the New Orleans Metropolitan transportation network near real-time. ABMs have grown in popularity because of their ability to analyze multifaceted community scale resilience with hundreds of thousands of links and millions of agents. Road closures and reduction in capacities are examples of influences on the weights or removal of edges which can affect the travel time, speed, and route of agents in the transportation model. Recent advances in high-performance computing (HPC) have made modeling networks on the city scale much less computationally intensive. We introduce an open-source ABM which utilizes parallel distributed computing to enable faster convergence to large scale problems. We simulate 50,000 agents on the entire southeastern Louisiana road network and part of Mississippi as well. This demonstrates the capability to simulate both city and regional scale transportation networks near real time.

scholarly journals Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk

2014 ◽  
Vol 2014 ◽  
pp. 1-24 ◽  
Author(s):  
Lu Gan ◽  
Jiuping Xu
Keyword(s):  
Seismic Risk ◽  
Construction Projects ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Fuzzy Variable ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Transportation Systems ◽  
Fuzzy Random

This paper focuses on the problem of hedging against seismic risk through the retrofit of transportation systems in large-scale construction projects (LSCP). A fuzzy random multiobjective bilevel programming model is formulated with the objectives of the retrofit costs and the benefits on two separate levels. After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. An approximation decomposition-based multi-objective AGLNPSO is developed to solve the model. The results of a case study validate the efficiency of the proposed approach.

A Learning-Based Optimization Approach for Autonomous Ridesharing Platforms with Service-Level Contracts and On-Demand Hiring of Idle Vehicles

2021 ◽  
Author(s):  
Breno A. Beirigo ◽  
Frederik Schulte ◽  
Rudy R. Negenborn
Keyword(s):  
New York ◽  
Autonomous Vehicles ◽  
Transportation Network ◽  
Supply And Demand ◽  
Service Level ◽  
Third Party ◽  
Optimization Approach ◽  
Marginal Value ◽  
On Demand ◽  
Demand Fluctuations

Current mobility services cannot compete on equal terms with self-owned mobility products concerning service quality. Because of supply and demand imbalances, ridesharing users invariably experience delays, price surges, and rejections. Traditional approaches often fail to respond to demand fluctuations adequately because service levels are, to some extent, bounded by fleet size. With the emergence of autonomous vehicles, however, the characteristics of mobility services change and new opportunities to overcome the prevailing limitations arise. In this paper, we consider an autonomous ridesharing problem in which idle vehicles are hired on-demand in order to meet the service-level requirements of a heterogeneous user base. In the face of uncertain demand and idle vehicle supply, we propose a learning-based optimization approach that uses the dual variables of the underlying assignment problem to iteratively approximate the marginal value of vehicles at each time and location under different availability settings. These approximations are used in the objective function of the optimization problem to dispatch, rebalance, and occasionally hire idle third-party vehicles in a high-resolution transportation network of Manhattan, New York City. The results show that the proposed policy outperforms a reactive optimization approach in a variety of vehicle availability scenarios while hiring fewer vehicles. Moreover, we demonstrate that mobility services can offer strict service-level contracts to different user groups featuring both delay and rejection penalties.

Analysis of Dynamic Impedance Functions in Urban Traffic Flow Guidance System

2012 ◽  
Vol 238 ◽  
pp. 503-506 ◽  
Author(s):  
Zhi Cheng Li
Keyword(s):  
Traffic Flow ◽  
Network Model ◽  
Large Scale ◽  
Choice Model ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Guidance System ◽  
Traffic Network ◽  
Dynamic Traffic ◽  
Run Time

The successful application of Intelligent Transportation Systems (ITS) depends on the traffic flow at any time with high-precision and large-scale assessments, it is necessary to create a dynamic traffic network model to evaluate and forecast traffic. Dynamic route choice model sections of the run-time function are very important to the dynamic traffic network model. To simplify the dynamic traffic modeling, improve the calculation accuracy and save computation time, the flow on the section of the interrelationship between the exit flow and number of vehicles are analyzed, a run-time functions into the flow using only sections of the said sections are established.

Large-Scale Parallel Thermal Elastic-Plastic Welding Simulation Using Balancing Domain Decomposition Method

2019 ◽  
Author(s):  
Yasunori Yusa ◽  
Yuma Murakami ◽  
Hiroshi Okada
Keyword(s):  
Domain Decomposition ◽  
Decomposition Method ◽  
Large Scale ◽  
Domain Decomposition Method ◽  
Gradient Methods ◽  
Point Of View ◽  
Computational Time ◽  
Elastic Plastic ◽  
Parallel Finite Element ◽  
Welding Processes

Abstract From the mechanical point of view, welding processes can be modeled as a coupling problem between heat conduction and thermal elastic–plastic problems. Such a welding mechanics problem generally requires a large amount of computational time due to its nonlinearity as well as a lot of time steps with a moving heat source. To overcome this difficulty, we are developing a large-scale welding simulator based on the domain decomposition method, which is one of parallel finite element methods. In the present paper, the methodology of the domain decomposition method that is applied to welding analysis is presented, followed by the algorithm. Then, a bead-on-plate problem, which is a popular benchmark problem in the field of computational welding mechanics, was analyzed by the simulator. The bead-on-plate problem was successfully analyzed within very small numbers of iteration steps of the Newton–Raphson and conjugate gradient methods.

scholarly journals Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

2021 ◽  
Vol 10 (3) ◽  
pp. 165
Author(s):  
Joerg Schweizer ◽  
Cristian Poliziani ◽  
Federico Rupi ◽  
Davide Morgano ◽  
Mattia Magi
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Choice Model ◽  
Demand Model ◽  
Agent Based ◽  
Traffic Lights ◽  
Gps Traces ◽  
The City

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

Hubbing in U.S. Air Transportation System: Economic Approach

1996 ◽  
Vol 1562 (1) ◽  
pp. 28-37
Author(s):  
Tara J. Weidner
Keyword(s):  
Choice Model ◽  
Transportation Network ◽  
Supply And Demand ◽  
Economic Cost ◽  
Air Transportation ◽  
Transportation System ◽  
Planning Tool ◽  
Development Scenarios ◽  
Airport Capacity ◽  
Economic Forces

The dynamics of airline hubbing in the postderegulation U.S. air transportation system are examined. It is asserted that hubbing is not the result of arbitrary decisions by airlines but of economic forces that allow hubbed traffic to be understood and possibly predicted. On the basis of three primary hubbing criteria, an economic model of hub choice is developed. In the model, hubbing is assumed to be (a) intricately tied to the national air network, (b) grounded in the airlines' economic cost structure, and (c) the result of a balance of supply and demand forces. The route choice model is estimated for domestic U.S. hub markets. The model results support the first two of the three hubbing criteria and produce relative valuations of in-flight time, airport capacity delays, and schedule delays for connecting flights. These results are valuable in understanding the interplay of forces that lead to hub development and decline. In addition, the model structure is promising as a planning tool to investigate the redistribution of traffic in the U.S. air transportation network under various development scenarios and policy actions.

scholarly journals Introduction: a brief overview of iterative algorithms in X-ray computed tomography

2015 ◽  
Vol 373 (2043) ◽  
pp. 20140399 ◽  
Author(s):  
M. Soleimani ◽  
T. Pengpen
Keyword(s):  
Computed Tomography ◽  
High Performance ◽  
Large Scale ◽  
Krylov Subspace ◽  
Iterative Algorithms ◽  
Computational Time ◽  
X Ray ◽  
Reconstruction Software ◽  
X Ray Computed ◽  
Linear Systems Of Equations

This paper presents a brief overview of some basic iterative algorithms, and more sophisticated methods are presented in the research papers in this issue. A range of algebraic iterative algorithms are covered here including ART, SART and OS-SART. A major limitation of the traditional iterative methods is their computational time. The Krylov subspace based methods such as the conjugate gradients (CG) algorithm and its variants can be used to solve linear systems of equations arising from large-scale CT with possible implementation using modern high-performance computing tools. The overall aim of this theme issue is to stimulate international efforts to develop the next generation of X-ray computed tomography (CT) image reconstruction software.

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