Critical enhancements of a dynamic traffic assignment model for highly congested, complex urban network

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 109-115).To accurately replicate the highly congested traffic situation of a complex urban network, significant challenges are posed to current simulation-based dynamic traffic assignment (DTA) models. This thesis discusses these challenges and corresponding solutions with consideration of model accuracy and computational efficiency. DynaMITP, an off-line mesoscopic DTA model is enhanced. Model success is achieved by several critical enhancements aimed to better capture the traffic characteristics in urban networks. A Path-Size Logit route choice model is implemented to address the overlapping routes problem. The explicit representation of lane-groups accounts for traffic delays and queues at intersections. A modified treatment of acceptance capacity is required to deal with the large number of short links in the urban network. The network coding is revised to maintain enough loader access capacity in order to avoid artificial bottlenecks. In addition, the impacts of bicycles and pedestrians on automobile traffic is modeled by calibrating dynamic road segment capacities. The enhanced model is calibrated and applied to a case study network extracted from the city of Beijing, China. Data used in the calibration include sensor counts and floating car travel time. The improvements of the model performance are indicated by promising results from validation tests.by Zheng Wei.S.M

A Fast Multi-Objective Optimization Approach to Solve the Continuous Network Design Problem with Microscopic Simulation

The capacity of microscopic traffic simulation to estimate the environmental and road safety impacts opens the possibility to address the Network Design Problem from a new multi-objective point of view. Computation time, however, has hindered the use of this tool. The aim of this thesis was to find a continuous optimization method that would require only a very limited number of evaluations, and thus reduce the computation time. For this purpose, the most recent optimization literature was studied and two algorithms were selected: PAL and SMS-EGO. Both these algorithms rely on Gaussian process meta-models, but they are distinct with respect to the assumptions, criteria and methods used. They were then compared on a real-world case-study with NSGA-II, a genetic algorithm considered as state-of-the-art. Within the very limited computational budget allowed, SMS-EGO was found to outperform PAL and NSGA-II in the three configurations studied. However, the computational time required was still too important to allow for large scale optimization. To further accelerate the optimization process, three main adjustments were proposed, based on variable noise modeling, gradient-based optimization and conditional updates of the meta-models. Considering 20 runs for each optimization process, only variable noise modeling exhibited a statistically significant positive impact. The two other modifications also accelerated the optimization process on average, but high variability in the results led to p-values in the order of 0.15. Overall, the proposed optimization methodology represents a useful tool for transportation researchers to solve multi-objective optimization problems of limited scale

Learning Augmented Optimization for Network Softwarization in 5G

The rapid uptake of mobile devices and applications are posing unprecedented traffic burdens on the existing networking infrastructures. In order to maximize both user experience and investment return, the networking and communications systems are evolving to the next gen- eration – 5G, which is expected to support more flexibility, agility, and intelligence towards provisioned services and infrastructure management. Fulfilling these tasks is challenging, as nowadays networks are increasingly heterogeneous, dynamic and expanded with large sizes. Network softwarization is one of the critical enabling technologies to implement these requirements in 5G. In addition to these problems investigated in preliminary researches about this technology, many new emerging application requirements and advanced opti- mization & learning technologies are introducing more challenges & opportunities for its fully application in practical production environment. This motivates this thesis to develop a new learning augmented optimization technology, which merges both the advanced opti- mization and learning techniques to meet the distinct characteristics of the new application environment. To be more specific, the abstracts of the key contents in this thesis are listed as follows: • We first develop a stochastic solution to augment the optimization of the Network Function Virtualization (NFV) services in dynamical networks. In contrast to the dominant NFV solutions applied for the deterministic networking environments, the inherent network dynamics and uncertainties from 5G infrastructure are impeding the rollout of NFV in many emerging networking applications. Therefore, Chapter 3 investigates the issues of network utility degradation when implementing NFV in dynamical networks, and proposes a robust NFV solution with full respect to the underlying stochastic features. By exploiting the hierarchical decision structures in this problem, a distributed computing framework with two-level decomposition is designed to facilitate a distributed implementation of the proposed model in large-scale networks. • Next, Chapter 4 aims to intertwin the traditional optimization and learning technologies. In order to reap the merits of both optimization and learning technologies but avoid their limitations, promissing integrative approaches are investigated to combine the traditional optimization theories with advanced learning methods. Subsequently, an online optimization process is designed to learn the system dynamics for the network slicing problem, another critical challenge for network softwarization. Specifically, we first present a two-stage slicing optimization model with time-averaged constraints and objective to safeguard the network slicing operations in time-varying networks. Directly solving an off-line solution to this problem is intractable since the future system realizations are unknown before decisions. To address this, we combine the historical learning and Lyapunov stability theories, and develop a learning augmented online optimization approach. This facilitates the system to learn a safe slicing solution from both historical records and real-time observations. We prove that the proposed solution is always feasible and nearly optimal, up to a constant additive factor. Finally, simulation experiments are also provided to demonstrate the considerable improvement of the proposals. • The success of traditional solutions to optimizing the stochastic systems often requires solving a base optimization program repeatedly until convergence. For each iteration, the base program exhibits the same model structure, but only differing in their input data. Such properties of the stochastic optimization systems encourage the work of Chapter 5, in which we apply the latest deep learning technologies to abstract the core structures of an optimization model and then use the learned deep learning model to directly generate the solutions to the equivalent optimization model. In this respect, an encoder-decoder based learning model is developed in Chapter 5 to improve the optimization of network slices. In order to facilitate the solving of the constrained combinatorial optimization program in a deep learning manner, we design a problem-specific decoding process by integrating program constraints and problem context information into the training process. The deep learning model, once trained, can be used to directly generate the solution to any specific problem instance. This avoids the extensive computation in traditional approaches, which re-solve the whole combinatorial optimization problem for every instance from the scratch. With the help of the REINFORCE gradient estimator, the obtained deep learning model in the experiments achieves significantly reduced computation time and optimality loss

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Stochastic process models for dynamic traffic assignment

This research explored the idea of unifying the deterministic and stochastic process approaches together, and developing a generalised framework of dynamic traffic assignment models to include day-to-day and within day variations in traffic flow. The framework of models is also aimed at capturing individual drivers’ adaptation of route choices based on the route costs experienced through suitable driver learning models. In this thesis, the route flows within a day in a given departure period are modelled as random variables, and their evolution over a period of time (a number of days) is modelled as stochastic processes based on the laws of probability. The interactions among the route flows from various departure periods over the network links in space and time, are modelled through dynamic link loading procedures. Stochastic processes under certain mild conditions admit a unique stationary probability distribution which can be modelled by using simulation techniques. Alternatively, the moments (e.g., mean and variance) of the equilibrium (stationary) probability distribution can also be estimated. This research has advanced the idea of estimating the properties of equilibrium probability distribution by making a particular contribution in formulating the methodology for computing the Jacobians of route travel times with respect to the route inflows in a doubly dynamic assignment context using an analytical procedure, which are necessary for estimating the variance-covariance matrices of stationary route flows. In this modelling framework, there are three modules - the first one is a day-to-day route choice model defined as a discrete time stochastic process, the second is a continuous time dynamic network loading of the route flows considering the complete spatio-temporal effects of the traffic flows that use the road links at the same time, and the third is the drivers’ learning and adjusting model based on a linear filter. The main idea of estimating the properties of stationary probability distribution in this research builds on two earlier results: firstly, when the demand is sufficiently large, the equilibrium probability distribution converges approximately to a Multivariate Normal distribution and its mean coincides with the SUE flows; secondly, the variance can be estimated by an approximation procedure. The equilibrium probability distribution can also be worked out using the commonly followed Monte Carlo simulation technique, which involves simulating the route choice process as a multinomial probability distribution over a number of days, and then summarising the properties e.g., the mean and the variance of the stationary probability distribution. This procedure though simple, is time consuming and the main difficulty lies in detecting the stationarity of the process. Based on the necessary conditions, simple and practically useable tests for identifying the stationarity of a stochastic process have been introduced. These tests involve analysing autocorrelations and computing large lag standard errors in autocorrelations. The stationary variance-covariance of route flows obtained by the variance approximation model, was compared with that computed by the simulation procedure. Overall, the variance approximation model performs satisfactorily. Variance-covariance of route flows has been found sensitive primarily to the input logit choice parameter, which defines the boundaries of the validity of the variance approximation model. Variance-covariance is also affected by the memory length with the shorter memory systems essentially producing highly variant systems. Similarly, the variance-covariance of route flows is also sensitive to the memory weight, and the lower memory weight (0 < memory weight « 1) produces the same effect as that of shorter memory systems. The Jacobians of the travel times worked out in this thesis have much wider applicability, and a few possible situations have been listed here among many others. Firstly, the optimisation based user equilibrium programs can be speeded up by defining the descent direction with the help of the Jacobians. Secondly, the Jacobians may be found very useful in defining the dynamic road user pricing problems. Finally, the sensitivity analysis of user equilibrium problems requires the computation of the Jacobians

Aeronautical engineering: A continuing bibliography with indexes, supplement 190

This bibliography lists 510 reports, articles and other documents introduced into the NASA scientific and technical information system in July 1985