Public transportation is undeniably an effective way to move a large number of people in a city. Its ineffectiveness, such as long travel times, poor coverage, and lack of direct services, however, makes it unappealing to many commuters. In this thesis, we address some of the shortcomings and propose solutions for making public transportation more preferable. The first part of this thesis is focused on improving existing bus services to provide higher levels of service. We propose an optimization model to determine limited-stop service to be operated in parallel with local service to maximize total user welfare. Theoretical properties of the model are established and used to develop an efficient solution approach. We present numerical results obtained using real-world data and demonstrate the benefits of limited-stop services. The second part of this thesis concerns the design of integrated vehicle-sharing and public transportation services. One-way vehicle-sharing services can provide better access to existing public transportation and additional options for trips beyond those provided by public transit. The contributions of this part are twofold. First, we present a framework for evaluating the impacts of integrating one-way vehicles haring service with existing public transportation. Using publicly available data, we construct a graph representing a multi-modal transportation service. Various evaluation metrics based on centrality indices are proposed. Additionally, we introduce the notion of a transfer tree and develop a visualization tool that enables us to easily compare commuting patterns from different origins. The framework is applied to assess the impact of Hubway (a bike-sharing service) on public transportation service in the Boston metropolitan area. Second, we present an optimization model to select a subset of locations at which installing vehicle-sharing stations minimizes overall travel time over the integrated network. Benders decomposition is used to tackle large instances. While a tight formulation generally generates stronger Benders cuts, it requires a large number of variables and constraints, and hence, more computational effort. We propose new algorithms that produce strong Benders cuts quickly by aggregating various variables and constraints. Using data from the Boston metropolitan area, we present computational experiments that confirm the effectiveness of our solution approach.
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility 'structural' analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data's impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility 'structural' analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data's impact on mobility, and an introduction to the tools necessary to apply new techniques.
This book presents papers based on the presentations and discussions at the international workshop on Big Data Smart Transportation Analytics held July 16 and 17, 2016 at Tongji University in Shanghai and chaired by Professors Ukkusuri and Yang. The book is intended to explore a multidisciplinary perspective to big data science in urban transportation, motivated by three critical observations: The rapid advances in the observability of assets, platforms for matching supply and demand, thereby allowing sharing networks previously unimaginable. The nearly universal agreement that data from multiple sources, such as cell phones, social media, taxis and transit systems can allow an understanding of infrastructure systems that is critically important to both quality of life and successful economic competition at the global, national, regional, and local levels. There is presently a lack of unifying principles and methodologies that approach big data urban systems. The workshop brought together varied perspectives from engineering, computational scientists, state and central government, social scientists, physicists, and network science experts to develop a unifying set of research challenges and methodologies that are likely to impact infrastructure systems with a particular focus on transportation issues. The book deals with the emerging topic of data science for cities, a central topic in the last five years that is expected to become critical in academia, industry, and the government in the future. There is currently limited literature for researchers to know the opportunities and state of the art in this emerging area, so this book fills a gap by synthesizing the state of the art from various scholars and help identify new research directions for further study.
This thesis tackles two major challenges of urban transportation optimization problems: (i) high-dimensionality and (ii) uncertainty in both demand and supply. These challenges are addressed from both modeling and algorithm design perspectives. The first part of this thesis focuses on the formulation of analytical transient stochastic link transmission models (LTM) that are computationally tractable and suitable for largescale network analysis and optimization. We first formulate a stochastic LTM based on the model of Osorio and Flötteröd (2015). We propose a formulation with enhanced scalability. In particular, the dimension of the state space is linear, rather than cubic, in the link’s space capacity. We then propose a second formulation that has a state space of dimension two; it scales independently of the link’s space capacity. Both link models are validated versus benchmark models, both analytical and simulation-based. The proposed models are used to address a probabilistic formulation of a city-wide signal control problem and are benchmarked versus other existing network models. Compared to the benchmarks, both models derive signal plans that perform systematically better considering various performance metrics. The second model, compared to the first model, reduces the computational runtime by at least two orders of magnitude. The second part of this thesis proposes a technique to enhance the computational efficiency of simulation-based optimization (SO) algorithms for high-dimensional discrete SO problems. The technique is based on an adaptive partitioning strategy. It is embedded within the Empirical Stochastic Branch-and-Bound (ESB&B) algorithm of Xu and Nelson (2013). This combination leads to a discrete SO algorithm that is both globally convergent and has good small sample performance. The proposed algorithm is validated and used to address a high-dimensional car-sharing optimization problem.
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility ‘structural’ analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data’s impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility ‘structural’ analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data’s impact on mobility, and an introduction to the tools necessary to apply new techniques. Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
Increasing traffic congestion, vehicle emissions and commuters delay have been major challenges for urban transportation systems for years. The economic cost of traffic congestion in the US is Increasing from 200 billion in 2013 to 293 billion in 2030. There is an increasing need for a better solution to long-term transportation demand forecasting for urban infrastructure planning, and solution to short-term traffic prediction for managing existing urban infrastructure. Accordingly, understanding how urban systems operate and evolve through modeling individuals' daily urban activities has been a major focus of transportation planners, urban planners, and geographers. Traffic data (loop sensors, surveillance cameras, and GPS in taxis, buses), survey data (ACS, CHTS), mobile phone signals (CDR and GPS) and Location Based Social Network (LBSN) data (Facebook, Twitter, Yelp, and Foursquare) have enabled data-driven research on transportation behavior research. The data-driven research, urban data analytics, is an interdisciplinary field where machine learning/ deep learning methods from computer science and optimization/ simulation methods from operation research are applied in conventional city-related fields using spatial-temporal data. In this dissertation, we aim to add the third dimension, social, to urban data analytics research using social-spatial-temporal data, whose key topic is understanding how friendship influences human behavior over time and space. In this era of transformative mobility, this can help better design policies and investment strategies for managing existing urban infrastructure and forecasting future urban infrastructure planning. In this dissertation, we explored two research directions on social-enabled urban data analytics. First, we developed new machine learning models for social discrete choice model, bridging the gap between discrete choice modeling research and computer science research. Second, we developed a methodology framework for synthetic population synthesis using both small data and big data. The first part of the dissertation focus on modeling social influence on human behavior from a graph modeling perspective, while conforming to the discrete choice modeling framework. The proposed models can be used to model how friends influence individual's travel mode choice and other transportation related choices, which is important to transportation demand forecasting. We propose two novel models with scalable training algorithms: local logistics graph regularization (LLGR) and latent class graph regularization (LCGR) models. We add social regularization to represent similarity between friends, and we introduce latent classes to account for possible preference discrepancies between different social groups. Training of the LLGR model is performed using alternating direction method of multipliers (ADMM), and training of the LCGR model is performed using a specialized Monte Carlo expectation maximization (MCEM) algorithm. Scalability to large graphs is achieved by parallelizing computation in both the expectation and the maximization steps. The LCGR model is the first latent class classification model that incorporates social relationships among individuals represented by a given graph. To evaluate our two models, we consider three classes of data: small synthetic data to illustrate the knobs of the method, small real data to illustrate one social science use case, and large real data to illustrate a typical large-scale use case in the internet and social media applications. We experiment on synthetic datasets to empirically explain when the proposed model is better than vanilla classification models that do not exploit graph structure. We illustrate how the graph structure and labels, assigned to each node of the graph, need to satisfy certain reasonable properties. We also experiment on real-world data, including both small scale and large scale real-world datasets, to demonstrate on which types of datasets our model can be expected to outperform state-of-the-art models. This dissertation also develops an algorithmic procedure to incorporate social information into population synthesizer, which is an essential step to incorporate social information into the transportation simulation framework. Agent-based modeling in transportation problems requires detailed information on each of the agents that represent the population in the region of a study. To extend the agent-based transportation modeling with social influence, a connected synthetic population with both synthetic features and its social networks need to be simulated. However, either the traditional manually-collected household survey data (ACS) or the recent large-scale passively-collected Call Detail Records (CDR) alone lacks features. This work proposes an algorithmic procedure that makes use of both traditional survey data as well as digital records of networking and human behaviors to generate connected synthetic populations. This proposed framework for connected population synthesis is applicable to cities or metropolitan regions where data availability allows for the estimation of the component models. The generated populations coupled with recent advances in graph (social networks) algorithms can be used for testing transportation simulation scenarios with different social factors.
Scheduled transportation networks give rise to very complex and large-scale networkoptimization problems requiring innovative solution techniques and ideas from mathematical optimization and theoretical computer science. Examples of scheduled transportation include bus, ferry, airline, and railway networks, with the latter being a prime application domain that provides a fair amount of the most complex and largest instances of such optimization problems. Scheduled transport optimization deals with planning and scheduling problems over several time horizons, and substantial progress has been made for strategic planning and scheduling problems in all transportation domains. This state-of-the-art survey presents the outcome of an open call for contributions asking for either research papers or state-of-the-art survey articles. We received 24 submissions that underwent two rounds of the standard peer-review process, out of which 18 were finally accepted for publication. The volume is organized in four parts: Robustness and Recoverability, Robust Timetabling and Route Planning, Robust Planning Under Scarce Resources, and Online Planning: Delay and Disruption Management.
In this thesis, we propose novel computationally efficient optimization algorithms that derive effective traffic management strategies to reduce congestion and improve the efficiency of urban transportation systems. The proposed algorithms enable the use of high-resolution yet computationally inefficient urban traffic simulators to address large-scale urban transportation optimization problems in a computationally efficient manner. The first and the second part of this thesis focus on large-scale offline transportation optimization problems with stochastic simulation-based objective functions, analytical differentiable constraints and high-dimensional decision variables. We propose two optimization algorithms to solve these problems. In the first part, we propose a simulation-based metamodel algorithm that combines the use of an analytical stationary traffic network model and a dynamic microscopic traffic simulator. In the second part, we propose a metamodel algorithm that combines the use of an analytical transient traffic network model and the microscopic simulator. In the first part, we use the first metamodel algorithm to solve a large-scale fixed-time traffic signal control problem of the Swiss city of Lausanne with limited simulation runs, showing that the proposed algorithm can derive signal plans that outperform traditional simulation-based optimization algorithms and a commercial traffic signal optimization software. In the second part, we use both algorithms to solve a time-dependent traffic signal control problem of Lausanne, showing that the metamodel with the transient analytical traffic model outperforms that with the stationary traffic model. The third part of this thesis focuses on large-scale online transportation problems, which need to be solved with limited computational time. We propose a new optimization framework that combines the use of a problem-specific model-driven method, i.e., the method proposed in the first part, with a generic data-driven supervised machine learning method. We use this framework to address a traffic responsive control problem of Lausanne. We compare the performance of the proposed framework with the performance of an optimization framework with only the model-driven method and an optimization framework with only the data-driven method, showing that the proposed framework is able to derive signal plans that outperform the signal plans derived by the other two frameworks in most cases.
This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.
