Updating of travel behavior parameters and estimation of vehicle trip-chain data based on plate scanning

This article proposes a maximum-likelihood method to update travel behavior model parameters and estimate vehicle trip chain based on plate scanning. The information from plate scanning consists of the vehicle passing time and sequence of scanned vehicles along a series of plate scanning locations (sensor locations installed on road network). The article adopts the hierarchical travel behavior decision model, in which the upper tier is an activity pattern generation model, and the lower tier is a destination and route choice model. The activity pattern is an individual profile of daily performed activities. To obtain reliable estimation results, the sensor location schemes for predicting trip chaining are proposed. The maximum-likelihood estimation problem based on plate scanning is formulated to update model parameters. This problem is solved by the expectation-maximization (EM) algorithm. The model and algorithm are then tested with simulated plate scanning data in a modified Sioux Falls network. The results illustrate the efficiency of the model and its potential for an application to large and complex network cases

Dynamic Modeling for Intelligent Transportation System Applications

Special Issue on Dynamic Modeling for Intelligent Transportation System Applicationspostprin

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

Development of Data Analytics and Modeling Tools for Civil Infrastructure Condition Monitoring Applications

This dissertation focuses on the development of data analytics approaches to two distinct important condition monitoring applications in civil infrastructure: structural health monitoring and road surface monitoring. In the first part, measured vibration responses of a major long-span bridge are used to identify its modal properties. Variations in natural frequencies over a daily cycle have been observed with measured data, which are probably due to environmental effects such as temperature and traffic. With a focus on understanding the relationships between natural frequencies and temperatures, a controlled simulation-based study is conducted with the use of a full-scale finite element (FE) model and four regression models. In addition to the temperature effect study, the identified modal properties and the FE model are used to explore both deterministic and probabilistic model updating approaches. In the deterministic approach (sensitivity-based model updating), the regularization technique is applied to deal with a trade-off between natural frequency and mode shape agreements. Specific nonlinear constraints on mode shape agreements are suggested here. Their capabilities to adjust mode shape agreements are validated with the FE model. To the best of the author's knowledge, the sensitivity-based clustering technique, which enables one to determine efficient updating parameters based on a sensitivity analysis, has not previously been applied to any civil structure. Therefore, this technique is adapted and applied to a full-scale bridge model for the first time to highlight its capability and robustness to select physically meaningful updating parameters based on the sensitivity of natural frequencies with respect to both mass and stiffness-related physical parameters. Efficient and physically meaningful updating parameters are determined by the sensitivity-based clustering technique, resulting in an updated model that has a better agreement with measured data sets. When it comes to the probabilistic approach, the application of Bayesian model updating to large-scale civil structures based on real data is very rare and challenging due to the high level of uncertainties associated with the complexity of a large-scale model and variations in natural frequencies and mode shapes identified from real measured data. In this dissertation, the full-scale FE model is updated via the Bayesian model updating framework in an effort to explore the applicability of Bayesian model updating to a more complex and realistic problem. Uncertainties of updating parameters, uncertainty reductions due to information provided by data sets, and uncertainty propagations to modal properties of the FE model are estimated based on generated posterior samples. In the second part of this dissertation, a new innovative framework is developed to collect pavement distress data via multiple vehicles. Vehicle vibration responses are used to detect isolated pavement distress and rough road surfaces. GPS positioning data are used to localize identified road conditions. A real-time local data logging algorithm is developed to increase the efficiency of data logging in each vehicle client. Supervised machine learning algorithms are implemented to classify measured dynamic responses into three categories. Since data are collected from multiple vehicles, the trajectory clustering algorithm is introduced to integrate various trajectories to provide a compact format of information about road surface conditions. The suggested framework is tested and evaluated in real road networks

Comparative Assessment on Static O-D Synthesis

Recognizing the benefits of data and the information it provides to travel demand is pertinent to network planning and design. Technological advances have led the ability to produce large quantities and types of data and as a result, many origin-destination (O-D) estimation techniques have been developed to accommodate this data. In contrast to the abundant choices on data types, data quantity and estimation procedures, there lacks a common framework to assess these methods. Without consistency in a baseline foundation, the performances of the methodologies can vary greatly based on each individual assumption. This research addresses the need for techniques to be tested on a common framework by establishing a baseline condition for static O-D estimation through a synthetic Vissim model of the Sioux Falls network as a case study area. The model is used to generate a master dataset, representing the ground-truth, and a subset of the master dataset, emulating the data collected from real world technologies. The subset of data is used as the input for the O-D estimation techniques where the input is varied to evaluate the effects of different levels of coverage/penetration of each data type on estimation results. A total of five estimation techniques developed by Cascetta and Postorino (2001), Castillo et al. (2008b), Parry and Hazelton (2012), Feng et al. (2015) and X. Yang et al. (2017) are tested with three data types (link counts, partial traces, and full traces) and two traffic assignment conditions (all-or-nothing and user equilibrium). The result of this research highlights the uniqueness of each network situation and highlights the outcomes of each approach. The wealth of data does not directly equal better information for every methodology. The insights that each data type provides each estimation technique reveals different results. The findings of this research demonstrate and supports that an established testbed framework supports and enhances future O-D estimation scenarios as it pertains to general O-D estimation and extensions of existing techniques

Link Travel Time Estimation Based on Network Entry/Exit Time Stamps of Trips

This dissertation studies the travel time estimation at roadway link level using entry/exit time stamps of trips on a steady-state transportation network. We propose two inference methods based on the likelihood principle, assuming each link associates with a random travel time. The first method considers independent and Gaussian distributed link travel times, using the additive property that trip time has a closed-form distribution as the summation of link travel times. We particularly analyze the mean estimates when the variances of trip time estimates are known with a high degree of precision and examine the uniqueness of solutions. Two cases are discussed in detail: one with known paths of all trips and the other with unknown paths of some trips. We apply the Gaussian mixture model and the Expectation-Maximization (EM) algorithm to deal with the latter. The second method splits trip time proportionally among links traversed to deal with more general link travel time distributions such as log-normal. This approach builds upon an expected log-likelihood function which naturally leads to an iterative procedure analogous to the EM algorithm for solutions. Simulation tests on a simple nine-link network and on the Sioux Falls network respectively indicate that the two methods both perform well. The second method (i.e., trip splitting approximation) generally runs faster but with larger errors of estimated standard deviations of link travel times

Driving Volatility in Instantaneous Driving Behaviors: Studies Using Large-Scale Trajectory Data

Increasing amounts of data, generated by electronic sensors from various sources that include travelers, vehicles, infrastructure and the environment, referred to as “Big Data”, represent an opportunity for innovation in transportation systems and toward achieving safety, mobility and sustainability goals. The dissertation takes advantage of large-scale trajectory data coupled with travel behavioral information and containing 78 million second-by-second driving records from 100 thousand trips made by nearly four thousand drivers. The data covers 70 counties across the State of California and Georgia, representing various land use types, roadway network conditions and population. The trajectories cover various driving practices made by vehicles of varied body types as well as different fuel types including conventional vehicles (CVs) consuming gasoline, hybrid electric vehicles (HEVs), battery electric vehicles (BEVs), diesel vehicles and other alternative fuel vehicles (AFVs). The dissertation establishes a framework for the research agenda in instantaneous driving behavior studies using the large-scale trajectory data. The dissertation makes both theoretical and empirical contributions: 1) Developing measures for driving volatility in instantaneous driving behaviors; 2) Understanding correlates of driving volatility in hierarchies & developing applications using large-scale trajectory data. Before using second-by-second trajectories, a study, answering research questions concerning the relationships between data sampling rates and information loss, was conducted. Then, a study for quantifying driving volatility in instantaneous driving behaviors was presented. “Driving volatility”, as the core concept in the dissertation, captures extreme driving patterns under seemingly normal conditions. After that, the dissertation presents a study on exploration of the hierarchical nature of driving volatility embedded in travel survey data using multi-level modeling techniques, and highlights the role of AFVs in travel. Last, the dissertation presents a study for customizing driving cycles for individuals using large-scale trajectory data, given heterogeneous driving performance across drivers and vehicle types. The customized driving cycles help generate more accurate fuel economy information to support cost-effective vehicle choices. The implications of the findings and potential applications to fleet vehicles and driving population are also discussed in the dissertation

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity