Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human's bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN's input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework. GPS is a ubiquitous positioning tool that records spatiotemporal information of moving objects carrying a GPS-enabled device (K. Heaslip). Transportation Research Part C 86 (2018) 360-371 0968-090X/ Published by Elsevier Ltd.T (e.g., a smartphone). The main advantageous of smart phones, compared to other GPS-equipped devices, is its enormous market penetration rate in a large number of countries and being relatively close to users nearly all of the time. As a consequence, such a dominant and area-wide sensing technology is capable of creating massive trajectory data of vehicles and people. A GPS trajectory, also called movement, of an object is constructed by connecting GPS points of their GPS-enabled device. A GPS point, here, is denoted as (lat, long, t), where lat, long, and t are latitude, longitude, and timestamp, respectively. The study of individuals' mobility patterns from GPS datasets has led to a variety of behavioral applications including learning significant locations, anomaly detection, locationbased activity recognition, and identification of transport modes (Lin and Hsu, 2014), in which the latter is the focus of this study. Nonetheless, GPS devices can only record time and positional characteristics of travels without any explicit information on utilized transport modes....

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Identifying users' transportation modes (e.g., bike, bus, train, and car) is a key step towards many transportation related problems including (but not limited to) transport planning, transit demand analysis, auto ownership, and transportation emissions analysis. Traditionally, the information for analyzing travelers' behavior for choosing transport mode(s) was obtained through travel surveys. High cost, low-response rate, time-consuming manual data collection, and misreporting are the main demerits of the survey-based approaches. With the rapid growth of ubiquitous GPS-enabled devices (e.g., smartphones), a constant stream of users' trajectory data can be recorded. A user's GPS trajectory is a sequence of GPS points, recorded by means of a GPS-enabled device, in which a GPS point contains the information of the device geographic location at a particular moment. In this research, users' GPS trajectories, rather than traditional resources, are harnessed to predict their transportation mode by means of statistical models. With respect to the statistical models, a wide range of studies have developed travel mode detection models using on hand-designed attributes and classical learning techniques. Nonetheless, hand-crafted features cause some main shortcomings including vulnerability to traffic uncertainties and biased engineering justification in generating effective features. A potential solution to address these issues is by leveraging deep learning frameworks that are capable of capturing abstract features from the raw input in an automated fashion. Thus, in this thesis, deep learning architectures are exploited in order to identify transport modes based on only raw GPS tracks. It is worth noting that a significant portion of trajectories in GPS data might not be annotated by a transport mode and the acquisition of labeled data is a more expensive and labor-intensive task in comparison with collecting unlabeled data. Thus, utilizing the unlabeled GPS trajectory (i.e., the GPS trajectories that have not been annotated by a transport mode) is a cost-effective approach for improving the prediction quality of the travel mode detection model. Therefore, the unlabeled GPS data are also leveraged by developing a novel deep-learning architecture that is capable of extracting information from both labeled and unlabeled data. The experimental results demonstrate the superiority of the proposed models over the state-of-the-art methods in literature with respect to several performance metrics.

quake may have a more meaningful measure of severity than the earthquake's magnitude because intensity refers to the effects experienced at that place. The Haitian earthquake was assigned an MMI value of 9, indicating severe damage to poorly built structures (fallen chimneys, smokestacks, columns, monuments, and walls; heavy furniture overturned), as well as considerable damage in specially and well-designed frame structures, which were thrown out of plumb. Substantial buildings also incurred severe damage with partial collapses and buildings shifted off their foundations (2).Almost 1 month later, a magnitude 8.8 earthquake rocked the Chilean coast; however, it was assigned an MMI value of 8. Despite the significantly stronger event in Chile, the MMI value was lower because damage was less significant. Whereas Haiti took months to recover, Chile recovered within weeks. The key difference between these two examples is resilience.Resiliency, if properly understood and applied, can preclude many of the devastating effects of disasters. Resilient transportation systems may reduce the probability of failure within the system and reduce the consequences of any failure that occurs, thus improving recovery time. Understanding the resiliency of a transportation system after a disaster has occurred does little to mitigate the effects of the event. Thus, this paper expands on the conceptual framework developed by Heaslip et al. to assess the network resiliency of a system before a destabilizing event (3). This process identifies weaknesses within the network and provides decision makers with a flexible and robust method for quantifying resiliency. The information can be used to properly prioritize transportation investments to enhance network resiliency. DEFINING RESILIENCEThe concept of resilience is broadly applied in many fields of study (e.g., engineering, psychology, sociology, economics). Similar concepts are flexibility, redundancy, reliability, elasticity, and risk management. In economics, the term "resilience" refers to the ability to recover quickly from a shock (shock counteraction), to withstand the effect of a shock (shock absorption), and to avoid the shock (vulnerability) (4). In social science, resilience is the capacity of a system that has been exposed to hazards to adapt by resisting or changing, so that it can reach and maintain an acceptable level of functioning and structure (5). In earthquake engineering, researchers define seismic resilience as the ability of social units (e.g., organizations, communities) to mitigate hazards, contain the effects of disasters, and carry out recovery activities in ways that minimize social disruption and reduce the effects of future earthquakes (6). Community seismic resilience is the capacity to absorb stress, manage it, and recover from it (7). More generally, resilience is the capacity to absorb shocks gracefully (8).The concept of resilience has been studied in the field of transportation engineering as well. Conceptual frameworks have been created Evaluation of...

The objective of this research was to develop analytical models and procedures for estimating the capacity of a freeway work zone by considering various geometric-, traffic-, and work zone–related parameters. The study was conducted in two stages: simulation-based modeling and field data collection. In the first stage, CORSIM (Version 5.1) was used to develop a comprehensive database for various work zone scenarios. Analytical models were developed to predict work zone capacity on the basis of these simulated data and previous literature findings considering three work zone configurations: two-to-one, three-to-two, and three-to-one lane closures. In the second stage, field data were collected at a freeway work zone to evaluate and refine the analytical models developed. Data were collected at the freeway work zone site during 15 evening peak periods, which included left- and right-lane closures as well as rainy weather conditions. The observed capacities were compared with those predicted by the new analytical models as well as to those estimated by the Highway Capacity Manual 2000. It was concluded that the analytical models developed predicted within 1% the capacity of the study work zone.

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