The architect's role in participatory planning processes : a case study of the Boston Transportation Planning Review.

Thesis: M. Arch. A.S., Massachusetts Institute of Technology, Department of Architecture, 1976Includes bibliographical references (leaves 268-274).M. Arch. A.S.M. Arch. A.S. Massachusetts Institute of Technology, Department of Architectur

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.Includes bibliographical references

Design revolutions: IASDR 2019 Conference Proceedings. Volume 3: People

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 3 includes papers from People track of the conference

Evaluation of Highway Geometrics Related to Large Trucks

One objective of this study was to determine the extent of highway safety and geometric problems associated with larger trucks using Kentucky\u27s highways. The accident analysis involved both a general analysis of all truck accidents statewide as well as the identification of specific high-accident locations. A second objective was to identify criteria which can be used in identifying roadway sections that cannot safely accommodate large trucks. The accident analysis given can be used to investigate locations which have a high number of truck accidents. The general accident statistics related to trucks can be used in the investigation of the high-accident locations to identify factors which may be contributing to the accident problem. The summary of information obtained from the review of literature can be used as a guide when determining the appropriate criteria to use in formalizing truck access criteria. For example, several references gave recommendations concerning lane width and horizontal curvature appropriate for highways that allowed large truck traffic

Identification and safety effects of road user related measures. Deliverable 4.2 of the H2020 project SafetyCube

Safety CaUsation, Benefits and Efficiency (SafetyCube) is a European Commission supported Horizon 2020 project with the objective of developing an innovative road safety Decision Support System (DSS). The DSS will enable policy-makers and stakeholders to select and implement the most appropriate strategies, measures, and cost-effective approaches to reduce casualties of all road user types and all severities. This document is the second deliverable (4.2) of work package 4, which is dedicated to identifying and assessing road safety measures related to road users in terms of their effectiveness. The focus of deliverable 4.2 is on the identification and assessment of countermeasures and describes the corresponding operational procedure and outcomes. Measures which intend to increase road safety of all kind of road user groups have been considered [...continues]

Review of advanced road materials, structures, equipment, and detection technologies

As a vital and integral component of transportation infrastructure, pavement has a direct and tangible impact on socio-economic sustainability. In recent years, an influx of groundbreaking and state-of-the-art materials, structures, equipment, and detection technologies related to road engineering have continually and progressively emerged, reshaping the landscape of pavement systems. There is a pressing and growing need for a timely summarization of the current research status and a clear identification of future research directions in these advanced and evolving technologies. Therefore, Journal of Road Engineering has undertaken the significant initiative of introducing a comprehensive review paper with the overarching theme of “advanced road materials, structures, equipment, and detection technologies”. This extensive and insightful review meticulously gathers and synthesizes research findings from 39 distinguished scholars, all of whom are affiliated with 19 renowned universities or research institutions specializing in the diverse and multidimensional field of highway engineering. It covers the current state and anticipates future development directions in the four major and interconnected domains of road engineering: advanced road materials, advanced road structures and performance evaluation, advanced road construction equipment and technology, and advanced road detection and assessment technologies

Modeling driving decisions with latent plans

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2007.Includes bibliographical references (p. 227-238).Driving is a complex task that includes a series of interdependent decisions. In many situations, these decisions are based on a specific plan. The plan is however unobserved or latent and only the manifestations of the plan through actions are observed. Examples include selection of a target lane before execution of the lane change, choice of a merging tactic before execution of the merge. Change in circumstances (e.g. reaction of the neighboring drivers, delay in execution) can lead to updates to the initially chosen plan. These latent plans are ignored in the state-of-the-art driving behavior models. Use of these myopic models in the traffic simulators often lead to unrealistic traffic flow characteristics and incorrect representation of congestion. A modeling methodology has been formulated to address the effects of unobserved plans in the decisions of the drivers and hence overcome the deficiency of the existing driving behavior models and simulation tools. The actions of the driver are conditional on the current plan. The current plan can depend on previous plans and be influenced by anticipated future conditions. A Hidden Markov Model is used to address the effect of previous plans in the choice of the current plan and to capture the state-dependence among decisions. Effects of anticipated future circumstances in the current plan are captured through predicted conditions based on current information. The heterogeneity in decision making and planning capabilities of drivers are explicitly addressed. The methodology has been applied in developing driving behavior models for four traffic scenarios: freeway lane changing, freeway merging, urban intersection lane choice and urban arterial lane changing. In all applications, the models are estimated with disaggregate trajectory data using the maximum likelihood technique.(cont.) Estimation results show that the latent plan models have a significantly better goodness-of-fit compared to the 'reduced form' models where the latent plans are ignored and only the choice of actions are modeled. The justifications for using the latent plan modeling approach are further strengthened by validation case studies within the microscopic traffic simulator MITSIMLab where the simulation capabilities of the latent plan models are compared against the reduced form models. In all cases, the latent plan models better replicate the observed traffic conditions.by Charisma Farheen Choudhury.Ph.D

Roundabout Safety: Econometric and Machine Learning Models and Applications

The rapid growth in the number of roundabouts raises some significant research challenges, especially around safety performance and evaluation in the U.S. Despite the safety advantages that roundabout geometric design brings, crashes still occur. While operationally roundabouts are good at preventing severe crashes, they may lead to more less severe crash outcomes. Much attention has been given to injury severity at signalized intersections in the past, but little to no work has been performed on roundabout crash severity. One possible reason for this is the lack of data and given that roundabouts are a growing trend among many states for their safety performance, data is still tricking in slowly. The challenge with current data is its lack of detail that would allow for a clearer understanding of how the complex interactions between roundabout crash related factors, crash types, injury severity and roundabout configurations can be captured with current econometric methods utilized by transportation safety analysts today. However, there have been considerable advancements over the last few years especially in econometrics methods that account for unobserved heterogeneity. These advancements have been shown to provide a more reasonable understanding of contributing factors to overall safety. In addition, there has been a greater push to improve predictability and performance of these econometric techniques by utilizing complementary approaches such as machine learning. With this in mind, the goal of this dissertation is to present an exploratory crash-based approaches that utilize both advanced econometric methods and machine learning techniques to better understand the factors that may influence less severe crashes to those of more severe crashes given various configurations and crash types utilizing Oregon and Washington state crash data at roundabouts. The first manuscript investigates a crash-based analysis to better understand the factors that may influence less severe crashes to those of more severe crashes given various roundabout configurations and crash types in Oregon. Using Oregon’s crash database from 2011 to 2015 in which 1,006 crashes occurred at roundabouts. A series of log-likelihood ratio tests were conducted to validate that four separate random parameters binary probit models by configuration type were warranted. The second manuscript develops a machine learning methodology that evaluates crash injury severity at roundabouts and compares this with traditional econometric techniques. This work estimates a Random Parameter Binary Probit model (RPBP) and compares its predictions with those rendered from machine learning techniques, namely, Support Vector Machine (SVM) with linear, radial, polynomial, and sigmoid kernels. This is accomplished by utilizing Oregon crash data from 2011 to 2015 and focuses on both three- and four-leg roundabouts. Two significant variable sets have been conducted by utilizing random forest and binary model. Finally, the third manuscript investigates risk factors that significantly contribute to driver injury severity at roundabout crashes while systematically accounting for unobserved heterogeneity and the variance in means of the random parameters within the crash data. In this method data from the Washington State Department of Transportation (WSDOT) over a six-year period (2013 to 2018) in which 8548 crashes occurred at roundabouts is used. A random parameter binary probit model with heterogeneity in random parameter means is estimated to explore the effects of a wide range of variables on driver injury severity outcomes. Although the results of this work are exploratory, they provide evidence that crashes are occurring at roundabouts and several factors lead to crashes that result in an injury. In addition, the modeling approaches in this work offer a methodology that can account for unobservable factors in roundabout crash data. The findings of this research underscore the importance of fully accounting for unobserved heterogeneity by considering possible heterogeneity in the means of parameters. With the growing and importance relating to roundabout safety, this work provides some essential initial findings with Washington data, but also hopefully can provide some guidance for the analysis of other roundabouts-crash databases from other geographic locations and time periods. Several low-cost mitigation measures can reduce the number of crashes at roundabouts. First, improving pavement marking and signage to guide the motorist better and enhance driver expectancy. Furthermore, educating the public, including public-private partnerships between law enforcement agencies, driver's education instructors, transportation engineering groups, and insurance companies

Vistas, Volume 31, Spring 2016

An interdisciplinary, multi-cultural journal celebrating the creativity of Sacred Heart University during the academic year 2015-2016. This year\u27s issue of Vistas is titled Heritage and Beyond.https://digitalcommons.sacredheart.edu/stupub_horizons/1021/thumbnail.jp