Advances in the Hierarchical Emergent Behaviors (HEB) approach to autonomous vehicles

Widespread deployment of autonomous vehicles (AVs) presents formidable challenges in terms on handling scalability and complexity, particularly regarding vehicular reaction in the face of unforeseen corner cases. Hierarchical Emergent Behaviors (HEB) is a scalable architecture based on the concepts of emergent behaviors and hierarchical decomposition. It relies on a few simple but powerful rules to govern local vehicular interactions. Rather than requiring prescriptive programming of every possible scenario, HEB’s approach relies on global behaviors induced by the application of these local, well-understood rules. Our first two papers on HEB focused on a primal set of rules applied at the first hierarchical level. On the path to systematize a solid design methodology, this paper proposes additional rules for the second level, studies through simulations the resultant richer set of emergent behaviors, and discusses the communica-tion mechanisms between the different levels.Peer ReviewedPostprint (author's final draft

CT-Mapper: Mapping Sparse Multimodal Cellular Trajectories using a Multilayer Transportation Network

Mobile phone data have recently become an attractive source of information about mobility behavior. Since cell phone data can be captured in a passive way for a large user population, they can be harnessed to collect well-sampled mobility information. In this paper, we propose CT-Mapper, an unsupervised algorithm that enables the mapping of mobile phone traces over a multimodal transport network. One of the main strengths of CT-Mapper is its capability to map noisy sparse cellular multimodal trajectories over a multilayer transportation network where the layers have different physical properties and not only to map trajectories associated with a single layer. Such a network is modeled by a large multilayer graph in which the nodes correspond to metro/train stations or road intersections and edges correspond to connections between them. The mapping problem is modeled by an unsupervised HMM where the observations correspond to sparse user mobile trajectories and the hidden states to the multilayer graph nodes. The HMM is unsupervised as the transition and emission probabilities are inferred using respectively the physical transportation properties and the information on the spatial coverage of antenna base stations. To evaluate CT-Mapper we collected cellular traces with their corresponding GPS trajectories for a group of volunteer users in Paris and vicinity (France). We show that CT-Mapper is able to accurately retrieve the real cell phone user paths despite the sparsity of the observed trace trajectories. Furthermore our transition probability model is up to 20% more accurate than other naive models.Comment: Under revision in Computer Communication Journa

Methods and Measures for Analyzing Complex Street Networks and Urban Form

Complex systems have been widely studied by social and natural scientists in terms of their dynamics and their structure. Scholars of cities and urban planning have incorporated complexity theories from qualitative and quantitative perspectives. From a structural standpoint, the urban form may be characterized by the morphological complexity of its circulation networks - particularly their density, resilience, centrality, and connectedness. This dissertation unpacks theories of nonlinearity and complex systems, then develops a framework for assessing the complexity of urban form and street networks. It introduces a new tool, OSMnx, to collect street network and other urban form data for anywhere in the world, then analyze and visualize them. Finally, it presents a large empirical study of 27,000 street networks, examining their metric and topological complexity relevant to urban design, transportation research, and the human experience of the built environment.Comment: PhD thesis (2017), City and Regional Planning, UC Berkele

Land Use and Transportation Alternatives: Constraint or Expansion of Household Choice, MTI Report 01-19

Transportation and land use research that considers such alternatives as New Urbanist development, jobs-housing balance, transit villages, or “smart growth” most typically tests the capacity of such physical forms to reduce vehicle miles traveled (VMT) or bring about other desired outcomes in the modification of travel behavior. Establishing such causality is broadly seen as a precondition for the urban planning interventions that are presumed to be necessary to bring these forms about. But such a view neglects the extent to which current interventions—notably zoning and transportation regulations—tend to preclude the development of such innovations in areas of high accessibility where they can potentially be of the greatest benefit. Payoffs in VMT reduction, though desirable, are hardly the necessary precondition for the relaxation of such regulations. Instead, the increased land use and transportation choice that such liberalization can engender is self-justifying in that it allows households to forge a closer link between their land use and transportation preferences on the one hand and their actual choices on the other. This framework is examined here through a comparison of two metropolitan areas: Boston, which offers its residents relatively rich opportunities for residence in transit and pedestrian friendly areas, and Atlanta, which offers many fewer such opportunities. The study is based on three principal components: A clustering of neighborhoods throughout each metropolitan area according to their transit and pedestrian characteristics; an urban design analysis of selected neighborhoods in each region; and a survey of 1600 households regarding their preferences for neighborhood environments. The study concludes that while residents of Atlanta are considerably less interested in transit- and pedestrian friendly neighborhoods than their Boston counterparts, the difference in preference is insufficient to explain the difference in the transit- and pedestrian quality of the neighborhoods the two groups inhabit. The neighborhood choices of the Boston residents was, as a consequence, considerably more sensitive to their transportation and land use preferences than the choices of their Atlanta counterparts

A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges

Vehicle re-identification (ReID) endeavors to associate vehicle images collected from a distributed network of cameras spanning diverse traffic environments. This task assumes paramount importance within the spectrum of vehicle-centric technologies, playing a pivotal role in deploying Intelligent Transportation Systems (ITS) and advancing smart city initiatives. Rapid advancements in deep learning have significantly propelled the evolution of vehicle ReID technologies in recent years. Consequently, undertaking a comprehensive survey of methodologies centered on deep learning for vehicle re-identification has become imperative and inescapable. This paper extensively explores deep learning techniques applied to vehicle ReID. It outlines the categorization of these methods, encompassing supervised and unsupervised approaches, delves into existing research within these categories, introduces datasets and evaluation criteria, and delineates forthcoming challenges and potential research directions. This comprehensive assessment examines the landscape of deep learning in vehicle ReID and establishes a foundation and starting point for future works. It aims to serve as a complete reference by highlighting challenges and emerging trends, fostering advancements and applications in vehicle ReID utilizing deep learning models

East City Precinct Design Code: Redevelopment through form-based codes

Includes bibliographical references.This thesis confines itself to a consideration of urban development opportunity in the East City Precinct through the understanding of it former historical character and memory which can be implemented through Form Based Codes. It locates the design process in the sub-regional context and puts forward notional spatial proposal for the physical area of the East City Precinct and its surrounds. The application of theory is tested at precinct level and emphasis remains firmly on the public elements ordering the spatial structure. With all these considerations, this dissertation presents a piece of history of District Six and the importance of memory in relation to the East City. This contested site of memory and heritage informs the area’s contextual development amid the often-essentialising multicultural in particular to the ‘new South Africa’. In turn, an understanding of District Six’s urban quality which frames the intricacies of a restitution and redevelopment plan. It also illustrates the genuine uniqueness of its principles of urbanism, in contrast to market-oriented urban development which reproduces spaces of social fragmentation, exclusion and inequality. Indeed, the vision for the East City concerns long-term urban sustainability, an investment in a city of fluid spaces, a city of difference and meaning. This dissertation contends that there is a real role for urban and social sustainability in the redevelopment potential of the study area, with its historical, social, cultural and symbolic significance. Therefore its outline the key elements and principles for a development framework prepared for the study area and discuss the prospects for urban and social sustainability. This will inform where and how to apply form based codes with in the East City context

CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins

The development of safety-oriented research and applications requires fine-grain vehicle trajectories that not only have high accuracy, but also capture substantial safety-critical events. However, it would be challenging to satisfy both these requirements using the available vehicle trajectory datasets do not have the capacity to satisfy both.This paper introduces the CitySim dataset that has the core objective of facilitating safety-oriented research and applications. CitySim has vehicle trajectories extracted from 1140 minutes of drone videos recorded at 12 locations. It covers a variety of road geometries including freeway basic segments, signalized intersections, stop-controlled intersections, and control-free intersections. CitySim was generated through a five-step procedure that ensured trajectory accuracy. The five-step procedure included video stabilization, object filtering, multi-video stitching, object detection and tracking, and enhanced error filtering. Furthermore, CitySim provides the rotated bounding box information of a vehicle, which was demonstrated to improve safety evaluations. Compared with other video-based critical events, including cut-in, merge, and diverge events, which were validated by distributions of both minimum time-to-collision and minimum post-encroachment time. In addition, CitySim had the capability to facilitate digital-twin-related research by providing relevant assets, such as the recording locations' three-dimensional base maps and signal timings.Comment: Transportation Research Record (2023

Understanding Land Use Grain: An Evaluation of Meaning and Measurement

Land use grain is a commonly-used measure of the mixture of land uses in the urban environment in transportation planning and public health, but there is no standard measurement practice in place. This thesis examines the meaning and common measurements of land use grain in these subfields. The entropy-based equation, the jobs-to-housing ratio, and the Herfindahl-Hirschman Index (HHI) are among the most common measures of land use grain, but results from these metrics differ depending upon how researchers choose a sample area and upon how land use categories are defined. All three metrics are performed, in a single context with varying assumptions, using the neighborhoods of Roxbury and Dorchester in Boston, MA. The entropy-based equation was deemed the most appropriate measure in a general context, with the HHI and the jobs-to-housing ratio potentially appropriate in specific contexts

IoT-driven scheduling of residential HVAC and virtual bus lanes for energy savings

The availability of commodity Internet connection and the decrease in price and form factor of consumer electronics led to the emergence of Internet of Things (IoT), with which our world becomes more connected and instrumented. IoT is a great vehicle for enabling solutions to problems in the connected environment that surrounds us (i.e., smart homes and smart cities). An example is the use of sensors and IoT to address issues related to energy efficiency, the broad area of this dissertation. Our hypothesis is that data processing and decision making need to be carried out at the network edge, specifically as close to the physical system as possible, where data are generated and used, to produce results in real-time and make sure the data is not exposed to privacy and security risks. To this end, we propose to leverage scheduling principles and statistical techniques in the context of two applications, namely aiming to reduce duty cycle of HVAC (Heating, Ventilation, and Air Conditioning) systems in smart homes and to mitigate road congestion in smart cities. The common goal in these two aims is the reduction of energy consumption and the reduction of atmospheric pollution. To achieve our first aim we propose intelligent scheduling of the duty cycles of HVAC systems in residential buildings. Our solution combines linear and polynomial regression enabled estimator that drives the calculations about the amounts of time thermally conditioned air should be supplied to each room. The output from our estimator is fed into our scheduler based on integer linear programming to decrease the duty cycle of the home's HVAC systems. We evaluate the effectiveness and efficiency of our HVAC solution with a dataset collected from several residential houses in the state of Pennsylvania. To achieve the second aim, we propose the concept of virtual bus lanes, that combines on-demand creation of bus lanes with dynamic control of traffic lights. Moreover, we propose to guide drivers through less congested routes using light boards that provide to drivers information in real-time for such routes. Our methods are anchored to priority scheduling, incremental windowed-based aggregation, and shortest path first Dijkstra's algorithm. We evaluate the effectiveness and efficiency of our virtual bus lanes solution with a real dataset from the city of Beijing, China, and a synthetic traffic scenario from the city of Luxembourg