The Penal Code of California forms the basis for the application of criminal law within the state of California. It was originally enacted in 1872 as one of the original four California Codes, and has been substantially amended and revised since then. This book contains the following parts: Part 1 - Of Crimes and Punishments, Part 2 - Of Criminal Procedure
TRB's second Strategic Highway Research Program (SHRP 2) Report S2-C46-RR-1: Activity-Based Travel Demand Models: A Primer explores ways to inform policymakers' decisions about developing and using activity-based travel demand models to better understand how people plan and schedule their daily travel. The document is composed of two parts. The first part provides an overview of activity-based model development and application. The second part discusses issues in linking activity-based models to dynamic network assignment models.
In this study, we are interested in the study of urban mobility from traces of mobile data that were provided by the operator Orange. The data provided relate to the region of the city of Rouen, during an ephemeral event that is the Armada of 2008. In a first study, a large amount of data is managed to extract characteristics allowing to qualify the uses of the city during Ephemeral events, depending on the days of activity of the individuals. Visualizations are given and make it possible to understand the mobilities generated in a specific way during the event. In the second part, we study the reconstruction of trajectories with aggregated approaches inspired by statistical physics techniques in order to reveal behaviors according to periods of activity and a spatial division in large urban areas. In order to obtain the general mobility law by observing distributions in power law characteristic for the studied complex system.
This book discusses the role of mobile network data in urban informatics, particularly how mobile network data is utilized in the mobility context, where approaches, models, and systems are developed for understanding travel behavior. The objectives of this book are thus to evaluate the extent to which mobile network data reflects travel behavior and to develop guidelines on how to best use such data to understand and model travel behavior. To achieve these objectives, the book attempts to evaluate the strengths and weaknesses of this data source for urban informatics and its applicability to the development and implementation of travel behavior models through a series of the authors’ research studies. Traditionally, survey-based information is used as an input for travel demand models that predict future travel behavior and transportation needs. A survey-based approach is however costly and time-consuming, and hence its information can be dated and limited to a particular region. Mobile network data thus emerges as a promising alternative data source that is massive in both cross-sectional and longitudinal perspectives, and one that provides both broader geographic coverage of travelers and longer-term travel behavior observation. The two most common types of travel demand model that have played an essential role in managing and planning for transportation systems are four-step models and activity-based models. The book’s chapters are structured on the basis of these travel demand models in order to provide researchers and practitioners with an understanding of urban informatics and the important role that mobile network data plays in advancing the state of the art from the perspectives of travel behavior research.
This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.
This contributed volume contains the conference proceedings of the Simulation of Urban Mobility (SUMO) conference 2014, Berlin. The included research papers cover a wide range of topics in traffic planning and simulation, including open data, vehicular communication, e-mobility, urban mobility, multimodal traffic as well as usage approaches. The target audience primarily comprises researchers and experts in the field, but the book may also be beneficial for graduate students.
Urban Mobility and the Smartphone: Transportation, Travel Behavior and Public Policy provides a global synthesis of the transformation of urban mobility by the smartphone, clarifying the definitions of new concepts and objects in mobility studies, accounting for the changes in transportation and travel behavior triggered by the spread of the smartphone, and discussing the implications of these changes for policy-making and research. Urban mobility is approached here as a system of actors: the perspectives of individual behavior (including lifestyles), the supply of mobility services (including actors, business models), and public policy-making are considered. The book is based on an extensive review of the academic literature as well as systematic observation of the development of smartphone-based mobility services around the world. In addition, case studies provide practical illustrations of the ongoing transformation of mobility services influenced by the dissemination of smartphones. The book not only consolidates existing research, but also picks up on weak signals that help researchers and practitioners anticipate future changes in urban mobility systems. Key Features • Synthesizes existing research into one reference, providing researchers and policy-makers with a clear and complete understanding of the changes triggered by the spread of the smartphone. • Analyzes numerous case studies throughout developed and developing countries providing practical illustrations of the influence of the smartphone on travel behavior, transportation systems, and policy-making. • Provides insights for researchers and practitioners looking to engage with the "smart cities" and "smart mobility" discourse. - Synthesizes existing research into one reference, providing researchers and policy-makers with a clear and complete understanding of the changes triggered by the spread of the smartphone - Analyzes numerous case studies throughout developed and developing countries providing practical illustrations of the influence of the smartphone on travel behavior, transportation systems, and policy-making - Provides insights for researchers and practitioners looking to engage with the "smart cities" and "smart mobility" discourse
This book discusses the role of mobile network data in urban informatics, particularly how mobile network data is utilized in the mobility context, where approaches, models, and systems are developed for understanding travel behavior. The objectives of this book are thus to evaluate the extent to which mobile network data reflects travel behavior and to develop guidelines on how to best use such data to understand and model travel behavior. To achieve these objectives, the book attempts to evaluate the strengths and weaknesses of this data source for urban informatics and its applicability to the development and implementation of travel behavior models through a series of the authors' research studies. Traditionally, survey-based information is used as an input for travel demand models that predict future travel behavior and transportation needs. A survey-based approach is however costly and time-consuming, and hence its information can be dated and limited to a particular region. Mobile network data thus emerges as a promising alternative data source that is massive in both cross-sectional and longitudinal perspectives, and one that provides both broader geographic coverage of travelers and longer-term travel behavior observation. The two most common types of travel demand model that have played an essential role in managing and planning for transportation systems are four-step models and activity-based models. The book's chapters are structured on the basis of these travel demand models in order to provide researchers and practitioners with an understanding of urban informatics and the important role that mobile network data plays in advancing the state of the art from the perspectives of travel behavior research.
Recent research has begun generating a much richer, activity-based behavioral framework to replace the conventional aggregate, four-step approaches. However, to date, the framework remains to be completed, at least enough to provide a robust behavioral foundation that incorporates household long-term behaviors with routine travel and activity patterns. The objective of this research is to explore aspects of activity-based urban modeling that could assist in understanding changing land use and transportation interactions as information technologies enable more complex measurement and modeling, and alter the economics of urban transportation by improving last-mile logistics and facilitating car sharing. The research focuses on specific issues and strategies for developing household, quasi-activity-based, urban modeling prototypes that could simulate the impacts of transport innovations in metropolitan areas. In our implementation and development of the Lisbon model, we started with case 0 first - the four-step travel demand model without considering any land use change. Then given the considerations of data and modeling purpose, what began as a standard version of the UrbanSim model linked to the four-step travel demand model (in Case 1) has evolved into a modified version of the UrbanSim connected to a uniquely formulated tour-based travel model (in Case 2) that not only adjusted the model specification for certain components, but also changed some of the assumptions about household behavior and heterogeneity. The modified UrbanSim model suggests some improvement over the standard version, in differentiating the accessibility for different types of households. However, it is still far from the considerations of household interactions that many planners consider important in the household long-term choices. One key objective of the research is to improve the ability of the models to simulate the impacts of transportation innovations on household-level activity patterns and residential location choice in metro Lisbon. Since transportation innovations and economic restructuring can trigger substantial changes in place/space/household interactions, household-level adjustments can involve changes in car ownership, trip chaining, repackaging of household trips and the like. Therefore, I propose an accessibility indicator that addresses these considerations when evaluating the attractiveness of destinations and modes. The indicators are measured at the household level and facilitate micro-simulation of residential location choice while accounting for household-specific trip chaining, scheduling, and mode choice options. This household quasi-activity- based urban modeling framework (Case 3), represents a progression of behavioral models that capture observably significant behavioral differences in Lisbon. In the simulation experiments, the quasi-household-activity-based urban modeling framework (Case 3) is applied only for two-worker households for which sufficient activity data are available in Lisbon. The quantitative results from simulating the urban development impacts of the proposed policy changes in the Lisbon Metropolitan in next 25 years under Case 0, Case 1, Case 2 and Case 3 demonstrate the progression of experiments with alternative strategies for incorporating key activity-based elements into LUTE models. The main contributions of the dissertation include the development and implementation of quasi-activity-based modeling framework and specific techniques to assess the impacts of transportation innovations and energy and environmental constraints on urban development patterns. This represents an alternative approach to the traditional land use and transportation interaction research and overcomes some major obstacles to model household activity and mobility. It also has significant applications for transportation and urban planning in the information and communication technology (ICT) age. The dissertation demonstrates the use of emerging information technologies, modern federated database management and distributed modeling techniques to facilitate the 'what if' analyses of changing land use and transportation circumstances, induced by the new ICTs in metropolitan areas.
A proposal for a new way to understand cities and their design not as artifacts but as systems composed of flows and networks. In The New Science of Cities, Michael Batty suggests that to understand cities we must view them not simply as places in space but as systems of networks and flows. To understand space, he argues, we must understand flows, and to understand flows, we must understand networks—the relations between objects that compose the system of the city. Drawing on the complexity sciences, social physics, urban economics, transportation theory, regional science, and urban geography, and building on his own previous work, Batty introduces theories and methods that reveal the deep structure of how cities function. Batty presents the foundations of a new science of cities, defining flows and their networks and introducing tools that can be applied to understanding different aspects of city structure. He examines the size of cities, their internal order, the transport routes that define them, and the locations that fix these networks. He introduces methods of simulation that range from simple stochastic models to bottom-up evolutionary models to aggregate land-use transportation models. Then, using largely the same tools, he presents design and decision-making models that predict interactions and flows in future cities. These networks emphasize a notion with relevance for future research and planning: that design of cities is collective action.
