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Advances in crowd analysis for urban applications through urban event detection
The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined
Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
Big data has been used widely in many areas including the transportation
industry. Using various data sources, traffic states can be well estimated and
further predicted for improving the overall operation efficiency. Combined with
this trend, this study presents an up-to-date survey of open data and big data
tools used for traffic estimation and prediction. Different data types are
categorized and the off-the-shelf tools are introduced. To further promote the
use of big data for traffic estimation and prediction tasks, challenges and
future directions are given for future studies
Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities
Traffic prediction plays a crucial role in alleviating traffic congestion
which represents a critical problem globally, resulting in negative
consequences such as lost hours of additional travel time and increased fuel
consumption. Integrating emerging technologies into transportation systems
provides opportunities for improving traffic prediction significantly and
brings about new research problems. In order to lay the foundation for
understanding the open research challenges in traffic prediction, this survey
aims to provide a comprehensive overview of traffic prediction methodologies.
Specifically, we focus on the recent advances and emerging research
opportunities in Artificial Intelligence (AI)-based traffic prediction methods,
due to their recent success and potential in traffic prediction, with an
emphasis on multivariate traffic time series modeling. We first provide a list
and explanation of the various data types and resources used in the literature.
Next, the essential data preprocessing methods within the traffic prediction
context are categorized, and the prediction methods and applications are
subsequently summarized. Lastly, we present primary research challenges in
traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies
(TR_C), Volume 145, 202
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
The novel application of optimization and charge blended energy management control for component downsizing within a plug-in hybrid electric vehicle
The adoption of Plug-in Hybrid Electric Vehicles (PHEVs) is widely seen as an interim solution for the decarbonization of the transport sector. Within a PHEV, determining the required energy storage capacity of the battery remains one of the primary concerns for vehicle manufacturers and system integrators. This fact is particularly pertinent since the battery constitutes the largest contributor to vehicle mass. Furthermore, the financial cost associated with the procurement, design and integration of battery systems is often cited as one of the main barriers to vehicle commercialization. The ability to integrate the optimization of the energy management control system with the sizing of key PHEV powertrain components presents a significant area of research. Contained within this paper is an optimization study in which a charge blended strategy is used to facilitate the downsizing of the electrical machine, the internal combustion engine and the high voltage battery. An improved Equivalent Consumption Method has been used to manage the optimal power split within the powertrain as the PHEV traverses a range of different drivecycles. For a target CO2 value and drivecycle, results show that this approach can yield significant downsizing opportunities, with cost reductions on the order of 2%–9% being realizable
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
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