131 research outputs found
A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective
One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computational capacities have increased the use of machine learning (ML) to address traffic prediction, which is mostly modeled as a supervised regression problem. Although some studies have presented taxonomies to sort the literature in this field, they are mostly oriented to classify the ML methods applied and a little effort has been directed to categorize the traffic forecasting problems approached by them. As far as we know, there is no comprehensive taxonomy that classifies these problems from the point of view of both traffic and ML. In this paper, we propose a taxonomy to categorize the aforementioned problems from both traffic and a supervised regression learning perspective. The taxonomy aims at unifying and consolidating categorization criteria related to traffic and it introduces new criteria to classify the problems in terms of how they are modeled from a supervised regression approach. The traffic forecasting literature, from 2000 to 2019, is categorized using this taxonomy to illustrate its descriptive power. From this categorization, different remarks are discussed regarding the current gaps and trends in the addressed traffic forecasting area
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
Maximum interpolable gap length in missing smartphone-based GPS mobility data
Passively-generated location data have the potential to augment mobility and transportation research, as demonstrated by a decade of research. A common trait of these data is a high proportion of missingness. NaĂŻve handling, including list-wise deletion of subjects or days, or linear interpolation across time gaps, has the potential to bias summary results. On the other hand, it is unfeasible to collect mobility data at frequencies high enough to reflect all possible movements. In this paper, we describe the relationship between the temporal and spatial aspects of these data gaps, and illustrate the impact on measures of interest in the field of mobility. We propose a method to deal with missing location data that combines a so-called top-down ratio segmentation method with simple linear interpolation. The linear interpolation imputes missing data. The segmentation method transforms the set of location points to a series of lines, called segments. The method is designed for relatively short gaps, but is evaluated also for longer gaps. We study the effect of our imputation method for the duration of missing data using a completely observed subset of observations from the 2018 Statistics Netherlands travel study. We find that long gaps demonstrate greater downward bias on travel distance, movement events and radius of gyration as compared to shorter but more frequent gaps. When the missingness is unrelated to travel behavior, total sparsity can reach levels of up to 20% with gap lengths of up to 10 min while maintaining a maximum 5% downward bias in the metrics of interest. Temporal aspects can increase these limits; sparsity occurring in the evening or night hours is less biasing due to fewer travel behaviors
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
Observing travel behaviour from GPS data - A tool comparison survey in the Torino metropolitan area
Travel surveys help researchers to paint a clear picture of specific aspects of travel behaviour. In the transport field, data quality is largely dictated by the data requirements of mathematical models, and by the rising complexity of individuals' travel behaviour. Beginning with an illustration of the most common transport models, this thesis will first present an overview of traditional survey tools, in order to understand their structural biases and current developments in the transport survey field. One of the recent solutions to common data collection problems has been the implementation of passive data collection tools in household and personal travel surveys. Passive data collection tools allow researchers to derive travel behaviour information from positional and navigational data, collected with devices that use location-aware technologies, such as GPS, GSM, and RFid. Passive data collection tools - in particular, GPS devices - have proven useful in household and personal travel surveys, and have shown themselves capable of providing researchers with high-quality travel data. The objective of this research is to evaluate the use of GPS as a survey tool in household and personal travel surveys. Technological advances and decreasing costs have helped GPS to achieve wide use in the survey field. Furthermore, GPS-equipped devices allow surveyors to collect high-quality data on the time and position of individuals and vehicles - data that are more difficult to ascertain using traditional survey tools, such as self-administered questionnaires and telephonic interviews. A research team at the Politecnico di Torino designed and carried out a multi-instrumental personal travel survey, in order to assess the context-specific problems of a GPS-based survey in the metropolitan area of Torino. Survey methods included both a paper-and-pencil travel diary, and locational data collected using GPS devices. The survey effort consisted of a 4-day pilot survey with a sample of 4 individuals, and a successive 14-day GPS survey with a sample of 8 individuals. Results from self-administered travel diaries and GPS-derived data provided surveyors with valuable data for assessing the quality and completeness of travel information, and for determining the data's ability to accurately describe respondents' travel behaviour. The final outcomes of the GPS survey effort and of supplementary passive data collection tests allowed researchers to identify strengths and weaknesses of the implementation of passive data collection tools. Actual trends and future developments in the field will supplement the overvie
From Data to Actions in Intelligent Transportation Systems: A Prescription of Functional Requirements for Model Actionability
Advances in Data Science permeate every field of Transportation Science and Engineering,
resulting in developments in the transportation sector that are data-driven. Nowadays, Intelligent
Transportation Systems (ITS) could be arguably approached as a “story” intensively producing and
consuming large amounts of data. A diversity of sensing devices densely spread over the infrastructure,
vehicles or the travelers’ personal devices act as sources of data flows that are eventually
fed into software running on automatic devices, actuators or control systems producing, in turn,
complex information flows among users, traffic managers, data analysts, traffic modeling scientists,
etc. These information flows provide enormous opportunities to improve model development and
decision-making. This work aims to describe how data, coming from diverse ITS sources, can be used
to learn and adapt data-driven models for efficiently operating ITS assets, systems and processes;
in other words, for data-based models to fully become actionable. Grounded in this described data
modeling pipeline for ITS, we define the characteristics, engineering requisites and challenges intrinsic
to its three compounding stages, namely, data fusion, adaptive learning and model evaluation.
We deliberately generalize model learning to be adaptive, since, in the core of our paper is the firm
conviction that most learners will have to adapt to the ever-changing phenomenon scenario underlying
the majority of ITS applications. Finally, we provide a prospect of current research lines within
Data Science that can bring notable advances to data-based ITS modeling, which will eventually
bridge the gap towards the practicality and actionability of such models.This work was supported in part by the Basque Government for its funding support through the EMAITEK program (3KIA, ref. KK-2020/00049). It has also received funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government
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