Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling

A Portfolio Theory of Route Choice

Although many individual route choice models have been proposed to incorporate travel time variability as a decision factor, they are typically still deterministic in the sense that the optimal strategy requires choosing one particular route that maximizes utility. In contrast, this study introduces an individual route choice model where choos- ing a portfolio of routes instead of a single route is the best strategy for a rational traveler who cares about both journey time and lateness when facing stochastic net- work conditions. The model is then tested with GPS data collected in metropolitan Minneapolis-St. Paul, Minnesota. Our data suggest strong correlation among link speed when analyzing morning commute trips. There is no single dominant route (de- fined here as a route with the shortest travel time for a 15 day period) in 18% of cases when links travel times are correlated. This paper demonstrates that choosing a port- folio of routes could be the rational choice of a traveler who wants to optimize route decisions under variability.Transportation planning, route choice, travel behavior, link performance

Doctor of Philosophy

dissertationData-driven analytics has been successfully utilized in many experience-oriented areas, such as education, business, and medicine. With the profusion of traffic-related data from Internet of Things and development of data mining techniques, data-driven analytics is becoming increasingly popular in the transportation industry. The objective of this research is to explore the application of data-driven analytics in transportation research to improve the traffic management and operations. Three problems in the respective areas of transportation planning, traffic operation, and maintenance management have been addressed in this research, including exploring the impact of dynamic ridesharing system in a multimodal network, quantifying non-recurrent congestion impact on freeway corridors, and developing infrastructure sampling method for efficient maintenance activities. First, the impact of dynamic ridesharing in a multimodal network is studied with agent-based modeling. The competing mechanism between dynamic ridesharing system and public transit is analyzed. The model simulates the interaction between travelers and the environment and emulates travelers' decision making process with the presence of competing modes. The model is applicable to networks with varying demographics. Second, a systematic approach is proposed to quantify Incident-Induced Delay on freeway corridors. There are two particular highlights in the study of non-recurrent congestion quantification: secondary incident identification and K-Nearest Neighbor pattern matching. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level. Lastly, a high-dimensional clustering-based stratified sampling method is developed for infrastructure sampling. The stratification process consists of two components: current condition estimation and high-dimensional cluster analysis. High-dimensional cluster analysis employs Locality-Sensitive Hashing algorithm and spectral sampling. The proposed method is a potentially useful tool for agencies to effectively conduct infrastructure inspection and can be easily adopted for choosing samples containing multiple features. These three examples showcase the application of data-driven analytics in transportation research, which can potentially transform the traffic management mindset into a model of data-driven, sensing, and smart urban systems. The analytic

Holiday travel behavior analysis and empirical study under integrated multimodal travel information service

Holidays are special periods and give rise to many kinds of non-mandatory trips, such as shopping trips and tourist trips. This study investigates the relationship between Integrated Multimodal Travel Information (IMTI) service and holiday travel behavior characteristics in a trip chain. The Exploratory Factor Analysis (EFA) method is first used to extract the common factors based on the RP-SP fusion data under the pre-trip IMTI and en-route IMTI services, respectively. The Structural Equation Modeling (SEM) method is then applied to examine causal effects and quantitative relationships between the influencing factors and trip chain characteristics based on the EFA results. The results show that pre-trip IMTI has a significant negative effect on the holiday travel behavior. The more pre-trip IMTI is obtained by the traveler, the simpler the trip chain spatiotemporal and structural complexity will be. In addition, although the effect of en-route IMTI is less than pre-trip IMTI, it still plays an important role compared to other factors. Therefore, providing IMTI is a new and good alternative to alleviate holiday traffic congestions

Self-Colocation: A Colocation Puzzle for Endurantists

The recent literature on the nature of persistence features a handful of imaginative cases in which an object seems to colocate with itself. So far, discussion of these cases has focused primarily on how they defy the standard endurantist approaches to the problem of temporary intrinsics. But in this article, I set that issue aside and argue that cases of apparent self-colocation also pose another problem for the endurantist. While the perdurantist seems to have a fairly straightforward account of self-colocation, the endurantist has a hard time saying exactly what it would be for an object to be self-colocated. After introducing this problem and explaining how the perdurantist can circumvent it with little difficulty, I discuss a number of tempting endurantist solutions that ultimately fail. Then I suggest an endurantist solution which I think is more promising, but which requires the endurantist to deny that apparent cases of self-colocation are genuine cases of self-colocation

Self-Colocation: A Colocation Puzzle for Endurantists

The recent literature on the nature of persistence features a handful of imaginative cases in which an object seems to colocate with itself. So far, discussion of these cases has focused primarily on how they defy the standard endurantist approaches to the problem of temporary intrinsics. But in this article, I set that issue aside and argue that cases of apparent self-colocation also pose another problem for the endurantist. While the perdurantist seems to have a fairly straightforward account of self-colocation, the endurantist has a hard time saying exactly what it would be for an object to be self-colocated. After introducing this problem and explaining how the perdurantist can circumvent it with little difficulty, I discuss a number of tempting endurantist solutions that ultimately fail. Then I suggest an endurantist solution which I think is more promising, but which requires the endurantist to deny that apparent cases of self-colocation are genuine cases of self-colocation

PRESERVING THE VERNACULAR POSTINDUSTRIAL LANDSCAPE: BIG DATA GEOSPATIAL APPROACHES TO HERITAGE MANAGEMENT AND INTERPRETATION

Redundant historical industrial sites, or postindustrial landscapes, face numerous preservation challenges. Functionally obsolete, and often derelict and decaying, these cultural landscapes often retain only a fraction of their original infrastructure. With their historical interconnections made indistinct by their physical separation and obscured by the passage of time, surviving remnants are isolated and disjunct, confounding both their legibility and their consideration for formal historic preservation. Nevertheless, they persist. This dissertation presents a theoretical understanding of the nature of postindustrial landscape preservation, and argues that the material persistence of its historical constituents is the result of previously overlooked processes of informal material conservation, here termed vernacular preservation. Further, this dissertation examines ways that heritage professionals can manage and interpret these vast, complex, and shattered landscapes, using 21st-century digital and spatial tools. Confronted by ongoing depopulation and divestment, and constrained by limited financial capacity to reverse the trend of blight and property loss, communities and individuals concerned with the preservation of vernacular postindustrial landscapes face many unique management and interpretation challenges. The successful heritagization of the postindustrial landscape depends on its comprehension, and communication, as a historically complex network of systems, and I argue that utilizing advanced digital and spatial tool such as historical GIS and procedural modeling can aid communities and heritage professionals in managing, preserving, and interpreting these landscapes. This dissertation presents heritage management and interpretation strategies that emphasize the historical, but now largely missing, spatial and temporal contexts of today’s postindustrial landscape in Michigan’s Copper Country. A series of case studies illustrates the demonstrated and potential value of using a big-data, longitudinally-linked digital infrastructure, or Historical GIS (HGIS), known as the Copper Country Historical Spatial Data Infrastructure (CC-HSDI), for heritage management and interpretation. These studies support the public education and conservation goals of the communities in this nationally-significant mining region through providing accessible, engaging, and meaningful historical spatiotemporal context, and by helping to promote and encourage the ongoing management and preservation of this ever-evolving postindustrial landscape