Spatial Temporal Incidence Dynamic Graph Neural Networks for Traffic Flow Forecasting

Urban traffic passenger flows prediction is practically important to facilitate many real applications including transportation management and public safety. Recently, deep learning based approaches are proposed to learn the spatio-temporal characteristics of the traffic passenger flows. However, it is still very challenging to handle some complex factors such as hybrid transportation lines, mixed traffic, transfer stations, and some extreme weathers. Considering the multi-channel and irregularity properties of urban traffic passenger flows in different transportation lines, a more efficient and fine-grained deep spatio-temporal feature learning model is necessary. In this paper, we propose a deep irregular convolutional residual LSTM network model called DST-ICRL for urban traffic passenger flows prediction. We first model the passenger flows among different traffic lines in a transportation network into multi-channel matrices analogous to the RGB pixel matrices of an image. Then, we propose a deep learning framework that integrates irregular convolutional residential network and LSTM units to learn the spatial-temporal feature representations. To fully utilize the historical passenger flows, we sample both the short-term and long-term historical traffic data, which can capture the periodicity and trend of the traffic passenger flows. In addition, we also fuse other external factors further to facilitate a real-time prediction. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing as well as bike flows in New York. The results show that the proposed DST-ICRL significantly outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.

Deep Irregular Convolutional Residual LSTM for Urban Traffic Passenger Flows Prediction

Subway passenger flow prediction is strategically important in metro transit system management. The prediction under event occurrences turns into a very challenging task. In this paper, we adopt a new kind of data source—social media—to tackle this challenge. We develop a systematic approach to examine social media activities and sense event occurrences. Our initial analysis demonstrates that there exists a moderate positive correlation between passenger flow and the rates of social media posts. This finding motivates us to develop a novel approach for improved flow forecast. We first develop a hashtag-based event detection algorithm. Furthermore, we propose a parametric and convex optimization-based approach, called optimization and prediction with hybrid loss function (OPL), to fuse the linear regression and the results of seasonal autoregressive integrated moving average (SARIMA) model jointly. The OPL hybrid model takes advantage of the unique strengths of linear correlation in social media features and SARIMA model in time series prediction. Experiments on events nearby a subway station show that OPL reports the best forecasting performance compared with other state-of-the-art techniques. In addition, an ensemble model is developed to leverage the weighted results from OPL and support vector machine regression together. As a result, the prediction accuracy and the robustness further increase.

Forecasting the Subway Passenger Flow Under Event Occurrences With Social Media

Recent advances in the probe vehicle deployment offer an innovative prospect for research in arterial travel time estimation. Specifically, we focus on the estimation of probability distribution of arterial route travel time, which contains more information regarding arterial performance measurements and travel time reliability. One of the fundamental contributions of this work is the integration of travel time correlation of route’s successive links within the methodology. In the proposed technique, given probe vehicles travel times of the traversing links, a two-dimensional (2D) diagram is established with data points representing travel times of a probe vehicle crossing two consecutive links. A heuristic grid clustering method is developed to cluster each 2D diagram to rectangular sub spaces (states) with regard to travel time homogeneity. By applying a Markov chain procedure, we integrate the correlation between states of 2D diagrams for successive links. We then compute the transition probabilities and link partial travel time distributions to obtain the arterial route travel time distribution. The procedure with various probe vehicle sample sizes is tested on two study sites with time dependent conditions, with field measurements and simulated data. The results are very close to the Markov chain procedure and more accurate once compared to the convolution of links travel time distributions for different levels of congestion, even for small penetration rates of probe vehicles.

/pdf/on-the-estimation-of-arterial-route-travel-time-distribution-18aj64m6om.pdf

On the estimation of arterial route travel time distribution with Markov chains

Visual analytics plays a key role in the era of connected industry (or industry 4.0, industrial internet) as modern machines and assembly lines generate large amounts of data and effective visual exploration techniques are needed for troubleshooting, process optimization, and decision making. However, developing effective visual analytics solutions for this application domain is a challenging task due to the sheer volume and the complexity of the data collected in the manufacturing processes. We report the design and implementation of a comprehensive visual analytics system, ViDX. It supports both real-time tracking of assembly line performance and historical data exploration to identify inefficiencies, locate anomalies, and form hypotheses about their causes and effects. The system is designed based on a set of requirements gathered through discussions with the managers and operators from manufacturing sites. It features interlinked views displaying data at different levels of detail. In particular, we apply and extend the Marey's graph by introducing a time-aware outlier-preserving visual aggregation technique to support effective troubleshooting in manufacturing processes. We also introduce two novel interaction techniques, namely the quantiles brush and samples brush, for the users to interactively steer the outlier detection algorithms. We evaluate the system with example use cases and an in-depth user interview, both conducted together with the managers and operators from manufacturing plants. The result demonstrates its effectiveness and reports a successful pilot application of visual analytics for manufacturing in smart factories.

ViDX: Visual Diagnostics of Assembly Line Performance in Smart Factories

An empirical, corridor-level method is proposed to divide the travel time unreliability or variability over a freeway section into the following components: incidents, weather, work zones, special events, and inadequate base capacity or bottlenecks. The method consists of three steps: (a) corridor-level aggregation of travel time and source data, (b) quantile regression to fit the 95th percentile of travel time on the source variables, and (c) calculation of the contribution of individual sources to the buffer time. It could be applied to other percentile-based travel time reliability measures such as planning time and 90th and 95th percentiles. Once the source data are defined, the method can be automatically applied to any site with minimum calibration. When applied to a 30.5-mi section of northbound I-880 in the San Francisco Bay Area, California, the method revealed that traffic accidents contributed 15.1% during the morning and 25.5% during the afternoon, among others, and most of the remaining reliability came from recurrent bottlenecks. Quantifying the components of travel time variability at individual freeway sites is essential in developing effective strategies to mitigate congestion.

Decomposition of Travel Time Reliability into Various Sources: Incidents, Weather, Work Zones, Special Events, and Base Capacity

The measurement of travel time reliability is of increasing importance as agencies seek to understand better the performance of their facilities and communicate trip expectations to the traveling public. Recent research suggests that modeling historical travel times with multistate distributions can add value to the process of monitoring reliability by linking travel times with the underlying traffic state. Although that research demonstrated the feasibility of breaking down travel time distributions into different operational states, the researchers did not evaluate the causal factors of the states. Models that link the travel time states of agencies' facilities with the factors that cause the time states, such as incidents or special events, allow agencies to predict travel times more accurately when these events occur in real time and enable agencies to develop targeted projects for the improvement of reliability over the long term. This paper extends existing reliability modeling in three ways. First,...

Relating Travel Time Reliability and Nonrecurrent Congestion with Multistate Models

Travel time reliability can be defined as consistency of travel time over time. The primary goal of the Strategic Highway Research Program 2 (SHRP 2) Reliability research is to improve the reliability of highway travel times by mitigating the effects of events that cause travel times to fluctuate unpredictably. Through this research, seven sources of unreliable travel times have been identified: traffic incidents, work zones, demand fluctuations, special events, traffic control devices, weather, and inadequate base capacity. This guide documents the research of the L02 Project, Establishing Monitoring Programs for Travel Time Reliability. It also provides further discussion of how to develop and use a travel time reliability monitoring system (TTRMS). The Guide will be useful for system operators in determining what actions they need to take to reduce the variability of travel time and enhance reliability. A TTRMS can help operating agencies monitor system performance, understand the impacts of the various factors that influence travel time, and provide credible information to system users about what travel time reliability to expect. With this information, operating agencies can make better decisions about what actions to take to help improve reliability. At the time this research was performed, most transportation agencies did not have these capabilities. The Guide has two parts. Chapters 1 through 5 describe the process of measuring, characterizing, identifying, and understanding the effects of recurrent congestion and nonrecurrent events that affect travel time reliability. The appendices provide more detailed information about the functional specification of a monitoring system, methods, a series of case studies, and a set of use cases that describe how different users of the TTRMS interact with the system. Accompanying the Guide is a brief, stand-alone document that provides suggestions for communicating information about time travel reliability using maps, figures, and tables.

Guide to Establishing Monitoring Programs for Travel Time Reliability

The original Marey graph, published in 1885, has become a frequent example of innovative design in data visualization. It plots a French train timetable on a time-space diagram, intuitively depicting the paths of trains throughout the day. These graphs continue to be used in transit-related applications such as the Google Transit Feed Specification (GTFS) Schedule Viewer. This paper repurposes the original Marey graph for use in transit performance measurement by adding schedule adherence and passenger load information. APC data preprocessing steps are described and technological issues related to the development of the visualization are discussed. Finally, this paper demonstrates how the Marey graph enables quick visual identification of vehicle performance trends across space and time.

Visualizing Bus Schedule Adherence and Passenger Load Through Marey Graphs

Automated performance-monitoring systems take in intelligent transportation system sensor data in real time, archive them, and analyze them. These systems are needed to help local agencies identify problem areas, develop improvement plans, and perform before and after evaluations on the impacts of traffic management changes. Research performed in the past few years has demonstrated the utility of these systems for local transportation agencies, particularly for evaluating signal progression quality. However, acquiring the critical data items for existing arterial intelligent transportation systems—signal phase event information—is often a practical challenge because the configuration of the existing system of most arterial systems does not record or communicate signal phase events to a central location. As a solution to that problem, this paper documents an approach to estimate signal phase data with in-pavement vehicle sensors, a data source that is generally available from arterial systems. On many arte...

Heuristic Approach for Estimating Arterial Signal Phases and Progression Quality from Vehicle Arrival Data

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