Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

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K-Prototypes Segmentation Analysis on Large-Scale Ridesourcing Trip Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120929338 ◽

2020 ◽

Vol 2674 (9) ◽

pp. 383-394 ◽

Cited By ~ 1

Author(s):

Jason Soria ◽

Ying Chen ◽

Amanda Stathopoulos

Keyword(s):

Spatial Data ◽

Large Scale ◽

Weather Conditions ◽

Data Types ◽

Adverse Weather Conditions ◽

Segmentation Analysis ◽

Adverse Weather ◽

Mobility System ◽

Spatio Temporal

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services interact with traditional mobility options and how they affect travel in cities. Improving data-sharing agreements are opening unprecedented opportunities for research in this area. This study examined emerging patterns of mobility using recently released City of Chicago public ridesourcing data. The detailed spatio-temporal ridesourcing data were matched with weather, transit, and taxi data to gain a deeper understanding of ridesourcing’s role in Chicago’s mobility system. The goal was to investigate the systematic variations in patronage of ridehailing. K-prototypes was utilized to detect user segments owing to its ability to accept mixed variable data types. An extension of the K-means algorithm, its output was a classification of the data into several clusters called prototypes. Six ridesourcing prototypes were identified and discussed based on significant differences in relation to adverse weather conditions, competition with alternative modes, location and timing of use, and tendency for ridesplitting. The paper discusses the implications of the identified clusters related to affordability, equity, and competition with transit.

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Multistep traffic speed prediction: A deep learning based approach using latent space mapping considering spatio-temporal dependencies

Expert Systems with Applications ◽

10.1016/j.eswa.2021.116140 ◽

2021 ◽

pp. 116140

Author(s):

Shatrughan Modi ◽

Jhilik Bhattacharya ◽

Prasenjit Basak

Keyword(s):

Deep Learning ◽

Space Mapping ◽

Latent Space ◽

Speed Prediction ◽

Traffic Speed ◽

Spatio Temporal ◽

Temporal Dependencies

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An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

Future Internet ◽

10.3390/fi13120306 ◽

2021 ◽

Vol 13 (12) ◽

pp. 306

Author(s):

Ahmed Dirir ◽

Henry Ignatious ◽

Hesham Elsayed ◽

Manzoor Khan ◽

Mohammed Adib ◽

...

Keyword(s):

Deep Learning ◽

Intelligent Transportation Systems ◽

Smart Cities ◽

Weather Conditions ◽

Research Area ◽

Transportation Systems ◽

Detection Accuracy ◽

Adverse Weather ◽

Object Counting ◽

The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

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Citywide traffic speed prediction: A geometric deep learning approach

Knowledge-Based Systems ◽

10.1016/j.knosys.2020.106592 ◽

2021 ◽

Vol 212 ◽

pp. 106592

Author(s):

James J.Q. Yu

Keyword(s):

Deep Learning ◽

Learning Approach ◽

Speed Prediction ◽

Traffic Speed

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Global Spatial-Temporal Graph Convolutional Network for Urban Traffic Speed Prediction

Applied Sciences ◽

10.3390/app10041509 ◽

2020 ◽

Vol 10 (4) ◽

pp. 1509 ◽

Cited By ~ 2

Author(s):

Liang Ge ◽

Siyu Li ◽

Yaqian Wang ◽

Feng Chang ◽

Kunyan Wu

Keyword(s):

Weather Conditions ◽

Urban Traffic ◽

Traffic Data ◽

Convolutional Network ◽

Temporal Dimension ◽

Temporal Correlations ◽

Speed Prediction ◽

External Component ◽

Temporal Graph ◽

Traffic Speed

Traffic speed prediction plays a significant role in the intelligent traffic system (ITS). However, due to the complex spatial-temporal correlations of traffic data, it is very challenging to predict traffic speed timely and accurately. The traffic speed renders not only short-term neighboring and multiple long-term periodic dependencies in the temporal dimension but also local and global dependencies in the spatial dimension. To address this problem, we propose a novel deep-learning-based model, Global Spatial-Temporal Graph Convolutional Network (GSTGCN), for urban traffic speed prediction. The model consists of three spatial-temporal components with the same structure and an external component. The three spatial-temporal components are used to model the recent, daily-periodic, and weekly-periodic spatial-temporal correlations of the traffic data, respectively. More specifically, each spatial-temporal component consists of a dynamic temporal module and a global correlated spatial module. The former contains multiple residual blocks which are stacked by dilated casual convolutions, while the latter contains a localized graph convolution and a global correlated mechanism. The external component is used to extract the effect of external factors, such as holidays and weather conditions, on the traffic speed. Experimental results on two real-world traffic datasets have demonstrated that the proposed GSTGCN outperforms the state-of-the-art baselines.

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Development of a Biologically-Based System for Detection and Tracking of Airborne Herbicides

Weed Technology ◽

10.1017/s0890037x00027809 ◽

1993 ◽

Vol 7 (2) ◽

pp. 404-410 ◽

Cited By ~ 18

Author(s):

Kassim Al-Khatib ◽

Gaylord I. Mink ◽

Guy Reisenauer ◽

Robert Parker ◽

Halvor Westberg ◽

...

Keyword(s):

Computer Model ◽

High Sensitivity ◽

Weather Conditions ◽

Weather Data ◽

Indicator Plant ◽

Field Exposure ◽

Detection And Tracking ◽

Adverse Weather Conditions ◽

Adverse Weather ◽

Herbicide Drift

This study was designed to develop a protocol for using a biologically-based system to detect and tract airborne herbicides. Common bean, lentil, and pea were selected for their quasi-diagnostic sensitivity to chlorsulfuron, thifensulfuron, metsulfuron, tribenuron, paraquat, glyphosate, bromoxynil, 2,4-D, and dicamba. Plants were grown in the greenhouse at Prosser, WA, and placed at 25 exposure sites at weekly intervals between Apr. 2 and Oct. 15, 1991. After 1 wk of field exposure plants were brought back and observed for herbicide symptoms over a 28-d period. Symptoms that developed were compared with symptoms caused by disease, insects, adverse weather conditions, and herbicides applied at different rates under controlled conditions on these species. In addition, if herbicide symptoms were observed, herbicide spray records and weather data in the area were used in a computer model to determine the source of potential herbicide drift. This study demonstrates that indicator plant species selected for high sensitivity to herbicides can be used to monitor the occurrence of herbicide movement.

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Evaluation of Spatial Resilience of Highway Networks in Response to Adverse Weather Conditions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9080480 ◽

2020 ◽

Vol 9 (8) ◽

pp. 480

Author(s):

Fei Han ◽

Su Zhang

Keyword(s):

Hot Spot ◽

Weather Conditions ◽

Extreme Weather Events ◽

K Nearest Neighbors ◽

Highway Infrastructure ◽

Spatial Resilience ◽

Highway Networks ◽

Weather Events ◽

Adverse Weather ◽

Traditional Service

Adverse weather poses a significant threat to the serviceability of highway infrastructure, as it causes more frequent and severe crash incidents. This study focuses on evaluating the resilience of highway networks by examining the crash-induced safety impact in response to extreme weather events. Unlike traditional service drop-based methods for resilience evaluation, this study endeavors to evaluate highway resilience in a spatial context. Three spatial metrics, including K-nearest neighbors, proximity to highways, and Kernel density hot spot, are introduced and employed to compare and analyze the spatial patterns (magnitude and distribution) of crashes in pre- and post-weather conditions. An illustrative example is also provided to showcase the applications of the proposed spatial resilience metrics for two study areas in the State of Illinois, U.S. The contribution of this study is to provide transportation practitioners with a tool to evaluate highway spatial resilience both visually and quantitatively, and ultimately improve highway safety and operation.

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