ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation

Bíl, Michal; Andrášik, Richard; Sedoník, Jiří; Cícha, Vojtěch

doi:10.1371/journal.pone.0208407

Cited by 27 publications

(15 citation statements)

References 21 publications

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“…Horizontal and vertical curves existed at the majority of the HVRSs. The data for the horizontal curves were derived from the ROad Curvature Analyst (ROCA) software package [61], whereas the data for the vertical curves were produced via Google Earth. Considering the maximum curve radius of 3000 m, the results indicate the existence of horizontal curve radii ranging between 215-2998 m at 18 out of 22 HVRSs, as shown in Appendix A.…”

Section: Resultsmentioning

confidence: 99%

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

et al. 2021

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Significant risk factors that influence the occurrence of heavy vehicle accidents have been explored in numerous studies in order to lower injury severity in traffic accidents. It is imperative to explore road sections with a high risk of heavy vehicle accident occurrence by considering the significant consequences of such accidents for road users, despite the low number of heavy vehicles in traffic flow. To address this, this study proposes a method to predict clustering hotspots for heavy vehicle accidents on the basis of three different criteria, namely, heavy vehicle accident cases, the number of heavy vehicles involved, and accident severity index values. Moran’s I spatial autocorrelation was employed to identify the clustering for each criterion, and the Getis–Ord Gi* statistic was applied to estimate the likelihood of risk along the network. This study considers the features of hotspot points at significance levels from 0.10 to 0.01 with a 1355 m buffer radius to create segments for each criterion. The three criteria for hotspots were considered within the overlapped buffer zone. A total of 22 heavy vehicle risk segments (HVRSs) were identified and then ranked by crash rate. Overall, this study demonstrates the application of different criteria to identify accident hotspots involving a specific vehicle type, which could help in prioritizing segments with a high risk of heavy vehicle accidents, as well as providing information for HVRSs for the purpose of developing appropriate countermeasures for the identified accident hotspots.

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Section: Resultsmentioning

confidence: 99%

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

et al. 2021

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“…The horizontal curve segments are generated as the primary data. To do so, the road network database provided by FHWA Highway Performance Monitoring System (https://maps.indiana.edu/layerGallery.html?category=Streets, accessed on 26 April 2021) was used to detect and extract horizontal curve segments by ROCA (ROad Curvature Analyst) [53] tool in ESRI ArcGIS. Figure 1 shows state-wide curve segment distribution and two typical road curve identification examples at the plain area and mountain area.…”

Section: Data Descriptionmentioning

confidence: 99%

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Wang

Shan

2021

IJGI

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Vehicle crashes on roads are caused by many factors. However, the influence of these factors is not necessarily homogenous across locations, which is a challenge for non-stationary modeling approaches. To address this problem, this paper adopts two types of methods allowing parameters to fluctuate among observations, that is, the random parameter approach and the geographically weighted regression (GWR) approach. With road curvature, curve length, pavement friction, and traffic volume as independent variables, vehicle crash frequencies are modeled by two non-spatial methods, including the negative binomial (NB) model and random parameter negative binomial (RPNB), as well as three spatial methods (GWR approach). These models are calibrated in microlevel using a dataset of 9415 horizontal curve segments with a total length of 1545 kilometers for a period of three years (2016–2018) over the State of Indiana. The results revealed that the GWR approach can capture spatial heterogeneity and therefore significantly outperforms the conventional non-spatial approach. Based on the Akaike Information Criterion (AICc), geographically weighted negative binomial regression (GWNBR) was proved to be a superior approach for statewide microlevel crash analysis.

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“…In addition, I would like to thank to my family for their trust and dedication during my master's studies, and my friends from all communities for their encouragement. [3], [4]. .…”

Section: Acknowledgementmentioning

confidence: 99%

Safe Driving Of Autonomous Vehicles Through Improved Deep Reinforcement Learning

Gupta¹

2021

Preprint

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In this thesis, we propose an environment perception framework for autonomous driving using deep reinforcement learning (DRL) that exhibits learning in autonomous vehicles under complex interactions with the environment, without being explicitly trained on driving datasets. Unlike existing techniques, our proposed technique takes the learning loss into account under deterministic as well as stochastic policy gradient. We apply DRL to object detection and safe navigation while enhancing a self-driving vehicle’s ability to discern meaningful information from surrounding data. For efficient environmental perception and object detection, various Q-learning based methods have been proposed in the literature. Unlike other works, this thesis proposes a collaborative deterministic as well as stochastic policy gradient based on DRL. Our technique is a combination of variational autoencoder (VAE), deep deterministic policy gradient (DDPG), and soft actor-critic (SAC) that adequately trains a self-driving vehicle. In this work, we focus on uninterrupted and reasonably safe autonomous driving without colliding with an obstacle or steering off the track. We propose a collaborative framework that utilizes best features of VAE, DDPG, and SAC and models autonomous driving as partly stochastic and partly deterministic policy gradient problem in continuous action space, and continuous state space. To ensure that the vehicle traverses the road over a considerable period of time, we employ a reward-penalty based system where a higher negative penalty is associated with an unfavourable action and a comparatively lower positive reward is awarded for favourable actions. We also examine the variations in policy loss, value loss, reward function, and cumulative reward for ‘VAE+DDPG’ and ‘VAE+SAC’ over the learning process.

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ROCA – An ArcGIS toolbox for road alignment identification and horizontal curve radii computation

Cited by 27 publications

References 21 publications

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Safe Driving Of Autonomous Vehicles Through Improved Deep Reinforcement Learning

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