Fig 5 - uploaded by Asma Thamir Ibraheem
Content may be subject to copyright.
Source publication
Problem statement: This study discusses the development of computer software of two phases. First phase contains the calculations of all curve elements of the four types of the horizontal curve and second phase contains the modeling of the ten methods of estimating the radius of the horizontal curve. Approach: The program is named as HCRET, which s...
Citations
... Roadway horizontal alignment data can be obtained through various means such as field surveys, global positioning system (GPS) methods, light detection and ranging (LiDAR), automated image processing, manual extraction via online mapping services, geographic information system (GIS) methods, and as-built plan sheets (Carlson et al. 2005;Ibraheem and Janan 2011). ...
Estimating horizontal alignment using discretized roadway data points, such as GIS maps, is complicated because the number of curved and tangent segments and their start and end points are not known a priori. This study proposes a two-step approach: The first step estimates the number and type of segments and their start and end points using an artificial neural network (ANN)-based approach. The second step estimates the segment-related attributes such as radii and length by circular curve-fitting. The novelty of this study lies in the simplicity of the input vector to the ANN model, which contains only the latitude and longitude readings of a point and those of its neighboring points. Training and test data were comprised of points extracted from curved and tangent segments of random horizontal alignments, generated synthetically using a computer programming code. The proposed approach was evaluated and compared with other available methods presented in the literature using real roadway horizontal alignment data from one freeway and one rural roadway with a total length of 47 km and 65 curved segments. The analysis results indicated that the proposed approach outperforms other approaches in terms of estimation performance, particularly when the roadway follows a winding alignment.
... CurvS runs the clustering algorithm by setting a large value for K, and terminates when SSE does not change by increasing K. A minimum cluster size restriction is imposed within the clustering method because at least three data points are required to calculate k in Equation 3. This is the default value for the minimum cluster size parameter in CurvS, and can be modified by the user. ...
This paper introduces CurvS, a web-based tool for researchers and analysts that automatically extracts, visualizes, and analyzes roadway horizontal alignment information using readily available geographic information system roadway centerline data. The functionalities of CurvS are presented along with a brief background on its methodology. The validation of its estimation results are presented using actual horizontal alignment data from two different roadway types: Route 83, a two-lane two-way rural roadway in New Jersey and I-80, a freeway segment in Nevada. Different metrics are used for validation. These are identification rates of curved and tangent sections, overlap ratio of curved and tangent sections between estimated and actual horizontal alignment data, and percent fit of curve radii. The validation results show that CurvS is able to identify all the curves on these two roadways, and the estimated section lengths are significantly close to the actual alignment data, especially for the I-80 freeway segment, where 90% of curved length and 94% of tangent section length are correctly matched. Even when curves have small central angles, such as the ones in Route 83, CurvS’s estimations covers 71% of curved length and 96% of tangent section length.
... The methods for collecting horizontal curvature data include ball bank indicator (BBI), chord method, compass, field surveys, lateral acceleration, global positioning system (GPS) units, as-built plan sheets, vehicle yaw rate, advisory speed plate method, automated image processing, the use of GIS shapefiles of roadway centerlines, and manual extraction. The majority of these methods are described in detail in Carlson et al. (5) and Ibraheem and Janan (6). ...
This paper presents the use of a clustering method for automatically estimating horizontal curvature data and crash modification factors (CMFs) using Geographic Information System (GIS) roadway shapefiles. The clustering method identifies distinct sections on a roadway, either curved or tangent, based on the proximity of the approximated curvature values of data points from GIS roadway centerline shapefiles, and calculates horizontal curvature data and the corresponding CMFs. The results of the clustering method are compared with two other methods: (1) the mobile access vehicle method based on field GPS measurements and (2) the manual data extraction method based on satellite images. The comparison was conducted on a total of 24.7 mi of four NJ rural two-lane roads. The results showed that the CMFs estimated by the clustering method were within 12.2 and 15.5% of the ones produced by the mobile asset vehicle and the manual data extraction method, respectively. In addition, the sensitivity of the manually extracted horizontal curvature data was examined by conducting three additional independent trials. The average percent difference in the calculated CMFs between trials was 15.5%. This study therefore concludes that the clustering method can produce CMF estimates as accurate as the two other methods method much more efficiently in relation to time and money.
Traffic safety and energy efficiency of vehicles are strictly related to driver’s behavior. The scientific literature has investigated on some specific dynamic parameters that, among the others, can be used as a measure of unsafe or aggressive driving style such as longitudinal and lateral acceleration of vehicle. Moreover, the use of modern mobile devices (smartphones and tablets), and their internal sensors (GPS receivers, three-axes accelerometers), allows road users to receive real time information and feedback that can be useful to increase awareness of drivers and promote safety. This paper focuses on the development of a prototype mobile application that can evaluate the grade of safety that drivers are keeping on the road by measuring of accelerations (longitudinal and lateral) and warning for users when it can be convenient to correct their driving style. The aggressiveness is evaluated by plotting vehicle’s acceleration on a g-g diagram specially studied and designed, where horizontal and lateral acceleration is displayed inside areas of “Good Driving Style”. Several experimental tests were carried out with different drivers and cars in order to estimate the system accuracy and the usability of the application. This work is part of the wider research project M2M, Mobile to Mobility: Information and communication technology systems for road traffic safety (PON National Operational Program for Research and Competitiveness 2007-2013) which is based on the use of mobile sensor computing systems for giving real-time information in order to reduce risks and to make the transportation system more safe and comfortable.
