Car-Following and Collision Constraint Models for Uninterrupted Traffic: Reexamination Using High-Precision Global Positioning System Data

2000 ◽  
Vol 1710 (1) ◽  
pp. 37-46 ◽  
Author(s):  
Sarosh Khan ◽  
Pawan Maini ◽  
Kittichai Thanasupsin
Keyword(s):  
High Precision ◽  
Global Positioning System ◽  
Critical Evaluation ◽  
Gps Data ◽  
Positioning System ◽  
Velocity Data ◽  
Car Following ◽  
Location Data ◽  
Global Positioning ◽  
System Data

In the last few decades several car-following models have been proposed and tested using mainly vehicle location data. The use of high-precision Global Positioning System (GPS) data to test several car-following and collision constraint models is reported, with a critical evaluation of these models and proposal of a modified collision constraint formulation. GPS receivers typically report time-stamped location or position fixes and velocity. For a pair of leading and following vehicles, location and velocity data were used to examine estimates of acceleration, velocity, and headway by Pipes’s; modified Pitt’s, or FRESIM; CARSIM; and INTELSIM car-following models. The important aspects of collecting accurate GPS data are also highlighted.

Subsidence is determined in the heart of the Central Valley using Post Processed Static and Precise Point Positioning techniques

2020 ◽  
Vol 14 (1) ◽  
pp. 113-118 ◽  
Author(s):  
Y. Facio ◽  
M. Berber
Keyword(s):  
Data Processing ◽  
Global Positioning System ◽  
Precise Point Positioning ◽  
Central Valley ◽  
Gps Data ◽  
Positioning System ◽  
Online Services ◽  
Global Positioning ◽  
System Data ◽  
Point Positioning

AbstractPost Processed Static (PPS) and Precise Point Positioning (PPP) techniques are not new; however, they have been refined over the decades. As such, today these techniques are offered online via GPS (Global Positioning System) data processing services. In this study, one Post Processed Static (OPUS) and one Precise Point Positioning (CSRS-PPP) technique is used to process 24 h GPS data for a CORS (Continuously Operating Reference Stations) station (P565) duration of year 2016. By analyzing the results sent by these two online services, subsidence is determined for the location of CORS station, P565, as 3–4 cm for the entire year of 2016. In addition, precision of these two techniques is determined as ∼2 cm. Accuracy of PPS and PPP results is 0.46 cm and 1.21 cm, respectively. Additionally, these two techniques are compared and variations between them is determined as 2.5 cm.

scholarly journals Quantification of the Accuracy of Low Priced GPS Receivers for Crash Location

2010 ◽  
Author(s):  
Thobias Sando ◽  
Renatus Mussa ◽  
John Sobanjo ◽  
Lisa Spainhour
Keyword(s):  
Global Positioning System ◽  
Time Of Day ◽  
Gps Data ◽  
Positioning System ◽  
Gps Receiver ◽  
Accuracy Improvement ◽  
Gps Receivers ◽  
Location Data ◽  
Global Positioning ◽  
Potential Tool

Global positioning system (GPS) has been identified as a potential tool for capturing crash location data. This study quantifies factors that could affect the accuracy of GPS receivers. The results showed that GPS receiver orientation, site obstructions, and weather have significant effects on the accuracy of GPS receivers. Time of day and number of satellites were not found to significantly affect the accuracy of GPS receivers. HDOP values of 1.2 or less were found to be adequate for crash location purposes. An accuracy improvement of 20.7% was realized by filtering GPS data based on HDOP values.

Using Global Positioning System Data to Understand Variations in Path Choice

2000 ◽  
Vol 1725 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Oliver Jan ◽  
Alan J. Horowitz ◽  
Zhong-Ren Peng
Keyword(s):  
Global Positioning System ◽  
Traffic Assignment ◽  
Positioning System ◽  
Location Data ◽  
Global Positioning ◽  
System Data ◽  
Assignment Models ◽  
Street Segments ◽  
Path Deviation ◽  
Research Recommendations

A comprehensive set of Global Positioning System (GPS) vehicle location data from Lexington, Kentucky, households was analyzed to determine if such data can be helpful in improving path choice assumptions in traffic assignment models. The portion of the data used consisted primarily of a reconstruction of the street network and the lists of street segments in each path. Analysis was based on matches of trips (e.g., pairs of trips with similar origins and destinations). Matches were obtained for trips within households and for trips across households. Statistics used to compare trips in matches were a path deviation index and the percentage of identical links. It was found that the path chosen on a trip was quite sensitive to the location of the origin and destination and that the chosen path most often differed considerably from the shortest time path across the network. Paths for trips made by the same driver were very consistent over time; paths by different drivers showed more deviations even when the trip ends were the same or very similar. As a result of this research, recommendations are made as to how GPS data on path choice can be better collected in the future for improvement of traffic assignment models.

scholarly journals Estimating velocity from noisy GPS data for investigating the temporal variability of slope movements

2014 ◽  
Vol 14 (9) ◽  
pp. 2503-2520 ◽  
Author(s):  
V. Wirz ◽  
J. Beutel ◽  
S. Gruber ◽  
S. Gubler ◽  
R. S. Purves
Keyword(s):  
Global Positioning System ◽  
Temporal Variability ◽  
Synthetic Data ◽  
Gps Data ◽  
Positioning System ◽  
Signal To Noise ◽  
Slope Movements ◽  
Global Positioning ◽  
System Data ◽  
Full Knowledge

Abstract. Detecting and monitoring of moving and potentially hazardous slopes requires reliable estimations of velocities. Separating any movement signal from measurement noise is crucial for understanding the temporal variability of slope movements and detecting changes in the movement regime, which may be important indicators of the process. Thus, methods capable of estimating velocity and its changes reliably are required. In this paper we develop and test a method for deriving velocities based on noisy GPS (Global Positioning System) data, suitable for various movement patterns and variable signal-to-noise-ratios (SNR). We tested this method on synthetic data, designed to mimic the characteristics of diverse processes, but where we have full knowledge of the underlying velocity patterns, before applying it to explore data collected.

scholarly journals Parking Places to Moped-Style Scooter Sharing Services Using GIS Location-Allocation Models and GPS Data

2021 ◽  
Vol 10 (4) ◽  
pp. 230
Author(s):  
Onel Pérez-Fernández ◽  
Juan Carlos García-Palomares
Keyword(s):  
Global Positioning System ◽  
Catchment Area ◽  
Public Spaces ◽  
Gps Data ◽  
Positioning System ◽  
Urban Centers ◽  
Location Allocation ◽  
Global Positioning ◽  
The City

Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city’s main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas.

Evaluation of CORSIM Car-Following Model by Using Global Positioning System Field Data

2000 ◽  
Vol 1710 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Sastry Chundury ◽  
Brian Wolshon
Keyword(s):  
Data Collection ◽  
Global Positioning System ◽  
Field Data ◽  
Goodness Of Fit ◽  
Positioning System ◽  
Goodness Of Fit Test ◽  
Distance Information ◽  
Car Following ◽  
Wide Range ◽  
Global Positioning

It has been recognized that CORSIM (and its constituent program, NETSIM) is one of the most widely used and effective computer programs for the simulation of traffic behavior on urban transportation networks. Its popularity is due in large part to the high level of detail incorporated into its modeling routines. However, the car-following models, used for the simulation of driver behavior in the program, have not been formally calibrated or validated. Since the model has performed well in a wide range of applications for so many years, it has always been assumed to have an implied validity. This study evaluated the NETSIM car-following models by comparing their results with field data. Car-following field data were collected using a new data collection system that incorporates new Global Positioning System and geographic information system technologies to improve the accuracy, ease, speed, and cost-effectiveness of car-following data collection activities. First, vehicle position and speed characteristics were collected under field conditions. Then simulated speeds and distances were based on identical lead vehicle actions using NETSIM car-following equations. Comparisons of simulated and field data were completed using both graphical and statistical methods. Although some differences were evident in the graphical comparisons, the graphs overall indicated a reasonable match between the field and simulated vehicle movements. Three statistical tests, including a goodness-of-fit test, appear to support these subjective conclusions. However, it was also found that definitive statistical conclusions were difficult to draw since no single test was able to compare the sets of speed and distance information on a truly impartial basis.

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