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Author: Raj Kumar Narla
Publisher:
ISBN:
Category : Detectors
Languages : en
Pages : 126
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Author: Raj Kumar Narla
Publisher:
ISBN:
Category : Detectors
Languages : en
Pages : 126
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Author: Ann M. Mechler
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 120
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Author: Robert Andrew Rescot
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :
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The modern roundabout is growing in popularity as an alternative intersection design, however it presents engineers a new quandary. At a traditional intersection, traffic movements may more easily counted given vehicle spacing, lane demarcations, and signal phasing. At a roundabout, counting techniques are much different. This research builds on other academic research to prove the feasibility of creating an automated traffic counting solution that is comprised of readily available parts, and a heuristic computer algorithm.
Author: Ali Gholami
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 332
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The purpose of this dissertation was to identify and investigate the possibility of obtaining turning volumes from inductive loops and investigate the accuracy of them. A large majority of signalized intersections operate under inductive loops. Experiences in cities such as Seattle, San Antonio, and Toronto show successful usage of inductive loop detectors to obtain traffic volume at intersections. Loop detectors are the most common method for obtaining data at intersections to operate and control traffic signals. A macroscopic study was performed on two intersections in Reno and Sparks. Both Reno and Sparks use sequential short loops. The detector accuracy was interpreted in terms of count errors. The preferred metric for count error is the Mean Absolute Percent Error (MAPE, %). Results showed the counts were not reliable and had a very high error. At the Kietzke/Moana intersection in Reno, NV, the MAPE was 15 percent northbound, 31 percent southbound, 20 percent eastbound, and 36 percent westbound. At Sparks/Prater in Sparks, NV, the MAPE was worse with all detector groups ranging from 48 to 74 percent. In Reno, advance detector counts could be modified because they showed a strong relationship with base (observed) counts; however, in Sparks, there was not a clear relationship between the two sets of counts. In Chapter 4, by using Genetic Programming (GP) and Adaptive Neuro-Fuzzy Inference System (ANFIS), detector counts were modified and again MAPE was calculated. At Kietzke/Moana, all approaches after data modification had MAPE less than 14 percent. However, at Sparks/Prater, because of the loops’ wiring, there was more irregularity in count detections and as a result, models were not able to reduce detector count errors significantly. Even when detector counts can be modified, detectors are unable to produce turning movement counts in shared lanes. Current practice involves gathering such information through manual counts, which is very costly. Chapter 5 proposes three methods to estimate turning movement proportions in shared lanes. These methods were tested using linear regression and Genetic Programming (GP). It was found that the hourly average error range at intersections was between 4 to 27 percent using linear regression and 1 to 15 percent using GP. The proposed method for modifying detector counts did not guarantee reliable counts in all situations. In Chapter 6, a method is proposed to obtain turning movement counts only from signal information without using detector counts. To produce the required data, a simulation was performed in VISSIM with different input volumes. To change turning volumes, a code was developed in COM interface. With this code, the inputs did not have to be changed manually. In addition, the COM code stored the outputs. Data were then exported to a single Excel file. Afterwards, regression and the Adaptive Neural Fuzzy Inference System (ANFIS) were used to build models to obtain turning volumes. The accuracy of the models was defined in terms of MAPE. Results of the two case studies showed that during peak hours, there was a high correlation between actuated green time and volumes. This method does not require extensive data collection and is relatively easy to employ. The results also showed that ANFIS produced more accurate results compared to regression. Chapter 7 proposes mid-intersection detector (MID) concept configuration to obtain more accurate counts. MIDs are departure doctors which have moved back to middle of intersection. Under this configuration, in addition to stop bar detectors, some mid-intersection detectors also are used to obtain more reliable counts. Due to intersection operation, stop bar detectors were still required, but compared to traditional departure detector configurations, MIDs were expected to produce more reliable and accurate data while requiring same number of detectors. Chapter 8 offers some recommendations to change the loop detector systems for the sake of improving turning movement counts. For obtaining more accurate counts, we recommend: 1) the cost-effective and non-intrusive replacements of inductive loops (Passive Infrared, Active Infrared, Radar and Passive Millimeter, Passive Acoustic, Ultrasonic-Pulse and Doppler). Several “non-intrusive” detection systems are becoming more prominent, being viewed as cost-effective replacements of inductive loops; 2) Changing the configuration and wiring of loops. Performance was significantly enhanced when the loops were connected such that the field generated by the individual loops was additive between the loops rather than subtractive. Counting results were likely to be fair to poor when the loops were separated by 10 or more feet or had a different number of turns or were connected in parallel. To obtain excellent to good counts from loops, each loop should be wired to an individual loop detector channel. If two or more are spliced together into one loop detector channel, the count accuracy would be fair to poor. (Abstract shortened by UMI.).
Author: Edward J. Smaglik
Publisher:
ISBN:
Category : Electronic traffic controls
Languages : en
Pages : 49
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This proof-of-concept study is to develop an automated data collection module for collection and management of traffic data at signalized intersections controlled by the Arizona Department of Transportation (ADOT). The objective of this proof-of-concept phase of the work was to determine the feasibility and cost of modifying an existing ADOT traffic control cabinet to collect operational data using the video equipment installed for presence detection to capture vehicle flow rate information. The goal was to use this data to develop event-based performance measures, leveraging existing infrastructure to its fullest extent. An intersection in Flagstaff, Arizona, was chosen as the test location. Researchers used the intersection's existing video detection cameras, installing additional video detector interface cards to produce contact-closure vehicle flow rate information. Researchers calculated performance measures (volume-to-capacity [V/C] ratio, equivalent hourly volume [EHV], and cumulative counts) from the video-generated data and compared them with measures generated from concurrent manually counted data over a 24-hour analysis period. The V/C values generated from the video data were shown to be statistically different than those calculated with manual-count data; however, on all but one phase, the difference was not operationally significant. An analysis of cumulative count data did show operationally significant differences. While the data had some inaccuracies, the proof of concept was successful in that the research team was able to generate traffic volume performance measures using existing video detection equipment. During the next phase of the project, the data inaccuracies can be investigated and possibly addressed with measures such as camera placement, choice of technology, etc. A cost analysis determined that the cost of equipping a similar intersection for this type of vehicle count capability is approximately $16,700 using the equipment specified for this project if the installation is performed as part of the initial construction or rehabilitation of the intersection. The researchers recommend that Phase 2 of this project be undertaken. Ultimately, assuming successful completion of all phased milestones, the investigators recommend that ADOT consider equipping future intersections as described in this report to improve the quality of future signal-timing plans while reducing costs over the long term.
Author: Jialin Tian
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 122
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Obtaining turning movement counts at signalized intersections is a routine task in traffic engineering and can be tedious and time consuming. Previous research in automating turning movement count has focused on estimating the turning movements from approach and departure volumes and developing detection systems for exclusive turning lanes. The accuracy of an alternative method, called the Time and Place System (TAPS), is examined in this research through a field study of five signalized intersections in Columbia, Missouri. TAPS uses both the locations and times of actuations from a small number of detectors to classify movements from shared approach lanes. The five intersections represent a range of geometries and signal timings. At four intersections a standard video camera was placed about 30 feet high, as close to the departure lanes as possible, to provide a reasonable view. Additional cameras showed current signal indications into the departure leg. At the fifth location a single elevated camera captured both vehicle movements and signal indications. The videotape data was used to compare TAPS results to actual flows. The errors in detections were apparently due to the sensitivity of detection system, camera angles, intersection geometries, traffic parameters and other factors. The ability of TAPS to identify turning movements at signalized intersections was supported by the study results. The information from TAPS could be used for advanced signal management, dynamic traffic assignment and traffic demand estimation.
Author: Matthew William Zoll
Publisher:
ISBN:
Category : Computer vision
Languages : en
Pages : 314
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Author: 林亦琴
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Author:
Publisher:
ISBN:
Category : Transportation
Languages : en
Pages : 574
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Author:
Publisher:
ISBN:
Category : Traffic engineering
Languages : en
Pages : 88
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