Traffic Data Collection And Analysis Methods And Procedures Pdf
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Home Consumer Insights Market Research. Data collection is defined as the procedure of collecting, measuring and analyzing accurate insights for research using standard validated techniques.
- Analysis Procedures Manual
- 6. What is involved in collecting data – six steps to success
- 6. What is involved in collecting data – six steps to success
This synthesis will be of interest to traffic engineers, highway planners, and others concerned with the collection of traffic data for traffic engineering studies, for long-range planning, and for evaluation of traffic law enforcement. Information is presented on current practice in traffic data collection and analysis. Although types of highway traffic data collected over the past 50 years have not changed significantly, the quantities, analysis procedure, and presentations of these data have changed as a result of changing policies, operational concerns, and capabilities resulting from new technologies.
The APM does not establish any accepted or preferred software. This data is intended for use in developing existing and future volumes and described in the APM version 2, Chapters 5 and 6. For information on how to use this table, see Section 6. Note: The future volumes are estimates only and local growth patterns and comprehensive plans may affect the actual outcome. The growth rates calculated from the Future Volumes Table are only a beginning point of estimating future volumes.
Analysis Procedures Manual
Metrics details. With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network WSN , which cannot only collect traffic flow data, but also record the speed and position of vehicles.
On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification.
The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals. Based on these steps, the trend of complexity change of mixed signal can be used to identify signal characteristics. The numerical experiments show that, based on incremental noise addition, the complexity trends of periodic data, random data, and chaotic data are different.
The application of the method opens a new way for traffic flow data collection and analysis. Traffic congestion is a daily phenomenon in large- and medium-sized cities all over the world.
With limited urban facilities and resources, it is an effective way to control traffic congestion by analyzing and predicting traffic flow. This involves two issues, collecting and analyzing traffic data.
There are many methods to collect traffic data, such as pneumatic road tubes [ 1 ], induction loop [ 2 ], and piezoelectric sensors [ 3 ]. These methods can collect traffic flow, but cannot record the speed and location of vehicles, which cannot meet the needs of traffic flow analysis algorithm. The paper proposes a novel traffic data collection scheme based on wireless sensor network WSN. The scheme measures vehicle flow and speed based on vehicle disturbances to geomagnetism and uses the slotted ALOHA protocol to communicate between data nodes.
Based on the scheme, vehicle speed and location are record every specific time slot. Chaos algorithm is widely used in traffic flow data processing, and chaotic identification is the premise of chaotic analysis.
However, because of the complexity of chaos, its intrinsic mechanism has not been fully revealed, so the academic community has not yet proposed a unified definition of chaos. Aiming at the chaotic identification, scholars have proposed many criterions, such as Poincare section [ 4 ], bifurcation diagram [ 5 ], power spectrum [ 6 ], Kolmogorov entropy [ 7 ], and topological entropy [ 8 ]. The most commonly used criteria are the largest Lyapunov exponent [ 9 , 10 ] and the fractal dimension [ 11 , 12 ], but these two parameters are based on phase space reconstruction [ 13 , 14 ].
Only in real phase space or near-real phase space that the two parameters can accurately analyze and identify the signal. The time delay method based on the Takens embedding theorem [ 15 ] is a main way for phase space reconstruction. However, this method has been influenced by many causes in practice, so the real phase space model of the object is often difficult to get, which leads to the unreliability of the identification results.
According to the idea of indirect method, the noise of different intensities is incrementally added to the signal, and it is found that the complexity trend of the mixed signal is an important feature for identifying signal characteristics. Then the delayed mutual information is used to measure the complexity of signals. This feature can be used as a significant criterion for identifying the differences of various kinds of signals, which achieves reliable chaotic recognition for traffic flow signals.
The scheme uses a magnetometer to count the vehicle and identify the speed. Generally, a magnetometer sensor can detect vehicles that are 10 m apart. The system architecture is shown in Fig. In the system, all data collection nodes are responsible for collecting road traffic data, and then these nodes transmit data to the aggregation node based on the ALOHA protocol and finally transmit to the remote server through the mobile internet. The system has strong scalability, and the cost of construction, maintenance and operation is low, which is suitable for urban traffic monitoring.
Traffic flow collecting system architecture based WSN. The magnetometer sensor identifies the vehicle flow and speed based on the vehicle's disturbance to the earth's magnetic field, and then the wireless module aggregates the data into the aggregation node.
Finally, the aggregation node data is transmitted to the remote cloud server via the mobile internet. RF69 is a wireless transceiver chip promoted by HopeRF. The module operates at MHz with a maximum spatial rate of kbps. It supports up to networks, each with nodes, and uses AES encryption to protect your data without restriction and transmits up to 66 bytes of packets. The FXOSCQ is a smart digital chipset that integrates a three-axis magnetometer and a three-axis accelerometer sensor.
The chip has a wide measurement range, high resolution, low noise density, high sensitivity, low output noise range, low cost, low power consumption, and the ability to manage high interference areas. ALOHA protocol is selected in this scheme. ALOHA is the earliest and most basic wireless data communication protocol.
Its idea is simple. As long as users have data to send, let them send it. And if, while sending that data, the data is received from another node, a collision has occurred. If this happens, try resending the data later. Chaos theory is an effective method of data analysis, but the premise of chaos analysis is chaos identification. A data analysis method based on incremental noise addition is proposed in the paper for traffic flow data, which provides a criterion for chaotic identification.
The method uses an improved surrogate data method to add noise to the signal to be analyzed incrementally, while using delayed mutual information to measure the complexity of mixing signals under different noise intensity.
The pseudo-periodic surrogate data method PPS [ 16 ] is proposed by Small in and has been successfully applied to the chaotic identification of ECG signals. The essence of this method is to add noise to the source signal by changing the signal phase order. Step 3: According to the Euclidean distance between s 1 and phase points in phase space X , calculate the transition probability of phase points and select s 2 randomly according to this probability, and so on.
The transition probability is set as. According to the PPS, the surrogate data of a sine signal under each noise intensity are shown in Fig. Sine signal and the surrogate data under different noise intensity. With the increase of noise, the structure of source signal is gradually annihilated. There are four subgraphs in Fig.
As the noise increases, the surrogate data of the sine signal gradually evolve from a regular sequence to a random sequence.
In summary, PPS can be used as a noise addition algorithm. Compared with the method of adding white noise, this algorithm can disrupt the phase order of the original data.
Randomizing the signal while preserving the statistical characteristics is an important feature of the algorithm. After adding the quantitative noise to the signal, it is necessary to evaluate the complexity of the signal with noise effectively. Kolmogorov proposed the first definition of the signal complexity in Later, Lempel and Ziv proposed the specific algorithm called LZ complexity, which is widely used in the research of nonlinear science. In addition, Skyllingstad et al.
Therefore, delayed mutual information is also often used to measure the complexity of a time series. The conclusion is that both parameters can effectively express the data complexity, and the values are negatively correlated. Calculating the two parameters using a four-segment or more detailed segmentation algorithm can more accurately reflect the essence of nonlinear signals.
The example analysis shows the delayed mutual information is more sensitive than the LZ complexity in expressing the intrinsic characteristics of the nonlinear dynamic system. Therefore, this paper uses delayed mutual information as the measurement for the signal complexity.
A and B can be regarded as two information sources, and their respective information entropy are. Mutual information can express the correlation between two sources A and B.
When A is the same as B , the value of mutual information is maximum; when A and B are independent of each other, the mutual information is 0. To express the complexity of a signal, the time series of a signal can be used to calculate the delayed mutual information.
The mutual information between A and B is called delayed mutual information of a signal. Counts the number num i , j of the joint information source AB in the section i , j , and calculates the joint probability of corresponding section i , j as below. Therefore, the value of D 0 can be used to normalize I. Applying the algorithm, the delayed mutual information sequences of a Lorenz chaotic signal and a periodic signal are shown in Fig.
Delayed mutual information sequence of two signals. This figure shows that the delayed mutual information can express the complexity of the signal. The simpler the signal is, the closer the parameter value is to 1; the more complex the signal is, and the closer the parameter value is to 0. In Fig. In this section, the method of the third section is used to carry out numerical experiments on typical periodic signals, chaotic signals, and random signals, respectively.
The periodic signals used for analysis are generated by logistic model [ 19 ]. The logistic model, proposed by biologist May, is also called model of insect, which is the most prestigious achievement in the early study of chaos. The expression of the model is. The logistic model is a famous example from regularity to chaos. There are two groups of chaotic signals for analysis. The two signals in the second group are generated by the Lorenz attractor [ 20 ] and Henon attractor [ 21 ].
In addition, to analyze the signal characteristics, a set of traffic flow data is selected in the second group. Finally, three white Gaussian noise signals are selected as random signals for numerical analysis. All the signals used for analysis above can be regarded as time series, and all of which have a length of The generation of surrogate data: First of all, the experimental signal is normalized. Finally, calculates the MD mean value of sets of surrogate data under each noise intensity.
For three periodic signals, according to the experimental design, the surrogate data are generated separately, and then the corresponding MD values are calculated. The curves corresponding to the periodic signals remain stable at first, and when the noise exceeds a certain threshold, these curves begin to decline.
According to Fig. For the periodic signal, when the added noise is small, the signal is still regular, and the complexity of the signal is not obviously changed. When the noise increases, the three curves range abilities are different, which is related to the structure of these signals. With the increase of period multiplier, the structure of the periodic signal is more complex.
6. What is involved in collecting data – six steps to success
If an organization is considering whether to collect data on its own or get help from an external consultant, it will need to have enough information to make an informed decision about how to proceed. This section outlines some of the key considerations that may arise during various steps in the data collection process. There is no requirement that these steps be followed or pursued in the order that they are written. The model presented is offered as a reference tool. How data is gathered and analyzed depends on many factors, including the context, the issue that needs to be monitored, the purpose of the data collection, and the nature and size of the organization. The main consideration is to make sure that any information collected is done in a way and for a purpose that is consistent with the Code and complies with freedom of information and privacy protection legislation. In the interest of effectiveness and efficiency, it is recommended that efforts be made to collect data that will shed light on issues or opportunities.
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Although types of highway traffic data collected over the past 50 years have not changed significantly, the quantities, analysis procedure, and presentations of these data have bpwnjfoundation.org.
6. What is involved in collecting data – six steps to success
Metrics details. With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network WSN , which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification. The method adds noise of different intensities to the signal incrementally by an improved surrogate data method and uses the delayed mutual information to measure the complexity of signals.
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book.
With the rapid development of urbanization, collecting and analyzing traffic flow data are of great significance to build intelligent cities. The paper proposes a novel traffic data collection method based on wireless sensor network WSN , which cannot only collect traffic flow data, but also record the speed and position of vehicles. On this basis, the paper proposes a data analysis method based on incremental noise addition for traffic flow data, which provides a criterion for chaotic identification.
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Мы выполняем свою работу. Мы обнаружили статистический сбой и хотим выяснить, в чем. Кроме того, - добавила она, - я хотела бы напомнить Стратмору, что Большой Брат не спускает с него глаз.
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Он сделал это из-за Сьюзан. Коммандер Тревор Стратмор - ее наставник и покровитель.
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