The authors would like to acknowledge the China Scholarship Council for partially funding this work was with the file No. 201506560015.

1. Preparation of the equipment
<ol>
	<li>Prepare two of each of the following devices to collect two-direction traffic flows: radars, laptops, batteries and cables for radars and laptops, cameras, and radar and camera tripods. 
	NOTE: The radar and its corresponding software are used to collect vehicle speed and trajectory, and this is more accurate than a speed gun. The radar is not the only choice if other equipment is available for collecting vehicle speed, trajectory, and volume. As radar signals can be easily blocked by large vehicles, videos shot by cameras can be used for vehicle counting. During the investigation, if the weather is rainy or sunny, protection of the equipment is needed. Especially on a sunny day, the equipment may reach a high temperature and shut down, so an umbrella or cooling equipment is needed for this situation.</li>
</ol>
2. Testing of the equipment
<ol>
	<li>Ensure that all investigators are wearing reflective vests.</li>
	<li>Prepare the radar tripod and extend it as tall as possible. Set the tripod taller than 2 m to avoid signals from being blocked on the roadside.</li>
	<li>Install the radar on top of the tripod and lock the radar.</li>
	<li>Set the radar about 0.5 m next to the roadside, adjust the radar vertically, and face the vehicle direction or opposite direction. Keep the angle between the road and radar as small as possible. 
	NOTE: The radar can detect 200 m at most. If the radar is set too close to the lane, it may blow over the passing vehicles. Thus, 0.5&#8722;1.0 m is the usual distance to the lane.</li>
	<li>Turn on the power battery and connect the laptop to the power battery. Plug in the radar power cable and plug in the radar data USB to the laptop. When all cables are connected, turn on the laptop.</li>
	<li>Set the camera next to the radar to shoot the vehicle flow.</li>
	<li>Opening the radar software
	<ol>
		<li>Click Communication check, then select the radar ID number from the dropdown list. It will show Radar Detected with an ID number.</li>
		<li>Click Investigation setup. In the pop-up menu, click Read RLU time, and the Device time on the left will change. Then, click Set up RLU time, and PC current time on the left will also change.</li>
		<li>Click Start investigation, and the device working status will change from Data recording is not proceeding and No data in device to Data recording in proceeding and Data in device. Click Close to close this dialog box.</li>
		<li>Click the Realtime view to check the radar status. A new dialog box will show, and the radar data will be rolled quickly. This means that the radar is detecting the vehicles and works well. Keep this dialog box open until the collection is finished. 
		NOTE: The vehicle can be captured by the radar when passing the radar.</li>
		<li>Click Close on the dialog box to finish the collection.</li>
		<li>Click Investigation setup | End investigation, and confirm in the dialog box. Click the Close button.</li>
		<li>Select Data download in the main menu. Click Browse to select a place to save the radar data. Input an individual name for the spreadsheet. Click the Start download button, a progress bar will show, and a dialog box will appear after downloading. Click Confirm to finish data collection.</li>
		<li>Click Investigation setup | Erase data record, and confirm it in the next dialog box to clear the internal memory of the radar. 
		NOTE: A test of all equipment is needed before departure to the data collection location. Move all the equipment to the data collection location if all the parts work well.</li>
	</ol>
	</li>
</ol>
3. Data collection
<ol>
	<li>Selection of data collection location (Figure 3)
	<ol>
		<li>Select a suitable location that is similar to the intersection type used in the research. 
		NOTE: This is the key requirement in location selection. The shape of the location, traffic flow situation, traffic light control, and other controls are all needed in consideration. The more similar the study site, the more accurate the results. A U-turn median opening on the freeway is needed. A sufficiently long line of sight and clearance are required, which is necessary for radar and safety for investigators. Based on the detect distance of the radar and vehicle stop distance, the line of sight should be at least 200 m from the location to an upstream direction.</li>
		<li>Check the clearance of the radar direction. Make sure that there are no trees, shrubs, footbridges, traffic signs or streetlights in sight.</li>
		<li>Ensure that the location is a safe place for the equipment and investigators. Whether the equipment is set on the roadside or above the road depends on the terrain.</li>
		<li>Place the equipment in a secluded place to avoid gaining the driver&#39;s attention. 
		NOTE: According to prior experience, some drivers may slow down if they see the investigation equipment, which will lead to errors. The data acquisition equipment can be regarded as a measuring device for traffic police to measure speeding vehicles.</li>
	</ol>
	</li>
	<li>Collection of traffic data
	<ol>
		<li>Choose the collection time.
		<ol>
			<li>Collect 3 h of data: 1 h in morning peak, 1 h at noon valley, and 1 h at evening peak.</li>
			<li>Check the accurate peak and valley time from the traffic research report, traffic police department, or traffic business companies25,26 (Figure 4). 
			NOTE: If there is no traffic report or analysis as reference, collect 3 h of data during the three periods mentioned above, and choose the highest data.</li>
			<li>Input the data with highest traffic volume over a 1 h period into the simulation model and analysis section. Use the remaining 2 h of data for verification at the end.</li>
		</ol>
		</li>
		<li>Setup of the equipment
		<ol>
			<li>Adjust the radar direction, and set the camera next to the radar where it can capture all lanes. Repeat the process of installing all equipment in section 2 on the pedestrian bridge. 
			NOTE: The clearance before the radar should be as long and wide as possible to cover the whole range of U-turn movements. The EW (east to west) radar faces the traffic flow, and the WE (west to east) radar faces towards the vehicle tails due to road alignment (Figure 5). There are no differences between results from setting up the equipment on the inner vs. outer side of the lanes. The inner or outer side of the radar location only affects the coordinate system of trajectory figures with radar data. When the radar faces traffic flow, the detected running speed is negative and needs to be reversed during data processing. When the radar faces traffic flow, the detected running speed is positive and can be used directly.</li>
			<li>Set the radars and cameras so that they are slightly taller than the bridge railings to ensure clearance before the radars and cameras. 
			NOTE: There is no need for the radars to be as tall as the roadside settlement.</li>
		</ol>
		</li>
		<li>Ensure that the timing of the radars, laptops, and cameras are consistent with real-time.</li>
		<li>Start two radars and cameras simultaneously to schedule time.</li>
		<li>Check whether the radars and cameras work normally every 5 min during data collection to ensure that all parts work well.</li>
		<li>End the data collection and output the radar data as a spreadsheet with an identified name (Table 1).</li>
	</ol>
	</li>
</ol>
4. Data analysis
<ol>
	<li>Using calculation software to extract the radar data and draw operating speed and trajectories figures from the spreadsheet. 
	NOTE: X/Y coordinates and X/Y speed are in the spreadsheet.</li>
	<li>Delete obviously discrete points in the figures. These points are radar errors. 
	NOTE: The radar detects a large range of area, so the data may contain target vehicles, opposite vehicles, and non-motor vehicles in non-motor vehicle lanes. When plotting all data as figures, the three-lane target vehicles are obvious, and the remaining points are &#34;obviously discrete points&#34;. The detection areas are straight in Figure 3, the width of the three lanes is known, and the &#34;obviously discrete points&#34; can be deleted in the software. Plot the necessary points as shown in Figure 6b,d.</li>
	<li>Replay the traffic videos and count manually to obtain the traffic volume and types. 
	NOTE: Vehicles can be divided into cars and trucks according to size. All cars, taxies, and small trucks within 6 m are classified here as cars. All large trucks and buses are classified as trucks.</li>
	<li>Select the highest traffic volume group as representative data and input it into the simulation described in section 5. 
	NOTE: Only one group of data is needed in the simulation and sensitivity analysis. Data from the other two groups will be simulated as verification.</li>
</ol>
5. Building the simulation model
<ol>
	<li>Building of the road
	<ol>
		<li>Open the simulation software. Click Map button at the top of the interface and zoom in the map to find the data collection location.</li>
		<li>Click Links on the left, then move the cursor to the start location of the link, and right-click. Select Add New Link, input the link name and number of lanes, and click OK. Drag the cursor to draw the link on the map.</li>
		<li>Right click the link and select Add Point. Add points and drag points to make the link smoother with real road alignment in the map.</li>
		<li>Repeat steps 5.1.2 and 5.1.3 3x to build four segments, except for the U-turn median opening.</li>
		<li>Hold the right button of the mouse and Ctrl button on the keyboard, then drag the endpoint of one link to the adjacent link to connect the links. This part is called the &#34;connector&#34; and can be smoother as more points are added.</li>
		<li>Repeat step 5.1.5 to connect all links and U-turn routes.</li>
	</ol>
	</li>
	<li>Input of the desired speed
	<ol>
		<li>Select Base Data from the top bar, then select Distributions | Desired speed.</li>
		<li>Click the green-cross Add button at the bottom to add a new desired speed, then name it.</li>
		<li>In the Desired Speed Distributions dialog box, input the maximum speed collected from the representative data as the maximum desired speed, then input the average speed calculated from the representative data as the minimum desired speed. Delete the default data.</li>
		<li>Input a name for this desired speed, which is usually named using a direction.</li>
		<li>Repeat steps 5.2.3 and 5.2.4 to build all desired speeds (WE, EW, WW U-turn, and EE U-turn).</li>
	</ol>
	</li>
	<li>Vehicle composition
	<ol>
		<li>Select the Lists button from the top bar, then click Private Transport | Vehicle Compositions.</li>
		<li>Click the green-cross Add button at the bottom to add a new vehicle composition. Select the desired speed built in step 5.2 as Car.</li>
		<li>Click the green-cross Add button to add the vehicle type bus/truck as HGV. Select the same desired speed as done in step 5.3.2.</li>
		<li>Input the volume of cars and trucks at RelFlow from the representative data.</li>
		<li>Repeat steps 5.3.2-5.3.5 to build all vehicle compositions (WE, EW, WW U-turn, and EE U-turn).</li>
	</ol>
	</li>
	<li>Vehicle routes
	<ol>
		<li>Select Vehicle Route from the left menu bar.</li>
		<li>Move the cursor to the upstream of one link as the start point, right-click, then select Add New Static Vehicle Routing Decision.</li>
		<li>Drag the blue cursor representing the vehicle routes in data collection. Repeat this step 4x in WE, EW, WW U-turn, and EE U-turn to draw all vehicle routes.</li>
	</ol>
	</li>
	<li>Reduced speed areas
	<ol>
		<li>Select Reduced Speed Areas from the left menu bar.</li>
		<li>Right-click at the upstream of U-turn opening, then select Add New Reduced Speed Area. 
		NOTE: The length of the area depends on the representative data and speed change length.</li>
		<li>Build this area in both directions.</li>
	</ol>
	</li>
	<li>Conflict areas
	<ol>
		<li>Select Conflict Areas from the left menu bar. Four yellow conflict areas will be shown in the median opening section.</li>
		<li>Right-click one yellow conflict area and select Set Status to Undetermined as the realistic situation and conflict areas turn red.</li>
		<li>Repeat step 5.6.2 for all four conflict areas.</li>
	</ol>
	</li>
	<li>Travel time measurement
	<ol>
		<li>Select Vehicle Travel Times from the left menu bar.</li>
		<li>Right-click at the beginning of one link and select Add New Vehicle Travel Time Measurement.</li>
		<li>Drag the cursor to the end of the link to build the one vehicle travel time measurement. Repeat this step for all vehicle routes (WE, EW, WW U-turn, and EE U-turn).</li>
		<li>Name each travel time measurement with the corresponding direction. 
		NOTE: To compare the operating situations with improvement designs, the length of the travel time measurements needs to be the same in both simulation models.</li>
	</ol>
	</li>
	<li>Vehicle input
	<ol>
		<li>Select Vehicle Inputs from the left menu bar. Click the starting point of one link and right click to add new vehicle input.</li>
		<li>Move the mouse to left bottom and input volume from representative data. Repeat this step for all links.</li>
	</ol>
	</li>
	<li>Build another ESUL simulation model as comparison, only the U-turn opening part needs to be modified (Figure 7 and Table 2).</li>
	<li>Click the blue play button at the top of the interface, and the simulation will start. Drag the scale at the left of the play button, which can adjust the simulation speed. 
	NOTE: The instrument button Quick mode can make the simulation speed to the maximum.</li>
	<li>When the simulation ends, all results will be shown at the bottom of the interface. Copy the results into a new spreadsheet. Here, travel time, delay, and the number of stops are evaluated in the analysis27.</li>
</ol>
6. Simulation model calibration
<ol>
	<li>Input the traffic volume of the representative data into simulation software and perform the simulation (Figure 8a).</li>
	<li>Compare the traffic volume from the simulation results with the collected data volume.</li>
	<li>Calculate the capacity using Equation 1 below: 
	<img align="center" src="/files/ftp_upload/60675/60675eq1.jpg" /> (1) 
	where C denotes the ideal capacity (veh/h) and ht denotes the average minimum headway (s).</li>
	<li>Using the capacity, estimate the simulation error as the mean absolute percent error (MAPE) following Equation 2: 
	<img align="center" src="/files/ftp_upload/60675/60675eq2.jpg" /> (2) 
	where n denotes the four different flows in this study, Civ is the capacity simulated in the simulation model (veh/h), and Cif is the capacity of the investigation (veh/h). The calculated MAPE is presented in Table 3. 
	NOTE: The simulation model can be used if the MAPE is small28,29,30.</li>
	<li>Modify the parameters (i.e., random seed, car follow model type, lane change rule, etc.) based on instructions of the simulation software, or check all steps described above when building the simulation model31,32,33,34.</li>
</ol>
7. Sensitivity analysis
NOTE: Sensitivity analysis process is shown in Figure 8b. The collected data can only reflect its own performance (Figure 9, Table 4, Table 5, and Table 6). To prove the effectiveness under all situations, all possible traffic situations and different combinations were input into the simulation model to ensure that all situations are covered between the MUTI and ESUL (Figure 10 and Table 7).
<ol>
	<li>Select the car/truck (bus) ratio and operating speed of the representative data. Maintain these parameters.</li>
	<li>Set the U-turn ratio from ~0.03-0.15 in the sensitivity analysis with an increase of 0.03, which means five U-turn ratios in the sensitivity analysis. 
	NOTE: According to the representative data in Table 1, the range of the U-turn rate is 0.04-0.15.</li>
	<li>Set traffic volume from ~0.2-1.0 V/C with an increase of 693 veh/h (0.1 V/C; Table 7), which means nine volumes in the sensitivity analysis. 
	NOTE: The maximum traffic volume is 6,930 veh/h in an urban freeway with a three-lane segment, corresponding to service level E according to the AASHTO&#39;s Highway Capacity Manual35 when the design speed is 80 km/h.</li>
	<li>Simulate all 45 situations and save the results in both the present situation (MUTI) and improved situation (ESUL).</li>
	<li>Verify improvements in travel time and delays by calculating the ratio = (MUTI - ESUL)/MUTI x 100%. Verify improvements in the number of stops by calculating reduced time = MUTI - ESUL. 
	NOTE: In the final results (Figure 10), a positive (&#62;0) result means that the ESUL improved the traffic situation, while a negative (&#60;0) result in sensitivity represents the opposite.</li>
</ol>

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K., Dhiman, I. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyses+of+a+continuum+traffic+flow+model+for+a+nonlane-based+system.">Analyses of a continuum traffic flow model for a nonlane-based system.</a> International Journal of Modern Physics C. 25 (10), 1450045 (2014).</li><li>Chen, H., Zhang, N., Qian, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=VISSIM-Based+Simulation+of+the+Left-Turn+Waiting+Zone+at+Signalized+Intersection.">VISSIM-Based Simulation of the Left-Turn Waiting Zone at Signalized Intersection.</a> 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA). , (2008).</li><li>PTV AG. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> PTV VISSIM 10 User Manual. , (2018).</li><li>AutoNavi Traffic Big-data. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> 2017 Traffic Analysis Reports for Major Cities in China. , (2018).</li><li>AutoNavi Traffic Big-data. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Xi'an realtime traffic congestion delay index. , (2019).</li><li>Xiang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+the+Operational+Features+of+an+Unconventional+Dual-Bay+U-Turn+Design+for+Intersections.">Evaluating the Operational Features of an Unconventional Dual-Bay U-Turn Design for Intersections.</a> PLoS ONE. 11 (7), e0158914 (2016).</li><li>Kuang, Y., Qu, X., Weng, J., Etemad, S. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=How+Does+the+Driver's+Perception+Reaction+Time+Affect+the+Performances+of+Crash+Surrogate+Measures?.">How Does the Driver's Perception Reaction Time Affect the Performances of Crash Surrogate Measures?.</a> PLoS ONE. 10 (9), e0138617 (2015).</li><li>Zhao, F., Sun, H., Wu, J., Gao, Z., Liu, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+of+Road+Network+Pattern+Considering+Population+Distribution+and+Central+Business+District.">Analysis of Road Network Pattern Considering Population Distribution and Central Business District.</a> PLoS ONE. 11 (3), e0151676 (2016).</li><li>Jian, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Guideline for microscopic traffic simulation analysis. , (2014).</li><li>Liu, K., Cui, M. Y., Cao, P., Wang, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Iterative+Bayesian+Estimation+of+Travel+Times+on+Urban+Arterials:+Fusing+Loop+Detector+and+Probe+Vehicle+Data.">Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data.</a> PLoS ONE. 11 (6), e0158123 (2016).</li><li>Ran, B., Song, L., Zhang, J., Cheng, Y., Tan, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+Tensor+Completion+Method+to+Achieving+Better+Coverage+of+Traffic+State+Estimation+from+Sparse+Floating+Car+Data.">Using Tensor Completion Method to Achieving Better Coverage of Traffic State Estimation from Sparse Floating Car Data.</a> PLoS ONE. 11 (7), e0157420 (2016).</li><li>Zhao, J., Li, P., Zhou, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Capacity+Estimation+Model+for+Signalized+Intersections+under+the+Impact+of+Access+Point.">Capacity Estimation Model for Signalized Intersections under the Impact of Access Point.</a> PLoS ONE. 11 (1), e0145989 (2016).</li><li>Wei, X., Xu, C., Wang, W., Yang, M., Ren, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+average+travel+delay+caused+by+moving+bottlenecks+on+highways.">Evaluation of average travel delay caused by moving bottlenecks on highways.</a> PLoS ONE. 12 (8), e0183442 (2017).</li><li>American Association of State and Highway Transportation Officials (AASHTO). <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Highway Capacity Manual 6th edition. , (2010).</li><li>Wang, Y., Qu, W., Ge, Y., Sun, X., Zhang, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+personality+traits+on+driving+style:+Psychometric+adaption+of+the+multidimensional+driving+style+inventory+in+a+Chinese+sample.">Effect of personality traits on driving style: Psychometric adaption of the multidimensional driving style inventory in a Chinese sample.</a> PLoS ONE. 13 (9), e0202126 (2018).</li><li>Shen, B., Qu, W., Ge, Y., Sun, X., Zhang, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+relationship+between+personalities+and+self-report+positive+driving+behavior+in+a+Chinese+sample.">The relationship between personalities and self-report positive driving behavior in a Chinese sample.</a> PLoS ONE. 13 (1), e0190746 (2018).</li><li>American Association of State and Highway Transportation Officials (AASHTO). <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A policy on geometric design of highways and streets 6th Edition. , (2011).</li><li>Ashraf, M. I., Sinha, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+"handedness"+of+language:+Directional+symmetry+breaking+of+sign+usage+in+words.">The "handedness" of language: Directional symmetry breaking of sign usage in words.</a> PLoS ONE. 13 (1), e0190735 (2018).</li><li>Lu, A. T., Yu, Y. P., Niu, J. X., John, X. Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Effect+of+Sign+Language+Structure+on+Complex+Word+Reading+in+Chinese+Deaf+Adolescents.">The Effect of Sign Language Structure on Complex Word Reading in Chinese Deaf Adolescents.</a> PLoS ONE. 10 (3), e0120943 (2015).</li><li>Fan, L., Tang, L., Chen, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimizing+location+of+variable+message+signs+using+GPS+probe+vehicle+data.">Optimizing location of variable message signs using GPS probe vehicle data.</a> PLoS ONE. 13 (7), e0199831 (2018).</li></ol>

The authors have nothing to disclose.

In this article, the procedure of solving a traffic problem at an intersection or short segment using simulation was discussed. Several points deserve special attention and are discussed in more detail here.
Field data collection is the first thing deserving attention. Some requirements for data collection location are as follows: 1) Finding a suitable location for data collection. The location should be similar to the road geometric shape in the study, which is the premise of data collection....

Some traffic problems, such as traffic congestion at an intersection or short segment, can be solved or improved by optimizing the road design, change signal timing, traffic management measurements, and other transportation technologies1,2,3,4. These improvements either have a positive or negative effect on traffic flow operations compared to the original situations. The changes in traffic operations can be compared in traffic simulation software rather than in actual reconstruction of the intersection or segment. The traffic simulation method is a quick and cheap option when one or more improvement plans are proposed, especially when comparing different improvement plans or evaluating the effectiveness of improvements. This article introduces the process of solving a traffic problem with simulation by evaluating traffic flow operational features of an exclusive spur dike U-turn lane design5.
U-turn movement is a widespread traffic demand that requires a U-turn median opening on the road, but this has been debated. Designing a U-turn opening can cause traffic congestion, while closing the U-turn opening can cause detours for the U-turn vehicles. Two movements, U-turn vehicles and direct left-turn vehicles, require a U-turn opening and cause traffic delays, stops, or even accidents. Some technologies have been proposed to solve the disadvantages of U-turn movements, such as signalization6,7, exclusive left turn lanes8,9, and autonomous vehicles10,11. Improvement potential still exists on U-turn issues, due to the above solutions having restrictive applications. A new U-turn design may be a better solution under certain conditions and be able to address existing problems.
The most popular U-turn design is the median U-turn intersection (MUTI)12,13,14,15, as shown in Figure 1. A significant limitation of the MUTI is that it cannot distinguish U-turn vehicles from passing vehicles and that traffic conflict still exists16,17. A modified U-turn design called the exclusive spur dike U-turn lane (ESUL; Figure 2) is proposed here and aims to diminish traffic congestion by introducing an exclusive U-turn lane on both sides of a median. The ESUL can significantly reduce travel time, delays, and the number of stops due to its channelization of the two flows.
To prove that the ESUL is more efficient than the normal MUTI, a rigorous protocol is needed. The ESUL cannot be actually constructed before a theoretical model; thus, simulation is needed18. Using traffic flow parameters, some key models have been used in simulation research19, such as driving behavior models20,21, car following models22,23, U-turn models4, and lane change models21. The accuracy of traffic flow simulations is widely accepted16,24. In this study, both the MUTI and ESUL are simulated with collected data to compare improvements made by the ESUL. To guarantee accuracy, a sensitive analysis of the ESUL is also simulated, which can apply to many different traffic situations.
This protocol presents experimental procedures for solving real traffic problems. The methods for traffic data collection, data analysis, and analysis of overall efficiency of traffic improvements are proposed. The procedure can be summarized in five steps: 1) traffic data collection, 2) data analysis, 3) simulation model build, 4) calibration of simulation model, and 5) sensitivity analysis of operational performance. If any one of these requirements in the five steps is not met, the process is incomplete and insufficient to prove effectiveness.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Battery</td>
			<td>Beijing Aozeer Technology Company</td>
			<td>LPB-568S</td>
			<td>Capacity: 3.7v/50000mAh. Two ports, DC 1 out:19v/5A (max), for one laptop. DC 2 out:12v/3A (max), for one radar.</td>
		</tr>
		<tr>
			<td>Battery Cable</td>
			<td>Beijing Aozeer Technology Company</td>
			<td>No Catalog Number</td>
			<td>Connect one battery with one laptop.</td>
		</tr>
		<tr>
			<td>Camera</td>
			<td>SONY</td>
			<td>a6000/as50r</td>
			<td>The videos shot by the cameras were 1080p, which means the resolution is 1920*1080.</td>
		</tr>
		<tr>
			<td>Camera Tripod</td>
			<td>WEI FENG</td>
			<td>3560/3130</td>
			<td>The camera tripod height is 1.4m.</td>
		</tr>
		<tr>
			<td>Laptop</td>
			<td>Dell</td>
			<td>C2H2L82</td>
			<td>Operate Windows 7 basic system.</td>
		</tr>
		<tr>
			<td>Matlab Software</td>
			<td>MathWorks</td>
			<td>R2016a</td>
			<td></td>
		</tr>
		<tr>
			<td>Radar</td>
			<td>Beijing Aozeer Technology Company</td>
			<td>SD/D CADX-0037</td>
			<td></td>
		</tr>
		<tr>
			<td>Radar Software</td>
			<td>Beijing Aozeer Technology Company</td>
			<td>Datalogger</td>
			<td></td>
		</tr>
		<tr>
			<td>Radar Tripod</td>
			<td>Beijing Aozeer Technology Company</td>
			<td>No Catalog Number</td>
			<td>Corresponding tripods which could connect with radars, the height is 2m at most.</td>
		</tr>
		<tr>
			<td>Reflective Vest</td>
			<td>Customized</td>
			<td>No Catalog Number</td>
			<td></td>
		</tr>
		<tr>
			<td>VISSIM Software</td>
			<td>PTV AG group</td>
			<td>PTV vissim 10.00-07 student version</td>
			<td></td>
		</tr>
	</tbody>
</table>

evaluation of an exclusive spur dike u-turn design with radar-collected data and simulation

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, conflicts, and delays. An exclusive spur dike U-turn lane design (ESUL) is proposed here to solve the disadvantages of traditional U-turn designs. To evaluate the operation performance of ESUL, a traffic simulation protocol is needed. The whole simulation process includes five steps: data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Data collection and simulation model build are two critical steps and are described later in greater detail. Three indexes (travel time, delay, and number of stops) are commonly used in the evaluation, and other parameters can be measured from the simulation according to experimental needs. The results show that the ESUL significantly diminishes the disadvantages of traditional U-turn designs. The simulation can be applied to solve microscopic traffic problems, such as in single or several adjacent intersections or short segments. This method is not suitable for larger scale road networks or evaluations without data collection.

Figure 2 shows the illustration of the ESUL for U-turn median opening. WENS mean four cardinal directions. The main road has six lanes with two directions. Greenbelts divide non-motorized lane on both sides and divide the two directions in the middle. Flow 1 is the east to west through traffic, flow 2 is east to east U-turn flow, flow 3 is west to east through traffic, and flow 4 is west to west U-turn traffic.
<p class="jove_content" fo:keep-together.wit...

Watch this Scientific Journal Video about Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation at JoVE.com

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

This protocol describes the process of solving a microscopic traffic problem with simulation. The whole process contains a detailed description of data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Modifications and troubleshooting of the method are also discussed.

Traditional U-turn designs can improve operational features obviously, while U-turn diversions and merge segments still cause traffic congestion, ...

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20.2K Views.  Chang'an University. This protocol describes the process of solving a microscopic traffic problem with simulation. The whole process contains a detailed description of data collection, data analysis, simulation model build, simulation calibration, and sensitive analysis. Modifications and troubleshooting of the method are also discussed.

Video: Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Simulation, Fabrication and Characterization of THz Metamaterial Absorbers

Metamaterials (MM), artificial materials engineered to have properties that may not be found in nature, have been widely explored since the first theoretical1 and experimental demonstration2 of their unique properties. MMs can provide a highly controllable electromagnetic response, and to date have been demonstrated in every technologically relevant spectral range including the optical3, near IR4, mid IR5 , THz6 , mm-wave7 , microwave8 and radio9 bands. Applications include perfect lenses10, sensors11, telecommunications12, invisibility cloaks13 and filters14,15. We have recently developed single band16, dual band17 and broadband18 THz metamaterial absorber devices capable of greater than 80% absorption at the resonance peak. The concept of a MM absorber is especially important at THz frequencies where it is difficult to find strong frequency selective THz absorbers19. In our MM absorber the THz radiation is absorbed in a thickness of ~ &lambda;/20, overcoming the thickness limitation of traditional quarter wavelength absorbers. MM absorbers naturally lend themselves to THz detection applications, such as thermal sensors, and if integrated with suitable THz sources (e.g. QCLs), could lead to compact, highly sensitive, low cost, real time THz imaging systems.

This protocol outlines the simulation, fabrication and characterization of THz metamaterial absorbers. Such absorbers, when coupled with an appropriate sensor, have applications in THz imaging and spectroscopy.

Metamaterials (MM),  artificial materials engineered to have properties that may not be found in  nature, have been widely explored since the first ...

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute transport in rivers and many important biogeochemical processes. To improve understanding of these processes through visual demonstration, we created a hyporheic flow simulation in the multi-agent computer modeling platform NetLogo. The simulation shows virtual tracer flowing through a streambed covered with two-dimensional bedforms. Sediment, flow, and bedform characteristics are used as input variables for the model. We illustrate how these simulations match experimental observations from laboratory flume experiments based on measured input parameters. Dye is injected into the flume sediments to visualize the porewater flow. For comparison virtual tracer particles are placed at the same locations in the simulation. This coupled simulation and lab experiment has been used successfully in undergraduate and graduate laboratories to directly visualize river-porewater interactions and show how physically-based flow simulations can reproduce environmental phenomena. Students took photographs of the bed through the transparent flume walls and compared them to shapes of the dye at the same times in the simulation. This resulted in very similar trends, which allowed the students to better understand both the flow patterns and the mathematical model. The simulations also allow the user to quickly visualize the impact of each input parameter by running multiple simulations. This process can also be used in research applications to illustrate basic processes, relate interfacial fluxes and porewater transport, and support quantitative process-based modeling.

This manuscript describes how to create regular bedforms in a flume, visualize flow through the bedforms, and use computer simulations to simulate the hyporheic flow. The computer simulations compare well with the experimental observations. This coupled simulation and experiment is well-suited for both research and educational purposes.

Advective exchange between the pore space of sediments and the overlying water column, called hyporheic exchange in fluvial environments, drives solute ...

Study of Siphon Breaker Experiment and Simulation for a Research Reactor

Under the design conditions of a research reactor, the siphon phenomenon induced by pipe rupture can cause continuous outward flow of water. To prevent this outflow, a control device is required. A siphon breaker is a type of safety device that can be utilized to control the loss of coolant water effectively.
To analyze the characteristics of siphon breaking, a real-scale experiment was conducted. From the results of the experiment, it was found that there are several design factors that affect the siphon breaking phenomenon. Therefore, there is a need to develop a theoretical model capable of predicting and analyzing the siphon breaking phenomenon under various design conditions. Using the experimental data, it was possible to formulate a theoretical model that accurately predicts the progress and the result of the siphon breaking phenomenon. The established theoretical model is based on fluid mechanics and incorporates the Chisholm model to analyze two-phase flow. From Bernoulli&#39;s equation, the velocity, quantity, undershooting height, water level, pressure, friction coefficient, and factors related to the two-phase flow could be obtained or calculated. Moreover, to utilize the model established in this study, a siphon breaker analysis and design program was developed. The simulation program operates on the theoretical model basis and returns the result as a graph. The user can confirm the possibility of the siphon breaking by checking the shape of the graph. Furthermore, saving the entire simulation result is possible and it can be used as a resource for analyzing the real siphon breaking system.
In conclusion, the user can confirm the status of the siphon breaking and design the siphon breaker system using the program developed in this study.

The siphon breaking phenomenon was investigated experimentally and a theoretical model was proposed. A simulation program based on the theoretical model was developed and the results of the simulation program were compared with experimental results. It was concluded that the results of the simulation program matched the experimental results well.

Under the design conditions of a research reactor, the siphon phenomenon induced by pipe rupture can cause continuous outward flow of water. To prevent ...

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Intelligent robots are part of a new generation of robots that are able to sense the surrounding environment, plan their own actions and eventually reach their targets. In recent years, reliance upon robots in both daily life and industry has increased. The protocol proposed in this paper describes the design and production of a handling robot with an intelligent search algorithm and an autonomous identification function.
First, the various working modules are mechanically assembled to complete the construction of the work platform and the installation of the robotic manipulator. Then, we design a closed-loop control system and a four-quadrant motor control strategy, with the aid of debugging software, as well as set steering gear identity (ID), baud rate and other working parameters to ensure that the robot achieves the desired dynamic performance and low energy consumption. Next, we debug the sensor to achieve multi-sensor fusion to accurately acquire environmental information. Finally, we implement the relevant algorithm, which can recognize the success of the robot&#39;s function for a given application.
The advantage of this approach is its reliability and flexibility, as the users can develop a variety of hardware construction programs and utilize the comprehensive debugger to implement an intelligent control strategy. This allows users to set personalized requirements based on their needs with high efficiency and robustness.

We present a protocol on modular design and production of intelligent robots to help scientific and technical workers design intelligent robots with special production tasks based on personal needs and individualized design.

Intelligent robots are part of a new generation of robots that are able to sense the surrounding environment, plan their own actions and eventually reach ...

Production and Characterization of Vacuum Deposited Organic Light Emitting Diodes

A method for producing simple and efficient thermally-activated delayed fluorescence organic light-emitting diodes (OLEDs) based on guest-host or exciplex donor-acceptor emitters is presented. With a step-by-step procedure, readers will be able to repeat and produce OLED devices based on simple organic emitters. A patterning procedure allowing the creation of personalized indium tin oxide (ITO) shape is shown. This is followed by the evaporation of all layers, encapsulation and characterization of each individual device. The end goal is to present a procedure that will give the opportunity to repeat the information presented in cited publication but also using different compounds and structures in order to prepare efficient OLEDs.

A protocol for the production of simple structured organic light-emitting diodes (OLEDs) is presented.

A method for producing simple and efficient thermally-activated delayed fluorescence organic light-emitting diodes (OLEDs) based on guest-host or exciplex ...

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle activities during food intake and other physical activities. We fabricated a 3D-printed frame of the glasses and a load cell-integrated printed circuit board (PCB) module inserted in both hinges of the frame. The module was used to acquire the force signals, and transmit them wirelessly. These procedures provide the system with higher mobility, which can be evaluated in practical wearing conditions such as walking and waggling. A performance of the classification is also evaluated by distinguishing the patterns of food intake from those physical activities. A series of algorithms were used to preprocess the signals, generate feature vectors, and recognize the patterns of several featured activities (chewing and winking), and other physical activities (sedentary rest, talking, and walking). The results showed that the average F1 score of the classification among the featured activities was 91.4%. We believe this approach can be potentially useful for automatic and objective monitoring of ingestive behaviors with higher accuracy as practical means to treat ingestive problems.

This study presents a protocol of designing and manufacturing a glasses-type wearable device that detects the patterns of food intake and other featured physical activities using load cells inserted in both hinges of the glasses.

This study presents a series of protocols of designing and manufacturing a glasses-type wearable device that detects the patterns of temporalis muscle ...

Design and Fabrication of an Optical Fiber Made of Water

In this report, an optical fiber of which the core is made solely of water, while the cladding is air, is designed and manufactured. In contrast with solid-cladding devices, capillary oscillations are not restricted, allowing the fiber walls to move and vibrate. The fiber is constructed by a high direct current (DC) voltage of several thousand volts (kV) between two water reservoirs that creates a floating water thread, known as a water bridge. Through the choice of micropipettes, it is possible to control the maximal diameter and length of the fiber. Optical fiber couplers, at both sides of the bridge, activate it as an optical waveguide, allowing researchers to monitor the water fiber capillary body waves through transmission modulation and, therefore, deducing changes in surface tension.
Co-confining two important wave types, capillary and electromagnetic, opens a new path of research in the interactions between light and liquid-wall devices. Water-walled microdevices are a million times softer than their solid counterparts, accordingly improving the response to minute forces.

This protocol describes the design and manufacture of a water bridge and its activation as a water fiber. The experiment demonstrates that capillary resonances of the water fiber modulate its optical transmission.

In this report, an optical fiber of which the core is made solely of water, while the cladding is air, is designed and manufactured. In contrast with ...

A 100 KW Class Applied-field Magnetoplasmadynamic Thruster

Applied-field magnetoplasmadynamic thrusters (AF-MPD thrusters) are hybrid accelerators in which electromagnetic and gas dynamic processes accelerate plasma to high speed; they have considerable potential for future space applications with the significant advantages of high specific impulse and thrust density. In this paper, we present a series of protocols for designing and manufacturing a 100 kW class of AF-MPD thruster with water-cooling structures, a 130 V maximum discharge voltage, a 800 A maximum discharge current, and a 0.25 T maximum strength of magnetic field. A hollow tantalum tungsten cathode acts as the only propellant inlet to inhibit the radial discharge, and it is positioned axially at the rear of the anode in order to relieve anode starvation. A cylindrical divergent copper anode is employed to decrease anode power deposition, where the length has been reduced to decrease the wall-plasma connecting area. Experiments utilized a vacuum system that can achieve a working vacuum of 0.01 Pa for a total propellant mass flow rate lower than 40 mg/s and a target thrust stand. The thruster tests were carried out to measure the effects of the working parameters such as propellant flow rates, the discharge current, and the strength of applied magnetic field on the performance and to allow appropriate analysis. The thruster could be operated continuously for significant periods of time with little erosion on the hollow cathode surface. The maximum power of the thruster is 100 kW, and the performance of this water-cooled configuration is comparable with that of thrusters reported in the literature.

The goal of this protocol is to introduce the design of a 100 kW class applied-field magnetoplasmadynamic thruster and relevant experimental methods.

Applied-field magnetoplasmadynamic thrusters (AF-MPD thrusters) are hybrid accelerators in which electromagnetic and gas dynamic processes accelerate ...

Crack Monitoring in Resonance Fatigue Testing of Welded Specimens Using Digital Image Correlation

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It is intended as a practical and reproducible procedure to identify macroscopic cracks at an early stage and monitor crack propagation during fatigue tests. It consists of strain field measurements at the weld using DIC. Images are taken at fixed load cycle intervals. Cracks become visible in the computed strain field as elevated strains. This way, the whole width of a small-scale specimen can be monitored to detect where and when a crack initiates. Subsequently, it is possible to monitor the development of the crack length. Because the resulting images are saved, the results are verifiable and comparable. The procedure is limited to cracks initiating at the surface and is intended for fatigue tests under laboratory conditions. By visualizing the crack, the presented procedure allows direct observation of macrocracks from their formation until rupture of the specimen.

Digital image correlation is used in fatigue tests on a resonance testing machine to detect macroscopic cracks and monitor crack propagation in welded specimens. Cracks on the specimen surface become visible as increased strains.

A procedure using digital image correlation (DIC) to detect cracks on welded specimens during fatigue tests on resonance testing machines is presented. It ...

Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with slopes of up to 84&#176;. The manufacturing method is valid for the fast pneunet bending actuators in general and might, therefore, be interesting for newcomers to the field of actuator manufacturing. The control of the robot is achieved by means of a pneumatic control box that can provide arbitrary pressures and can be built by only using purchased components, a laser cutter, and a soldering iron. For the walking performance of the robot, the pressure-angle calibration plays a crucial role. Therefore, a semi-automated method for the pressure-angle calibration is presented. At high inclines (&#62; 70&#176;), the robot can no longer reliably fix itself to the walking plane. Therefore, the gait pattern is modified to ensure that the feet can be fixed on the walking plane.

This protocol provides a detailed list of steps to be performed for the manufacturing, control and evaluation of the climbing performance of a gecko-inspired soft robot.

This protocol presents a method for manufacturing, control, and evaluation of the performance of a soft robot that can climb inclined flat surfaces with ...