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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Q., Wang, Y. P., Yang, X. B., Huang, H. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+multilane+traffic+flow+model+accounting+for+lane+width,+lanechanging+and+the+number+of+lanes.">A multilane traffic flow model accounting for lane width, lanechanging and the number of lanes.</a> Networks and Spatial Economics. 14 (14), 465-483 (2014).</li><li>Gupta, A. 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><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.
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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, ...

evaluation-an-exclusive-spur-dike-u-turn-design-with-radar-collected

Research

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Civil Engineering

Engineering

Unit 1

Angles and Directions

SCIENTISTS IN ACTION

20.3K Views.  Chang'an University. Deze video is gericht op het proces van het verzamelen van verkeersgegevens door radars en evaluatie door simulatie model. Elke keer als we een U-bocht mediane opening passeren, heb ik een manier gevonden voor auto's om te vertragen en zelfs te stoppen. Dus we willen een speciale U-bocht rijstrook ontwerpen om de door- en u-bochtvoertuigen uit elkaar te halen, en om het verkeersprobleem veroorzaakt door de U-bocht voertuigen te verminderen.We gebruiken radars om echte verkeersgegevens ...

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

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

We propose a method for increasing the resolution of an object and overcoming the diffraction limit of an optical system installed on top of a moving imaging system, such as an airborne platform or satellite. The resolution improvement is obtained in a two-step process. First, three low resolution differently defocused images are being captured and the optical phase is retrieved using an improved iterative Gerchberg-Saxton based algorithm. The phase retrieval allows to numerically back propagate the field to the aperture plane. Second, the imaging system is shifted and the first step is repeated. The obtained optical fields at the aperture plane are combined and a synthetically increased lens aperture is generated along the direction of movement, yielding higher imaging resolution. The method resembles a well-known approach from the microwave regime called the Synthetic Aperture Radar (SAR) in which the antenna size is synthetically increased along the platform propagation direction. The proposed method is demonstrated through laboratory experiment.

A method for overcoming the optical diffraction limit is presented. The method includes a two-step process: optical phase retrieval using iterative Gerchberg-Saxton algorithm, and imaging system shifting followed by repetition of the first step. A synthetically increased lens aperture is generated along the direction of movement, yielding higher imaging resolution.

We propose a method for increasing the resolution of an object and overcoming the diffraction limit of an optical system installed on top of a moving ...

Kinematic History of a Salient-recess Junction Explored through a Combined Approach of Field Data and Analog Sandbox Modeling

Within fold-thrust belts, the junctions between salients and recesses may hold critical clues to the overall kinematic history. The deformation history within these junctions is best preserved in areas where thrust sheets extend from a salient through an adjacent recess. We examine one such junction within the Sevier fold-thrust belt (western United States) along the Leamington transverse zone, northern Utah. Deformation within this junction took place by faulting and cataclastic flow. Here, we describe a protocol that examines these fault patterns to better understand the kinematic history of the field area. Fault data is supplemented by analog sandbox experiments. This study suggests that, in detail, deformation within the overlying thrust sheet may not directly reflect the underlying basement structure. We demonstrate that this combined field-experimental approach is easy, accessible, and may provide more details to the deformation preserved in the crust than other more expensive methods, such as computer modeling. In addition, the sandbox model may help to explain why and how these details formed. This method can be applied throughout fold-thrust belts, where upper-crustal rocks are well preserved. In addition, it can be modified to study any part of the upper crust that has been deformed via elastico-frictional mechanisms. Finally, this combined approach may provide more details as to how fold-thrust belts maintain critical-taper and serve as potential targets for natural resource exploration.

Kinematic histories of fold-thrust belts are typically based on careful examinations of high-grade metamorphic rocks within a salient. We provide a novel method of understanding fold-thrust belts by examining salient-recess junctions. We analyze the oft-ignored upper crustal rocks using a combined approach of detailed fault analysis with experimental sandbox modeling.

Within fold-thrust belts, the junctions between salients and recesses may hold critical clues to the overall kinematic history. The deformation history ...

Environment

Research and Development of High-performance Explosives

Developmental testing of high explosives for military applications involves small-scale formulation, safety testing, and finally detonation performance tests to verify theoretical calculations. For newly developed formulations, the process begins with small-scale mixes, thermal testing, and impact and friction sensitivity. Only then do subsequent larger scale formulations proceed to detonation testing, which will be covered in this paper. Recent advances in characterization techniques have led to unparalleled precision in the characterization of early-time evolution of detonations. The new technique of Photonic Doppler Velocimetry (PDV) for the measurement of detonation pressure will be shared and compared with traditional fiber-optic detonation velocity and plate-dent calculation of detonation pressure. In particular, the role of aluminum in explosive formulations will be discussed. Recent developments led to the development of explosive formulations that result in reaction of aluminum very early in the detonation product expansion. This enhanced reaction leads to changes in the detonation velocity and pressure due to reaction of the aluminum with oxygen in the expanding gas products.

Developmental testing of high explosives for military applications involves small-scale formulation, safety testing, and finally detonation performance tests to verify theoretical calculations. This paper will share typical development tests associated with the measurement of detonation velocity and detonation pressure.

Developmental testing of high explosives for military applications involves small-scale formulation, safety testing, and finally detonation performance ...

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration experiment conducted at a test site near the Tottori Sand Dune, Japan. The antenna array used in this study consists of 10 transmitting antennas (Tx) and 11 receiving antennas (Rx). For this experiment, the system was configured to use all possible Tx-Rx pairings, resulting in a Multi-Offset Gather (MOG) consisting of 110 Tx-Rx combinations. The array was left stationary at a position directly above the infiltration area and data were collected every 1.5 seconds using a time-based trigger. Common-Offset Gather (COG) and Common Mid-Point (CMP) data cubes were reconstructed from the MOG data during post-processing. There have been few studies that used time-lapse CMP data to estimate changes in velocity of propagation. In this study, electromagnetic (EM) wave velocity was estimated heuristically at 1-minute intervals from the reconstructed CMP data through curve fitting, using the hyperbola equation. We then proceeded to calculate the depth of the wetting front. The evolution of the wetting front over time obtain through this method is consistent with the observations from a soil moisture sensor which was placed at a depth below 20 cm. The results obtained in this study demonstrate the ability of such array GPR system to monitor a subsurface dynamic process like water infiltration accurately and quantitatively.

Here we present a Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array for monitoring the dynamic process of subsurface water infiltration. A time-lapse radar image of the infiltration process allowed estimating the depth of the wetting front during the course of the infiltration process.

A Ground Penetrating Radar (GPR) system based on a ground-coupled, densely populated antenna array was used to collect data during an infiltration ...

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180&#176; Curved Artery Test Section

The arterial network in the human vasculature comprises of ubiquitously present blood vessels with complex geometries (branches, curvatures and tortuosity). Secondary flow structures are vortical flow patterns that occur in curved arteries due to the combined action of centrifugal forces, adverse pressure gradients and inflow characteristics. Such flow morphologies are greatly affected by pulsatility and multiple harmonics of physiological inflow conditions and vary greatly in size-strength-shape characteristics compared to non-physiological (steady and oscillatory) flows 1 - 7.
Secondary flow structures may ultimately influence the wall shear stress and exposure time of blood-borne particles toward progression of atherosclerosis, restenosis, sensitization of platelets and thrombosis 4 - 6, 8 - 13. Therefore, the ability to detect and characterize these structures under laboratory-controlled conditions is precursor to further clinical investigations.
A common surgical treatment to atherosclerosis is stent implantation, to open up stenosed arteries for unobstructed blood flow. But the concomitant flow perturbations due to stent installations result in multi-scale secondary flow morphologies 4 - 6. Progressively higher order complexities such as asymmetry and loss in coherence can be induced by ensuing stent failures vis-&#224;-vis those under unperturbed flows 5. These stent failures have been classified as &#34;Types I-to-IV&#34; based on failure considerations and clinical severity 14.
This study presents a protocol for the experimental investigation of the complex secondary flow structures due to complete transverse stent fracture and linear displacement of fractured parts (&#34;Type IV&#34;) in a curved artery model. The experimental method involves the implementation of particle image velocimetry (2C-2D PIV) techniques with an archetypal carotid artery inflow waveform, a refractive index matched blood-analog working fluid for phase-averaged measurements 15 - 18. Quantitative identification of secondary flow structures was achieved using concepts of flow physics, critical point theory and a novel wavelet transform algorithm applied to experimental PIV data 5, 6, 19 - 26.

Stent implants in stenosed arterial curvatures are prone to "Type IV" failures involving the complete transverse fracture of stents and linear displacement of the fractured parts. We present a protocol for detection of secondary flow (vortical) structures in a curved artery model, downstream of clinically relevant "Type IV" stent failures.

The arterial network in the human vasculature comprises of ubiquitously present blood vessels with complex geometries (branches, curvatures and ...

Bioengineering

A Novel Single Animal Motor Function Tracking System Using Simple, Readily Available Software

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor deficits. Motor impairments were manually quantified through an open field grid test to measure the gross motor function and through a ladder test to measure the fine motor function. Here, we discuss a technique for the automated quantification of the video-recorded tests using our custom Capadona Behavioral Video Analysis System: Grid and Ladder Test, or BVAS. Leveraging simple and readily available coding software (see the Table of Materials), this program allows for the tracking of a single animal on both the open field grid and the ladder tests. In open field grid tracking, the code thresholds the video for intensity, tracks the position of the rat over the 3 min duration of the grid test, and analyzes the path. It then computes and returns measurements for the total distance traveled, the maximum velocity achieved, the number of left- and right-handed turns, and the total number of grid lines crossed by the rat. In ladder tracking, the code again thresholds the video for intensity, tracks the movement of the rat across the ladder, and returns calculated measurements including the time it took the rat to cross the ladder, the number of paw slips occurring below the plane of the ladder rungs, and the incidence of failures due to stagnation or reversals. We envision that the BVAS developed here can be employed for the analysis of motor function in a variety of applications, including many injury or disease models.

The current study aimed to automate the quantification of motor deficits in rats. The initial evaluation model assesses motor loss resulting from an intracortical microelectrode implantation in the motor cortex. We report on the development and use of a tracking algorithm using easily adaptable, simple, and readily available coding software.

We have recently demonstrated that implanting intracortical microelectrodes in the motor corteces of rats&#160;results in immediate and lasting motor ...

Behavior

Installation Method to Enhance Quality Control for Fiber Reinforced Polymer Spike Anchors

Fiber reinforced polymer (FRP) anchors are a promising way to enhance the performance of externally-bonded FRPs applied to existing structures, as they can delay or even prevent debonding failure. However, a major concern faced by designers is the premature failure of the anchors due to stress concentration. Poor installation quality and preparation of the clearance holes can result in stress concentration that provokes this premature failure. This paper deals with an installation method that aims to reduce the impact of the stress concentration and to provide a proper quality control of the preparation of the drill hole. The method involves three parts: the drilling and cleaning of the holes, the smoothing of the hole edges with a customized drill bit, and the installation of the anchor itself, including the impregnation of the anchor dowel and its insertion. Anchor fans (the free length of the spikes) are then bonded to the external FRP reinforcement. For the end anchorage, and in the case of reinforcements with multiple plies, it is recommended that the anchor fan be inserted between two plies to assist the stress-transfer mechanism. 
The proposed procedure is complemented with a design approach for spike anchors, based on an extensive database. It is proposed that the design follow a number of steps, namely: selection of the anchor diameter and subsequent tensile strength of the connector (that is to say, the anchor before fanning out the free end), evaluation of the reduction in the tensile strength due to bending, provision of enough embedment to prevent slippage failure, and consideration of the number and spacing of anchors for a given reinforcement. In this sense, it should be noted that further research is needed in order to obtain a general expression for the contribution of spike anchors to overall bond strength of FRP reinforcements.

This manuscript presents a method for controlling the quality of installation for spike anchors designed to delay delamination of externally bonded fiber reinforced polymers. The protocol includes the preparation of the drill hole and the insertion process. The most influential parameters on the efficiency of the anchors are discussed.

Fiber reinforced polymer (FRP) anchors are a promising way to enhance the performance of externally-bonded FRPs applied to existing structures, as they ...

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Electro-hydrostatic actuators (EHAs) have been considerably researched in academia, and their applications in various industrial fields are expanding. The variable-speed EHA has now taken priority over the variable-displacement EHA, but its driving motor and associated electronics encounter issues when applied in high-power applications: low-dynamics, high thermal dissipation, high price, etc. Therefore, a variable-displacement EHA equipped with an electro-variable displacement pump (EVDP) has been considered. The EVDP itself is a mechatronic system that integrates a piston pump, a ball screw, a gearbox, and a permanent magnet synchronous motor (PMSM). Consequently, the EVDP needs to be investigated to ensure its system-level performance when applied in an EHA. In addition to the previous research on the technical parameters of the EVDP, a dedicated design method is necessary for further reducing the cost of using the EVDP and exploring its performance potential. Here, a simulation based EVDP preliminary design method is selected for designing a 37 kW EVDP. Firstly, a previously proposed multidisciplinary model of the EVDP is extended by improving the parameter generation, including the EVDP life, reliability, control models, etc. Secondly, the proposed model is partially verified using a downsized prototype. Thirdly, the EVDP is simulated at a system level, supported by the proposed model. The EVDP performance is evaluated according to the specified design requirements. The temperature, bandwidth and accuracy, reliability and lifetime, etc., are all predicted for the EVDP. The simulation results demonstrate the EVDP's applicability in variable-displacement EHA. The proposed modeling and simulation method can be used to evaluate diverse EVDP performance and respond to general design requirements. The method can also support the resolution of the preliminary design challenges in terms of limited information and robustness. Therefore, the proposed method is appropriate for the realization of the simulation-based EVDP preliminary design method.

A simulation model specifically supporting the preliminary design of an electro-variable displacement pump (EVDP) is developed and partially verified by experiments. The control performance, life, reliability, etc., can all be evaluated using the proposed model, which covers the main performance requirements under the EVDP preliminary design task.

Electro-hydrostatic actuators (EHAs) have been considerably researched in academia, and their applications in various industrial fields are expanding. The ...

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

The electro-hydrostatic actuator (EHA) is a promising actuating apparatus used in flight control systems for more electric aircraft (MEA) due to its high power density and low maintenance. Since the reliability of the system decreases with increasing complexity, fault detection is becoming increasingly important. In this paper, an adaptive filter was designed based on a normalized least mean square (NLMS) algorithm, which could identify the resistance of the motor windings online to detect electrical faults in the EHA. Additionally, based on the analytical relationship between rotational speed and displacement, a rotational speed estimation method was designed. By comparing the actual rotational speed with the estimated one, hydraulic faults could be detected. To verify the efficacy of the aforementioned method, software was applied for the modeling and simulations, which included fault injection and detection. On this basis, an experimental platform was built and then subjected to a series of validation experiments. The results indicate that the fault detection method has the potential to detect electrical and hydraulic faults in an EHA.

In this paper, an adaptive filter based on a normalized least mean square (NLMS) algorithm and a rotational speed estimation method are introduced to detect the electrical and hydraulic faults of the electro-hydrostatic actuator (EHA). The efficacy and feasibility of the aforementioned methods are verified through simulations and experiments.

The electro-hydrostatic actuator (EHA) is a promising actuating apparatus used in flight control systems for more electric aircraft (MEA) due to its high ...

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Roadside parking is a common traffic phenomenon in China. Narrow urban streets, high parking demands, and a shortage of parking lots force the public to engage in random parking along the roadside. A protocol is proposed to determine the impact of a roadside-parked vehicle on passing vehicles. In this investigation, a dual-direction and two-lane urban street in which one vehicle is parked on the roadside is selected for the collection of traffic data. Based on these data, the impact of the roadside-parked vehicles on the trajectory and speed of passing vehicles is determined. In addition, a microsimulation model is applied to determine the impact of roadside parking on the maximum queue length, delay, emissions, and other indicators under different traffic volumes according to the sensitivity analysis. The results show that roadside-parked vehicles affect the trajectory of passing vehicles for approximately 80 m and have a negative effect on speed, with the lowest speed being observed at the location of the roadside-parked vehicle. The sensitivity analysis results suggest that traffic volume increases synchronously with indicator values. The protocol provides a method for determining the effect of roadside parking on travel trajectory and speed. The research contributes to the refined management of future roadside parking.

In this study, the effect of roadside parking on an urban street is analyzed. The entire process consists of traffic data gathering, data processing, operation simulation, simulation calibration, and sensitivity analysis.

Roadside parking is a common traffic phenomenon in China. Narrow urban streets, high parking demands, and a shortage of parking lots force the public to ...

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

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