The authors would like to acknowledge the Scientific Research Program Funded by Shaanxi Provincial Education Department (Program No. 21JK0908).

1. Preparation of the equipment
<ol>
	<li>Ensure that all the equipment required is available: radars, roadside laser device, laptops, batteries, a camera, a drone, a reflective tripod, the corresponding cables, and device tripods.</li>
</ol>
2. Selection of the data collection location (Figure 1)
<ol>
	<li>Select the data collection location. Ensure the location selected is on a two-direction and two-lane road. 
	NOTE: The location choice is key in this research. The two-lane width is easy to observe.</li>
	<li>Ensure that the location does not have any intersections. 
	NOTE: Vehicles coming from a third direction may cause chaos in the observation.</li>
	<li>Ensure that there are no barriers on the road except one parked vehicle placed by investigators. 
	NOTE: Barriers may interrupt vehicle behavior and block radar detection.</li>
	<li>Ensure that there is at least a sight distance and clearance of 300 m. This is required for the radar investigation and the investigators&#39; safety. 
	NOTE: One radar can detect 200 m at the most. The radars are located 100 m upstream and downstream from the parked vehicle in the investigation.</li>
	<li>Ensure that the location is a straight-line segment. 
	&#8203;NOTE: If the segment is not straight, it is not possible to determine whether the vehicle offset is caused by the roadside parking.</li>
</ol>
3. Selection of the investigation time
<ol>
	<li>Select the investigation time. At least 3 h in total is needed, with 1 h during the morning peak, 1 h at the noon, and 1 h during the evening peak23,24,25.</li>
	<li>Obtain the time of the peak traffic volume hour from traffic research reports, traffic police departments, or traffic business companies26 (Figure 2).</li>
	<li>In the absence of traffic reports or analyses as a reference, collect several hours of data during the three periods above, and then select the data with the highest peak traffic volume27,28.</li>
	<li>Use the hour data with the highest traffic volume to conduct data analysis and as an input into the simulation model. Use all 3 h of data for the model verification. 
	&#8203;NOTE: The selected road was flanked by restaurants, and the peak hour for catering is the peak hour for the roadside parking demand. The peak hour for traffic volume is the off-time, and the off-time is also the peak time for catering. Therefore, the peak hour for traffic volume and the peak hour for parking demand are almost synchronous.</li>
</ol>
4. Data collection (Figure 3)
<ol>
	<li>Park the vehicle approximately 20 cm away from the curb in the intended place so that the roadside laser device can be placed.</li>
	<li>Place the reflective tripod at the back of the vehicle. Do not place it too far away to ensure that it does not affect the vehicles&#39; behavior. 
	NOTE: A reflective tripod is necessary to alert and/or avoid collision based on the relevant provisions of the Chinese Road Traffic Safety Law. The tripod is placed a certain distance behind the parked vehicle to alert the vehicles behind that a parked vehicle is in front and, thus, to avoid a collision. The distance between the reflective tripod and the parked vehicle is kept low in order to minimize the effect of the reflective tripod on the behavior of the passing vehicles, so its effect on the study results is negligible.</li>
	<li>Set the radar tripod. Set the tripod at a height of no less than 2 m to avoid signal blockage. Lock the radar with the tripod. Adjust the radar vertically, and turn it toward the parked vehicle. Connect the radar data cable with the laptop USB port. 
	NOTE: One radar is 100 m upstream and one is 100 m downstream of the parked vehicle. Both radars are placed on the same side of the parked vehicle to catch the traffic data.</li>
	<li>Open the radar software, and perform the following steps.
	<ol>
		<li>Click on Communication Check. Select the Serial Port, and click on Connect. Click on Confirm after the software shows Radar Detected.</li>
		<li>Click on Investigation Set Up. Click on Read RLU Time and Set RLU Time. Click on Erase Data Record, and confirm it to clear the internal memory of the radar. Click on Start Investigation, and close the dialog box.</li>
		<li>Click on Real-Time View to check the radar status, and the traffic data should get collected as vehicles pass.</li>
	</ol>
	</li>
	<li>Prepare the roadside laser device and the cable. Connect the roadside laser device data cable with the port. Connect the roadside laser device data cable with the laptop USB port.</li>
	<li>Place the roadside laser device in the middle of the parked vehicle. Rotate the four adjustment columns on the device to level it. 
	NOTE: The roadside laser device must be working under the standard position.</li>
	<li>Open the roadside laser device software, and perform the following operations.
	<ol>
		<li>Click on Communication Check. Select the RLU Serial Port Number, and click on Connect. Click on Confirm after the software shows New RLU Connection Detected.</li>
		<li>Click on View Investigation. When vehicles pass, the traffic flow will be shown in real time.</li>
		<li>Click on Investigation Set Up. Click on Read RLU Time and Set RLU Time, successively. Set the Start Time and End Time, and click on Set Task. Click on Confirm after the software shows RLU Investigation Set Up Succeeded.</li>
		<li>Click on Finish. Click on Device Status to view the status of the roadside laser device.</li>
	</ol>
	</li>
	<li>Set the camera about 30 m upstream of the parked vehicle. 
	NOTE: The traffic data can be collected by radars and the roadside laser device. Traffic operating videos are prepared for data validation.</li>
	<li>Set all the equipment on the double-lane double-side road (here, Dian Zi Yi Road). Check if the radars, roadside laser device, and camera are working well every 5 min. 
	NOTE: Ensure that the time of the laptops and camera are the same as real time. Start two radars, the roadside laser device, and the camera simultaneously at the scheduled time. Two radars facing each other, combined with an intermediate roadside laser device, provide a continuous trajectory of the affected traffic.</li>
	<li>End the data collection, and close the real-time check window in the radar software.
	<ol>
		<li>Click on Investigation Set Up, select End Investigation, and confirm it. Close the dialog box.</li>
		<li>Select Data Download, browse the computer to save the data, and input a name for the file. Click on Open, and then click on Start Download. Click on Confirm to finish the radar data collection.</li>
	</ol>
	</li>
	<li>Click on Device Status in the roadside laser device software, and then click on Stop Task to end the data collection. Select Data Download, browse, and input a name for the file. Click on Open, and click on Start Download. Click on Confirm to finish the data collection of the roadside laser device.</li>
</ol>
5. Data analysis
NOTE: Through data collection, 3 h of data are acquired, including the morning peak, middle noon hour, and evening peak. Playback traffic videos are provided by the camera to calibrate the traffic volumes and vehicle types manually. Select the group data with the highest volume (i.e., the morning peak data in this case) as the representative hour for conducting the data analysis.
<ol>
	<li>Use software to collect the trajectories and speed from the radars. 
	NOTE: The radar is located 100 m away from the parked vehicle, and the road is 10 m wide. So, all the data points beyond that range are radar errors and should be deleted.</li>
	<li>Ensure that the roadside laser device provides the offset value, passing speed, number of vehicles, and the types of vehicles at the parked vehicle position.</li>
	<li>Draw the whole range of trajectories and speed provided by the two radars and one roadside laser device as the representative data using calculation software (Figures 4-6).</li>
</ol>
6. Building the simulation model
NOTE: The microscopic simulation model is established by simulation software for traffic simulation. The results of the data collection, including the traffic volume, vehicle speed, and vehicle type composition, are vital parameters in the traffic simulation and form the basis of the model building. Only the representative data group is needed in the simulation.
<ol>
	<li>Road building
	<ol>
		<li>Open the simulation software. Import the background map of the investigated road segment.</li>
		<li>Click on Obstacles on the left, right-click, and select Add New Obstacle. Input the length and width of the obstacle, and then click on OK. Drag the cursor to move the obstacle to the roadway. 
		NOTE: &#34;Obstacle&#34; refers to the roadside parked vehicle. The length and width of the obstacle are set according to the actual size of the parked vehicle.</li>
		<li>Click on Links on the left, move the cursor to the start of the link, and right-click. Select Add New Link, input the lane width, and click on OK. Drag the cursor to draw the link on the map.</li>
		<li>Repeat step 6.1.3 to build four road segments.</li>
		<li>Hold the right button of the mouse and the Ctrl button on the keyboard to drag the endpoint of one link to the adjacent link to connect the two links. 
		NOTE: This part is called the &#34;connector&#34;, and it becomes smoother when more points are added.</li>
		<li>Repeat step 6.1.5 to connect all the links.</li>
	</ol>
	</li>
	<li>Desired speed
	<ol>
		<li>Select Base Data from the top bar, and then select Distributions | Desired Speed.</li>
		<li>Click on the green-cross Add button at the bottom to add a new desired speed distribution and name it.</li>
		<li>Input the average speed and the maximum speed taken from the representative data as the minimum and maximum desired speeds. Delete the default data.</li>
		<li>Repeat steps 6.2.2-6.2.3 to establish all the desired speed distributions (the direction of east to west, the direction of west to east, and the reduced speed area). 
		NOTE: In the following text, the direction of east to west is abbreviated as E-W, and the direction of west to east is abbreviated as W-E.</li>
	</ol>
	</li>
	<li>Vehicle compositions
	<ol>
		<li>Select Lists from the top bar, and then select Private Transport | Vehicle Compositions.</li>
		<li>Click on the green-cross Add button to add a new vehicle composition.</li>
		<li>Click on the Add button to add two vehicle types: heavy goods vehicles (HGVs) and buses.</li>
		<li>Select the desired speed distribution set in step 6.2 for cars, HGVs, and buses.</li>
		<li>Repeat steps 6.3.2-6.3.4 to establish two vehicle compositions (E-W and W-E). Input the flow of cars, HGVs, and buses from the representative data.</li>
	</ol>
	</li>
	<li>Vehicle routes
	<ol>
		<li>Select Vehicle Routes from the left menu bar.</li>
		<li>Move the cursor to the upstream of one link, right-click, and select Add New Static Vehicle Routing Decision.</li>
		<li>Drag the blue cursor to draw the vehicle routes on the map from real routes in the data collection.</li>
	</ol>
	</li>
	<li>Reduced speed areas
	<ol>
		<li>Select Reduced Speed Areas from the left menu bar.</li>
		<li>Right-click on the area upstream of the parking position, and select Add New Reduced Speed Area. 
		NOTE: The length of the area depends on the data analysis results.</li>
		<li>Right-click at the margin of the screen, select Add, and select the desired speed set in step 6.2 for the reduced speed area as the area speed.</li>
		<li>Repeat steps 6.5.2-6.5.3 to set all the reduced speed areas.</li>
	</ol>
	</li>
	<li>Priority rules
	<ol>
		<li>Select Priority Rules from the left menu bar.</li>
		<li>Right-click on the reduced speed area upstream of the parked vehicle in the W-E direction, and select Add New Priority Rule. Input the minimum gap time and clearance.</li>
		<li>Repeat step 6.6.2 to set the priority rule downstream of the parked vehicle in the E-W direction. 
		NOTE: The setting of priority rules depends on the real traffic operation reflected by the data collection.</li>
	</ol>
	</li>
	<li>Vehicle travel times
	<ol>
		<li>Select Vehicle Travel Times from the left.</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 a vehicle travel time measurement.</li>
		<li>Repeat step 6.7.3 for all the vehicle routes.</li>
	</ol>
	</li>
	<li>Vehicle inputs
	<ol>
		<li>Select Vehicle Inputs from the left. Right-click at the beginning of one link, and select Add New Vehicle Input.</li>
		<li>Move the mouse to the left bottom, and input the volume for the representative data.</li>
		<li>Repeat steps 6.8.1-6.8.2 for all the links.</li>
	</ol>
	</li>
	<li>Nodes
	<ol>
		<li>Select Nodes from the left. Right-click to select Add New Node, and then click on OK.</li>
		<li>Left-click and move the mouse to adjust a moderate node range. 
		NOTE: The node range is related to the simulation results and depends on the road section geometry.</li>
	</ol>
	</li>
	<li>Click on Evaluation at the top of the simulation interface, and select Result Lists. Click on Nodes Results and Vehicle Travel Time Results.</li>
	<li>Click on the blue play button at the top to start the simulation. Click on the device button Quick Mode to maximize the simulation speed.</li>
	<li>After the simulation, the node results and vehicle travel time results are shown at the bottom of the interface, including the maximum queue length, parking times, delay, number of vehicles, fuel consumption, CO emissions, NO emissions, VOC emissions, and travel time.</li>
</ol>
7. Simulation model calibration
NOTE: In this study, the traffic observations showed that the morning peak data had the highest volume, but the three data groups were simulated for verification to fully illustrate the simulation model&#39;s reliability.
<ol>
	<li>Input the collected data into the simulation model, run the simulation, and obtain the simulation result (Figure 7A). 
	NOTE: The simulation volume can be generated from the simulation result.</li>
	<li>Compare the simulation volume with the collected volume. 
	NOTE: Calculate the capacity using Equation 1: 
	<img alt="Equation 1" src="/files/ftp_upload/63384/63384eq01.jpg" style="margin: auto;" />&#160; &#160; &#160;(1) 
	where C denotes the ideal capacity (veh/h), and ht&#160;denotes the average minimum headway (s). 
	NOTE: The difference between the collected volume and simulation volume is called the mean absolute percent error (MAPE), as shown in Equation 2: 
	<img alt="Equation 2" src="/files/ftp_upload/63384/63384eq02.jpg" style="margin: auto;" />&#160; &#160; &#160; (2) 
	where n denotes the four different flows in this study, <img alt="Equation 3" src="/files/ftp_upload/63384/63384eq03.jpg" style="margin: auto;" /> is the capacity simulated in the simulation model (veh/h), and <img alt="Equation 4" src="/files/ftp_upload/63384/63384eq04.jpg" style="margin: auto;" /> is the capacity of the investigation (veh/h). The calculated MAPE is listed in Table 2. 
	NOTE: The simulation accuracy is acceptable when the MAPE is small.</li>
</ol>
8. Sensitivity analysis
NOTE: Figure 7B shows the sensitivity analysis process. The sensitivity analysis process only reflects the performance of the collected data (Table 3). To understand situations with different traffic volumes in real-time scenarios, all possible traffic volume combinations are input into the simulation model to ensure that all situations are covered in the roadside parking analysis (Figure 8 and Table 4).
<ol>
	<li>Ensure that the representative data contains three groups of data (i.e., W-E volume, E-W volume, and other parameters).</li>
	<li>Divide the W-E volume into six categories, divide the E-W volume into seven categories, and keep the other parameters stable in the simulation. 
	NOTE: The W-E traffic volume was 150-400 veh/h, with an increase of 50 veh/h during the peak hour, and the E-W traffic volume was 150-450 veh/h, with an increase of 50 veh/h during the peak hour. The maximum service traffic volume of one lane in the urban street was 1,140 veh/h.</li>
	<li>Simulate 42 situations, and verify the effectiveness under all situations.</li>
</ol>

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H., 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+operational+features+of+three+unconventional+intersections+under+heavy+traffic+based+on+CRITIC+method.">Evaluating operational features of three unconventional intersections under heavy traffic based on CRITIC method.</a> Sustainability. 13 (8), 4098 (2021).</li><li>Sun, 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> Guideline for Microscopic Traffic Simulation Analysis. , (2014).</li><li>Koohpayma, J., Tahooni, A., Jelokhani, N. M., Jokar, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+analysis+of+curb-park+violations+and+their+relationship+with+points+of+interest:+A+case+study+of+Tehran,+Iran.">Spatial analysis of curb-park violations and their relationship with points of interest: A case study of Tehran, Iran.</a> Sustainability. 11 (22), 6336 (2019).</li><li>Zoika, S., Tzouras, P. G., Tsigdinos, S., Kepaptsoglou, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Causal+analysis+of+illegal+parking+in+urban+roads:+The+case+of+Greece.">Causal analysis of illegal parking in urban roads: The case of Greece.</a> Case Studies on Transport Policy. 9 (3), 1084-1096 (2021).</li></ol>

The authors have nothing to disclose.

The effect of roadside parking on urban streets cannot be ignored, and random parking needs to be addressed30,31. A protocol to determine the impact of roadside parking on traffic flow in a dual-direction urban street is presented here. The data collection specifies the trajectory and speed changes of passing vehicles caused by roadside parking. The traffic simulation quantifies roadway indices such as maximum queue length, delay, and emissions.
<p class="jov...

The acceleration of urbanization is accompanied by an obvious increase in motor vehicle ownership and urban traffic flow. In 2021, China&#39;s car ownership reached 378 million, representing an increase of 25.1 million compared to that in 20201. However, the current situation with insufficient road capacity and limited traffic management technology has led to an increasingly evident discrepancy between urban traffic supply and demand. Therefore, road traffic congestion has gradually intensified. As the most widespread problem in urban transportation, traffic congestion causes many hazards and has attracted wide attention from researchers2,3,4. In addition to extending travel time, traffic congestion also aggravates environmental pollution, intensifies energy consumption, and increases pollutant emissions5,6,7,8. There is a positive correlation between traffic congestion and accident rates9,10. Apart from the abovementioned effects, increasing traffic congestion undercuts income and employment11, and this effect is closely related to people&#39;s daily life thereby making this one of the main problems in cities. With the development of cities, the adverse impact of road congestion on society will continue to increase.
Traffic congestion is a comprehensive reflection of many urban traffic problems, among which parking is the major one. The expansion of the urban population and the increase in motor vehicles have a negative impact on the parking supply and outstanding parking demand. In the parking system, roadside parking is common in urban traffic and is an important means of addressing the imbalance between parking supply and demand. Roadside parking utilizes resources on both sides of the road to provide parking spaces. Roadside parking is convenient, quick, flexible, and space-saving compared to other parking facilities. However, roadside parking occupies road resources, and its adverse effects cannot be ignored. In cities undergoing rapid development in developing countries, the soaring parking demands make roadside parking overloaded, thus reducing traffic safety, air quality, and public space12. Therefore, the roadside parking issue needs to be addressed.
Roadside parking space can be located in two scenarios: (1) the non-motorized lane (i.e., on wide roads with separate motorized and non-motorized lanes, roadside parking takes up space on the rightmost non-motorized lane); and (2) the motor vehicle and non-motor vehicle mixed lane, which is often a narrow road with a low traffic volume. As motor and non-motor vehicles share road resources, roadside parking frequently leads to chaos in traffic operations in the second scenario. However, most existing studies have focused on the first scenario13,14,15,16,17,18.
When a roadside parking space is present in the non-motorized lane, and if there is no compulsory isolation of the motorized and non-motorized lanes, roadside parking indirectly leads to mixed traffic. A roadside parking space significantly decreases the effective width of the non-motorized lane, thereby increasing the probability of non-motor vehicles passing through the non-motorized lane and occupying the adjacent motorized lane. The behavior is called lane-crossing16. Many studies have explored the impact of roadside parking in the non-motorized lane on mixed traffic flow. Based on the cellular automata model, Chen et al.13 evaluated the impact of roadside parking on heterogeneous traffic operations in urban streets through the study of friction and congestion conflicts between motor and non-motor vehicles13. Chen et al. proposed a road resistance model of mixed traffic flow by considering the effect of roadside parking17. In addition, some studies have examined the impact of roadside parking only on motor vehicles. Guo et al. proposed a method based on risk duration, which was used to quantitatively analyze the driving time of motor vehicles on roadside parking sections19, and the results showed that roadside parking significantly impacted travel time.
Traffic simulation is a common tool to investigate the impact of roadside parking. Yang et al.&#160;used VISSIM software to explore the impact of roadside parking on dynamic traffic (especially on the capacity), developed a vehicle average delay traffic model, and verified the model reliability through simulation20. Gao et al. analyzed the effect of roadside parking on mixed traffic under four types of traffic interference using the same software18. Guo et al. used a cellular automata model to analyze the influence of roadside parking on vehicle traffic characteristics (lane capacity and vehicle speed) through Monte Carlo simulation under different scenarios21. Under the framework of Kerner&#39;s three-phase traffic theory, Hu et al. analyzed the impact of temporary roadside parking behavior on traffic flow based on the cellular automata model22. These studies show that roadside parking has a large negative impact on traffic efficiency.
The traffic management department is interested in understanding the effect of roadside-parked vehicles on the traffic flow. The specific length and degree of the effect are important for managing issues with roadside parking, for example, by providing information on how to delimit parking lots, determine non-parking zones, and regulate parking durations. In this study, a protocol was designed to examine the effect of a single roadside-parked vehicle on traffic operation. The procedure can be summarized in the following steps: 1) preparing the equipment, 2) selecting the data collection location, 3) selecting the investigation time, 4) collecting the data, 5) performing the data analysis, 6) building the simulation model, 7) calibrating the simulation model, and 8) performing the sensitivity analysis. If any requirement in these eight steps is not satisfied, 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>Shenzhen Saiqi Innovation Technology Co., Ltd</td>
			<td>LPB-568S</td>
			<td></td>
		</tr>
		<tr>
			<td>cables for radar</td>
			<td>BEIJING AOZER TECH &#38; DEVELOPMENT CO.,LTD</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>cables for roadside laser device</td>
			<td>MicroSense</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>camera</td>
			<td>Sony Group Corp</td>
			<td>HDR-CS680</td>
			<td></td>
		</tr>
		<tr>
			<td>camera tripod</td>
			<td>Sony Group Corp</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>drone</td>
			<td>SZ DJI Technology Co.,Ltd.</td>
			<td>DA2SUE1</td>
			<td></td>
		</tr>
		<tr>
			<td>laptop</td>
			<td>Dell</td>
			<td>C2H2L82</td>
			<td></td>
		</tr>
		<tr>
			<td>radar</td>
			<td>BEIJING AOZER TECH &#38; DEVELOPMENT CO.,LTD</td>
			<td>CADS-0037</td>
			<td></td>
		</tr>
		<tr>
			<td>radar tripod</td>
			<td>BEIJING AOZER TECH &#38; DEVELOPMENT CO.,LTD</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>reflective tripod</td>
			<td>Beijing Shunan liandun Technology Co., Ltd</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>roadside laser device</td>
			<td>MicroSense</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

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.

This paper presents a protocol to determine the effect of roadside parking on passing vehicles on a two-direction and two-lane urban road through traffic data collection and simulation. A road was selected as the study site (Figure 1), and a vehicle was parked at the planned roadside location. Radars, a roadside laser device, and a camera were applied to collect the vehicle trajectory, speed, volume, and type composition to determine the changes in vehicle trajectory and speed under roadside...

Watch this Scientific Journal Video about Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street at JoVE.com

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

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 ...

evaluating-effect-roadside-parking-on-dual-direction-urban

Research

JoVE Journal

Engineering

3.1K Views.  Chang’an University. 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.

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

Evaluating Plasmonic Transport in Current-carrying Silver Nanowires

Plasmonics is an emerging technology capable of simultaneously transporting a plasmonic signal and an electronic signal on the same information support1,2,3. In this context, metal nanowires are especially desirable for realizing dense routing networks4. A prerequisite to operate such shared nanowire-based platform relies on our ability to electrically contact individual metal nanowires and efficiently excite surface plasmon polaritons5 in this information support. In this article, we describe a protocol to bring electrical terminals to chemically-synthesized silver nanowires6 randomly distributed on a glass substrate7. The positions of the nanowire ends with respect to predefined landmarks are precisely located using standard optical transmission microscopy before encapsulation in an electron-sensitive resist. Trenches representing the electrode layout are subsequently designed by electron-beam lithography. Metal electrodes are then fabricated by thermally evaporating a Cr/Au layer followed by a chemical lift-off. The contacted silver nanowires are finally transferred to a leakage radiation microscope for surface plasmon excitation and characterization8,9. Surface plasmons are launched in the nanowires by focusing a near infrared laser beam on a diffraction-limited spot overlapping one nanowire extremity5,9. For sufficiently large nanowires, the surface plasmon mode leaks into the glass substrate9,10. This leakage radiation is readily detected, imaged, and analyzed in the different conjugate planes in leakage radiation microscopy9,11. The electrical terminals do not affect the plasmon propagation. However, a current-induced morphological deterioration of the nanowire drastically degrades the flow of surface plasmons. The combination of surface plasmon leakage radiation microscopy with a simultaneous analysis of the nanowire electrical transport characteristics reveals the intrinsic limitations of such plasmonic circuitry.

Silver nanowires can simultaneously transport electrons and optical information in the form of surface plasmons. A procedure is described here to realize such a shared circuitry and the limitations at propagating both information carriers are evaluated.

Plasmonics is an emerging technology capable of simultaneously transporting a plasmonic signal and an electronic signal on the same information ...

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 ...

Casting Protocols for the Production of Open Cell Aluminum Foams by the Replication Technique and the Effect on Porosity

Metal foams are interesting materials from both a fundamental understanding and practical applications point of view. Uses have been proposed, and in many cases validated experimentally, for light weight or impact energy absorbing structures, as high surface area heat exchangers or electrodes, as implants to the body, and many more. Although great progress has been made in understanding their structure-properties relationships, the large number of different processing techniques, each producing material with different characteristics and structure, means that understanding of the individual effects of all aspects of structure is not complete. The replication process, where molten metal is infiltrated between grains of a removable preform material, allows a markedly high degree of control and has been used to good effect to elucidate some of these relationships. Nevertheless, the process has many steps that are dependent on individual &#8220;know-how&#8221;, and this paper aims to provide a detailed description of all stages of one embodiment of this processing method, using materials and equipment that would be relatively easy to set up in a research environment. The goal of this protocol and its variants is to produce metal foams in an effective and simple way, giving the possibility to tailor the outcome of the samples by modifying certain steps within the process. By following this, open cell aluminum foams with pore sizes of 1&#8211;2.36 mm diameter and 61% to 77% porosity can be obtained.

Replication is one of the processing techniques used for the production of porous metal sponges. In this paper one implementation of the method for the production of open celled porous aluminum is shown in detail.

Metal foams are interesting materials from both a fundamental understanding and practical applications point of view. Uses have been proposed, and in many ...

Experiments on Ultrasonic Lubrication Using a Piezoelectrically-assisted Tribometer and Optical Profilometer

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at a frequency above the acoustic range (20 kHz). As a solid-state technology, ultrasonic lubrication can be used where conventional lubricants are unfeasible or undesirable. Further, ultrasonic lubrication allows for electrical modulation of the effective friction coefficient between two sliding surfaces. This property enables adaptive systems that modify their frictional state and associated dynamic response as the operating conditions change. Surface wear can also be reduced through ultrasonic lubrication. We developed a protocol to investigate the dependence of friction force reduction and wear reduction on the linear sliding velocity between ultrasonically lubricated surfaces. A pin-on-disc tribometer was built which differs from commercial units in that a piezoelectric stack is used to vibrate the pin at 22 kHz normal to the rotating disc surface. Friction and wear metrics including effective friction force, volume loss, and surface roughness are measured without and with ultrasonic vibrations at a constant pressure of 1 to 4 MPa and three different sliding velocities: 20.3, 40.6, and 87 mm/sec. An optical profilometer is utilized to characterize the wear surfaces. The effective friction force is reduced by 62% at 20.3 mm/sec. Consistently with existing theories for ultrasonic lubrication, the percent reduction in friction force diminishes with increasing speed, down to 29% friction force reduction at 87 mm/sec. Wear reduction remains essentially constant (49%) at the three speeds considered.

We present a protocol for using a piezoelectrically-assisted tribometer and optical profilometer to investigate the dependence of ultrasonic wear and friction reduction on linear velocity, contact pressure, and surface properties.

Friction and wear are detrimental to engineered systems. Ultrasonic lubrication is achieved when the interface between two sliding surfaces is vibrated at ...

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in industrialized countries. Here, the development of optical mesosensors/adsorbents (MSAs) for efficient recognition and selective recovery of Pd(II), Au(III), and Co(II) from urban mine was achieved. A simple, general method for preparing MSAs based on using high-order mesoporous monolithic scaffolds was described. Hierarchical cubic Ia3d wagon-wheel-shaped MSAs were fabricated by anchoring chelating agents (colorants) into three-dimensional pores and micrometric particle surfaces of the mesoporous monolithic scaffolds. Findings show, for the first time, evidence of controlled optical recognition of Pd(II), Au(III), and Co(II) ions and a highly selective system for recovery of Pd(II) ions (up to ~95%) in ores and industrial wastes. Furthermore, the controlled assessment processes described herein involve evaluation of intrinsic properties (e.g., visual signal change, long-term stability, adsorption efficiency, extraordinary sensitivity, selectivity, and reusability); thus, expensive, sophisticated instruments are not required. Results show evidence that MSAs will attract worldwide attention as a promising technological means of recovering and recycling palladium, gold and cobalt metals.

Because of the importance and extensive use of palladium, gold and cobalt metals in high-technology equipment, their recovery and recycling constitute an important industrial challenge. The metal recovery system described herein is a simple, low-cost means for the effective detection, removal, and recovery of these metals from the urban mine.

Developing low-cost, efficient processes for recovering and recycling palladium, gold and cobalt metals from urban mine remains a significant challenge in ...

Effect of Bending on the Electrical Characteristics of Flexible Organic Single Crystal-based Field-effect Transistors

The charge transport in an organic semiconductor depends highly on the molecular packing in the crystal, which influences the electronic coupling immensely. However, in soft electronics, in which organic semiconductors play a critical role, the devices will be bent or folded repeatedly. The effect of bending on the crystal packing and thus the charge transport is crucial to the performance of the device. In this manuscript, we describe the protocol to bend a single crystal of 5,7,12,16-tetrachloro-6,13-diazapentacene (TCDAP) in the field-effect transistor configuration and to obtain reproducible I-V characteristics upon bending the crystal. The results show that bending a field-effect transistor prepared on a flexible substrate results in nearly reversible yet opposite trends in charge mobility, depending on the bending direction. The mobility increases when the device is bent toward the top gate/dielectric layer (upward, compressive state) and decreases when bent toward the crystal/substrate side (downward, tensile state). The effect of bending curvature was also observed, with greater mobility change resulting from higher bending curvature. It is suggested that the intermolecular &#960;-&#960; distance changes upon bending, thereby influencing the electronic coupling and the subsequent carrier transport ability.

This manuscript describes the bending process of an organic single crystal-based field-effect transistor to maintain a functioning device for electronic property measurement. The results suggest that bending causes changes in the molecular spacing in the crystal and thus in the charge hopping rate, which is important in flexible electronics.

The charge transport in an organic semiconductor depends highly on the molecular packing in the crystal, which influences the electronic coupling ...

Experimental Implementation of a New Composite Fabrication Method: Exposing Bare Fibers on the Composite Surface by the Soft Layer Method

The bipolar plate is a key component in proton exchange membrane fuel cells (PEMFCs) and vanadium redox flow batteries (VRFBs). It is a multi-functional component that should have high electrical conductivity, high mechanical properties, and high productivity.
In this regard, a carbon fiber/epoxy resin composite can be an ideal material to replace the conventional graphite bipolar plate, which often leads to the catastrophic failure of the entire system because of its inherent brittleness. Though the carbon/epoxy composite has high mechanical properties and is easy to manufacture, the electrical conductivity in the through-thickness direction is poor because of the resin-rich layer that forms on its surface. Therefore, an expanded graphite coating was adopted to solve the electrical conductivity issue. However, the expanded graphite coating not only increases the manufacturing costs but also has poor mechanical properties.
In this study, a method to expose fibers on the composite surface is demonstrated. There are currently many methods that can expose fibers by surface treatment after the fabrication of the composite. This new method, however, does not require surface treatment because the fibers are exposed during the manufacture of the composite. By exposing bare carbon fibers on the surface, the electrical conductivity and mechanical strength of the composite are increased drastically.

A protocol to expose bare fibers on the composite surface by eliminating resin rich area is presented. The fibers are exposed during fabrication of the composites, not by the post surface treatment. The exposed carbon composites exhibit high electrical conductivity in the through-thickness direction and high mechanical property.

The bipolar plate is a key component in proton exchange membrane fuel cells (PEMFCs) and vanadium redox flow batteries (VRFBs). It is a multi-functional ...

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 ...

Predicting Catalyst Extrudate Breakage Based on the Modulus of Rupture

The mechanical strength of extruded catalysts and their natural or forced breakage by either collision against a surface or by a compressive load in a fixed bed are important phenomena in catalyst technology. The mechanical strength of the catalyst is measured here by its bending strength or flexural strength. This technique is relatively new from the perspective of applying it to commercial catalysts of typical sizes used in the industry. Catalyst breakage by collision against a surface is measured after a fall of the extrudates through the ambient air in a vertical pipe. Quantifying the impact force is done theoretically by applying Newton's second law. Measurement of catalyst breakage due to stress in a fixed bed is done following the standard procedure of the bulk crush strength test. Novel here is the focus on measuring the reduction in the length to diameter ratio of the extrudates as a function of the stress.

Here we present a protocol to measure the modulus of rupture of an extruded catalyst and the breakage of said catalyst extrudates by collision against a surface or by compression in a fixed bed.

The mechanical strength of extruded catalysts and their natural or forced breakage by either collision against a surface or by a compressive load in a ...

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Resistive switching crossbar architecture is highly desired in the field of digital memories due to low cost and high-density benefits. Different materials show variability in resistive switching properties due to the intrinsic nature of the material used, leading to discrepancies in the field because of underlying operation mechanisms. This highlights a need for a reliable technique to understand mechanisms using nanostructural observations. This protocol explains a detailed process and methodology of in&#160;situ nanostructural analysis as a result of electrical biasing using transmission electron microscopy (TEM). It provides visual and reliable evidence of underlying nanostructural changes in real time memory operations. Also included is the methodology of fabrication and electrical characterizations for asymmetric crossbar structures incorporating amorphous vanadium oxide. The protocol explained here for vanadium oxide films can be easily extended to any other materials in a metal-dielectric-metal sandwiched structure. Resistive switching crossbars are predicted to serve the programmable logic and neuromorphic circuits for next-generation memory devices, given the understanding of the operation mechanisms. This protocol reveals the switching mechanism in a reliable, timely, and cost-effective way in any type of resistive switching materials, and thereby predicts the device&#39;s applicability.

In Situ Transmission Electron Microscopy with Biasing and Fabrication of Asymmetric Crossbars Based on Mixed-Phased a-VOx

Presented here is a protocol for analyzing nanostructural changes during in&#160;situ biasing with transmission electron microscopy (TEM) for a stacked metal-insulator-metal structure. It has significant applications in resistive switching crossbars for the next generation of programmable logic circuits and neuromimicking hardware, to reveal their underlying operation mechanisms and practical applicability.