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>

<ol>
	<li>He, Y. X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+traffic+capacity+model+of+lane+occupation.">A traffic capacity model of lane occupation.</a> Applied Mechanics and Materials. 599-601, 2083-2087 (2014).</li><li>Hua, S. Y., Wang, J. L., Zhu, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cause+analysis+and+countermeasures+of+Beijing+city+congestion.">Cause analysis and countermeasures of Beijing city congestion.</a> Procedia-Social and Behavioral Sciences. 96, 1426-1432 (2013).</li><li>Yang, H. X., Li, J. D., Zhang, H., Liu, S. Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+on+the+governance+of+urban+traffic+jam+based+on+system+dynamics.">Research on the governance of urban traffic jam based on system dynamics.</a> Systems Engineering-Theory &amp; Practice. 34 (8), 2135-2143 (2014).</li><li>Raj&#233;, F., Tight, M., Pope, F. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Traffic+pollution:+A+search+for+solutions+for+a+city+like+Nairobi.">Traffic pollution: A search for solutions for a city like Nairobi.</a> Cities. 82, 100-107 (2018).</li><li>Abdull, N., Yoneda, M., Shimada, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Traffic+characteristics+and+pollutant+emission+from+road+transport+in+urban+area.">Traffic characteristics and pollutant emission from road transport in urban area.</a> Air Quality, Atmosphere &amp; Health. 13 (6), 731-738 (2020).</li><li>Shi, K., Di, B. F., Zhang, K. S., Feng, C. Y., Svirchev, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detrended+cross-correlation+analysis+of+urban+traffic+congestion+and+NO+2+concentrations+in+Chengdu.">Detrended cross-correlation analysis of urban traffic congestion and NO 2 concentrations in Chengdu.</a> Transportation Research Part D: Transport and Environment. 61, 165-173 (2018).</li><li>Lu, Q. Y., Chai, J., Wang, S. Y., Zhang, Z. G., Sun, X. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+energy+conservation+and+CO2+emissions+reduction+related+to+China's+road+transportation.">Potential energy conservation and CO2 emissions reduction related to China's road transportation.</a> Journal of Cleaner Production. 245, 118892 (2020).</li><li>S&#225;nchez Gonz&#225;lez, S., Bedoya-Maya, F., Calatayud, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+the+effect+of+traffic+congestion+on+accidents+using+big+data.">Understanding the effect of traffic congestion on accidents using big data.</a> Sustainability. 13 (13), 7500 (2021).</li><li>Fuente, J., Rolloque, A. C., Azas, P., Alcantara, M. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Young+road+safety+advocate+program,+the+&amp;#34;peer+to+peer&amp;#34;+approach+in+teaching+pedestrian+safety.">Young road safety advocate program, the &amp;#34;peer to peer&amp;#34; approach in teaching pedestrian safety.</a> Injury Prevention. 22, 67 (2016).</li><li>Jin, J., Rafferty, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Does+congestion+negatively+affect+income+growth+and+employment+growth?+Empirical+evidence+from+US+metropolitan+regions.">Does congestion negatively affect income growth and employment growth? Empirical evidence from US metropolitan regions.</a> Transport Policy. 55, 1-8 (2017).</li><li>Ajeng, C., Gim, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analyzing+on-street+parking+duration+and+demand+in+a+metropolitan+city+of+a+developing+country:+A+case+study+of+Yogyakarta+City,+Indonesia.">Analyzing on-street parking duration and demand in a metropolitan city of a developing country: A case study of Yogyakarta City, Indonesia.</a> Sustainability. 10 (3), 591 (2018).</li><li>Chen, J. X., Li, Z. B., Jiang, H., Zhu, S. L., Wang, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simulating+the+impacts+of+on-street+vehicle+parking+on+traffic+operations+on+urban+streets+using+cellular+automation.">Simulating the impacts of on-street vehicle parking on traffic operations on urban streets using cellular automation.</a> Physica A: Statistical Mechanics and its Applications. 468, 880-891 (2017).</li><li>Ye, X. F., Chen, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+curbside+parking+on+travel+time+and+space+mean+speed+of+nonmotorized+vehicles.">Impact of curbside parking on travel time and space mean speed of nonmotorized vehicles.</a> Transportation Research Record. 2394 (1), 1-9 (2013).</li><li>Ye, X., Yan, X. C., Chen, J., Wang, T., Yang, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Impact+of+curbside+parking+on+bicycle+lane+capacity+in+Nanjing,+China.">Impact of curbside parking on bicycle lane capacity in Nanjing, China.</a> Transportation Research Record. 2672 (31), 120-129 (2018).</li><li>Guo, H. W., Gao, Z. Y., Zhao, X. M., Yang, X. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Traffic+behavior+analysis+of+non-motorized+vehicle+under+influence+of+curb+parking.">Traffic behavior analysis of non-motorized vehicle under influence of curb parking.</a> Journal of Transportation Systems Engineering and Information Technology. 11 (1), 79-84 (2011).</li><li>Chen, J., Mei, Z. Y., Wang, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Road+resistance+model+under+mixed+traffic+flow+conditions+with+curb+parking.">Road resistance model under mixed traffic flow conditions with curb parking.</a> China Civil Engineering Journal. (09), 103-108 (2007).</li><li>Gao, L. P., Sun, Q. X., Liu, M. J., Liang, X., Mao, B. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Delay+models+and+simulation+on+mixed+traffic+system+with+curb+parking.">Delay models and simulation on mixed traffic system with curb parking.</a> Journal of System Simulation. 22 (003), 804-808 (2010).</li><li>Guo, H. W., Gao, Z. Y., Yang, X. B., Zhao, X. M., Wang, W. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+travel+time+under+the+influence+of+on-street+parking.">Modeling travel time under the influence of on-street parking.</a> Journal of Transportation Engineering. 138 (2), 229-235 (2012).</li><li>Yang, X. G., Long, L., Pu, W. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimal+distance+between+one-side+curbside+parking+location+and+signalized+intersection.">Optimal distance between one-side curbside parking location and signalized intersection.</a> Journal of Tongji University (Natural Science). 33 (3), 297-300 (2005).</li><li>Guo, H. W., Wang, W. H., Guo, W. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Micro-simulation+study+on+the+effect+of+on-street+parking+on+vehicular+flow.">Micro-simulation study on the effect of on-street parking on vehicular flow.</a> 2012 15th International IEEE Conference on Intelligent Transportation Systems. , 1840-1845 (2012).</li><li>Hu, X. J., Hao, X. T., Wang, H., Su, Z. Y., Zhang, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Research+on+on-street+temporary+parking+effects+based+on+cellular+automaton+model+under+the+framework+of+Kerner's+three-phase+traffic+theory.">Research on on-street temporary parking effects based on cellular automaton model under the framework of Kerner's three-phase traffic theory.</a> Physica A: Statistical Mechanics and its Applications. 545, 123725 (2020).</li><li>Shao, 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=Evaluation+of+two+improved+schemes+at+non-aligned+intersections+affected+by+a+work+zone+with+an+entropy+method.">Evaluation of two improved schemes at non-aligned intersections affected by a work zone with an entropy method.</a> Sustainability. 12 (14), 5494 (2020).</li><li>Shao, 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+sustainable+traffic+flow+operational+features+of+an+exclusive+spur+dike+U-turn+lane+design.">Evaluating the sustainable traffic flow operational features of an exclusive spur dike U-turn lane design.</a> PLoS One. 14 (4), 0214759 (2019).</li><li>Shao, Y., Han, X. Y., Wu, H., Claudel, C. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+signalization+and+channelization+selections+at+intersections+based+on+an+entropy+method.">Evaluating signalization and channelization selections at intersections based on an entropy method.</a> Entropy. 21 (8), 808 (2019).</li><li>Xi'an realtime traffic congestion delay index. AutoNavi Traffic Big-data Available from: <a target="_blank" href="https://trp.autonavi.com/detail.do?city=610100">https://trp.autonavi.com/detail.do?city=610100</a> (2021)</li><li>Pan, B. 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=Evaluation+and+analysis+model+of+the+length+of+added+displaced+left-turn+lane+based+on+entropy+evaluation+method.">Evaluation and analysis model of the length of added displaced left-turn lane based on entropy evaluation method.</a> Journal of Advanced Transportation. 2021, 2688788 (2021).</li><li>Pan, B. 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><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 &amp;amp; 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 &amp;amp; DEVELOPMENT CO.,LTD</td><td>CADS-0037</td><td></td></tr><tr><td>radar tripod</td><td>BEIJING AOZER TECH &amp;amp; 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

Universidad de Cartagena UDEC

Research

JoVE Journal

Engineering

3.2K 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

Design and Construction of an Urban Runoff Research Facility

As the urban population increases, so does the area of irrigated urban landscape. Summer water use in urban areas can be 2-3x winter base line water use due to increased demand for landscape irrigation. Improper irrigation practices and large rainfall events can result in runoff from urban landscapes which has potential to carry nutrients and sediments into local streams and lakes where they may contribute to eutrophication. A 1,000 m2 facility was constructed which consists of 24 individual 33.6 m2 field plots, each equipped for measuring total runoff volumes with time and collection of runoff subsamples at selected intervals for quantification of chemical constituents in the runoff water from simulated urban landscapes. Runoff volumes from the first and second trials had coefficient of variability (CV) values of 38.2 and 28.7%, respectively. CV values for runoff pH, EC, and Na concentration for both trials were all under 10%. Concentrations of DOC, TDN, DON, PO4-P, K+, Mg2+, and Ca2+ had CV values less than 50% in both trials. Overall, the results of testing performed after sod installation at the facility indicated good uniformity between plots for runoff volumes and chemical constituents. The large plot size is sufficient to include much of the natural variability and therefore provides better simulation of urban landscape ecosystems.

This paper describes the design, construction, and function of a 1,000 m2 facility containing 24 individual 33.6 m2 field plots equipped for measuring total runoff volumes with time and collection of runoff subsamples at selected intervals for quantification of chemical constituents in the runoff water from simulated home lawns.

As the urban population increases, so does the area of irrigated urban landscape. Summer water use in urban areas can be 2-3x winter base line water use ...

Environment

Implementation of Portable Emissions Measurement Systems (PEMS) for the Real-driving Emissions (RDE) Regulation in Europe

Vehicles are tested in controlled and relatively narrow laboratory conditions to determine their official emission values and reference fuel consumption. However, on the road, ambient and driving conditions can vary over a wide range, sometimes causing emissions to be higher than those measured in the laboratory. For this reason, the European Commission has developed a complementary Real-Driving Emissions (RDE) test procedure using the Portable Emissions Measurement Systems (PEMS) to verify gaseous pollutant and particle number emissions during a wide range of normal operating conditions on the road. This paper presents the newly-adopted RDE test procedure, differentiating six steps: 1) vehicle selection, 2) vehicle preparation, 3) trip design, 4) trip execution, 5) trip verification, and 6) calculation of emissions. Of these steps, vehicle preparation and trip execution are described in greater detail. Examples of trip verification and the calculations of emissions are given.

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

The European Commission has developed a Real-Driving Emissions (RDE) test procedure to verify pollutant emissions during real-world vehicle operation using the Portable Emissions Measurement Systems (PEMS). This paper presents the experimental procedures required by the newly-adopted RDE test.

Vehicles are tested in controlled and relatively narrow laboratory conditions to determine their official emission values and reference fuel consumption. ...

Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects

Patients suffering from homonymous hemianopia after infarction of the posterior cerebral artery (PCA) report different degrees of constraint in daily life, despite similar visual deficits. We assume this could be due to variable development of compensatory strategies such as altered visual scanning behavior. Scanning compensatory therapy (SCT) is studied as part of the visual training after infarction next to vision restoration therapy. SCT consists of learning to make larger eye movements into the blind field enlarging the visual field of search, which has been proven to be the most useful strategy1, not only in natural search tasks but also in mastering daily life activities2. Nevertheless, in clinical routine it is difficult to identify individual levels and training effects of compensatory behavior, since it requires measurement of eye movements in a head unrestrained condition. Studies demonstrated that unrestrained head movements alter the visual exploratory behavior compared to a head-restrained laboratory condition3. Martin et al.4 and Hayhoe et al.5 showed that behavior demonstrated in a laboratory setting cannot be assigned easily to a natural condition. Hence, our goal was to develop a study set-up which uncovers different compensatory oculomotor strategies quickly in a realistic testing situation: Patients are tested in the clinical environment in a driving simulator. SILAB software (Wuerzburg Institute for Traffic Sciences GmbH (WIVW)) was used to program driving scenarios of varying complexity and recording the driver's performance. The software was combined with a head mounted infrared video pupil tracker, recording head- and eye-movements (EyeSeeCam, University of Munich Hospital, Clinical Neurosciences).
The positioning of the patient in the driving simulator and the positioning, adjustment and calibration of the camera is demonstrated. Typical performances of a patient with and without compensatory strategy and a healthy control are illustrated in this pilot study. Different oculomotor behaviors (frequency and amplitude of eye- and head-movements) are evaluated very quickly during the drive itself by dynamic overlay pictures indicating where the subjects gaze is located on the screen, and by analyzing the data. Compensatory gaze behavior in a patient leads to a driving performance comparable to a healthy control, while the performance of a patient without compensatory behavior is significantly worse. The data of eye- and head-movement-behavior as well as driving performance are discussed with respect to different oculomotor strategies and in a broader context with respect to possible training effects throughout the testing session and implications on rehabilitation potential.

Patients with visual deficits after stroke report about different constraints in daily life most likely due to variable compensatory strategies, which are difficult to differentiate in clinical routine. We present a clinical set-up which allows measurement of different compensatory head- and eye-movement-strategies and evaluating their effects on driving performance.

Patients suffering from homonymous hemianopia after infarction of the posterior cerebral artery (PCA) report different degrees of constraint in daily ...

Medicine

Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which people walk on a treadmill to intercept gaps between two moving vehicles in an immersive virtual environment. Virtual reality allows for a safe and ecologically varied investigation of gap crossing behavior. Manipulating the initial starting distance can further the understanding of a participant&#8217;s speed regulation while approaching a gap. The speed profile may be assessed for various gap crossing variables, such as initial distance, vehicle size, and gap size. Each walking simulation results in a position/time series that can inform how velocity is adjusted differently depending on the gap characteristics. This methodology can be used by researchers investigating pedestrian behavior and behavioral dynamics while employing human participants in a safe and realistic setting.

This protocol describes use of a walking simulator that serves as a safe and ecologically valid method to study pedestrian behavior in the presence of moving traffic.

To cross a road successfully, individuals must coordinate their movements with moving vehicles. This paper describes use of a walking simulator in which ...

Behavior

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of tactile warnings in reducing driver&#8217;s brake response time. At the same time, tactile warnings have been proved effective in take-over request (TOR) for partially autonomous vehicles.
How the performance of tactile warnings can be optimized is an ongoing hot research topic in this field. Thus, the presented low-cost driving simulation software and methods are introduced to attract more researchers to take part in the investigation. The presented protocol has been divided into five sections: 1) participants, 2) driving simulation software configuration, 3) driving simulator preparation, 4) vibrating toolkit configuration and preparation, and 5) conducting the experiment.
In the exemplar study, participants wore the tactile vibrating toolkit and performed an established car-following task using the customized driving simulation software. The front vehicle braked intermittently, and vibrating warnings were delivered whenever the front vehicle was braking. Participants were instructed to respond as quickly as possible to the sudden brakes of the front vehicle. Driving dynamics, such as the brake response time and brake response rate, were recorded by the simulation software for data analysis.
The presented protocol offers insight into the exploration of the effectiveness of tactile warnings on different body locations. In addition to the car-following task that is demonstrated in the exemplar experiment, this protocol also provides options&#160;to apply other paradigms to the driving simulation studies by making simple software configuration without any code development. However, it is important to note that due to its affordable price, the driving simulation software and hardware introduced here may not be able to fully compete with other high-fidelity commercial driving simulators. Nevertheless, this protocol can act as an affordable and user-friendly alternative to the general high-fidelity commercial driving simulators.

This protocol describes a driving simulation platform and a tactile vibrating toolkit for the investigation of driving-related research. An exemplar experiment exploring the effectiveness of tactile warnings is also presented.&#160;

Collision warning system plays a key role in the prevention of driving distractions and drowsy driving. Previous studies have proven the advantages of ...

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.

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, ...

Determination of the Friction Coefficients of Icy Pavements Under Different Amounts of Snowfall

Ice on road surfaces can lead to a significant decrease in the friction coefficient, thus endangering driving safety. However, there are still no studies that provide exact friction coefficient values for pavements covered in ice, which is detrimental to both road design and the selection of winter road maintenance measures. Therefore, this article presents an experimental method to determine the friction coefficient of icy road surfaces in the winter. A British portable tester (BPT), also known as a pendulum friction coefficient meter, was employed for the experiment. The experiment was divided into the following five steps: the preparation of the equipment, the calculation and analysis of the snowfall, equipment calibration, friction coefficient determination, and data analysis. The accuracy of the final experiment is directly affected by the equipment accuracy, which is described in detail. Moreover, this article suggests a method for calculating the ice thickness for corresponding amounts of snowfall. The results illustrate that even patchy ice formed by very light snowfall may lead to a significant decrease in the friction coefficient of the pavement, thus endangering driving safety. Additionally, the friction coefficient is at its peak when the ice thickness reaches 5 mm, meaning protection measures should be taken to avoid the formation of such ice.

Here, we present a method for determining the friction coefficient of pavements with different ice thicknesses indoors. The complete procedure includes the preparation of the equipment, the calculation and analysis of the snowfall, equipment calibration, friction coefficient determination, and data analysis.

Ice on road surfaces can lead to a significant decrease in the friction coefficient, thus endangering driving safety. However, there are still no studies ...

Trajectory Data Analyses for Pedestrian Space-time Activity Study

It is well recognized that human movement in the spatial and temporal dimensions has direct influence on disease transmission1-3. An infectious disease typically spreads via contact between infected and susceptible individuals in their overlapped activity spaces. Therefore, daily mobility-activity information can be used as an indicator to measure exposures to risk factors of infection. However, a major difficulty and thus the reason for paucity of studies of infectious disease transmission at the micro scale arise from the lack of detailed individual mobility data. Previously in transportation and tourism research detailed space-time activity data often relied on the time-space diary technique, which requires subjects to actively record their activities in time and space. This is highly demanding for the participants and collaboration from the participants greatly affects the quality of data4.
Modern technologies such as GPS and mobile communications have made possible the automatic collection of trajectory data. The data collected, however, is not ideal for modeling human space-time activities, limited by the accuracies of existing devices. There is also no readily available tool for efficient processing of the data for human behavior study. We present here a suite of methods and an integrated ArcGIS desktop-based visual interface for the pre-processing and spatiotemporal analyses of trajectory data. We provide examples of how such processing may be used to model human space-time activities, especially with error-rich pedestrian trajectory data, that could be useful in public health studies such as infectious disease transmission modeling.
The procedure presented includes pre-processing, trajectory segmentation, activity space characterization, density estimation and visualization, and a few other exploratory analysis methods. Pre-processing is the cleaning of noisy raw trajectory data. We introduce an interactive visual pre-processing interface as well as an automatic module. Trajectory segmentation5 involves the identification of indoor and outdoor parts from pre-processed space-time tracks. Again, both interactive visual segmentation and automatic segmentation are supported. Segmented space-time tracks are then analyzed to derive characteristics of one's activity space such as activity radius etc. Density estimation and visualization are used to examine large amount of trajectory data to model hot spots and interactions. We demonstrate both density surface mapping6 and density volume rendering7. We also include a couple of other exploratory data analyses (EDA) and visualizations tools, such as Google Earth animation support and connection analysis. The suite of analytical as well as visual methods presented in this paper may be applied to any trajectory data for space-time activity studies.

A suite of spatiotemporal processing methods are presented to analyze human trajectory data, such as that collected using a GPS device, for the purpose of modeling pedestrian space-time activities.

It is well recognized that human movement in the  spatial and temporal dimensions has direct influence on disease transmission1-3.  An infectious disease ...

Biology

Motor Dual-Tasks for Gait Analysis and Evaluation in Post-Stroke Patients

Eighteen stroke patients were recruited for this study involving the evaluation of cognition and walking ability and multitask gait analysis. Multitask gait analysis consisted of a single walking task (Task 0), a simple motor dual-task (water-holding, Task 1), and a complex motor dual-task (crossing obstacles, Task 2). The task of crossing obstacles was considered to be equivalent to the combination of a simple walking task and a complex motor task as it involved more nervous system, skeletal movement, and cognitive resources. To eliminate heterogeneity in the results of the gait analysis of the stroke patients, the dual-task gait cost values were calculated for various kinematic parameters. The major differences were observed in the proximal joint angles, especially in the angles of the trunk, pelvis, and hip joints, which were significantly larger in the dual motor tasks than in the single walking task. This research protocol aims to provide a basis for the clinical diagnosis of gait function and an in-depth study of motor control in stroke patients with motor control deficits through the analyses of dual-motor walking tasks.

This paper presents a protocol specifically for dual motor task gait analysis in stroke patients with motor control deficits.

Eighteen stroke patients were recruited for this study involving the evaluation of cognition and walking ability and multitask gait analysis. Multitask ...

Neuroscience

Crowding

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
Human vision depends on light-sensitive neurons that are arranged in the back of the eye on a tissue called the retina. The neurons, called the rods and cones because of their shapes, are not uniformly distributed on the retina. Instead, there is a region in the center of the retina called the macula where cones are densely packed, and especially so in a central sub-region of the macula called the fovea. Outside the fovea there are virtually no cones, and rod density decreases considerably with greater distance from the fovea. Figure 1 schematizes this arrangement. This kind of arrangement is also replicated in the visual cortex: Many more cells represent stimulation at the fovea compared to the periphery.
<img alt="Figure 1" src="/files/ftp_upload/10280/10280fig1.jpg" /> 
Figure 1. Schematic depiction of the human eye and the distribution of light-sensitive receptor cells on the retina. The pupil is the opening in the front of the eye that allows light to enter. Light is then focused onto the retina, a neural tissue in the back of the eye that is made of rods and cones, light-sensitive cells. At the center of the retina is the macula, and in the center of the macula is the fovea. The graph schematizes the density of rod and cone receptors on the retina as a function of their position. Cones, which are responsible for color vision, are found almost exclusively in the fovea. Rods, which support seeing in low-light conditions, are similarly clustered more heavily near the fovea, with quickly falling densities outside the macula.
The result: We see very well in the part of space that our eyes are pointing at directly, the part of space stimulating the fovea; but we actually don&#39;t see very well in the periphery. We don&#39;t really notice it though, because our eyes move around constantly, building a representation of space from many individual fixations.
One way to study the properties of peripheral vision is with a phenomenon known as crowding.1 Crowding refers to an inability to recognize objects in clutter, and we experience crowding especially strongly when objects are shown in the periphery. Figure 2a is an example in which you should be able to experience crowding: Look at the cross in the center, and see if you can report the letter that is in the middle of the pack on the right. It is probably pretty hard. Now in Figure 2b try to report the letter on the left. Much easier! In this figure the letter is not crowded and there is no clutter around it, so it is easier to recognize.
<img alt="Figure 2a" src="/files/ftp_upload/10280/10280fig2a.jpg" />
Figure 2a. Crowded stimulus. Fixate the cross in the center, and see if you can recognize the letter in the middle of the pack on the left. It should be difficult, because the letters are in the periphery, and the central letter is crowded by the letters around it.
<img alt="Figure 2b" src="/files/ftp_upload/10280/10280fig2b.jpg" />
Figure 2b. Uncrowded stimulus. This stimulus is identical to Figure 2a, except that the letter G is uncrowded-no other letters surround it. Even while fixating the cross, the letter should be easy to recognize, even though it is just as much in the periphery as the G in Figure 2a.
This video will demonstrate how to design and implement a crowding experiment with letters as stimuli.

Investigating Crowding in Peripheral Vision

Source: Laboratory of Jonathan Flombaum&#8212;Johns Hopkins University
Human vision depends on light-sensitive neurons that are arranged in the back of ...