Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
January 20th, 2023
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.
This video is aimed to show the process of collecting traffic data by radars, roadside laser device, and evaluation by simulation model. Roadside parking is very common, which often is to traffic congestion, especially, on narrow urban streets, so we want to discuss the effect of roadside parking on a new direction Urban Street. Determine its influence lanes and ranging.
We use radars and roadside laser device to collect traffic data and use simulation software to evaluate the impact of roadside parking on traffic operation effect. Traffic Data collection needs two direction data, at least. The equipment needed is as follows.
Two radars, batteries and cables, laptops, roadside laser device, drone, camera, and corresponding tripods for camera and radars. The reflective tripod is shown in the section data collection. The location must be on a two direction and two-lane road.
A street line segment without intersection is required. No barrier is on the road, except one parking vehicle provided by investigators. Long enough line of site and clearance are required, which is required for radar investigation and investigator safety.
The location must have enough and safe space for equipment and investigators. Park the vehicle approximately 20 centimeter away from the curb in the intended place, so that the roadside laser device can be placed. Place the reflective tripod at the back of the vehicle.
Do not place it too far to ensure that it does not affect the vehicle's behavior. Set the radar tripod. Set the tripod taller than two meters to avoid signal blockage.
Lock the radar with the tripod. Adjust the radar vertically and make it toward the parked vehicle. Connect the radar data cable with the laptop USB port.
Open radar software. Click Communication Check. Connect the serial port and click connect.
It will show Radar detected and click Confirm. Click Investigation set up. Click Read RLU time and set RLU time successively.
Click Erase data record and confirm it. Clear the internal memory of radar. Click Start investigation and close dialogue box.
Click realtime view to check the radar status and the traffic data will be rolling with the vehicle passing. Prepare the roadside laser device and the cable. Connect the roadside laser device data cable fit to port.
Connect the roadside laser device data cable with the laptop USB port. Place the roadside laser device in the middle of the parked vehicle. Rotate the full adjustment columns on the device to level the laser device.
Open roadside laser device software. Click Communication check, select RLU serial port number, and click Connect. It will show new RLU connection detected and click Confirm.
Click View investigation. When vehicles pass, it will view the traffic flow in real time. Click Investigation set up.
Click Read RLU time and set RLU time successively. Set Start time and End time and click Set task. It will show RLU investigation set up succeeded and Confirm it.
Click Finish. Click Device status to view the status of roadside laser device. Set the camera above 30 meters upstream of the parked vehicle.
Set all equipment on the dense road. Check radars, roadside laser device and camera work well every five minutes. And the data collection, close the Realtime check window in radar software.
Click Investigation set up. Select End investigation and confirm it. Close the dialogue box.
Select the Data download. Browse computer to save the data and input a name for the file. Click Open, and then, click Start download.
Click Confirm to finish radar data collection. Click Device status in roadside laser device software, and then, click Stop task to end the data collection. Select the Data download, browse, and input the name for the file.
Click Open and click Start download. Click Confirm to finish data collection of the roadside laser device. Radars collect trajectories and speed by calculation software.
The roadside laser device provides the offset value, passing speed, numbers of vehicles and types of vehicles at the parked vehicle position. Draw the whole range trajectories and speed provided by the two radars and one roadside laser device at the representative data using calculation software. Open the simulation software.
Import the background map of the investigated road segment. Click Obstacles at left, right click, and select Add New Obstacle. Input the length and width of the obstacle and click OK.Track the cursor to move the obstacle to the roadway.
Click Links at left. Move the cursor to the start of the link and right click. Select Add New Link.
Input the link width and click OK.Track the cursor to draw the link on the map. Repeat this step three times to build four road segments. Hold the right button of the mouse and the Control button on the keyboard to drag the endpoint of one link to the adjacent link for connecting the two links.
Repeat this step to connect all links. Select the Base Data from the top bar and then, select Distributions, Desire Speed. Click the green cross add button at the bottom to add a new desired speed distribution and name it.
Input the average speed and the maximum speed taken from the representative data as the minimum and the maximum desired speed. Delete the default data. Repeat the steps two times to establish all desired speed distributions.
Select Lists from the top bar, and then, select the Private Transport, Vehicle Compositions. Click the green cross add button to add a new vehicle composition. Click add button to add two vehicle types, HGV and bus.
Select the desire speed distribution setting, last step for car, HGV, and bus. Repeat the steps to establish two vehicle compositions. Input the flow of car, HGV and bus from the representative data.
Select the Vehicle Routes from the left menu bar. Move the cursor to the upstream of one link. Right click, and select Add New Static Vehicle Routing Decision.
Track the blue cursor to draw vehicle routes on the map as real routes in data collection. Select Reduced Speed Areas from the left menu bar. Right click at the upstream of the parking position and select Add New Reduced Speed Area.
The length of the area depends on the data analysis results. Right click in the margin of the screen. Select Add and select the desired speed for reduced speed area set in previous steps as the area speed.
Repeat the steps two times to set or reduce the speed areas. Select the Priority Rules from the left menu bar. Right click the Reduced Speed Area upstream of the parked vehicle in the west to east direction and select Add New Priority Rule.
Input minimum depth time and clearance. Repeat this step to set the priority rule downstream of the parked vehicle in the east to west direction. The setting of priority rules depends on the real traffic operation reflected by the data collection.
Select the Vehicle Travel Times from the left. Right click at the beginning of one link and select Add New Vehicle Travel Time Measurement. Direct the cursor to the end of the link to build a vehicle travel time measurement.
Repeat this step for all vehicle routes. Select Vehicle Input from the left. Right click at the beginning of one link and select Add New Vehicle Input.
Move the mouse to the left bottom and input the volume at the representative data. Repeat the steps for all links. Select Nodes from the left.
Right click to select Add New Node and then click OK.Left click and move the mouse to adjust a moderate node range. Click Evaluation at the top of the simulation interface and select Results Lists. Click Nodes Results and Vehicle Travel Time Results.
Click the blue play button at the top to start the simulation. Click the device button Quick Mode to maximize the simulation speed. After the simulation, node results and vehicle travel time results are shown at the bottom of the interface including the maximum queue length, parking times, and so on.
Input the collected data into the simulation model. Round the simulation and gather simulation result. Simulation volume can be generated from the simulation result.
Compare the simulation volume with the collective volume. The difference between the collective volume and simulation volume is called Mean Absolute Percent Error. The simulation accuracy is acceptable when the MAPE is small.
Representative data concludes three groups of data. West to the East volume, East to the West volume, and other parameters. Divide West to the East volume into six categories.
Divide East to the West volume into seven categories and keep other parameters stable in the simulation. Simulate 42 situations and the results verify effectiveness under all situations. Different traffic volumes are shown in the results with no indexes.
Maximum queue length, number of vehicles, delay, number of stops, carbon monoxide emissions, electric oxide emissions, volatile organic compound emissions, fuel consumption and travel time. The results show the significant effect of roadside parking under all traffic volume situations. Now, indicators are evaluated investment simulation.
With the collective data and simulation result, the influence of roadside parking is significant. We want to determine the list and range of the impact of roadside parking vehicle on traffic flow in good direction urban Street using this method. It could be applied to determine the parking position and lengths by transportation department.
The key study in this method is real traffic data collection and simulation model built.
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Overview
This study analyzes the impact of roadside parking on urban street traffic. It involves collecting traffic data using advanced equipment and evaluating the effects through simulation models.
Key Study Components
Area of Science
- Traffic Engineering
- Urban Planning
- Transportation Systems
Background
- Roadside parking is prevalent in urban areas.
- It can lead to traffic congestion, especially on narrow streets.
- Understanding its effects is crucial for urban traffic management.
- Data collection is essential for accurate analysis.
Purpose of Study
- To assess the influence of roadside parking on traffic flow.
- To identify the impact on lane usage and traffic operations.
- To provide insights for urban street design improvements.
Methods Used
- Traffic data collection using radars and roadside laser devices.
- Simulation modeling to evaluate traffic operations.
- Calibration of simulation models for accuracy.
- Sensitivity analysis to understand variable impacts.
Main Results
- Roadside parking significantly affects traffic flow.
- Data collection methods proved effective in capturing traffic dynamics.
- Simulation results highlighted critical congestion points.
- Recommendations for urban planning were derived from findings.
Conclusions
- Roadside parking has a measurable impact on urban traffic.
- Effective data collection and simulation are vital for analysis.
- Insights gained can inform better urban street designs.
Frequently Asked Questions
Executive Industry Relevance
This traffic engineering study provides a methodological framework for assessing how localized obstructions affect flow dynamics in constrained environments. The approach combines empirical data collection with simulation modeling to quantify impacts on trajectory, speed, and system-wide indicators such as delay and emissions. Such methodologies support predictive modeling in urban planning and infrastructure decision-making.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Enables interrogation of how localized perturbations influence system behavior, supporting hypothesis testing in complex biological environments.
- Operational Value: Provides a framework for isolating variables to assess directional effects on measurable outputs.
Screening & Assay Development
- Scientific Value: Generates quantitative, spatially resolved data on entity movement and velocity under defined conditions.
- Operational Value: Supports assay standardization through reproducible data collection protocols and calibration against simulation outputs.
Translational & Preclinical Research
- Scientific Value: Facilitates modeling of obstruction effects in disease-relevant systems to assess functional consequences.
- Operational Value: Enables translation of bench-derived measurements into predictive simulations for cross-functional risk assessment.
Pipeline & Workflow Integration
The method positions itself as a discovery-phase tool for characterizing perturbation impacts, with outputs informing downstream modeling and decision-making in translational workflows.
- Discovery Biology: Supports hypothesis testing by quantifying how localized changes affect directional flow and entity kinetics.
- Screening: Delivers standardized, reproducible inputs for modeling workflows through calibrated sensor fusion and trajectory reconstruction.
- Analytics: Yields multivariate outputs including path deviation, velocity profiles, and system-level metrics for comparative condition analysis.
- Translational Research: Connects empirical observations to simulation-based projections, enabling continuity from measurement to predictive forecasting.
- Enterprise Reuse: Establishes a reusable capability for evaluating obstruction effects across diverse model systems and scales.
Operational & Enterprise Impact
- Scientific Value: Reduces ambiguity in attributing system changes to specific perturbations through controlled, measurable outputs.
- Operational Value: Ensures reproducibility via standardized equipment setup, calibration procedures, and data validation against simulation.
- Strategic Value: Improves go/no-go decisions by providing quantifiable thresholds for impact significance.
- Portfolio Impact: Supports risk-adjusted prioritization by identifying conditions under which localized effects propagate system-wide.
Implementation Considerations
- Requires expertise in sensor-based traffic monitoring and microsimulation modeling.
- Depends on synchronized data collection from multiple modalities (radar, laser, video) for trajectory and speed reconstruction.
- Necessitates cross-team alignment on data formats, simulation parameters, and validation metrics such as MAPE.
- Involves adaptation considerations when transferring protocols to different geometric or operational contexts.
- Practical limitations include line-of-sight requirements, equipment spacing constraints, and sensitivity to environmental interference.
