1 EZ Pass 2.0 for DOD Installation Entry Jefferey Barr, Peter Liedman, Scherard Lindsay College of Engineering, Florida State University ESI 5590: Human Factors for Systems Engineering Advisor: Dr David C. Gross May 4, 2023 2 Abstract Entry points of DoD installations become quickly congested due to a bottleneck of vehicles coming from different paths paired with traffic lights causing vehicles to stop and increasing check point visits while personnel verify user credentials to ensure secure entry. Traffic studies were conducted to determine the average time of traffic light intervals, peak traffic hours and time required for a certain number of vehicles to pass through the check point. The installation of a pre-verified user check in would mitigate the issues of bottlenecking at peak hours of installation access and increase the level of security by having user data logged once a mile away. Keywords: Key Words here 3 Purpose/Introduction The purpose of this project is to understand the design limitations of the roadway entrance to a secure facility as a system and to provide recommendations for decreasing the wait time at the gate to improve traffic flow easing frustration and decrease security concerns. As service members, civil servants and contractors begin to slowly go back into the office the consequence of increased traffic comes alongside. Some installations allowed entry by selfscanning their Common Access Card (CACs) but with some army “bases will conduct the checks in phases through mid-June with the exact timing determined by the base.”( Kheel,R.) Hypothesis A general simulation can be developed to understand the flow of traffic through a Entry Control Point (ECP) and used to suggest improvements to decrease the wait time through the ECP. Human Factors issues The human factors issues of our project are: 1. Understanding the drivers' behaviors (arrival times, how long their wait is, cellphone use while driving, getting ID, etc.) 2. Understanding gate personnel/staffing needs to support surge in traffic 3. Incorporating both influences into a system model for future access design and entrance criteria. Can these be linked to the Human Factor’s Goals (page 4 in the text)? Literature Review Method Link to system engineering – especially the system engineering V 4 The general methodology that was utilized when building this project was as follows: Observe: Observe an analogous system to the one being tested (in our case the entrance to NSWC PCD), from that observation collect preliminary data. In parallel perform a literature review to understand the challenges for the project. Simulate: Using the preliminary data and the literature review generate a simulation, validate the simulation against the data to ensure the simulation is correct, collect additional data as needed. Analyze: Once the simulation is complete, run various scenarios to better understand the system and what the causes and effects are that make the system perform well/poorly. Report: Summarize and report findings The flow chart below shows the methodology that was used to develop the model for this project. To first understand the system, the system was observed using some basic questions: What’s the situation (are cars backed up)? How many cars are entering the Entry Control Point? Where do things seem to be backing up? 5 After the initial observations, a basic simulation (in Rockwell Arena 2022) was created using the questions and answers generated from the Observe Scenario step. While generating that model some additional questions were generated. Armed with additional data and a developing model, some statistical analysis was performed on the data that was gathered. This analysis was used to determine if additional data would be required or if enough data had been gathered. Once complete, the simulation could then be used to complete the research. It’s important to note that the data and model are not complete at this point but are enough to understand the system in question and make some additional recommendations to the system users and follow on simulation authors. When analyzing the data the following procedure was used to determine if enough (sample size) data had been gathered. 1. Gather some data (small sample ≤ 30) 6 2. Determine maximum relative error (gamma) by educated guess. 3. Perform Statistical analysis including sample number (N), mean, and variance for gathered data. 4. Calculate gamma prime 5. Calculate the relative error (n*), based on the predetermined significance level (alpha), mean, and variance for a gathered data. 6. Very sample size (i) until calculated relative error (n*) ≤ gamma prime. This is your predicted sample size. Data Data was gathered to understand the traffic light into the Entry Control Point, the Travel time from the traffic light to the guard, the time it takes for the guard to clear a vehicle, and how many cars are allowed through the turn signal at one time. As discussed later in this 7 paper/presentation, the most important data was the timing of the turn signal, and the time for a guard to clear a vehicle. Once the data was gathered a basic statistical analysis was performed to determine if the data gathered was enough to meet the appropriate relative error requirement (10%).If the data wasn’t enough, but close enough the data would be considered good, if the sample size seemed inadequate additional data would be gathered. After the relative error analysis, in which the sample size was predicted, it was determined that the data was adequate to continue developing the simulation, however it would be noted that additional data should be gathered to improve the simulation. Data Mean Max Min Standard Deviation Sample size Min required data Guard Processing Time (sec) Travel Time (sec) 10.67 34.77 6.72 25.77 29.00 21.21 Turn Signal Schedule Left Green Red Turn Light Light (sec) (sec) (sec) 37.22 27.28 31.51 42.45 37.24 38.89 34.32 20.54 22.19 5.14 3.46 2.46 4.30 6.80 27 5 10 10 10 78 8 4 11 18 For the most part when comparing the sample size of the data to the “Minimum required data”, most data was adequate. The Guard processing time data was grossly inadequate, but valid enough to develop the model. In later iterations of the project, attention will need to be paid to gathering more data in that area. Explain how minimum required data was determined. Simulation 8 This is the final simulation (Rockwell Arena), the “left turn lane” create blocks generate incremental series of cars, each generates 15 cars every 96 seconds based on the observations of the traffic light before the ECP. There are four in case the user wants to simulate a different traffic light pattern or real world scenario. This process is repeated 4 times to simulate the morning rush from 0600 to 0620. The Time to Guard circle is a delay to simulate the time it takes the vehicles to get to the guards, then the decision block (diamond shape “Entrance Lane”) splits the cars between the two guard shacks and the fast pass lane. The decision block can be adjusted to direct all the traffic to one guard or a combination of the three. Each guard process block is set to process vehicles on a 10.67 sec mean processing time, with a standard deviation of 5.14. The three dispose blocks wrap up the simulation, and the pass inspection is just for fun, for future simulations. The counter (blue 0) and graph are set up to log the number of vehicles in line in “real time”. 9 Results While observing the traffic flow the team realized that the two key components of the simulation was the traffic entering the facility, in this case how many cars the turn signal was allowing each light cycle, and how many cars the guard(s) were able to able to process. If the guards couldn’t process all the cars each cycle, then it would create a backup and the problem would start to compound. This graph shows how many cars accumulate in line if just one guard tries to process 15 cars per light cycle. The distance between the ECP and the turn signal can hold a maximum of 20 cars. If the simulation consistently gets above 20 cars, then the guards are having trouble processing the incoming traffic. This can be shown in the graph below, there are time’s when the standing traffic approaches the 20 car mark (left axis of graph). In this scenario the guards are just holding their own. This simulation is set up to generate 15 cars every 96 seconds, and two guards are processing cars as the same rate (10.67 mean, 5.14 stdv) as before. Think two guards two lanes. 10 On this third simulation the team has added a “fast pass” lane, it doesn’t require a guard but does take the same amount of time as a guard. So, three lanes clearing cars. This graph shows that the three lanes are better keeping up with the cars, the red line which indicates number of cars, reaches or gets near zero more often than the other two simulations. Since most peaks top out at around 15 it shows that the process can clear all 15 cars entering the facility each traffic light cycle. 11 Results Probably should explain the results Simulation One Guard Time 58’50” Two Guards Two Guards & Fast Pass 33’50” 33’15” Max backup >128, unacceptable 20, 21, Borderline 20, acceptable Observations Backup out of control Barely handled Barely handled Discussion To better understand the effects that guard processing time had on entrance queue length the team build a simulation. This simulation allowed the team to very the number of vehicles entering the check point and number of guard stations to determine the main influences of the system. 12 One main goal of the project was to understand if adding additional lanes increased the efficiency in the process of entering DoD installations during peak traffic hours. “Additional lanes” might include the construction of additional lanes, or the creation of a fast pass system. With the additional use of technology base security forces can operate with minimal stress and keep a consistent flow of traffic mitigating the congestion of base entry points at peak times. The additional use of multiple guards in one lane provides positive results and anticipation of peak traffic hours mitigate traffic congestion. Key Takeaways / Conclusion Reference some of the human factors interventions from pages 6 & 7 in the textbook and link them back to the goals If the guard(s) can clear the 15 cars from the Left Turn Lane every 105 seconds, there will never be a backup. It doesn’t matter if this takes 2 guards or 12. But there’s no need to add extra guards, they’ll just cost more money. A corollary to this would be, an EZ Pass lane would need to clear people as fast or faster than a guard to be of any value to the system. Having the guards in place early will help, the simulation shows that the traffic backup compounds quickly (within 3-4- light cycles with one guard processing vehicles); having two guards ready for rush hour will keep the backup to a minimum. Two guards, or even two guards and the fast pass are barely holding on so any delay in vehicle processing (extra long look at badge or car has to turn around) will begin the compounding problem. It’s hard to recover from this delay in the height of rush hour. Implementation of ride sharing & public transportation programs is an additional and cost-effective alternative. 13 Users should avoid peak hours Because of the constant and relentless flow of traffic, 3 lanes are only slightly more efficient than 2 lanes, consideration should be given to the cost of introducing a third lane. At peak hours and extra delay in processing time can have large effects in entrance delay times. 1. Implement staggered work schedules: One of the most effective ways to reduce congestion during entry in the mornings is to implement staggered work schedules. This means that employees will start and end their workday at different times, which will help to spread out the traffic and reduce congestion. 2. Increase public transportation options: Another way to reduce congestion is to increase public transportation options. This can include providing shuttle buses or encouraging employees to carpool or use public transportation. 3. Improve traffic flow: Improving traffic flow can also help to reduce congestion during entry in the mornings. This can include adding additional lanes, improving traffic signals, or implementing traffic calming measures. 4. Encourage telecommuting: Encouraging employees to work from home or telecommute can also help to reduce congestion during entry in the mornings. This can be especially effective for employees who do not need to be physically present at the installation Suggestion for future work 14 15 References Human Factors | FHWA. (n.d.). Retrieved February 9, 2023, from https://highways.dot.gov/research/research-programs/safety/human-factors Human Factors | NHTSA. (n.d.). [Text]. Retrieved February 9, 2023, from https://www.nhtsa.gov/research-data/human-factors Salimifard, K., & Ansari, M. (2013). Modeling and Simulation of Urban Traffic Signals. International Journal of Modeling and Optimization, 172–175. https://doi.org/10.7763/IJMO.2013.V3.261Links to an external site. Yuniawan, D., Fajar, A., Hariyanto, S., & Setiawan, R. (2018). Traffic Queue Modeling Using Arena Simulation Software (a Case study of Mergan 4-Way Intersection in Malang City). 6. National Academies of Sciences, Engineering, and Medicine. (2018). Improving operational capabilities and reducing congestion in the national airspace system: Proceedings of a workshop. The National Academies Press. Kheel,R. (Apr 28, 2023) Soldiers and Other Visitors to Army Bases Could See Heavy Traffic at Gates Amid '100% ID Checks'. Retrieved Apr 28,2023 from https://www.military.com/daily-news/2023/04/28/major-delays-getting-army-basespossible-service-launches-100-id-checks.html A note on references: References should be used and cited in the paper, otherwise they are a bibliography. And Dr Gross says “I don’t know why anyone would read a source and not use it in the paper”. 16 17 Appendix A If we need it 18 Appendix B If we need it