Real-Time Traffic Control for Isolated Intersections. A Thesis. Sai Prathyusha Peddi. Graduate Program in Computer Science and Engineering

Transcription

1 Real-Time Traffic Control for Isolated Intersections A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Sai Prathyusha Peddi Graduate Program in Computer Science and Engineering The Ohio State University 2013 Master's Examination Committee: Dr. Bruce W. Weide, Advisor Dr. Paul Sivilotti

2 Copyright by Sai Prathyusha Peddi 2013

3 Abstract Efficient design is required for a cyber-physical system (CPS), as we need to trade off the complexity and performance benefits. The CPS of a traffic-light controller is considered at an isolated intersection in a mixed-traffic highway-type situation with autonomous, semi-autonomous, and human-driven vehicles. Since conventional traffic controllers with fixed signal timings lead to intersection losses (loss of time waiting at the signal) and fuel wastage, we follow an approach based on the idea that reactive variable light timings can better control traffic and at the same time ensure safety. We use information about upcoming vehicles (position, velocity, acceleration and maximum braking power) to adjust the red-to-green ratios of the traffic signal to minimize the average loss per vehicle, using software to simulate a real-time traffic scenario. The results are analyzed to determine the optimal red-to-green ratios for a given cycle length and various traffic flow rates. ii

4 This document is dedicated to my father, Sambasiva Reddy Peddi iii

5 Acknowledgments I would like to express my gratitude to my adviser, Bruce Weide, for his support, patience, and encouragement throughout my graduate studies. It is not often that one finds an advisor who always finds the time for listening to the little problems and roadblocks that unavoidably crop up in the course of performing research. His technical and editorial advice was essential to the completion of this thesis and has taught me innumerable lessons and insights on the workings of academic research. I would also like to thank the other member of my committee, Prof. Paul Sivilotti, for providing many valuable suggestions that improved the presentation and contents of this thesis. I am also grateful to Dr. Ted Pavlic, for his valuable insights and helping me with the various aspects of the mathematical model followed. Last but not the least, I would like to thank my parents and my husband for their constant support and encouragement. This research is supported by NSF grants EECS and CCF iv

6 Vita B.E. Computer Science, BITS-Pilani, India Software Engineer, Symantec & Xilinx, India 2011 to present...graduate Research Associate, Department of Computer Science, The Ohio State University Publications S. P. Peddi, Real-Time Adaptive Signaling for Isolated Intersections, Proceedings of the 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems (ICCPS 2013), April 8 11, Won the Best Poster Award. Pavlic, T.P., S. P. Peddi, P.A.G. Sivilotti, and B.W. Weide, Getting Out of the Way Safety Verification without Compromise, Proceedings of the 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems (ICCPS 2012), April 17 19, Fields of Study Major Field: Computer Science and Engineering v

7 Table of Contents Abstract... ii Acknowledgments... iv Vita...v Publications...v Fields of Study...v Table of Contents... vi List of Figures... ix Chapter 1: Introduction Cyber Physical Systems Problem The Thesis Related Work and Background Information Outline...5 Chapter 2: The Mathematical Model The Basic Model Arrival of Cars Poisson Process...8 vi

8 2.2.2 Modified Poisson Arrivals Safety Constraints Stopping Distance Safe State Unsafe State Safe following distance Safe following State Unsafe following state The actual flow of the model Optimal yellow time Performance Metrics...15 There can be several performance metrics for the system. We consider the following: Distance Lost (dl) Time Lost ( ) Optimal Signal timings Using distance lost to calculate signal timings...19 Chapter 4: The Software...20 vii

9 4.1 High level Design Classes and Methods Car Class...22 State Behavior Central Controller class...24 State Behavior Outer Controller class...27 Chapter 5: Results...28 Chapter 6: Conclusions & Future Work...36 REFERENCES...37 viii

10 List of Figures Figure 1: Basic Model... 6 Figure 2: Safe Stopping Figure 3: Unsafe Stopping Figure 4: Safe Following Figure 5: Unsafe Following Figure 6: Distance Lost Figure 7: Time Lost Figure 8: Average Loss Figure 9: High-level Design Figure 10: Pseudo code Figure 11: Result Figure 12: Result Figure 13: Result Figure 14: Result Figure 15: Result Figure 16: Results Summary ix

11 Chapter 1: Introduction 1.1 Cyber Physical Systems A cyber-physical system (CPS) integrates the computing world with the physical world and defines the co-ordination between the two elements within a shared complex environment. A CPS transforms the way in which we interact with the physical world. It does this by merging computing and communications with the physical processes. This ability broadens the potential of CPS in a wide range of domains including aerospace, defense, energy and industrial automation, healthcare and biomedical devices, agriculture, environmental science, civil infrastructure, manufacturing, materials, and transportation. The technological advantages that CPS can offer are immense. The major benefits of implementing CPS include increased safety and reliability, and potential reduction in the cost and complexity of a system. The economic advantages brought about by integrating the computing world and cyber infrastructure are increasing the demand for autonomous systems in every field; a remarkable example being transportation systems. The inter-vehicle and intra-vehicle applications with the integration of computing and communication technologies transform the way vehicles are connected. It also connects them with the transportation infrastructure through wireless networks. 1

12 It has long been recognized that existing transportation systems are vulnerable to accidents, earthquakes, floods, traffic jams and undesired traffic delays. The next generation of these cyber physical systems must simultaneously protect against these disruptions while integrating new generation sources and maintaining active, automated controls of the system. The integration of a traditional transportation system with a cyber physical system gives rise to the Transportation Cyber-Physical Systems (TCPS). To accelerate the production of these systems, designers must also take care to reduce unnecessary complexities and employ reliable analysis framework to help maintain the design, safety and reliability of these systems. By taking the input of real-time dynamic traffic information, these systems will be able to predict the near-future behavior of vehicles and avoid collision, increase traffic throughput, decrease traffic delays, improve fuel efficiency and optimize routes in realtime. Another important concern for the CPS is the integration of human behaviors with the autonomous computational system. In the far future, we might expect to see homogeneous automation, i.e., highways controlled entirely by automation with no human intervention. Human decision making may actually change with the introduction of new technologies, and so engineers may have to collaborate to form new models of human decision making in the presence of this technology. 1.2 Problem As seen earlier, efficient design is required for a TCPS, as we may need to trade off the complexity and performance benefits. Here we consider the CPS of a traffic-light 2

13 controller at an isolated intersection that is used by autonomous, semi-autonomous, and human-driven automobiles. Conventional traffic controllers with fixed signal timings lead to: Excessive waiting time at intersections Fuel wastage (largely in re-acceleration) Driver frustration The next generation of traffic-light control systems can minimize the negative aspects while maintaining enhanced control of the system thereby optimizing key features. Possible areas for improvement include: Improved fuel and time efficiency - as less time is wasted in queuing, decelerating/re-accelerating Improved safety in denser traffic Decreased demands on drivers to make critical decisions while driving Reactive variable light timings can better control traffic and still ensure safety. 1.3 The Thesis This document defends the following thesis: Information about approaching vehicles such as position, velocity, acceleration and maximum braking power can be used to adjust the red-to-green ratios of a traffic signal to minimize the average loss per vehicle. 3

14 To support this thesis, we develop software to simulate a real-time traffic scenario and use it to optimize vehicle losses. 1.4 Related Work and Background Information In the 1950s, initial research [5] on minimizing delays at intersections was done by Webster on pre-timed isolated intersection operations. According to Webster, once we know the degree of saturation (flow to capacity ratio) of the critical phases for a given cycle length and lost time, the minimum delay cycle length equation, is given as: Where C is the resulting cycle length, L is the total lost time (time wasted waiting at the intersection) and Y is the summation of all the flow-to-saturation ratios. In developing this equation for the optimal minimum delay cycle length, the assumption is that the effective green time of the phases are in the ratio of their respective flow ratios which means that more green is allocated to the lanes with higher flow rates. Using this equation, the resulting delay at an isolated signalized intersection can be minimized under the assumption above. However, when the traffic demand at an intersection is high, which causes a high value of degrees of saturation, the optimal cycle length based on Webster s equation will be extremely high. Related work in this domain also includes the delay models at intersections [1], which are described in terms of two components. One is a deterministic component and the other is a stochastic component; to reflect the random properties of a traffic flow. In the deterministic component, the flow rates are continuous variables which vary over the 4

15 time and space domain. On the other hand, the stochastic component of delays is based on a queuing model which defines the traffic arrival and service time distributions. Another study [2] on controlling traffic signals using information collected via vehicle-to-infrastructure (V2I) communication is to use vehicle speed and position as state variables and construct a state-space representation of the control problem. This is solved via dynamic programming, using speed and position as state variables instead of defining a queue and estimating queue length in real-time. This method aims to optimize control performance successively in real-time. Other models [3], [4] have been developed to quantify fuel consumption with agent-based approaches for intersection control with minimization of fuel consumption as an explicit design objective. More references to related work appear throughout this document. 1.5 Outline This document is organized as follows: Chapter 2 provides a detailed discussion of the mathematical model used in developing the software, including the assumptions and implications. Chapter 3 discusses optimization and the required performance metrics. Chapter 4 presents the software used to simulate the real-time traffic scenario, its design, algorithm and flow. Chapter 5 contains the results of applying the software for a set of simulations and the related analysis. Finally, Chapter 6 provides the conclusions and discusses the possibilities for future work. 5

16 Chapter 2: The Mathematical Model In this chapter we discuss in detail the mathematical model used for simulating the real-time traffic network scenario. We also discuss the assumptions of this model and its implications for the software. 2.1 The Basic Model Figure 1: Basic Model 6

17 The basic model depicted above shows the isolated intersection with a horizontal lane and a vertical lane. Cars arrive in both directions and leave at the end of respective lanes (marked as end of world). The basic parameters of each car are given below: 1. Position (x): The displacement from the start of the lane to its current position on the lane 2. Velocity (v): Speed at which the car is travelling 3. Acceleration (a): Nominal acceleration for a car (assumed to be the same for all vehicles) 4. Deceleration (b): Nominal braking power of a car (assumed to be the same for all vehicles) The basic parameters of the system are given below: 1. Lane distance (l): The distance from the start of the lane to the traffic controller 2. Simulation Period (s): The amount of time the simulation needs to run 3. Red Signal Duration - Horizontal ( ): The red duration in the horizontal direction 4. Red Signal Duration - Vertical ( ): The red duration in the vertical direction 5. Yellow Time Duration (y): The fixed amount of yellow time required for the clearance of traffic before issuing a red 7

18 2.2 Arrival of Cars In our current model, vehicles arriving at the intersection are modified Poisson arrivals Poisson Process In a Poisson process, cars arrive independently one at a time, at a constant average rate with the inter-arrival time determined by the negative exponential distribution. For a given arrival rate λ, the probability density function is: And the cumulative distribution function is defined as: The inter-arrival times between any two subsequent car arrivals follow the above distribution. Hence the inter-arrival times are generated by using the inverse transformation technique: 1. Generate R from a uniform distribution in the range [0,1] : = R 2. Solve for : 3. Use this as the inter-arrival time Modified Poisson Arrivals Due to the safety constraints imposed, any two cars need to maintain a minimum safe distance (d) to avoid collisions. Hence this safe distance is converted into a safe time τ: 8

19 where is the speed of a car The actual arrival rate of the distribution is for which the average inter-arrival time is. This time is not guaranteed to be greater than the minimum safe time calculated above. To adjust for this, safe time is added to the generated inter-arrival time. This now guarantees the minimum safe distance between vehicles. This modification results in a new effective arrival rate (labeled ) for the distribution. Hence our modified arrival rate is 2.3 Safety Constraints Two safety constraints have been imposed. One is to avoid collisions and the other is to avoid violation of traffic signals. These have been explained below in detail Stopping Distance The stopping distance of a car is defined as the distance in which it can stop after applying its brakes. If the car's current velocity is and the nominal braking power is, the stopping distance is calculated as 9

20 Safe State A car is said to be in a safe state with respect to stopping at the intersection, when its current position is such that it can stop before the intersection line, that is: The sum of its position and its stopping distance should be less than the total lane distance: Figure 2: Safe Stopping Unsafe State A car is said to be in an unsafe state with respect to stopping at the intersection when its current position is such that it cannot stop before the intersection line, that is: The sum of its position and its stopping distance is greater than the total lane distance: 10

21 Figure 3: Unsafe Stopping Safe following distance Safe following distance is defined as the distance that needs to be maintained between two cars, say lead car and follower car, such that the follower car does not collide with the lead car. This takes the following factors into consideration: The reaction time of the driver of the follower car (same for all cars) This is converted to a reaction distance by multiplying with its current velocity The length of the follower car - L (same for all cars) Minimum distance between cars - m Stopping distance of both the lead car and follower car 11

22 Once these factors are computed, safe and unsafe following states can be defined, under the assumption that both the lead car and follower car use the same braking power b Safe following State A follower car is said to be in safe state, if it satisfies the following: Which means that, if the follower car reacts in t seconds and stops the car at a particular state, its position should still be at a distance of at least m behind the stopped car. Figure 4: Safe Following Unsafe following state A follower car is said to be in an unsafe state, if it satisfies the following: 12

23 This means that the follower cannot avoid colliding with a lead car that stops in front of it. Figure 5: Unsafe Following 2.4 The actual flow of the model At the starting state of the system, the light is green in the horizontal direction (which means red on vertical). Vehicles enter the system from both the horizontal and vertical directions according to the given modified Poisson arrival processes with the respective effective arrival rates. At the beginning, vehicles seek to travel at some cruising speed, defined for the system of cars. Now as the light turns yellow/red the approaching vehicles calculate their stopping distance D, then decelerate and brake if needed. 13

24 The first vehicle that approaches the intersection with a red light stops if it can. Others behind it maintain a minimum safe distance (as would be determined by adaptive cruise control). Now as the light switches back to green, vehicles that have stopped or slowed, accelerate until they reach their cruising velocity, while maintaining an assured safe following distance Optimal yellow time The fixed yellow time is calculated such that any car traveling at its cruising speed, but unable to stop at red is able to clear the intersection before the light turns red. Stopping distance for a car is: Hence, the stopping time for yellow should be at least To account for the reaction time of the drivers and allow a tolerance according to standards, we use the yellow time: 14

25 Chapter 3: Optimization Once the mathematical model is defined, and the cars move by following the safety constraints to avoid collisions and signal violations, we can now move on to determine the optimal signal timings for the traffic controller. The optimal signal timings are determined for each cycle, where a cycle is defined as: In order to calculate the optimal signal timings, we need few performance metrics to be optimized. Once we have these performance metrics, we can aim at formulating this as an optimization problem with the given safety constraints and other operational features, and assign the signal timings as the control variables. 3.1 Performance Metrics There can be several performance metrics for the system. We consider the following: Distance Lost Time Lost Fuel Lost 15

26 3.1.1 Distance Lost (dl) Distance lost by a car can be defined as the distance the car would have been ahead of the actual location if there had been no traffic signal (or, no red). Basically, it is the difference of two positions of a car: If the car had no red signal and would have traveled at cruising speed at all times If the car faced the situation with the signal The graph below gives a better understanding of distance lost: Figure 6: Distance Lost 16

27 The graph is plotted with time on the horizontal axis and position on the vertical axis. The straight line here represents the location of the car at cruising speed, if there were no red signals. The curved red line represents the location of the car with the presence of a red signal. The curve starts bending towards right when the car starts to slow for the intersection. Once the green is issued, the curve starts sloping up again and it picks up back to its cruising speed. Therefore, the difference between these two locations (labeled dl) gives us the lost distance of a car Time Lost ( ) This is similar to the distance lost defined above. Time lost by a car can be defined as the time the car would have been ahead of the actual timing if there had been no traffic signal. Basically, it is the difference of the times of a car: If the car had no red signal and would have traveled at cruising speed for a distance d If the car faced the situation with the signal The graph below gives a better understanding of time lost: 17

28 Figure 7: Time Lost 3.2 Optimal Signal timings Now that we have the performance metrics, we know the values that need to be minimized for calculating the optimal signal timings. Ideally, the score that needs to be minimized for the optimization problem is: where, are defined above. The coefficients and ( ) are the weights for the respective performance metrics. We focus on the distance lost ( ) and use it to calculate the optimal signal timings, but, the other performance measures are similar. 18

29 3.2.1 Using distance lost to calculate signal timings Once we have the distance lost for each car, we can calculate the cumulative distance lost for the whole system of cars. Average loss per car is: We can then pick the red-to-green ratio that gives the minimum average loss per car from different combinations. Thereby we can calculate the optimal red-to-green ratio for each cycle length. We can compute these for varying flow rates and plot the average loss per car as a function of effective green ratio (green/cycle-length). For any particular flow rates, we might expect the graph to look like this: Figure 8: Average Loss 19

30 Chapter 4: The Software The software used for simulating the real-time network traffic scenario is built on the mathematical model given in Chapter 2. It aims at producing the optimal signal timings; that is the green-to-red ratio (with a fixed yellow) for a given set of input parameters and a simulation period. The software minimizes the performance metrics as described in Chapter 3 and gives the required optimal values. It also produces graphs of the average loss plotted against the effective green time for varying flow rates in both the horizontal and vertical lanes. 4.1 High level Design The high-level design and flow of the software is depicted below: 20

31 Figure 9: High-level Design Cycle length: The total time allotted for the red, green and yellow signals for each phase is considered as cycle length Red-Green split: The ratio of red/green signal timing to the total cycle length 21

32 Simulation period: The total amount of time from the beginning of the initial cycle phase to the end of the given time period for which the simulation runs Horizontal/Vertical car queue: In each direction, the queue of cars lined from the start to the end of the lane. 4.2 Classes and Methods Three classes are defined here: 1. Car class The car class defines the behavior of the car driver and incorporates the safety constraints that the driver should follow (as defined in Chapter 2). 2. Central controller class The central controller is responsible for the movement of cars that arrive at the intersection and also computes the cumulative distance lost for the whole set of cars. 3. Outer controller class The outer controller class is used to start the central controller for each set of redgreen splits, compute its corresponding cumulative distance lost and thereby output the optimal signal timings Car Class State Position of the car 22

33 Road (Horizontal/vertical) Car length Car Number (ID) Speed of the Car, acceleration, braking power Cruising Speed of the car Mode (accel/decel/idle) Stopping distance of the car Starting and ending positions for the distance lost calculation Starting and ending times for the distance lost calculation Minimum distance between cars after they are stopped Reaction time of the driver Behavior The following methods are defined for the car driver behavior: void stopatsignal (int signal, Car carinfront) This method defines the car behavior when it is approaching the intersection. With the input of the current signal (when it is red), the car decides to stop at the intersection. In the case of yellow, based on its stopping distance the car decides to stop or proceed depending on whether it can or cannot stop at the intersection. Once the signal changes from red-to-green it starts accelerating which also depends on the state of the car in front of it. double stoppingdis() 23

34 This method calculates the stopping distance, based on the current state of the car. void move() This method moves the car to accelerate / decelerate / cruise based on its current mode. void checkcarinfront (Car carinfront) This method is used to maintain the safe following distance between two cars. By checking with the car in front, a car decides whether it needs to decelerate in order to maintain the safe following distance. Once the safe following distance is maintained, it triggers the car to accelerate from idle state Central Controller class State Duration of running the simulation Lane distance Horizontal car queue and vertical car queue Red duration for horizontal and vertical directions Flow rates for horizontal and vertical directions Number of cars in horizontal and vertical directions Distance lost in horizontal and vertical directions Fixed amount of yellow time Safety distance between two cars converted to time 24

35 Delta interval for update Behavior This class schedules events for generating the cars, updating the queues and updating the signal timings. It contains three inner classes, which are instantiated in its constructor: 1. Signal update This class is used to keep updating the red, green and yellow signals, the events for which are generated according to their duration. 2. Cararrivalh and Cararrivalv The cararrivalh is used to generate the cars and add them to the queue in the horizontal direction. Cars are generated from the modified Poisson arrival process as defined. On the other hand, the cararrivalv is used to generate the cars and add them to the queue in the vertical direction. Cars are generated from the modified Poisson arrival process as defined. 3. Carupdate This class keeps updating both the horizontal and vertical car queues for every "delta" interval of time defined. Also it updates the starting and ending positions for the distance lost calculation. 25

36 Pseudo code: The below figure shows pseudo code for the overall process: Figure 10: Pseudo code Other methods: updatecumdist() 26

37 This method computes the lost distance for each car, based on its starting and ending positions and updates the global distance lost, variable in the corresponding direction getexp() This method generates the Poisson distributed inter-arrival times for the cars with an added safety distance converted to time Outer Controller class For a given cycle length and simulation period, this class calls the inner controller class with different values of red-green splits. Then it outputs the red-green split with the minimum cumulative distance lost value. It also takes different flow rates as input and plots the effective green ratio against the average distance lost per car. 27

38 Chapter 5: Results The results given here plot the average loss per car as a function of effective green ratio (green/cycle-length). For both the curves in each graph, flow rates for both the horizontal and vertical lanes are given. The red curve, which is used for comparison, has equal flow rates on both the lanes and remains the same in all the results. In all the given results, flow rates of red curve = 1.0, 1.0. Here the unit of flow rate is vehicles per second. The following are the parameter values used in the simulation: Cruising speed, Acceleration, Braking power, Length of the car, Minimum distance between cars, Reaction time of the driver, Cycle length, Yellow time, 28

39 The following are the results with the red curve being the same and decreasing flow rates in horizontal lane for the blue curve. Result1: Flow rates (blue curve) = 0.8, 1.0 Figure 11: Result1 29

40 Result2: Flow rates (blue curve) = 0.6, 1.0 Figure 12: Result2 30

41 Result3: Flow rates (blue curve) = 0.4, 1.0 Figure 13: Result3 31

42 Result4: Flow rates (blue curve) = 0.2, 1.0 Figure 14: Result4 32

43 Result5: Flow rates (blue curve) = 0.1, 1.0 Figure 15: Result5 Analysis When the traffic flow rates are equal on both the directions: Minimum loss occurs when Optimal Effective green is 33

44 Here we have Now by substituting the above values into the equation, we get As seen in the results, minimum is at 0.35 on X-axis, which is the optimal effective green. When the flow rates are unequal on both the directions: In this, the flow rate on the horizontal lane is less than the flow rate on the vertical lane. In order to maintain the loss at minimum, as the flow rate on the horizontal lane decreases, the effective green also decreases. This is depicted in the graph below, which plots the optimal effective green ratio as a function of horizontal flow rates. 34

45 Figure 16: Results Summary 35

46 Chapter 6: Conclusions & Future Work In this report, a mathematical model for the system of cars approaching a traffic signal at an isolated intersection is presented. A real-time traffic scenario with the cars arriving in a modified Poisson process is simulated. Various optimization metrics with respect to minimizing the fuel loss and average waiting time of a driver are discussed. The main focus is on the distance lost by the whole system of cars during red signal. Results from the software are presented which plot the effective green (ratio of green to cycle length) to the distance lost. For a given cycle, the red-to-green ratio at which the distance lost is marginal can be determined with the help of resulting graphs. Future Work: This field of work can be extended to other intersections such as Single Point Urban Interchange. In addition, vehicle-to-infrastructure communication can be used to provide the parameters that can be obtained from sensors, cameras, etc. Infrastructure-tovehicle communication can also be added to alert the driver (or autonomous vehicle) about ideal driving speed and acceleration/deceleration. 36

47 REFERENCES [1] Rouphail N., Tarko, A., and Jing Li (1997). "Traffic Flow at Signalized Intersections," ninth chapter in monograph: Traffic Flow Theory - A State-of-the- Art Report, an update and expansion of Transportation Research Board Special Report 165, pp.9-1 to [2] C. Cai, Y. Wang and G. Geers, Adaptive traffic signal control using vehicle-toinfrastructure communication: A technical note, Proc. Int l Workshop on Computational Transportation Science (IWCTS), pp.43-47, [3] N. Pulter, H. Schepperle and K. Bohm, How agents can help curbing fuel combustion: a performance study of intersection control for fuel-operated vehicles, AAMAS 2011: [4] Liao, Tsai-Yun, et al., Fuel Consumption Estimation and Optimal Traffic Signal Timing, U.S. Department of Transportation, Report No. SWUTC/98/ , Texas A&M University, College Station, Texas, [5] Cheng, D., C.J. Messer, Z. Tian, and J. Liu, Modification of Webster s Minimum Delay Cycle Length Equation Based on HCM 2000, the 81st 2003 Annual Meeting of the Transportation Research Board in Washington, D.C., January [6] Sarah M. Loos, Andre Platzer, and Ligia Nistor, Adaptive Cruise Control: Hybrid, Distributed, and Now Formally Verified, School of Computer Science, Carnegie Mellon University, CMU-CS ,