Development of an Internet-Based Traffic Simulation Framework for Transportation Education and Training
Chen-Fu Liao* Center for Transportation Studies and the Intelligent Transportation Systems Institute University of Minnesota 200 Transportation and Safety Building 511 Washington Ave SE Minneapolis, MN 55455 E-mail:
[email protected] Ph: 612-626-1697 Fax: 612-625-6381
Ted Morris Center for Transportation Studies and the Intelligent Transportation Systems Institute University of Minnesota 200 Transportation and Safety Building 511 Washington Ave SE Minneapolis, MN 55455 E-mail:
[email protected] Ph: 612-626-8499 Fax: 612-625-6381
Max Donath Intelligent Transportation Systems Institute University of Minnesota 200 Transportation and Safety Building 511 Washington Ave SE Minneapolis, MN 55455 E-mail:
[email protected] Ph: 612-625-2304 Fax: 612-625-6381
Word Count: Body text (3,531) + 11 Figures (222) = 3,753 *Corresponding Author
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ABSTRACT Many traffic simulation software packages are available to help traffic engineers and researchers study and evaluate the potential impact of proposed traffic management strategies and policies. However, existing tools require a significant investment in time for learning how to create models, perform calibrations and finally analyze the results. This substantial learning curve severely restricts their application and makes it difficult for engineering students, the general public and policy makers to take advantage of these tools. An Internet-based traffic simulation framework was developed to enhance the learning experience for transportation students and engineers. Pre-generated traffic scenarios were first implemented as part of a Civil Engineering undergraduate class. Based on feedback, an interactive simulation tool was developed to allow users to make changes to the model and examine the traffic impacts. This now allows students to for example, minimize the queue length by changing the cycle length or splits. This interactive traffic simulation tool was deployed and tested in an undergraduate class of 73 students and feedback was collected from instructors and students that will facilitate additional enhancements of the lab module. This web-based traffic simulation framework can also incorporate larger road networks that allow one to consider a multiplicity of control strategies, thus providing a valuable tool for educating and training transportation professionals.
KEYWORDS Traffic Simulation, Distance Learning, Transportation Visualization
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INTRODUCTION Traffic simulation tools have been widely used by transportation engineers and consultants to assist traffic managers and operators with the evaluation and analysis of the potential impact of design or control strategy changes. As an early example, Hourdakis and Michalopoulos [1] created a framework that allowed traffic operations personnel to try various strategies for managing a 'virtual' freeway incident. This was achieved by real time integration of traffic microsimulation software with a GUI (Graphical User Interface) front end. Their GUI displayed a 2D traffic map of the modeled freeway network (traffic volume is mapped to a color), variable message sign (VMS) I/O, and an interface to remote cameras (for live video feeds). The GUI and the traffic management strategies were formulated from rules written with a 'natural language' expert system programming language (http://www.gensym.com). Thus, they were able to evaluate how traffic operations personnel managed incidents while varying the content and UI (User Interface) presentation of the 'sensed' traffic data. The framework could also be 'switched' to use 'live' traffic instead of the simulated traffic (i.e., by collecting freeway loop detector data in real-time). These tools are however rather complex and have been developed for and used by researchers, and trained traffic engineers. Existing tools, usually only available in dedicated laboratories, require a significant investment in time for learning how to create models, perform calibrations and finally analyze the results. This substantial learning curve severely restricts their application and makes it difficult for engineering students, the general public and policy makers to take advantage of these tools. The benefit of using simulation to train people who deal with matters of life and death (e.g. pilots) is clear. However, many do not see the benefit of using simulation to teach material that has been taught using traditional methods in the classroom [2]. Yet, it is well understood that conveying complex concepts can be achieved by exploring them through simulation. Accordingly, in order to enhance the learning experience and understanding of intelligent transportation systems (ITS) by students and traffic engineers, we created a web-based traffic simulation module for an undergraduate transportation engineering course at the University of Minnesota. This web-based traffic simulation lab module was developed based on a microscopic traffic simulation package, AIMSUN (Advanced Interactive Microscopic Simulator for Urban and Non-urban Networks, http://www.aimsun.com) [3]. AIMSUN is embedded in GETRAM (Generic Environment for TRaffic Analysis and Modeling), a simulation environment inspired by modern trends in the design of graphical user interfaces adapted to traffic modeling requirements [3]. GETRAM comprises a traffic network graphical editor (TEDI), a network database, a module for storing results, and an Application Programming Interface (API) to allow interfacing to other simulation or assignment models. An additional library of DLL functions (GETRAM Extensions) enables the system to communicate with external applications [3, 8]. AIMSUN has been used successfully for numerous large-scale traffic modeling research projects within the lab [4, 5] and provides a well-documented API to access and modify all elements of the simulation state (signal control, sensing, vehicle characteristics and state) while the simulation is running. Of course, other simulation packages with similar capabilities can be integrated into our web-based traffic simulation framework. These include, CORSIM [6] (Corridor Simulation, a comprehensive microscopic traffic simulation, applicable to surface streets, freeways, and integrated networks,
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http://ops.fhwa.dot.gov/trafficanalysistools/corsim.htm) operated under the TSIS (Traffic Software Integrated System) environment and VISSIM (a microscopic, behavior-based multi-purpose traffic simulation program, http://www.ptvamerica.com/vissim.html). Our goal was to help students and engineers understand issues related to traffic management and operations. Using this new simulation tool accessed in real time over the Internet, students were able to analyze existing traffic situations and provide potential solutions to improve traffic operational efficiency.
TRAFFIC SIMULATION ON THE INTERNET There are many commercial traffic simulation software packages available to help traffic engineers and researchers study and evaluate the potential impact of proposed traffic management strategies and policies. Creating a traffic model and calibrating the model is a fairly complicated process which often discourages students or engineers from learning and understanding the importance and impact of traffic control. The availability and interactive nature of web-based traffic simulation provides an excellent medium for students to experience the complexity and dynamism of traffic operation, management and control. From an educational point of view, this interactive web-based tool allows people to try their own design/control approach in the simulated network without disrupting the actual traffic. From an operation and management point of view, traffic simulation helps the user test and verify different control strategies (ramp metering, signal timing control, etc.) for different traffic conditions. Clearly this approach can be integrated with other distance learning approaches already in place for teaching ITS technologies (Web Research Modules, http://www.its.umn.edu/education/modules.html#webmodule, for high school students). For example, Helbing et al [7] developed multilane freeway traffic models to help people better understand on-ramp vehicle merging, lane-changing, car following, lane-closing, and signal control through online traffic simulation and visualization (http://www.mtreiber.de/MicroApplet/index.html). Lastly, new traffic control strategies that are proposed often meet considerable resistance from the general public. We believe that by providing traffic simulations which incorporate visualization tools available on the Internet along with the ability to investigate and analyze different cause and effect scenarios, various stakeholders will better understand the impact of changes in traffic operations or proposed ITS technologies.
OUR APPROACH An initial study site of Washington Avenue SE on the Minneapolis campus of the University of Minnesota was chosen. The Public Works Department of the City of Minneapolis provided intersection signal timing and traffic flow data. Digital Orthorectified Quad aerial images (DOQs) were acquired from the Metropolitan Council (the MPO for the greater twin cities 7 county region) for network geometry layout. Scaling factors of the aerial images were calculated to reflect the corresponding scale in the simulation model. Network geometry was created using the graphical traffic editor (TEDI) that is integrated into the GETRAM environment [8] with the aerial images placed in the background. In addition to traffic network geometry modeling, a software interface was developed to access
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traffic simulation data through the GETRAM extension API (Application Program Interface) and store data to a central database (MySQL, http://www.mysql.com) server while the simulation is running. Java client/server applications were developed to handle data communication between the client computer, traffic simulators, web server and database server. Java has been widely used in web based application and web simulation. The Java technology provides an ideal platform for developing reliable, portable and scalable applications for web-based traffic simulations.
Traffic simulation The vehicle data on a user’s PC is updated and graphically displayed during every simulation step by sending queries to the web server. The web server processes and forwards the request to the database server and returns the requested data. Intersection signal timing information and time-space diagrams can also be displayed, as shown in Figure 1, with the selected vehicle(s) traced in real time. Vehicle information (such as vehicle ID, type, length, width, location and heading data) can be logged and saved from the graphical user interface.
Interactive simulation model In the initial phase, our web-based 2D traffic simulation module limited users to choosing only from a fixed set of pre-generated scenarios for their analysis. The student could not design new signal control strategies or traffic demand models for the network. We have since developed an “interactive” traffic simulation lab module that will allow on campus or distance-learning students to configure their own signal control strategies and traffic demand patterns—and see the results either in ‘real-time’ or in ‘accelerated time’ using the client web interface. As before, users can select, for example, a bus and watch it run its route through the network while monitoring the distance traveled on time-space plots overlaid with the signal phases along its route (Figure 1). Simulation results are saved in a traffic simulation database. Measures of Effectiveness (MOEs), including traffic flow, travel time, delay time, speed, density, stop time, number of stops, total travel distance and total travel time, for the simulation can be accessed for each simulation run for further analysis. The ability to run the simulator in ‘real-time’ can be used to test the efficacy of various operational management strategies. For example, students can create ‘incidents’ or lane blockages and monitor how well their incident management strategies work. The new module design consists of an interactive web-based application, a database server, web server, and several custom developed JAVA back-end services (i.e., Java servlets) as shown in Figure 2. The web application displays the vehicle movements and signal states either ‘as they happen’ in real-time or by scrolling through post-processed simulation data. The servers act as a communication ‘bridge’ between the web application and the microsimulation software running on a PC within the ITS Laboratory network. From here on, we define the PC which runs the traffic microsimulation software a node, and each execution run of the software a simulation instance. The backend services also manage simulation node selection and subsequent instance execution by remotely monitoring the CPU loads and process executions using freely available software (http://www.beyondlogic.org). Finally, the back-end services interface with the lab’s OpenLDAP authentication system as a means of user access and authentication control.
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Before the simulation starts, the web interface first communicates with the web server and scans through the available simulation instances (our lab can execute up to 4 instances simultaneously) and determines the least loaded node available to execute the simulation in batch mode. User inputs (such as traffic demands, signal timing, simulation period) are formatted into corresponding data files and sent to the remote simulation engine prior to the simulation. Resulting statistics (MOEs, such as traffic flow, travel time, delay time, speed, density, stop time, number of stops, total travel distance and total travel time) are stored in a centralized database during the simulation at minute intervals. The graphical interface on the user’s computer will start rendering the real-time simulation as soon as data are available. When the simulator finishes the batch mode execution (a batch execution could take about one minute for an hour of simulation for the single intersection example), a simulation time slider on the screen becomes enabled which allows users to jump forward or backward in time within the simulation period. Users can use this feature to examine traffic conditions such as queue length or delay, more efficiently. Finally, simulation statistics, as shown in Figure 6, can also be saved to the user’s PC and transferred into a Microsoft Excel for later analysis. Previously, students relied mainly on formula suggested in the Highway Capacity Manual [9] to calculate estimated cycle time, green splits, lost time, delays and make the necessary adjustments for left-turn movement. The design of a feasible signal timing plan is a complex and iterative process that is generally carried out with the assistance of computer software (for example, Highway Capacity Software, http://mctrans.ce.ufl.edu/hcs/). However, there was little or no verification or feedback on how well the design of the signal timing performs. The traffic simulation tool helped students visualize traffic parameters, such as lost time, gap acceptance, queue length and delay, and make adjustments to the signal timing plan as needed. We shall now present an example of a prototype module recently used for an undergraduate transportation engineering class of 73 students which addressed some of these issues.
Single intersection example In a single intersection example, as shown in Figure 3, the web interface allows users to specify traffic demands from each approach (Figure 4) and design desired signal cycle time and green splits (Figure 5). In the lab module designed by the course teaching assistants, existing intersection traffic demand and turning movements were provided. Students were asked to develop a new signal timing plan. They needed to evaluate three alternative plans, each with different objectives: 1.
Minimize the intersection delay
2.
Minimize the number of stops per vehicle
3.
Based on a combination of the above two, consider alternatives for the optimization using the performance index (PI), PI = 1 * delay (sec)/vehicle + 10 * number of stops/vehicle. Students needed to find the timing plan with the lowest performance index.
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Students were also asked to develop a signal timing plan based on the traffic demand forecast (e.g. 5% increase for NB and SB directions, and 2% increase for the EB (East Bound) and WB (West Bound) directions over the next 5 years while the turning proportions remain the same) using the same performance index as described above. Students were to investigate timing plan changes if the cycle length remained the same. We collected feedback from students on the interactive web-based traffic simulation model. One of the comments was to display the statistics graphically on the web interface without the need for post analysis of data using a spreadsheet. We realized the need for a graphical interpretation and an explanation of the simulation results. We therefore added several graphs from the resulting statistics including traffic flow, number of stops per vehicle per Km (Figure 7), travel time, delay (Figure 8), speed, queue length (Figure 9), stop time and a performance index within the simulation period. Providing these simulation statistical results helps the users visualize the impact of the applied control strategy and allow him or her to make adjustments more efficiently. For example, the user can readily identify the vehicle queue length (17 vehicles) at 9:18AM (or 18 minutes from the beginning of the simulation) as shown in Figure 9. The user can then adjust the simulation time slider (Figure 3) to a time stamp a few minutes before 9:18AM and inspect the forming queue and the traffic conditions around the interested simulation timeframe. A portion of queue length from 9:15AM to 9:23PM (or simulation time from 900 sec to 1380 sec) where the maximum queue occurred in northbound traffic was plotted and overlaid with signal phases (see Figure 10) as an example to better understand the queue formation associated with the signal timing.
FUTURE WORK We will continue to enhance the design to include larger road networks that allow one to consider a multiplicity of control strategies, for example, adding a bus signal priority module or a ramp-metering module. We currently continue to integrate the web-based traffic simulation with a 3D visualization interface. The visualization interface can render the simulation results in a virtual reality environment and provide real-time interactivity. Future enhancements for the 3D software will allow the user to program signal timing plans and select vehicle probes similar to the 2D applications.
In order to further enhance the user’s ability to understand the traffic effects within the context of the infrastructure, we are currently exploring a 3D component to the simulation. Users can interactively view the simulation from any perspective—including from within or above a moving vehicle. The user can gain additional insights into the nature of the simulated traffic flow, such as shockwave propagation behavior [10], car-following model [11], driverroadside traffic information infrastructure, and context design and urban planning issues [TRB visualization in transportation, http://www.trbvis.org], [12,13]. We first developed a 3D web-based traffic simulation for the Washington Ave corridor in Minneapolis, as shown in Figure 11(a & b), using VRML (Virtual Reality Modeling Language) EAI (External Authoring Interface). The VRML EAI interface allowed users to control the content of a VRML browser window embedded in a web page from a Java applet interface. It enabled embedded objects on a
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web page to communicate with each other through a browser plug-in. Unfortunately, we found that the VRML EAI interface scales poorly (memory and 3D rendering performance) as more vehicles were added to the network. We are therefore developing a standalone 3D web application based on OSG (OpenSceneGraph, http://www.OpenSceneGraph.org) and CommonC++ (http://www.cplusplus.org) that can interface with the webbased traffic simulation framework previously discussed.
We believe that the framework represents a novel, flexible approach to further engaging students and the general public in the transportation decision making and planning process. Our primary intention was to use our framework to allow individuals to internalize important concepts in traffic engineering and ITS issues in their mind’s eye in an efficient and scalable manner. Note that similar approaches are now available for other more generalized numerical simulation software (www.mathworks.com) [14]. However, one interesting issue arises from a software licensing standpoint. Essentially, we have created an interface for our site that automates the process of running the software for a desired set of conditions specified by another person and to then send back the results. The software is run under our authority at our site on our behalf for a person whom we have knowledge of the request. Certainly, a less automated interface would be for that same person to use e-mail communication in order to request the input and outputs to the model for a given scenario. Further discussion is needed on how traffic simulation software, can best be integrated to emerging pedagogical distance e-learning approaches. For example, we wish to integrate actual state-of-the-art sensing and signal control hardware into our framework. Other facilities and laboratories have done this [15, 16], although our framework will allow individuals to learn and ‘experience’ these technologies from any location having Internet access.
ACKNOWLEDGEMENT We would like to acknowledge the Intelligent Transportation Systems Institute, University of Minnesota, for supporting this work and the Institute for New Media Studies, University of Minnesota, for providing some resources for this effort. We would also like to thank Scott Tacheny, traffic engineer in the City of Minneapolis, for providing the traffic and signal timing data, Professor David Levinson for giving access to the students and his teaching assistants, Nebiyou Tilahun and Lei Zhang, and other transportation engineering faculty and students of the Civil Engineering Department for providing us with their invaluable feedback.
REFERENCES [1]
Hourdakis J., Michalopoulos P.G., “Development And Implementation of A Virtual Traffic Management Center”, in Proceedings of the World Congress on Intelligent Transportation Systems, Turin, Italy November 2000.
[2]
Aldrich C., “Simulations and the Future of Learning: An Innovative (and Perhaps Revolutionary) Approach to E-Learning”, Pfeiffer, September, 2003
[3]
“AIMSUN Version 4.1 User’s Manual”, Transport Simulation Systems (TSS), Barcelona, Spain, Mar. 2002.
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[4]
Hourdakis, J., Michalopoulos, P. and J. Kottommannil. “Practical procedure for calibrating microscopic traffic simulation models” Transportation Research Record 1852: pp. 130-139, 2003.
[5]
Xin, W., Michalopoulos, P., Hourdakis, J., and D. Lau, “Minnesota's new ramp-control strategy: Design overview and preliminary assessment” Transportation Research Record (in press).
[6]
Owen, L., Zhang, Y., Rao, L., and McHale, G. “Traffic Flow Simulation Using CORSIM,” Proceedings of the 2000 Winter Simulation Conference, 2000: pp. 1143-1147.
[7]
D. Helbing, A. Hennecke, V. Shvetsov, and M. Treiber “Micro- and macro-simulation of freeway traffic”, Mathematical and Computer Modeling 35(5/6), pp. 517-547, 2002.
[8]
“GETRAM Extensions Version 4.1 User’s Manual”, TSS, Barcelona, Spain, Mar. 2002.
[9]
Highway Capacity Manual – “Chapter 16: Signalized Intersections”, Transportation Research Board, National Research Council, Washington DC, 2000.
[10]
Franklin, R.E. (1961), The Structure of a Traffic Shock Wave. Civil Engineering Pulb. Wks. Rev. 56, 11861188.
[11]
Del Castillo, J.M. (1996). A Car-Following Model based on the Lighthill-Whitham Theory. In: Lesort, J.B. (ed), Proceedings of the 13th International Symposium of Transportation and Traffic Theory, Lyon, 517-538.
[12]
Hearne, L. P., and Matthews, D., “Improving the Geospatial Data Extraction and Analysis Process Using Stereo Imagery Datasets,” American Congress on Surveying and Mapping (ACSM), Nashville TN, April 1621, 2004. http://www.acsm.net/HearnePhotogrammetry42004.pdf
[13]
Jones, Ted, Hamilton-Smith, G., and Matthews, N.D., “Integrating Remotely Sensed Imagery And Information For Transportation Infrastructure Management,”, American Society for Photogrammetry & Remote Sensing, ISPRS-Pecora, Denver Colorado, November 10-15, 2002.
[14]
Sysel, M. and Pomykacz, I., “Extension of MATLAB Web Server”, In Proceedings: Advances in Computer Science and Technology, 2004.
[15]
Engelbrecht, R., C. Poe, and K. Balke, “Development of a Distributed Hardware-in-the-Loop Simulation System for Transportation Networks,” in Proceedings of the 78th Annual Conference of the Transportation Research Board, 1999, Washington, D.C.
[16]
Bullock, D. and A. Catarella, “Real-Time Simulation Environment for Evaluating Traffic Signal Systems,” Transportation Research Record 1634, 1998: pp. 130-135.
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List of Figures Figure 1. Java-Based User Interface Figure 2. Web-Based Traffic Simulation Framework Figure 3. Interactive Traffic Simulation Interface Figure 4. Intersection Traffic Demand and Turning Proportion Interface Figure 5. Intersection Traffic Signal Timing Interface Figure 6. Traffic Simulation Statistics Figure 7. Traffic Simulation Result – Number of Stops per Vehicle per Km Figure 8. Traffic Simulation Result – Average Vehicle Delay Figure 9. East Bound Queue Length of Timing Plan A Figure 10. North Bound Queue Length Overlaid with Signal Phases Figure 11(a). Snapshot of VRML Model of Intersection at Union St. and Washington Ave. SE in Minneapolis Figure 11(b). Snapshot of VRML Model of Intersection at Harvard St. and Washington Ave. SE in Minneapolis
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HTTP Applications
LDAP Authenticate
LDAP Authorize Generate & return session key
Application check validation of session key
Web Server
Traffic demand and signal control settings, authentication and trigger execution
Return session key validation and user application ID
Traffic simulation nodes PC 2 GETRAM EXT uid.dll
Traffic Simulation Database Server
OpenLDAP Database Server
PC 3 GETRAM EXT uid.dll
Simulation resulting statistics, vehicle data and trajectories
Figure 2. Web-Based Traffic Simulation Framework
Figure 3. Interactive Traffic Simulation Interface
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Figure 4. Intersection Traffic Demand and Turning Proportion Interface
Figure 5. Intersection Traffic Signal Timing Interface
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Figure 6. Traffic Simulation Statistics
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Figure 7. Traffic Simulation Result – Number of Stops per Vehicle per Km
Figure 8. Traffic Simulation Result – Average Vehicle Delay
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Figure 9. East Bound Queue Length of Timing Plan A
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NB Queue Length vs. Signal Phases 7
Queue (veh/lane)
6 5 4 3 2 1 0 900
1000
1100
1200
1300
1400
Time (sec) Red
Green
Amber
Queue
Figure 10. North Bound Queue Length Overlaid with Signal Phases
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Figure 11(a). Snapshot of VRML Model of Intersection at Union St. and Washington Ave. SE in Minneapolis
Figure 11(b). Snapshot of VRML Model of Intersection at Harvard St. and Washington Ave. SE in Minneapolis
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