Joshua Auld (corresponding), Argonne National Laboratory

Transcription

1 Paper Author (s) Joshua Auld (corresponding), Argonne National Laboratory Michael B Hope, Argonne National Laboratory (hope@anl.gov) Hubert Ley, Argonne National Laboratory (hley@anl.gov) Vadim Sokolov, Argonne National Laboratory (vs@anl.gov) Bo Xu, Argonne National Laboratory (bxu@anl.gov) Kuilin Zhang, Michigan Technological University (klzhang@mtu.edu) Paper Title & Number POLARIS: An integrated agent-based simulation model of activity travel behavior and network operations [ITM # 37] Abstract This paper documents the development of a fully integrated dynamic simulation of travel demand, network supply and network operations using a newly developed high-performance agent-based modeling framework called POLARIS. The utility of developing complex, integrated, agent-based microsimulation models using an extensible agent-based framework is shown. The paper briefly introduces a new generalized, agent-based simulation model development suite of tools and processes, and demonstrates its use in the creation of a prototype integrated ABM model for the Chicago region used to evaluate Traffic Management and ITS operations. The prototype demonstration model is a fully functional agent-based simulation model including travel demand, traffic simulation and ITS operations, which could be extended to a tool appropriate for planning purposes with substantial calibration and validation effort. The simulation model built from the POLARIS core modeling language is unique in that it is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning modes to be extended to include such previously separate aspects of the urban system, such as network operational characteristics, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management centers to various network events such as accidents, congestion and weather events show the potential of the system. Statement of Financial Interest The authors have no financial interest in the paper described as the model system is federally funded open-source software, other than as a source of research funding. Statement of Innovation The project is innovative in that it is one of the few examples of a fully agent-based integrated travel demand model, where the network supply portion of the model is implemented as individual agent

2 behaviors of the same agents which generate the travel demand. In other words, route choice, network simulation, etc. are individual actions of persistent agents in the system. The model is implemented with a new high-performance agent-based discrete event engine called POLARIS.

3 POLARIS: A fully integrated agent-based simulation model of activity travel behavior and network operations. Joshua Auld*, Michael Hope, Hubert Ley, Vadim Sokolov, Bo Xu and Kuilin Zhang Transportation Research and Analysis Computing Center Argonne National Laboratory 9700 S. Cass Ave. Argonne, IL Phone: jauld@anl.gov *corresponding author Paper Submitted for Presentation at the 5th Conference on Innovations in Travel Modeling Submission Date: December 15, 2013 Word Count: (Text) 250 = 2991 words (1 Figure)

4 ABSTRACT This paper documents the development of a fully integrated dynamic simulation of travel demand, network supply and network operations using a newly developed high-performance agent-based modeling framework called POLARIS. The utility of developing complex, integrated, agent-based microsimulation models using an extensible agent-based framework is shown. The paper briefly introduces a new generalized, agent-based simulation model development suite of tools and processes, and demonstrates its use in the creation of a prototype integrated activity-based model for the Chicago region used to evaluate Traffic Management and Intelligent Transportation Systems (ITS) operations. The prototype demonstration model is a fully functional agent-based simulation model including travel demand, traffic simulation and ITS operations, which could be extended to a tool appropriate for planning purposes with substantial calibration and validation effort. The simulation model built from the POLARIS framework is unique in that it is a single, shared-memory process for handling all aspects of the integrated urban simulation. The resulting gains in computational efficiency and performance allow planning modes to be extended to include such previously separate aspects of the urban system, such as network operational characteristics, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management centers to various network events such as accidents, congestion and weather events show the potential of the system. Keywords: Agent-based modeling, Activity-based modeling, Integration, Dynamic Traffic Assignment STATEMENT OF FINANCIAL INTEREST The authors have no financial interest, other than as a source of research funding, in the model described in this paper as the model system is federally funded open-source software. STATEMENT OF INNOVATION The project is innovative in that it is one of the few examples of a fully agent-based integrated travel demand model, where the network supply portion of the model is implemented as individual agent behaviors of the same agents which generate the travel demand. In other words, route choice, network simulation, etc. are individual actions of persistent agents in the system. The integrated nature of the model, and the high performance provided by the underlying discrete event modeling framework on which it is developed, allow for the integration of more aspects than just demand and supply, in this case also considering network operations. The model is implemented with a new high-performance agent-based discrete event engine called POLARIS. 2

5 INTRODUCTION Historically, transportation-related models have analyzed aspects of the transportation system (travel demand, traffic flows, etc.) independently. The realization developed that these phenomena needed to be modeled in an integrated manner, but transportation models generally lacked a common framework to do so. Additionally, as the complexity of models increased there was need for better computational performance. This is especially true when modeling Intelligent Transportation Systems and network operations improvements. There is a need to evaluate the impacts of ITS as for any other system investment, requiring integration with travel demand. This has often been done using sketch planning tools that rely on existing macro-level demand model, such as IDAS (McHale 2000), or the integrated corridor management analysis, modeling, and simulation methodologies that combine macroscopic travel demand model with traffic flow simulation (Alexiadis 2008). However, a fully disaggregate model which simulates travel demand along with ITS requires more direct integration. The Planning and Operations Language for Agent-based Regional Integrated Simulation (POLARIS) model framework was designed to address these issues. The framework consists of a high-level modeling object repository and an low-level SDK (software development kit). The framework allows the user to develop an integrated simulation of a transportation system in a standardized, extensible manner. The framework handles the discrete event simulation scheduling and execution, memory management and threading for the developer, as discussed in a companion paper (Hope et al 2014). The use of an agent-based modeling approach to integrated transportation system modeling allows some of the limitations of traditional aggregated transportation models to be overcome. This paper discusses a model built using the POLARIS framework for evaluating network operations and ITS from a planning perspective, as in MITSIMLab (Yang et al 2000), DynaMIT (Ben-Akiva et al. 2002) and Dynasmart (Mahmassani et al 1993). The model consists of an activity-based demand model implemented as a series of actions and behaviors that the traveler agents perform during the simulation, following the activity-based paradigm. Along with the ABM is the network model, which includes an individual traveler route choice model under information, a route generation model using simulated travel costs, and a traffic simulation based on the Kinematic Wave theory of traffic flow. Finally, there is a Traffic Management Center component which controls the ITS system. The innovative aspect of this model is the complete integration of the activity-based and network simulation model elements through their implementation as behaviors of a single, persistent traveler agent which interacts directly with the network and ITS agents in a shared memory space. This work builds on previous integration efforts such as MATSIM (Balmer et. al 2008), SimTRAVEL (Pendyala et al 2012), and the DaySim-TRANSIMS integration (Lawe et al. 2011). POLARIS INTEGRATED ABM OVERVIEW The activity-based model consists of a series of agent classes which implement events corresponding to typical components found in travel demand, network simulation and operations 3

6 models. At the center of the model is a person-agent which represents the travelers in the system and their activity and travel planning behavior. The person agents operate in an environment represented by the transportation network agents to handle movements through the system. A set of ITS components and an automated Traffic Management Center (TMC) agent controls the ITS system and monitors the network agents. An overview of the ABM is shown in Figure 1, which shows how the main actions and behaviors of the three main components of the model and how they fit together. The various components are discussed in the following sections, followed by a section which describes the case studies carried out using the model. Throughout these sections, it is important to note that both the simulation model itself and the Chicago-area network event case studies are currently only intended as demonstration prototypes. Activity Generation Route Choice Activity Scheduling Activity Planning Person Traveler Movements Person Network Monitoring ITS Response Strategies Information Dissemination ITS Infrastructure Intersection Activity Planning Simulation ITS Responses Link Simulation Network Traffic Management Center Figure 1. POLARIS ABM Overview ACTIVITY-TRAVEL DEMAND MODELING COMPONENTS The first set of components in the simulation is the activity-based demand model. The demand behaviors modeled include time-dependent activity generation, activity attribute planning and replanning, and an activity scheduling model which resolve conflicts and maintains a consistent schedule for the agent. The demand components are also responsive to network and traffic management events, which can result in agent re-planning. The demand components derive from previous work in modeling activity-planning and scheduling behaviors found in the development of the ADAPTS (Agent-based Dynamic Activity Planning and Travel Scheduling) model (Auld and Mohammadian 2009). The ADAPTS model components have been reorganized to more closely fit the agent-based nature of the simulation. The ADAPTS model was developed to simulate the underlying activity and travel planning and scheduling processes which lead to observed activity-travel patterns (Auld and Mohammadian 4

7 2009). The model was explicitly designed to continuously integrate with traffic simulation as a truly dynamic model, with planning and scheduling occurring in a time-dependent manner. By considering planning and scheduling steps as discrete events within the simulation, a more complete picture of the planning dynamics is developed. ADAPTS is implemented here as a set of agents and sub-agents which perform the planning and scheduling. The primary agent in the demand model is the Person, which has several sub-agents representing different cognitive and physical capabilities (implemented as discrete events) including: Perception agent - gather and process information from world Planner agent - activity generation, planning and re-planning Scheduler agent - maintains consistent daily activity-travel schedule Movement handler agent - initiates simulation of physical movements in the system Routing agent - choose routes (discussed in network model section) Activity planning and scheduling occurs throughout the simulation, as does routing and traveling. Agent actions can be classified into four categories: Activity Generation, Planning, Scheduling, and Traveling. Activity generation in ADAPTS is implemented as a joint hazardduration model which continuously estimates the need for a new activity to be planned during the simulation, see Auld et al (2011). After the activities are generated the attributes of the activities are planned at time horizons set by a series of linked multivariate ordered probit regression models described in Auld and Mohammadian (2012). The attribute planning behaviors for destination choice and mode choice are handled by discrete choice models, while start time and duration are set by random draw from segmented observed probability distributions. The planned activities are then scheduled using a set of heuristic scheduling rules and a conflict resolution model. The conflict resolution model determines the overall strategy while the scheduling rules implement the strategy while ensuring a consistent final schedule. Scheduling occurs whenever a new activity is planned, an existing activity is re-planned, or deviations from the original schedule are observed. When an activity start is reached the person plans a route using its router, then is loaded to the network through the movement handler. While the person is being moved by the network simulation, all other aspects of the person agent continue to operate, such as activity generation. The travel simulation is discussed next. NETWORK SIMULATION MODEL COMPONENTS The network model components include three key models: an individual route choice model, a route generation model using simulated travel costs, and a mesoscopic traffic simulation model. The network model is seamlessly integrated with the demand model by providing pre-trip and/or en-route path information for travelers, and the ITS model by publishing sensor information to the TMC agent for real-time traffic information provision. In turn, the demand and TMC operations also impact route choice and traffic flow pattern. In the route choice model agents make route choice decisions with respect to their own user characteristics and in response to pretrip and/or en-route traffic information. The router calculates the least-time routes for the 5

8 individual traveler using simulated travel costs. The traffic simulation simulates the movement of each individual traveler based on the Kinematic Wave theory of traffic flow. In addition, intersection operations are simulated for signal controls, as well as stop and yield signs. The traffic simulation model also captures dynamic capacity reductions due to special events such as weather and accidents. The route choice model describes dynamic route choice decisions under various information provision types. The purpose of the route choice model is to address en-route switching behavior of travelers. It models travelers with pre-trip traffic information only, and equipped travelers with en-route information (e.g. navigation devices). Both traveler types are assumed to be able to access prevailing traffic information at their origins, but only equipped travelers can access realtime traffic information during their trip. The unequipped travelers can, however, access realtime traffic information disseminated through ITS infrastructures such as VMS and radio to respond to both recurrent and nonrecurrent traffic congestions. A bounded rationality en-route switching model (Mahmassani and Stephan, 1988, Jarakrishnan et al. 1994) is used. The route generation model calculates the least-time routes for travelers, and is implemented as a sub-agent of the traveler. Each routing agent has its own copy of the network topology and costs, which enables the use of heterogenous sources of traffic information such as historical, prevailing, experienced and predictive travel costs for different user classes. The router uses the A-Star shortest path algorithm, which allows the parallelization of route calculation by each vehicle. It is also consistent with the integration with activity-based demand model to calculate routes between two activity locations instead of two traffic analysis zones. Finally, the traffic simulation model is implemented using Newell s Simplified Kinematic Waves Traffic Flow model (Newell, 1993), which is a link-based solution and has been recognized as an efficient and effective method for large-scale networks (Zhou et. al, 2012) and dynamic traffic assignment formulations (Zhang et al., 2013). The traffic simulation model includes a set of agents for intersections, links, and traffic controls. The model simulates traffic operations and controls to provide capacities and driving rules on links and turn movements at intersections. With these capacity and driving rule constraints, link and intersection agents simulate the traffic flows using cumulative departures and arrivals as decision variables based on the Newell s model, which determines the network performance. TRAFFIC MANAGEMENT / ITS COMPONENTS The final primary set of components in the model relates to the Traffic Management and Intelligent Transportation System simulation. This includes three elements that were developed to simulate network operations, the Network Events manager, ITS Infrastructure Agents and TMC Agent. The Event Manager provides information about network events, such as accidents, weather conditions, special events, etc. The Sensor Model imitates sensor readings by adding noise to the ground truth speed data calculated in the Traffic Simulation Model. The locations and sensor types are specified in the input data that is stored in ITS Infrastructure object. The 6

9 goal of the automated TMC agent is to monitor the status of the transportation network (speed, travel times, etc) as well as network-related events (weather, incidents, etc.) and decide on a response that would allow to mitigate unusual congestion level on the network. This aspect of the model is intended to allow planning agencies to analyze the benefits of network operations improvements. CASE STUDY AND INITIAL RESULTS To evaluate the effectiveness of the model, a case study was conducted for the Chicago metropolitan area. The model was used to analyze the benefit of a simple ITS infrastructure in terms of the improved network performance and the adapted travel demand. The simulated area is an area surrounding the Chicago CBD. The simulated ITS infrastructure includes VMS (Variable Message Sign) and HAR (Highway Advisory Radio). Three scenarios were studied, including a normal day, a scenario with incidents but no ITS, and a scenario with incidents and the ITS infrastructure. The incidents include multiple accident events on the expressways and a heavy snow in the region, which reduce link capacities. The ITS responses include accident notifications displayed on VMS located along the expressways and a continuously broadcasted weather advisory via the HAR. The simulations were performed on standard desktop computers, with Intel i7-980x six-core processors, and 12GB of RAM. The run-time for the CBD case study averaged 10.5 minutes and used a maximum of 4GB of RAM. The impact was measured using the network load and total delay. The results show the impact that the network events have on the network performance, with many more vehicles in the network at a given time than on an average day, indicating congestion and delay due to the events. The case with ITS responses shows the network load generally decreasing when vehicles are informed of events occurring. The provision of the accident/weather information allows the vehicle to reroute prior to experiencing the congestion, thereby reducing the load on the network. Overall, ITS saves 30,000 hours of delay which is a reduction of 23% of the excess delay caused by the events. Note that the case study is intended solely to demonstrate the capabilities of the modeling system and does not represent real-world expected results as the model is uncalibrated. CONCLUSION AND FUTURE WORK This paper documents the development of an agent-based microsimulation model using the POLARIS framework, which was designed to address a lack of integration and performance in transportation simulations. The demonstration model is a fully functional agent-based simulation model including travel demand, traffic simulation and rudimentary ITS operations, which could be extended to a tool appropriate for planning purposes with continued model development, calibration and validation. The use of a high performance modeling framework, optimized for agent-based simulations allows planning models to incorporate network operational characteristics, enhancing the utility of such models from the planning perspective. Initial tests with case studies involving traffic management center responses to various network events such as accidents and weather events show the potential of the system. 7

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