Nan Jiang (corresponding), University of Texas, Austin Chandra R. Bhat, University of Texas, Austin

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1 Paper Author (s) Nan Jiang (corresponding), University of Texas, Austin Subodh Dubey, University of Texas, Austin Natalia Ruiz Juri, University of Texas, Austin Chandra R. Bhat, University of Texas, Austin Jennifer Duthie, University of Texas, Austin Alireza Khani, University of Texas, Austin Sebastian Astroza, University of Texas, Austin Tyler James Beduhn, University of Texas, Austin Ankita Chaudhary, University of Texas at Austin Zeina Wafa, University of Texas at Austin Paper Title & Number On the impacts of time resolution in the integration of activity-based and assignment models [ITM # 39] Abstract The extended abstract is submitted. There is no separated short abstract. Statement of Financial Interest There would be no financial gain for any of the authors from the publishing of this brief. Modeling results are analyzed from a methodological standpoint, and not directly related to the characteristics of the selected software tools. Statement of Innovation The incorporation of dynamic traffic assignment (DTA) models and activity-based-models (ABM) into the planning process holds enormous potential. Among others, it would allow planners to evaluate policies that cannot be captured using the traditional four-step process, and to obtain a realistic assessment of the impact of a variety of transportation-related decisions. While the integration of ABM and DTA has been shown to be feasible, there are a number of implementation decisions that deserve further analysis. This research explores the impacts of the temporal resolution used for the travel cost skims on the results and performance of integrated ABM-DTA models. This research is innovative in that it explores fine aspects of the ABM-DTA integration that will be key in practical implementations. Further, this work will also identify data analysis and visualization methodologies that can capture the differences across various integration approaches. The outcomes of this work can assist practitioners in

2 the process of selecting and implementing modeling tools, and in the interpretation and analysis of results from complex models.

3 On the impacts of time resolution in the integration of activity-based and assignment models Nan Jiang Center for Transportation Research, The University of Texas at Austin 1616 Guadalupe St. Suite 4.202, Austin, TX Phone: , Fax: , Subodh Dubey Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin 301 E. Dean Keeton St. Stop C1700, Austin, TX Phone: , Fax: , Natalia Ruiz Center for Transportation Research, The University of Texas at Austin 1616 Guadalupe St. Suite 4.202, Austin, TX Phone: , Fax: , Chandra R. Bhat Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin 301 E. Dean Keeton St. Stop C1700, Austin, TX Phone: , Fax: , Jennifer Duthie (corresponding author) Center for Transportation Research, The University of Texas at Austin 1616 Guadalupe St. Suite 4.202, Austin, TX Phone: , Fax: , 1. Introduction The adoption of advanced models in transportation planning has increased significantly in the past decade. Activity-based models (ABM), which estimate travel demand based on daily activity patterns, allow planning agencies to evaluate the impacts of transportation policies that cannot be represented using traditional modeling approaches. From the supply side, dynamic traffic assignment (DTA) models are increasingly used due to their ability to capture the variability of traffic conditions during the day, and to explicitly model traffic control and a variety of traffic management strategies. While the incorporation of either of these models into the planning process can lead to more realistic modeling results, the capabilities of ABM and DTA models are better utilized when both approaches are integrated. ABM s detailed time-dependent demand provides the inputs that DTA models require to produce realistic traffic patterns. In turn, the time-dependent traffic conditions produced by DTA models can be used by ABMs to better predict activity patterns and corresponding travel demand. An ABM-DTA integrated modeling approach can answer policy questions, such as those related to (dynamic) congestion pricing and land-use change instruments, better than other alternative models.

4 The feasibility of integrating ABM and DTA models has been assessed by researchers and practitioners. Several ABM models, including DaySim (Resource Systems Group et al., 2013), CT-RAMP (Parsons Brinckerhoff, 2012), CEMDAP (Lin et al., 2008; Goulias et al., 2013) and THSHA (Hao et al., 2010) have been successfully used in combination with both static (most MPOs that have developed ABM) and dynamic (e.g., Resource Systems Group et al., 2013; Cambridge Systematics et al., 2010; Hao et al., 2010) traffic assignment models. Feasibility studies include both microscopic (e.g., TRANSIMS (Resource Systems Group et al., 2013), MATsim (Hao et al., 2010)) and mesoscopic (e.g., VISTA (Lin et al., 2008), DynusT (Cambridge Systematics et al., 2010)) DTA models. Most of these integrated modeling systems are further integrated with emission models such as MOVES to estimate greenhouse gas emission (Resource Systems Group et al., 2013; Cambridge Systematics et al., 2010; Hao et al., 2010). Lin et al. (2008, 2009) discusses possible frameworks for the ABM-DTA integration, as well as different convergence criteria for approaches that include an iterative feedback. Figure 1 presents a simplified description of the most common integration framework, in which ABM models generate travel demand information as input to DTA models. The DTA model then produces time-dependent network flow patterns that can be used to estimate emissions, among other performance metrics. If the integrated modeling system does have a feedback feature, the level-of-service information generated by DTA models (origin-destination travel costs by mode) is used to generate a new travel demand estimate by ABM. Employment & Demographic data OD Skim estimates ABM Updated OD Skims Time-dependent travel demand Input Framework Output Intermediate Output DTA Network Performance Network & traffic control data Figure 1: Schematic ABM-DTA integration framework Another paradigm for integrated transportation modeling is the dynamic integration of demand and supply, called SimTRAVEL (Pendyala et al., 2012). In this integrated model, the handshaking between the daily activity simulator and the dynamic traffic assignment model is in a dynamic way, allowing the modeling of within day activity adjustment based on network conditions. More specifically, the demand and supply models run in parallel, with information passing between the models at each iteration, until

5 both are converged. One potentially advantageous property of this method for integration is the ability to consider travel decision adjustments based on sudden changes to network conditions. For example, the arrival time of each traveler, after being simulated in the DTA, is given to the ABM model in realtime, so the decision regarding the next activity will be adjusted for the simulation in DTA model. While previous efforts have shown the feasibility and advantages of an integrated ABM-DTA approach, there are a number of challenges that remain to be addressed in order to facilitate the use of integrated models in practice (Parsons Brinckerhoff, 2012). Further, there are a number of implementation decisions that are likely to affect modeling performance and results, and the impacts of these decisions are not yet thoroughly understood. Among these is the temporal resolution of the level-of-service information generated by the traffic assignment component. In an ideal framework, the origindestination (OD) travel costs (also referred to as skims) used as inputs to the ABM would realistically reflect the variation of traffic conditions throughout the day. In practice, time and computational constraints may require simplifying assumptions, including utilizing a single skim for the entire day, or dividing the day into a few periods with similar characteristics (e.g. AM peak, PM peak and off-peak) during which skims are considered constant. This work studies the impacts on model performance, sensitivity and results of utilizing different temporal resolution levels for the skims fed to the travel demand component of an integrated ABM-DTA modeling approach. Numerical experiments on real networks will be used to identify trends and provide guidelines when appropriate. Central to this effort will be the definition of meaningful methodologies to compare modeling results under different assumptions. Given the complexity of the outputs of both ABM and DTA models, data analysis and visualization techniques become extremely important in order to identify adequate performance metrics. 2. Methodology This work implements two mature models, CEMDAP (for ABM) and VISTA (for DTA), to evaluate the impacts of using different temporal aggregations in the integration of ABM and DTA models. Experimental results and conclusions from this effort are expected to be applicable to any integrated framework using modeling tools based on similar principles. VISTA (Waller and Ziliaskopoulos, 1998) is a simulation-based DTA model implemented in C++ and run in Linux servers through a web-interface. VISTA takes as inputs travel demand and a detailed network representation. Its simulator extends the cell transmission model (CTM Daganzo, 1994, 1995) framework to model urban intersections and other traffic management techniques, and is used to evaluate travel costs under any given assignment of vehicles to paths. Travel demand is split into assignment intervals, typically 10 to 15 minutes long, and the model uses an iterative process (Figure 2) to assign vehicles to paths until Dynamic User Equilibrium (DUE) conditions are reached. In the context of DTA, DUE conditions imply equal and minimum travel costs across all used paths, for every OD pair and assignment time interval. DUE is widely accepted as a good approximation to recurrent traffic conditions, and it is a desirable modeling paradigm when traffic conditions under different scenarios are to be compared (Chiu et al., 2011).

6 Figure 2: DTA solution framework CEMDAP (Comprehensive Econometric Microsimulator for Daily Activity-travel Patterns) is a simulationbased model of human activity-travel movement that has been developed at The University of Texas at Austin by Bhat and students (Bhat et al., 2004; Bhat et al., 2013). It is a disaggregate (individual-level), continuous-time, activity-travel forecasting system. It takes as input the disaggregate agent level demographics, land use patterns, transportation system level-of-service characteristics, and model parameters for a study area, and provides as output the detailed individual-level daily activity-travel patterns for all the individuals in the study area. It is grounded in the fundamental paradigm that the needs and desires to participate in activities are more basic than the human movement that some of these participations may entail. Thus, activity episodes and their generation/scheduling are the building blocks of the system. The activity-travel patterns of workers, non-workers, students, children (persons under 16 years of age), and adults are all explicitly modeled, while also accommodating space-time constraints and interactions. The predictions are made at a continuous-time level (one minute), which enables the assessment of time-of-day specific transportation policies such as dynamic pricing mechanisms. CEMDAP is a generic system that can be applied to any metropolitan area, and at any level of spatial and temporal resolution, as long as the appropriate models are estimated for the local area. Alternatively, the analyst can choose to borrow and update model parameters estimated elsewhere. CEMDAP has already been applied to the population of the Dallas-Fort Worth area and the Los Angeles region, and extensive validation tests have been undertaken between CEMDAP outputs and observed link counts and Census-based work flow and related data. It is currently being deployed to simulate and determine the effects of potential transportation and land-use policies for improving urban and metropolitan mobility in the Los Angeles region.

7 The proposed experimental framework involves using CEMDAP to generate demand patterns based on inter-zonal travel costs provided at different aggregation levels, and evaluating the corresponding network performance using VISTA. The considered skims will include the results of a typical static traffic assignment model (single skim), as well as skims at different temporal aggregation levels from a DTA model. The proposed framework will be also used to test the sensitivity of the integrated model to pricing policies, zoning decisions and other relevant planning scenarios. Table 1 provides a summary of some of the possible scenarios to be analyzed using the integrated framework. Traffic Assignment Approach Static Dynamic Table 1: Numerical Experiments OD Skim aggregation 24 hours AM Peak, PM Peak AM Peak, PM Peak 1 hour ½ hour 15 minutes The results of each analyzed scenario will consist of very large datasets describing the time-dependent OD matrix, and DUE vehicle trajectories. A crucial component of this research will be the development of methodologies to analyze, visualize and compare such results across scenarios. The proposed techniques will be focused on contrasting the temporal and spatial characteristics of the travel demand pattern generated by the ABM model under different skim aggregations. Analyses will also be conducted to assess the differences in the resulting traffic flow patterns, as estimated by the DTA model, and to derive meaningful network-level performance metrics. 3. Expected results The goal of this work is to generate a better understanding of the impacts of the temporal aggregation of skims on the results of integrated ABM-DTA models. It is expected that more disaggregate approaches will increase model sensitivity and produce more realistic results. However, it may not always be feasible (due to time or monetary constraints) to utilize fine levels of temporal resolution. This research is expected to demonstrate the possible impacts of various simplifying assumptions, and identify systematic trends and errors when possible. Further, researchers will explore different approaches to analyze and compare the results of integrated models, seeking to identify meaningful performance metrics that can be used in practice. References Bhat, C.R., Guo, J.Y., Srinivasan, S. and Sivakumar, A. (2004) Comprehensive Econometric Microsimulator for Daily Activity-Travel Patterns. Transportation Research Record, Vol. 1894, pp

8 Bhat, C.R., Goulias, K.G., Pendyala, R.M., Paleti, R., Sidharthan, R., Schmitt, L., and Hu, H-H. (2013) A Household-Level Activity Pattern Generation Model with an Application for Southern California. Transportation, Vol. 40, No. 5, pp Cambridge Systematics, Inc., Sacramento Area Council of Governments, University of Arizona, University of Illinois at Chicago, Sonoma Technology, Inc. and Fehr & Peers Associates (2010) SHRP 2 C10B Partnership to Develop an Integrated, Advanced Travel Demand Model and a Fine-Grained, Time- Sensitive Network. Model Design Plan Chiu, Y.-C., Bottom, J., Mahut, M., Paz, A., Balakrishna, R., Waller, S.T., Hicks, J. (2011) Dynamic Traffic Assignment - A Primer. Transportation Research Circular E-C153 Daganzo, C. F. (1994) The Cell Transmission Model: A Simple Dynamic Representation of Highway Traffic Consistent with the Hydrodynamic Theory. Transportation Research Part B: Methodology 28B, pp Daganzo, C. F. (1995) The Cell Transmission Model, Part II: Network Traffic. Transportation Research Part B: Methodology 29B, pp Davidson, W., Peter Vovsha, P., Freedman, J. and Donnelly, R. (2010) CT-RAMP Family of Activity-Based Models. Australasian Transport Research Forum 2010 Proceedings, September 29 October 1, Canberra, Australia Goulias, K.G., Bhat, C.R., Pendyala, R.M. Chen, Y., Paleti, R., Konduri, K., Yoon, S.Y., and Tang, D. (2013) Simulator of Activities, Greenhouse Emissions, Networks, and Travel (SimAGENT) in Southern California. Final Report Hao, J.Y., Hatzopoulou, M. and Miller, E.J. (2010) Integrating an Activity-Based Travel Demand Model with Dynamic Traffic Assignment and Emission Models. Implementation in the Greater Toronto, Canada, Area Lin, D.-Y., Eluru N., Waller, S.T., and Bhat, C.R. (2008) Integration of Activity-Based Modeling and Dynamic Traffic Assignment, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2076, pp Lin, D.-Y., Eluru N., Waller, S.T., and Bhat, C.R. (2009) Evacuation Planning Using the Integrated System of Activity-Based Modeling and Dynamic Traffic Assignment. Transportation Research Record, Vol. 2132, pp Parsons Brinckerhoff (2012) San Francisco Dynamic Traffic Assignment Project. Moel Integration Options Report. Working Draft for Peer Review Panel. Pendyala, R.M., Konduri, K.C., Chiu, Y.-C., Hickman, M, Noh, H., Waddell, P., Wang, L., You, D. and Gardner, B. (2012) An Integrated Land Use Transport Model System with Dynamic Time-Dependent

9 Activity-Travel Microsimulation. Transportation Research Record: Journal of the Transportation Research Board, No. 2303, pp Resource Systems Group, AECOM, Mark Bradley Research & Consulting, John Bowman Research & Consulting, Mohammed Hadi, Ram Pendyala, Chandra Bhat and Travis Waller (2013) SHRP 2 C10A Partnership to Develop an Integrated, Advanced Travel Demand Model and a Fine-Grained Time- Sensitive Network. Final Report Waller, S.T. and Ziliaskopoulos, A.K. (1998) A Visual Interactive System for Transportation Algorithms Presented at the 78th Annual Meeting of the Transportation Research Board, Washington, D.C.