Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over conventional OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model. The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrixbased demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over usual OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

Cadyts a free calibration tool for dynamic traffic simulations

This article reports on the realization and on first applications of the Cadyts (Calibration of dynamic traffic simulations) calibration tool. The presented first version of Cadyts calibrates disaggregate demand models of dynamic traffic assignment simulators from traffic counts. The tool is broadly applicable in that it (i) makes only very mild assumptions about the calibrated simulators workings and (ii) allows for various modes of technical interaction with the simulation software. The article provides a both conceptual and technical overview of the tool and exemplary demonstrates its applicability to two different traffic microsimulators

Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation

This article reports on the calibration and analysis of a fully disaggregate (agent-based) transport simulation for the metropolitan area of Zurich. The agent-based simulation goes beyond traditional transport models in that it equilibrates not only route choice but all-day travel behavior, including departure time choice and mode choice. Previous work has shown that the application of a novel calibration technique that adjusts all choice dimensions at once from traffic counts yields cross-validation results that are competitive with any state-of-the-art four-step model. While the previous study aims at a methodological illustration of the calibration method, this work focuses on the real-world scenario, and it elaborates on the usefulness of the obtained results for further demand analysis purpose

Dynamic Modeling for Intelligent Transportation System Applications

Special Issue on Dynamic Modeling for Intelligent Transportation System Applicationspostprin

An Integrated Agent-Based Microsimulation Model for Hurricane Evacuation in New Orleans

Mass evacuation of urban areas due to hurricanes is a critical problem that requires extensive basic and applied research. Knowing the accurate evacuation time needed for the entire region in advance such that the evacuation order can be issued on a timely basis is crucial for the officials. Microsimulation modeling, which focuses on the characteristics of individual motorists and travel behavior, has been used widely in traffic simulation as it can lead to the most accurate result. However, because detailed driver response modeling and path processing must be incorporated, vehicle-based microscopic models have always been used only to simulate small to medium sized urban areas. Few studies have attempted to address problems associated with mass evacuations using vehicle-based microsimulation at a regional scale. This study develops an integrated two-level approach by separating the entire road network of the study area into two components, highways (i.e., interstate highways and causeways) and local roads. A vehicle-based microsimulation model was used to simulate the highway part of the road traffic, whereas the local part of the road traffic simulation utilized an agent-based model. The integrated microsimulation model was used to simulate hurricane evacuation in New Orleans. Validation results confirm that the proposed model performs well in terms of high model accuracy (i.e., close agreement between the real and simulated traffic patterns) and short model running time. Sufficient evacuation time is a premise to protect people’s life safety when an area is threatened by a deadly disaster. To decrease the network clearance time, this study also examined the effectiveness of three evacuation strategies for disaster evacuation, including a) simultaneous evacuation strategy, b) staged evacuation strategy based on spatial vulnerabilities, and c) staged evacuation strategy based on social vulnerabilities. The simulation results showed that both staged evacuation strategies can decrease the network clearance time over the simultaneous evacuation strategy. Specifically, the spatial vulnerability-based staged evacuation strategy can decrease the overall network clearance time by about four hours, while the social vulnerability-based staged evacuation strategy can decrease the network clearance time by about 2.5 hours

Agent Behaviour Issues Arising with Urban System Micro-Simulation

A large co-ordinated program of work is underway exploring techniques for integrated landuse transport modelling, including elements of agent-based micro-simulation. The intentionis to demonstrate the practical viability of these techniques and help provide guidance intheir further development and use in policy analysis considering transport policy and theimpacts of transport on society. This has given rise to a number of questions about the natureof the behaviour of the agents being considered (including people, households, businessestablishments and developers) and about potential methods for implementing practicalrepresentations of this behaviour. This paper describes the modelling system and techniquesbeing considered, and sets out some of the questions about behaviour and its representationthat have arisen together with some of the more promising ideas and approaches beingconsidered for addressing these questions

FACILITATING NO-NOTICE EVACUATION THROUGH OPTIMAL PICK-UP LOCATION SELECTION

Under no-notice disasters, dependents in facilities such as schools and daycare centers usually wait for their families to pick them up. This family pickup behavior could increase individual evacuation time and cause extra delay to other vehicles in the network. Relocating the dependents to other pickup sites may facilitate no-notice evacuation. This study developed an optimization model to determine optimal pickup locations, assuming that all evacuating families have personal vehicles; the objective is to maximize the number of evacuees who can successfully pick up dependents and then escape from the dangerous zones within a safe evacuation time threshold. The optimization model was based on anticipated travel time output from the simulation model (VISSIM in this study); iteration between the two models was performed. The methodology was applied to a case study based on a simplified version of Chicago Heights, Illinois. The case study involved three facilities with 492 dependents and three safe time thresholds (i.e., 30, 45 and 60 minutes). Improvements in total travel time, average speed, total delay time and average delay time per vehicle and increases in the number of successful evacuations of dependents were used to evaluate the performance of the relocation strategy. This study also examined the sensitivity of the strategy to parents' arrival time, number of dependents, and safe time. Finally, relocation sites were recommended based on the results of all scenarios. The results found that the relocation strategy was sensitive to safe evacuation time and number of pickup evacuees (pickup evacuees refer to those persons with a need to pick up their dependents inside the dangerous zones). The relocation strategy was prominently effective when safe evacuation time fell into a moderate range or the number of pickup evacuees was fairly high. Application of the proposed methodology to a certain area can assist local decision-makers to take effective measures during no-notice evacuation and the relocation sites could be part of local evacuation management plans. Abstract Under no-notice disasters, dependents in facilities such as schools and daycare centers usually wait for their families to pick them up. This family pickup behavior could increase individual evacuation time and cause extra delay to other vehicles in the network. Relocating the dependents to other pickup sites may facilitate no-notice evacuation. This study developed an optimization model to determine optimal pickup locations, assuming that all evacuating families have personal vehicles; the objective is to maximize the number of evacuees who can successfully pick up dependents and then escape from the dangerous zones within a safe evacuation time threshold. The optimization model was based on anticipated travel time output from the simulation model (VISSIM in this study); iteration between the two models was performed. The methodology was applied to a case study based on a simplified version of Chicago Heights, Illinois. The case study involved three facilities with 492 dependents and three safe time thresholds (i.e., 30, 45 and 60 minutes). Improvements in total travel time, average speed, total delay time and average delay time per vehicle and increases in the number of successful evacuations of dependents were used to evaluate the performance of the relocation strategy. This study also examined the sensitivity of the strategy to parents' arrival time, number of dependents, and safe time. Finally, relocation sites were recommended based on the results of all scenarios. The results found that the relocation strategy was sensitive to safe evacuation time and number of pickup evacuees (pickup evacuees refer to those persons with a need to pick up their dependents inside the dangerous zones). The relocation strategy was prominently effective when safe evacuation time fell into a moderate range or the number of pickup evacuees was fairly high. Application of the proposed methodology to a certain area can assist local decision-makers to take effective measures during no-notice evacuation and the relocation sites could be part of local evacuation management plans. 3 The primary purpose of a no-notice evacuation is to save people's lives Evacuation time In a short-notice case, people can choose whether to evacuate or not, and when to evacuate In a no-notice scenario, almost everyone in the impacted area evacuates at the time of the disaster occurrence Origin evacuation place In a "short notice" evacuation case, evacuees often start from their homes together with their families. In a "no-notice" scenario, evacuees start evacuation from wherever they are at that time, (i.e., schools, work places, entertainment place, etc.) most likely by themselves. Family gathering process Families gather together before they start to evacuate. Families gather together during or after the evacuation When a no-notice disaster occurs during the daytime, household members may be scattered throughout the road network. Household dependents in facilities such as schools and daycare centers may wait for their families to pick them up; this family pickup behavior could increase individual evacuation time and cause extra delay for other vehicles in the network. When a large amount of vehicles rush into certain places to pick up dependents within a short period of time, bottlenecks could easily be formed for those locations whose entries/exits cannot accommodate heavy traffic. Facilities' current locations may not be well designed for the emergency case and limited entry/exit by itself could be a bottleneck. Relocating facilities' dependents to appropriate sites would eliminate unnecessary bottlenecks and smooth road traffic. Therefore, this study addresses selecting appropriate pickup locations to facilitate no-notice evacuation. A school is a typical facility having a number of carless dependents who need to be picked up. Most school districts have developed an emergency plan, which in summary, requests three kinds of action, i.e., shelter in place, lockdown, and off-site evacuation. According to the Office of the Superintendent, Arlington High School, Massachusetts 7 (2006), shelter-in-place is used when a danger happens outside the school, such as a chemical spill; lock-down is used when a danger is inside the school and makes evacuation impractical; off-site evacuation is used in an extreme emergency situation, and an evacuation location such as another school, church, Boys & Girls Club, Town Hall or ice skating rink, are prearranged for each individual school. This school's existing emergency plans demonstrate that moving dependents to other sites is a feasible strategy. Moreover, some previous studies have involved this issue; for example, Sinuany-Stern and Stern (1993) studied relocating carless people under an emergency situation, where carless households are assumed to move to a certain point first and are then picked up by organized transportation and transported to the shelter, and the households are assumed to use shortcuts and not interfere with road traffic. This study develops a mathematical model to determine optimal pickup locations for facilities; the objective is to maximize the number of evacuees who can successfully pick up dependents and escape from the dangerous zones afterwards. The model is tested for a given network based on the City of Chicago Heights, Illinois. This report is organized as follows. Chapter 1 describes the background and purpose of this study. Chapter 2 reviews the previous studies on evacuation modeling and the location problem. Chapter 3 formulates the optimization model and explains the methodologies adopted in this study. Chapter 4 describes basic information of the case study in Chicago Heights, such as the network, demand, assumptions and scenarios, and presents the results and the sensitivity analysis. Chapter 5 concludes the study and discusses the future directions. Chapter 2 Literature Review This chapter mainly reviews the previous studies on evacuation modeling. Numerous studies on evacuation planning and modeling were conducted since the 1980s, driven by tragic events such as the Three Mile Island nuclear reactor incident in 1979, September 11 terrorist attacks in 2001, and Hurricanes Katrina and Rita in 2005. Those studies generally focused on estimating evacuation time and determining optimal evacuation routes and optimal shelter locations, using operations research methods and simulation models. Evacuation studies, according to scopes and features of impacted areas, fall into five general categories: regions, neighborhoods, buildings, ships, and airplanes Regional Evacuation Regional (urban) evacuation models can be classified into aggregate models and disaggregate models. An aggregate model investigates a group of vehicles as a whole, while a disaggregate model evaluates each individual driver's behavior. An aggregate model overlooks the difference of individual driver's behavior among the population. The model developed in this study is a disaggregate model that relies on microsimulation. Aggregate Evacuation Modeling Most evacuation models were developed on an aggregate level and simulation-based (macroscopic), such as NETVAC, DYNEV, MASSVAC, and TEVACS; most of these models were dealing with hurricanes or nuclear plant incidents, as both are among the most frequent and severe disasters in the United Stated. Few previous works exist regarding regional evacuation models on the micro simulation level In general, most of the aforementioned models are capable of estimating network clearance time and identifying evacuation bottlenecks of the network. Most of these simulators assume that the evacuation process has reached equilibrium, thereby estimating evacuation time based on determined equilibrium traffic flow. However, under abnormal situations such as evacuation, equilibrium of road traffic is hard to achieve due to the practical reason that no historical experience exists for evacuees to choose routes and minimize their evacuation time; this is contrary to normal situations recurring almost every day, in which travelers can learn from past experience to choose routes 9 with minimum travel time Disaggregate Evacuation Modeling The previously mentioned models are aggregate as they do not consider an individual's behavior while modeling the evacuation progress. Stern and Sinuany-Stern (1989) first incorporated some behavior-related parameters, including diffusion time of evacuation instruction and individuals' preparation time, in a microscopic simulation model for an urban evacuation. Later Sinuany-Stern and Stern (1993) developed the SLAM Network Evacuation Model (SNEM) based on this behavioral-based model to test the effects of traffic factors (e.g. household size, car ownership and intersection traversing time) and route choice parameters on network clearance time. Sinuany-Stern and Stern's work assumes that household members are together when a disaster occurs; therefore it takes households as entities, instead of individual household members. In reality, family members could be scattered at different places under nonotice evacuation. Murray-Tuite and Mahmassani Neighborhood Evacuation Less attention was paid to the subject of neighborhood-scale evacuation under an emergency during the last twenty years, compared to region-scale evacuation or building evacuation No-notice Evacuation Recently, more and more focus is placed on no-notice evacuation. As a no-notice disaster requires quick response, real time estimation tools are important, for which computation time is a critical issue. Chiu et al. Shelter Location Many other previous studies focused on different specific aspects of an emergency evacuation. One is that the location of shelters may influence network clearance time significantly under hurricane evacuation. Facility Location Problem This study involves relocating dependents at facilities to make an evacuation efficient, so we here provide an overview of basic facility location problems. The facility location problem is a critical issue for strategic planning of a wide range of enterprises, e.g., a retailer chooses where to locate a store or a city planner selects locations of fire stations based on a set of rules (Owen and Diskin, 1998). Basic location problems, such as the P-median, P-center, set covering and maximal covering problems, are reviewed by Owen and Diskin (1998). The P-median problem is to locate P facilities in order to minimize the total travel cost between demands and facilities; the P-Center problem, also called the minmax problem, is to locate P facilities so as to minimize the maximum travel cost between a demand and its nearest facility; the set covering problem is to locate the minimum number of facilities that will serve all demands within a specified time; the maximal covering problem is to place P facilities with the goal to maximize the amount of demand covered within an acceptable distance between demands and facilities (Owen and Diskin, 1998). The P-Center, set covering and maximal covering problems can all be applied to locate emergency 11 medical services (EMS); the P-center and maximal covering problems are used to locate a given number of EMS, and the set covering problem is used to determine the least number of EMS to cover all population of a certain area. The above mentioned basic location problems do not account for location costs, which limits their application to practical problems. The fixed charge facility location problems are thus introduced with a fixed cost for each potential location site, and categorized as uncapacitated and capacitated according to whether facility capacities are incorporated or not (Owen and Diskin, 1998). The fixed charge facility location problems determine the number of facilities located endogenously, rather than pre-specified as in median and center problems. However, without considering varying costs associated with flows between facilities and demands, fixed charge problems still cannot solve such a problem as locating a warehouse, which is a general case in industry and needs to find the best shipments between facilities and customers. Hence, the location-allocation problems incorporate flow allocation between demands and facilities into a basic location problem (usually a median problem or a fixed charge problem) (Owen and Diskin, 1998). The location problem presented in this study is essentially a P-center problem that locates students to minimize the maximum of a pickup travel time. The flow allocation is not the case of this study as it is predetermined which parent picks up which child. The previous works provide valuable contributions to the emergency evacuation field, however most of them omit family gathering behavior under no-notice evacuation conditions. This omission could lead to optimistic estimates of evacuation time. This report explores this issue and considers the fact that parents need to pick up their carless household members during an evacuation. A strategy of relocating dependents to more accessible sites to facilitate no-notice evacuation is proposed and evaluated in this report. Chapter 3 Methodology This chapter describes the methodology adopted in this study. First, an integer optimization model is formulated to determine optimal relocation sites for facilities. The microscopic simulation model is then introduced to provide zonal travel time information for the optimization model. As the two models interact with each other, iteration between them is performed to achieve the "real" optimal point. A procedure to accomplish this iteration process is illustrated by a chart and explained step by step. This chapter also includes the methodology to generate the trip chain. Optimization Model A no-notice evacuation usually starts at the moment when a disaster is confirmed or announced, then evacuees surge onto the road network in a very short time and cause traffic to dramatically change. In this study, time-dependent road traffic is taken into consideration by discretizing the evacuation period into several time intervals and capturing the travel time for each time interval. A mathematical optimization model was developed to find optimal relocation sites for facilities such as schools and daycare centers. Unlike a short-notice evacuation that seeks reductions in total evacuation time or personal property loss, a no-notice evacuation aims at maximizing the number of evacuees successfully escaping from the dangerous zones or minimizing total fatalities or injuries within a given time threshold (Chiu et al., 2006; is an index of pickup evacuees' origin nodes; j is an index of pickup evacuees' destination nodes; k is an index of current locations of facilities; l is an index of possible relocation sites for facilities; a is an index of time interval, a th ; x kl are binary integer decision variables. x kl = 1, if we assign facility k to site l; 0 otherwise; Ф is the average number of dependents a pickup evacuee gathers at a facility. Equation (1-1) calculates that the number of successful pickup evacuees for each i, j, l; p ijl S is the number of pickup evacuees (originating from i and evacuating to j) with dependent(s) relocated to l from all facilities before the last safe time interval, ijl A . Equation (1) determines the total number of successful pickup evacuees by summating p ijl S over i, j, l. Equation (2) requires the number of dependents relocated to possible site l to be no greater than facility l's capacity. The average number of dependents for a pickup evacuee is assumed to be the same over all of the facilities. Multiple intermediate stops for a pickup trip are not considered in this study; parents who have more than one dependent in the dangerous zone are assumed to have them in one facility. Equation (3) restricts the relocation site to a walkable distance (0.5 miles) from the original site. Equation (4) guarantees that a facility is assigned to one and only one relocation site. Facilities' current locations are also treated as possible relocation sites. Equation A ijl is an input to the optimization model and based on travel time from micro-simulation. Micro-simulation provides travel time of multiple paths for each OD pair; the path with the least travel time is assumed to be selected by pickup evacuees. Equations is the duration of a time interval (sec); ) ( E is the expected value of (sec). Equation Traffic Simulation Model Microscopic simulation outputs travel time among origins, destinations, and facility/relocation sites for the optimization model. Micro-simulation was chosen instead of simulation models on other levels, because it can model the road network in great detail, has the ability to model queues, and reflects the impacts of facilities' entry/exit configuration on travel time, which is crucial for the special case here. VISSIM, part of the PTV VISION traffic analysis package, was used in this study. VISSIM is a driver behavior based, second by second microscopic traffic simulation program, and developed to model major elements of transportation systems, such as lane configuration, vehicle composition, driver behavior, traffic controls and so on VISSIM's built-in dynamic traffic assignment algorithm was used to find routes for pickup evacuees. VISSIM accomplishes dynamic assignment procedures by iterated simulation runs. For each iterated run, drivers make decisions on route choice based on road traffic situations they experienced from the previous iterations. After multiple simulation runs, the iterations end when network traffic reaches stability, defined in VISSIM as when travel times or volumes do not vary significantly between two consecutive runs Framework The road traffic situation determines optimal relocation sites; reversely, relocation sites affect road traffic. In this study, relocation sites are determined by the optimization model and the network traffic is modeled using the simulation model, VISSIM. The optimization model uses travel time output from VISSIM, which assumes current facility locations as pickup points at the beginning. When the optimization model finds new relocation sites, travel time from VISSIM should be updated accordingly; as a result, these determined optimal sites may not be "real". In order to achieve "real" optimal sites, iteration between the two models is performed until convergence is reached. A procedure to accomplish this iteration process is shown in Figure 1 Flowchart of the Study In this procedure, first, the road network under normal conditions is simulated in VISSIM and normal travel times are achieved and adopted by the optimization model to be initial travel time. Then, micro simulation for emergency situations is iteratively executed with the optimization model until the termination criteria are satisfied, to determine optimal relocation sites. Facility current locations are set to be initial pickup locations; during each iteration, new relocation sites are found, and the travel time corresponding to those new sites is updated accordingly. The procedure follows the steps below: Step 0. Run VISSIM to gain travel time in a normal situation (without pickup evacuees considered). As VISSIM can only output travel time for those OD pairs with actual vehicles dispatched, we generate dummy trips for specific OD pairs to collect the travel times needed. a) Specify OD pairs we need to collect travel times for; b) Divide the simulation time period into several periods 1 ; for each period, generate one du

Bayesian calibration of dynamic traffic simulations

We present an operational framework for the calibration of demand models for dynamic traffic simulations. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, at a likewise individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of sensor data such as traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet applicable in a purely simulation-based environment, and, as our experimental results show, capable of estimating practically relevant scenarios in real-time