A traffic network can be disrupted by work zones and incidents. Calculating diversion rate is a core issue for estimating demand changes, which is needed to select a suitable work zone configuration and work schedule. An urban network can provide multiple alternative routes, so traffic assignment is the best tool to analyze diversion rates on network level and the local level. Compared with the results from static traffic assignment, dynamic traffic assignment predicts a higher network diversion rate in the morning peak period and off-peak period, a lower local diversion rate in the morning peak period. Additionally, travelers may benefit from knowing real-time traffic condition to avoid the traffic incident areas. Deploying variable message signs (VMSs) is one possible solution. One key issue is optimizing locations of VMSs. A planning model is created to solve the problem. The objective is minimize total system travel time. The link transmission model is used to evaluate the performance of the network, and bounded rational behavior is used to represent drivers' response to VMSs. A self-adapting genetic algorithm (GA) is formulated to solve the problem. This model selects the best locations to provide VMSs, typically places are that allow travelers to switch to alternative routes. Results show that adding more VMSs beyond a certain threshold level does not further reduce travel time. / text

Dynamic traffic assignment (DTA) can be used to model impacts of network modification scenarios, including traffic control plans (TCPs), on traffic flow. However, using DTA for modeling construction project impacts is limited by the computational time required to simulate entire roadway networks. DTA modeling of a portion of the larger network surrounding these work zones can decrease the overall run time. However, impacts are likely to extend beyond typical boundaries, and determining the proper extents to be analyzed is necessary. Therefore, a methodology for selecting an adequate portion to analyze using DTA, along with provision for properly analyzing the resultant subnetwork, is necessary to determine the magnitude of construction impacts. The primary objectives of this research center on evaluating subnetwork sizes to determine the appropriate extents required to analyze network modifications and developing a strategy to account for impacts extending beyond the subnetwork boundary. The first objective is accomplished through an in-depth review of subnetwork sizes relative to multiple impact scenarios. Three statistical measures are implemented to evaluate the adequacy of a chosen subnetwork relative to the derived impact scenarios based on an assessment of boundary demand. Ultimately, the root mean squared error is used successfully to provide a series of recommended subnetwork sizes associated with an array of possible impact scenarios. These recommendations are validated, and application of the proposed methodology demonstrated, using five scenarios selected from real-world network modifications observed in the field. When a subnetwork is not large enough and impacts to inbound trips pass beyond the boundary, there is a change in flow at this location that can be represented by a change in the demand assigned to the subnetwork at each entry point. As such, two strategies for adjusting the demand at subnetwork boundaries are implemented and evaluated. This includes use of results from static traffic assignment (STA) models to identify where flow changes occur, and implementation of a logit formulation to estimate demand adjustments based on differences in internal travel times between base and impact scenario models. Based on preliminary results, the logit method was selected for large-scale implementation and testing. In the end, an inconsistent performance of the logit method for full implementation highlights the limitations of the methodology as applied for this study. However, the results suggest that a refined strategy that builds on the foundation established could work more effectively and produce valuable subnetwork demand estimates in the future. This research is used to provide recommendations for selecting and analyzing subnetworks using DTA for an array of common impact scenarios involving network modifications. The tradeoffs between improved efficiency and reduced accuracy associated with using subnetworks are thoroughly demonstrated. It is shown that a considerable amount of computational time and space, as well as effort on the part of an analyst, can be saved. A number of limitations associated with subnetworks are also identified and discussed. The proposed methodology is implemented and evaluated using several software programs and as a result, a number of useful tools and software scripts are developed as part of the research. Ultimately, the valuable experience gained from performing an extensive review of subnetwork analysis using DTA can be used as a basis from which to develop future research initiatives. / text

The focus of this dissertation is a methodology to select an appropriate subnetwork from a large urban transportation network that experiences changes to a small fraction of the whole network. Subnetwork selection techniques are most effective when using a regional dynamic traffic assignment model. The level of detail included in the regional model relieves the user of manually coding subnetwork components because they can be extracted from the full model. This method will reduce the resources necessary for an agency to complete an analysis through time and cost savings. Dynamic traffic assignment also has the powerful capability of determining rerouting due to network changes. However, the major limitation of these new dynamic models is the computational demand of the algorithms, which inhibit use of full regional models for comparing multiple scenarios. By examining a smaller window of the network, where impacts are expected to occur, the burden of computer power and time can be overcome. These methods will contribute to the accuracy of dynamic transportation systems analysis, increase the tractability of these advanced traffic models, and help implement new modeling techniques previously limited by network size. The following describes how to best understand the effects of reducing a network to a subarea and how this technique may be implemented in practice. / text

published_or_final_version / abstract / Civil Engineering / Master / Master of Philosophy

In this study, a comprehensive set of transit, intermodal and multimodal assignment models (FAST-TrIPs) is developed for transportation planning and operations purposes. The core part of the models is a schedule-based transit assignment with capacity constraint and boarding priority. The problem is defined to model the system performance dynamically by taking into account the scheduled transit service and to model user behavior more realistically by taking into account capacity of transit vehicles and boarding priority for passengers arriving early to a stop or a transfer point. An optimization model is proposed for both deterministic and stochastic models, which includes a penalty term in the objective function to model the boarding priority constraint. The stochastic model is proposed based on logit route choice with flexibility on the level of stochasticity in route choice. Optimality conditions show that the models are consistent with network equilibrium and user behavior. An extension of the optimization models is proposed for multimodal assignment problem, in which the transit and auto networks interact dynamically. To solve the proposed models, since the penalty term is non-linear and makes the model an asymmetric nonlinear optimization model with side constraints, a simulation-based approach is developed. The solution method incorporates the path assignment models and a mesoscopic transit passenger simulation in conjunction with Dynamic Traffic Assignment (DTA) models. The simulation model can capture detailed activities of transit passengers and determines the nonlinear penalty explicitly by reporting passengers' failure to board experience. Therefore, the main problem can be solved iteratively, by solving a relaxed problem and simulating the transit network in each iteration, until the convergence criterion is met. The relaxed problem is a path generation model and can be either a shortest/least-cost path or a logit-based hyperpath in the schedule-based transit network. An efficient set of path models are developed using Google's General Transit Feed Specification (GTFS) files, taking into account the transit network hierarchy for computational efficiency of the model. A multimodal assignment model is also developed by integration of the proposed transit assignment model with DTA models. The model is based on simulation and is able to capture the effect of transit and auto mode on each other through an iterative solution method and feedback loop from the transit assignment model to the DTA models. In the multimodal assignment model, more realistic transit vehicle trajectories are generated in the DTA models and are used for assigning transit passengers to transit vehicles. In an application of the multimodal assignment, intermodal tours are modeled considering the timing of auto trips and transit connections, the schedule-based transit network, and the constraint on park-n-ride choice in a tour. The model can simulate the transit, auto, and intermodal tours together with high resolution and realistic user behavior.

Figure assignment entails competition between object properties on opposite sides of borders. The figure is perceived on the side of the border that wins the competition. Ample evidence indicates that configural familiarity is among the competing object properties. We investigated whether priming the semantics of a familiar object suggested along one side of a border can increase its likelihood of winning the competition. To prime the semantics, we presented brief masked exposures of object names before brief masked exposures of displays where a portion of a familiar object was suggested on one side of a central border separating two equal-area, black-and-white regions. Participants reported whether the figure lay on the left or right side of the central border and were unaware of the presence of the word prime. These experimental primes named either the Same Object (SO) or a Different Object (DO) as the familiar object suggested in the display. In the DO condition, the word named an object either in the Same Category (DO-SC) or a Different Category (DO-DC) as the familiar object suggested in the display, where superordinate category was defined as natural versus artificial objects. We also used non-words as control primes. We hypothesized that, if semantic activation influences figure assignment, participants in the SO and DO-SC conditions should be more likely than participants in the DO-DC condition to perceive the figure on the side where the familiar object lies following experimental primes than control primes. We did not observe differences between experimental and control prime in any condition. However, we did obtain a Prime Context Effect, in that participants were more likely to perceive the figure on the familiar side of the border in the SO and DO-SC conditions than in the DO-DC condition. The Prime Context Effect shows that participants discerned the relationship between the masked word prime and the semantics of the familiar object suggested in the display, and this led them to change their strategy on both experimental and control trials. We also found that behavior changed over the course of the experiment: Participants in the DO-DC condition perceived the figure on the familiar side of the border more often in the second half of the experiment, on both experimental and control trials. This pattern suggests that over the course of the experiment, they learned to rely more on information from the display than from the prime, perhaps by restricting their attention to the time when the figure-ground display appeared. Participants in the DO-SC condition perceived the figure on the familiar side of the border more often on experimental trials in the second half of the experiment, whereas their performance on control trials did not differ in the first and second half. We hypothesize that participants in the DO-SC condition learned to match the superordinate semantics of the experimental prime and the display, leading to semantic priming. Taken together, these results show that (1) participants can quickly learn the relationship between experimental primes and target displays and can change their strategy accordingly, and (2) semantic activation can affect figure assignment.

Figure-ground perception can be modeled as a competitive process with mutual inhibition between shape properties on opposite sides of an edge. This dissertation reports brain-based evidence that such competitive inhibition can be induced by access to preexisting object memory representations during figure assignment. Silhouette stimuli were used in which the balance of properties along an edge biased the inner, bounded, region to be seen as a novel figure. Experimental silhouettes (EXP) suggested familiar objects on their outside edges, which nonetheless appeared as shapeless grounds. Control silhouettes (CON) suggested novel shapes on the outside.In an initial task, human observers categorized masked EXP and CON silhouettes (175 ms exposure) as "novel" versus a third group of silhouettes depicting "familiar" objects on the inside. Signal detection measures verified that observers were unconscious of the familiar shapes within the EXP stimuli. Across three experiments, novel categorizations were highly accurate with shorter RTs for EXP than CON. Event-related potential (ERP) indices of observers' brain activity (Experiments 2 and 3) revealed a Late Potential (~300 ms) to be less positive for EXP than CON, a reduction in neural activity consistent with the presence of greater competitive inhibition for EXP stimuli. After controlling for stimulus confounds (Experiment 3), the P1 ERP (~100 ms) was larger for EXP than CON conditions, perhaps reflecting unconscious access to object memories.In a second task, observers were informed about familiar shapes suggested on the outsides of the EXP silhouettes before viewing masked (Experiments 1 and 2) or unmasked (Experiment 3) EXP and CON silhouettes to report whether they saw familiar shapes on the outside. Experiment 3 observers were more accurate to categorize CON vs. EXP stimuli as novel vs. familiar, with shorter RTs for EXP than CON. Task 2 N170 ERPs (~170 ms) were larger for EXP than CON in Experiments 2 and 3, reflecting the conscious perception of familiar shape in the outsides of EXP silhouettes. LP magnitudes were greater for CON than EXP, although ERP polarity was dependent on the presence/absence of a mask. Task 2 LPs may reflect competitive inhibition or longer processing times for CON stimuli.

Wireless communication is a rapidly growing segment of the communication industry, with the potential to provide low-cost, high-quality, and high-speed information exchange between portable devices. To harvest the available bandwidth efficientlyin a wireless network, they employ multiple orthogonal channels over multiple ra-dios at the nodes. In addition, nodes in these networks employ directional antennasas radios to improve spatial throughput. This dissertation develops algorithms forrouting and broadcasting with channel assignment in such networks. First, we com-pute the minimum cost path between a given source-destination pair with channelassignment on each link in the path such that no two transmissions interfere witheach other. Such a path must satisfy the constraint that no two consecutive links onthe path are assigned the same channel, referred to as "channel discontinuity con-straint." To compute such a path, we develop two graph expansion techniques basedon minimum cost perfect matching and dijkstra's algorithm. Through extensive sim-ulations, we study the effectiveness of the routing algorithms developed based onthe two expansion techniques and the benefits of employing the minimum cost per-fect matching based solution. Secondly, we study the benefits of sharing channelbandwidth across multiple flows. We model the routing and channel assignmentproblem in two different ways to account for the presence and absence of inter-flowbandwidth sharing. Benefits of multiple paths between a source-destination pairmotivates the problem of computing multiple paths between a source-destinationpair with channel assignment such that all the paths can be active simultaneouslyto achieve maximal flow between the pair in the considered network. Since finding even two such paths is NP-hard, we formulate the problem as an integer linearprogram and develop efficient heuristic to find these paths iteratively. Thirdly, wecompute a broadcast tree from a given root with channel assignment such that all the links in the broadcast tree can be active simultaneously without interferingwith each other. Since finding such a tree is an NP-hard problem, we formulatethe problem as an integer linear program (ILP) and develop heuristics to find thebroadcast tree with channel assignment. We evaluate and compare the performanceof the developed heuristics with respect to their success rate, average depth of theobtained tree, and average path length from root to a node in the network. Thisdissertation also analyzes the blocking performance of a channel assignment schemein a multi-channel wireless line network. We assume that the existing calls in thenetwork may be rearranged on different channels to accommodate an incoming call.The analysis is limited to single-hop calls with different transmission ranges.Finally, this dissertation evaluates the performance of disjoint multipath routingapproaches for all-to-all routing in packet-switched networks with respect to packetoverhead, path lengths, and routing table size. We develop a novel approach basedon cycle-embedding to obtain two node-disjoint paths between all source-destinationpairs with reduced number of routing table entries maintained at a node (hence thereduced look up time), small average path lengths, and less packet overhead. Westudy the trade-off between the number of routing table entries maintained at anode and the average length of the two disjoint paths by: (a) formulating the cycle-embedding problem as an integer linear program; and (b) developing a heuristic.We show that the number of routing table entries at a node may be reduced toat most two per destination using cycle-embedding approach, if the length of thedisjoint paths are allowed to exceed the minimum by 25%.

With the advances in process technology, comes the domination of interconnect in the overall propagation delay in modern VLSI designs. Hence, interconnect synthesis techniques, such as buffer insertion, wire sizing and layer assignment play critical roles in the successful timing closure for EDA tools. In this thesis, while our aim is to satisfy timing constraints, accounting for the overhead caused by these optimization techniques is of another primary concern. We utilized a Lagrangian relaxation method to minimize the usage of buffers and metal resources to meet the timing constraints. Compared with the previous work that extended traditional Van Ginneken’s algorithm, which allows for bumping up the wire from thin to thick given significant delay improvement, our approach achieved around 25% reduction in buffer + wire capacitance under the same timing budget.

Group role assignment (GRA) is an important task in Role-Based Collaboration (RBC). The complexity of group role assignment becomes very high as the constraints are introduced. According to recent studies, considerable efforts have been put towards research on complex group role assignment problems. Some of these problems are clearly defined and initial solutions are proposed. However some of these solutions were unable to guarantee an optimal result, or the time complexity is very high. In fact, many real world collaboration problems concern many types of constraints. Therefore, to make them practical, the accuracy and efficiency of the algorithms should be improved. Role is the center of a role-based collaboration mechanism. Role plays a very essential part in the whole process of a collaboration system, without the roles, there would be no collaboration. One important function of the role is that it defines the features or requirements of a position which can be used to filter or access the candidates. The definition of roles greatly influences the evaluation results of candidates, which in turn influence the RBC algorithms significantly. Based on previous research, the role-based evaluation is associated with multiple attribute decision making (MADM). Role-based evaluation methods can be adopted from MADM methods. Selecting an appropriate method for a specific problem is difficult and domain oriented. Therefore, a dynamic evaluation model which can be expanded by domain experts and adapted to many cases is required. At present, there is limited research related to this requirement. This thesis first focuses on two complex role-based collaboration problems. The first being group role assignment problems with constraints of conflicting agents, and the iv second an agent training problem for a sustainable group. Practical solutions to these problems are proposed and resolved by IBM ILOG CPLEX. Simulations are conducted to demonstrate the performance of these solutions. From which I compare the solutions’ performances with the initial solutions, and indicate the improvement of these proposed solutions. Secondly, this thesis clarifies the difficulties of connecting evaluation methods with real world requirements. In order to overcome these difficulties, I introduce an additional parameter, propose a dynamic evaluation model, and provide four synthesis methods to facilitate the requirements of a co-operation project which is funded by NSERC (Natural Sciences and Engineering Research Council of Canada). The contributions of this thesis includes: clarifying the complexity of two complex role-based collaboration problem; proposing a better solution and verifying its efficiency and practicability; discussing the difficulties of connecting evaluation methods with real world problems; introducing an additional parameter to improve the accuracy of evaluation to some problems; proposing a role-based evaluation model to meet the requirements of adaptive and expandable.