From systems to patterns and back - Exploring the spatial role of dynamic time and direction patterns in the area of regional planning

This master thesis presents a data-driven framework to explore the role of dynamic time and direction patterns in the area of Finnish Lapland in order to improve decision-making in urban planning and design tasks. The Arctic Ocean Railway project is chosen as a case study. In an era marked by dramatic environmental, political and societal changes, the Arctic region becomes more global and complex. An increasing number of actors are involved in its spatial transformations. Due to melting ice, the Northern Sea Route gains attention from the shipping and trade industries that are manifested in new port and infrastructure projects. Eco-tourism is booming in the Arctic due to its imaginary remoteness, while local Indigenous People try to preserve traditional livelihoods. In order to cope with the increasing complexity of such dynamic urban and regional challenges, Systems Thinking, dynamic patterns, modelling and use of simulation are researched to open up novel ways for complex regional planning methods. This is achieved by designing an agent-based model and using different representation and abstraction features for different dynamic data packages. The project is integrated within the GAMA simulation platform (a modelling and simulation development environment for building spatially explicit agent-based simulations) and embedded in the MIT CityScope framework - a medium for both, analyzing agent’s behavioural patterns and displaying them to the relevant stakeholders. The project attempts to address the necessity to handle the increasing complexity by presenting a dynamic, evidence-based planning and decision support tool called CityScope Lapland. The main goal of CityScope Lapland is to use digital technologies to incorporate variables like time and direction in urban spatial analysis and methodology; secondly, to improve the accessibility of the decision-making process for non-experts through a tangible user interface, and third, to help users evaluate their decisions by creating a feedback through real-time visualization of urban simulation results when facing less and less predictable futures. The project provides an alternative design approach, introducing new forms of urban imagination and different ways of perceiving and measuring complex spatial transformations

Optimization of headway, stops, and time points considering stochastic bus arrivals

With the capability to transport a large number of passengers, public transit acts as an important role in congestion reduction and energy conservation. However, the quality of transit service, in terms of accessibility and reliability, significantly affects model choices of transit users. Unreliable service will cause extra wait time to passengers because of headway irregularity at stops, as well as extra recovery time built into schedule and additional cost to operators because of ineffective utilization of allocated resources. This study aims to optimize service planning and improve reliability for a fixed bus route, yielding maximum operator’s profit. Three models are developed to deal with different systems. Model I focuses on a feeder transit route with many-to-one demand patterns, which serves to prove the concept that headway variance has a significant influence on the operator profit and optimal stop/headway configuration. It optimizes stop spacing and headway for maximum operator’s profit under the consideration of demand elasticity. With a discrete modelling approach, Model II optimizes actual stop locations and dispatching headway for a conventional transit route with many-to-many demand patterns. It is applied for maximizing operator profit and improving service reliability considering elasticity of demand with respect to travel time. In the second model, the headway variance is formulated to take into account the interrelationship of link travel time variation and demand fluctuation over space and time. Model III is developed to optimize the number and locations of time points with a headway-based vehicle controlling approach. It integrates a simulation model and an optimization model with two objectives - minimizing average user cost and minimizing average operator cost. With the optimal result generated by Model II, the final model further enhances system performance in terms of headway regularity. Three case studies are conducted to test the applicability of the developed models in a real world bus route, whose demand distribution is adjusted to fit the data needs for each model. It is found that ignoring the impact of headway variance in service planning optimization leads to poor decision making (i.e., not cost-effective). The results show that the optimized headway and stops effectively improve operator’s profit and elevate system level of service in terms of reduced headway coefficient of variation at stops. Moreover, the developed models are flexible for both planning of a new bus route and modifying an existing bus route for better performance

Locating Mobile Telecommunication Facilities in Extreme Events Evacuation

Large regional evacuations caused by severe weather such as hurricane’s and tsunami’s are fraught with complexity, uncertainty and risk. During such events, evacuees have to make decisions on route planning and point-of-destination while emergency managers need to ensure that appropriate personnel and infrastructure are available and capable of facilitating the evacuation. In parallel, the widespread usage of social media and micro-blogs enabled by mobile technology is leading to more dynamic decision-making and real-time communication by evacuees. This research uses deterministic and simulation techniques to model regional hurricane evacuation. A mixed integer formulation for telecommunication equipment location is used to identify gaps or strains in mobile service and to locate mobile telecommunications equipment to temporarily alleviate system stress. This problem unifies location allocation and routing characteristics with signal interference processing to maximize the number of served users through the evacuation. A Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and a Lagrangian Relaxation-based heuristic are used to solve larger problem instances. Agent-based simulation modeling is used to investigate the reliability, robustness and effectiveness of telecommunications equipment location given the inherent diversity and uncertainty of individual decision-making during evacuation. The agent-based simulation adopts Fuzzy Cognitive Maps to model individual evacuation decision-making that dynamically integrates external information (e.g., physical environment, interpersonal communication) and internal data (e.g., historical empirical, demographic trends). This research shows how social communication among evacuees positively impacts travel patterns as well as overall evacuation time and the usage of mobile telecommunications equipment

Travel Demand Growth: Research on Longer-Term Issues. The Potential Contribution of Trip Planning Systems

INTRODUCTION 1.1 The growth in demand for travel Over the 20 years hm 1965, National Travel Survey (NTS) data shows a 61% growth in total person - km of travel. More detailed analysis suggests that this is made up roughly as follows:- due to increased population 4% due to more journeys 22% due to longer journeys 35% This implies that around 60% of the growth in travel has been due to people travelling further, rather than making more journeys. It is interesting to note, too, that the same phenomenon occurs even in the most congested areas. Between 1975 and 1985, NTS shows an 11% growth in person -km by London residents, at a time when population fell by 5%. In this case, the growth is made up roughly as follows:- due to lost population -5% due to more journeys 4% due to longer journeys 12% It is of course difficult to estimate the extent to which future growth in travel will be generated by longer journeys. The NRTF, which predicts a growth in car-km of between 120% and 180% between 1985 and 2025, is not based on a procedure which enables the effects of journey making and journey length to be separated. However, it is worth noting that if the same pattern were to exist at a national level in future, the predicted growth in car travel due to longer journeys could be equivalent to between 75% and 100% of today's car travel. It seems appropriate to ask whether it is a wise use of scarce resources to provide the infrastructure and energy needed to enable people to carry out their activities further from home. (Continues...

Automatic collision avoidance of ships

One of the key elements in automatic simulation of ship manoeuvring in confined waterways is route finding and collision avoidance. This paper presents a new practical method of automatic trajectory planning and collision avoidance based on an artificial potential field and speed vector. Collision prevention regulations and international navigational rules have been incorporated into the algorithm. The algorithm is fairly straightforward and simple to implement, but has been shown to be effective in finding safe paths for all ships concerned in complex situations. The method has been applied to some typical test cases and the results are very encouraging

A Patient Risk Minimization Model for Post-Disaster Medical Delivery Using Unmanned Aircraft Systems

The purpose of this research was to develop a novel routing model for delivery of medical supplies using unmanned aircraft systems, improving existing vehicle routing models by using patient risk as the primary minimization variable. The vehicle routing problem is a subset of operational research that utilizes mathematical models to identify the most efficient route between sets of points. Routing studies using unmanned aircraft systems frequently minimize time, distance, or cost as the primary objective and are powerful decision-making tools for routine delivery operations. However, the fields of emergency triage and disaster response are focused on identifying patient injury severity and providing the necessary care. This study addresses the misalignment of priorities between existing routing models and the emergency response industry by developing an optimization model with injury severity to measure patient risk. Model inputs for this study include vehicle performance variables, environmental variables, and patient injury variables. These inputs are used to construct a multi-objective mixed-integer nonlinear programming (MOMINLP) optimization model with the primary objective of minimizing total risk for a set of patients. The model includes a secondary aim of route time minimization to ensure optimal fleet deployment but is constrained by the risk minimization value identified in the first objective. This multi-objective design ensures risk minimization will not be sacrificed for route efficiency while still ensuring routes are completed as expeditiously as possible. The theoretical foundation for quantifying patient risk is based on mass casualty triage decision-making systems, specifically the emergency severity index, which focuses on sorting patients into categories based on the type of injury and risk of deterioration if additional assistance is not provided. Each level of the Emergency Severity Index is assigned a numerical value, allowing the model to search for a route that prioritizes injury criticality, subject to the appropriate vehicle and environmental constraints. An initial solution was obtained using stochastic patient data and historical environmental data validated by a Monte Carlo simulation, followed by a sensitivity analysis to evaluate the generalizability and reliability of the model. Multiple what-if scenarios were built to conduct the sensitivity analysis. Each scenario contained a different set of variables to demonstrate model generalizability for various vehicle limitations, environmental conditions, and different scales of disaster response. The primary contribution of this study is a flexible and generalizable optimization model that disaster planning organizations can use to simulate potential response capabilities with unmanned aircraft. The model also improves upon existing optimization tools by including environmental variables and patient risk inputs, ensuring the optimal solution is useful as a real-time disaster response tool

The dynamics of iterated transportation simulations

Iterating between a router and a traffic micro-simulation is an increasibly accepted method for doing traffic assignment. This paper, after pointing out that the analytical theory of simulation-based assignment to-date is insufficient for some practical cases, presents results of simulation studies from a real world study. Specifically, we look into the issues of uniqueness, variability, and robustness and validation. Regarding uniqueness, despite some cautionary notes from a theoretical point of view, we find no indication of ``meta-stable'' states for the iterations. Variability however is considerable. By variability we mean the variation of the simulation of a given plan set by just changing the random seed. We show then results from three different micro-simulations under the same iteration scenario in order to test for the robustness of the results under different implementations. We find the results encouraging, also when comparing to reality and with a traditional assignment result. Keywords: dynamic traffic assignment (DTA); traffic micro-simulation; TRANSIMS; large-scale simulations; urban planningComment: 24 pages, 7 figure

Look-ahead strategies for dynamic pickup and delivery problems

In this paper we consider a dynamic full truckload pickup and delivery problem with time-windows. Jobs arrive over time and are offered in a second-price auction. Individual vehicles bid on these jobs and maintain a schedule of the jobs they have won. We propose a pricing and scheduling strategy based on dynamic programming where not only the direct costs of a job insertion are taken into account, but also the impact on future opportunities. Simulation is used to evaluate the benefits of pricing opportunities compared to simple pricing strategies in various market settings. Numerical results show that the proposed approach provides high quality solutions, in terms of profits, capacity utilization, and delivery reliability

Guidelines for assessing pedestrian evacuation software applications

This paper serves to clearly identify and explain criteria to consider when evaluating the suitability of a pedestrian evacuation software application to assess the evacuation process of a building. Guidelines in the form of nine topic areas identify different modelling approaches adopted, as well as features / functionality provided by applications designed specifically for simulating the egress of pedestrians from inside a building. The paper concludes with a synopsis of these guidelines, identifying key questions (by topic area) to found an evaluation

Solving the detour problem in navigation: a model of prefrontal and hippocampal interactions.

Adapting behavior to accommodate changes in the environment is an important function of the nervous system. A universal problem for motile animals is the discovery that a learned route is blocked and a detour is required. Given the substantial neuroscience research on spatial navigation and decision-making it is surprising that so little is known about how the brain solves the detour problem. Here we review the limited number of relevant functional neuroimaging, single unit recording and lesion studies. We find that while the prefrontal cortex (PFC) consistently responds to detours, the hippocampus does not. Recent evidence suggests the hippocampus tracks information about the future path distance to the goal. Based on this evidence we postulate a conceptual model in which: Lateral PFC provides a prediction error signal about the change in the path, frontopolar and superior PFC support the re-formulation of the route plan as a novel subgoal and the hippocampus simulates the new path. More data will be required to validate this model and understand (1) how the system processes the different options; and (2) deals with situations where a new path becomes available (i.e., shortcuts)