DEVELOPMENT OF A MIXED-FLOW OPTIMIZATION SYSTEM FOR EMERGENCY EVACUATION IN URBAN NETWORKS

In most metropolitan areas, an emergency evacuation may demand a potentially large number of evacuees to use transit systems or to walk over some distance to access their passenger cars. In the process of approaching designated pick-up points for evacuation, the massive number of pedestrians often incurs tremendous burden to vehicles in the roadway network. Hence, one critical issue in a multi-modal evacuation planning is the effective coordination of the vehicle and pedestrian flows by considering their complex interactions. The purpose of this research is to develop an integrated system that is capable of generating the optimal evacuation plan and reflecting the real-world network traffic conditions caused by the conflicts of these two types of flows. The first part of this research is an integer programming model designed to optimize the control plans for massive mixed pedestrian-vehicle flows within the evacuation zone. The proposed model, integrating the pedestrian and vehicle networks, can effectively account for their potential conflicts during the evacuation. The model can generate the optimal routing strategies to guide evacuees moving toward either their pick-up locations or parking areas and can also produce a responsive plan to accommodate the massive pedestrian movements. The second part of this research is a mixed-flow simulation tool that can capture the conflicts between pedestrians, between vehicles, and between pedestrians and vehicles in an evacuation network. The core logic of this simulation model is the Mixed-Cellular Automata (MCA) concept, which, with some embedded components, offers a realistic mechanism to reflect the competing and conflicting interactions between vehicle and pedestrian flows. This study is expected to yield the following contributions * Design of an effective framework for planning a multi-modal evacuation within metropolitan areas; * Development of an integrated mixed-flow optimization model that can overcome various modeling and computing difficulties in capturing the mixed-flow dynamics in urban network evacuation; * Construction and calibration of a new mixed-flow simulation model, based on the Cellular Automaton concept, to reflect various conflicting patterns between vehicle and pedestrian flows in an evacuation network

A Petri Net Model of Train Operation Simulation for Harmonizing Train Timetables of Neighbor Dispatching Sections

Train timetable is the key document to regulate railway traffic through sequencing train movements to keep the appropriate order. Timetable stability and on-schedule rate are closely related. Delays caused by disturbances in train operations can be absorbed by a high quality timetable with high stability, and the on-schedule rate then can be assured. This paper improves the stability of timetables of several connected railway sections to assure the on-schedule rate with a simulation method. Firstly, we build a macroscopic network model of train operation in a railway network using the Petri net theory. Then we design the train tracking subnet model, the station subnet model and arrival-departure track subnet model. At last we propose a computing case, simulating the train operation process based on the presented models, and the simulation results prove the feasibility and availability of the models. The approach presented in this paper can offer valuable decision-support information for railway operators preparing train timetables.</p

Understanding the costs of urban transportation using causal inference methods

With urbanisation on the rise, the need to transport the population within cities in an efficient, safe and sustainable manner has increased tremendously. In serving the growing demand for urban travel, one of the key policy question for decision makers is whether to invest more in road infrastructure or in public transportation. As both of these solutions require substantial spending of public money, understanding their costs continues to be a major area of research. This thesis aims to improve our understanding of the technology underlying costs of operation of public and private modes of urban travel and provide new empirical insights using large-scale datasets and application of causal econometric modelling techniques. The thesis provides empirical and theoretical contributions to three different strands in the transportation literature. Firstly, by assessing the relative costs of a group of twenty-four metro systems across the world over the period 2004 to 2016, this thesis models the cost structure of these metros and quantifies the important external sources of cost-efficiency. The main methodological development is to control for confounding from observed and unobserved characteristics of metro operations by application of dynamic panel data methods. Secondly, the thesis provides a quantification of the travel efficiency arising from increasing the provision of road-based urban travel. A crucial pre-condition of this analysis is a reliable characterisation of the technology describing congestion in a road network. In pursuit of this goal, this study develops novel causal econometric models describing vehicular flow-density relationship, both for a highway section and for an urban network, using large-scale traffic detector data and application of non-parametric instrumental variables estimation. Our model is unique as we control for bias from unobserved confounding, for instance, differences in driving behaviour. As an important intermediate research outcome, this thesis also provides a detailed association of the economic theory underlying the link between the flow-density relationship and the corresponding production function for travel in a highway section and in an urban road network. Finally, the influence of density economies in metros is investigated further using large-scale smart card and train location data from the Mass Transit Railway network in Hong Kong. This thesis delivers novel station-based causal econometric models to understand how passenger congestion delays arise in metro networks at higher passenger densities. The model is aimed at providing metro operators with a tool to predict the likely occurrences of a problem in the network well in advance and materialise appropriate control measures to minimise the impact of delays and improve the overall system reliability. The empirical results from this thesis have important implications for appraisal of transportation investment projects.Open Acces

人・ユーザー中心の移動サービスと群集マネジメントのためのモデリング，シミュレーションと最適化

Tohoku University博士（情報科学）thesi