A multi-dimensional rescheduling model in disrupted transport network using rule-based decision making

Apart from daily recurrent traffic congestion, unforeseen events such as flood induced road damages or bridge collapses can degrade the capacity of traffic supply and cause a significant influence on travel demand. An individual realising the unexpected events would take action to reschedule its day plan in order to fit into the new circumstance. This paper analyses the potential reschedule possibilities by augmenting the Within-Day Replanning simulation model implemented in the Multi-Agent Transport Simulation (MATSim) framework. Agents can adjust day plan through multi-dimensional travel decisions including route choice, departure time choice, mode switch, trip cancellation. The enhanced model not only improves the flexibility of MATSim in rescheduling a plan during an execution day, but also lays the foundation of integrating more detailed heterogeneity decision rules into the travel behaviour simulation to cope with unexpected incidents. Furthermore, the proposed rescheduling model is capable of predicting the network performance in the real-world picture and gives a hint on how best react to transport disruptions for transport management agency

On agent-based modeling: Multidimensional travel behavioral theory, procedural models and simulation-based applications

This dissertation proposes a theoretical framework to modeling multidimensional travel behavior based on artificially intelligent agents, search theory, procedural (dynamic) models, and bounded rationality. For decades, despite the number of heuristic explanations for different results, the fact that "almost no mathematical theory exists which explains the results of the simulations" remains as one of the large drawbacks of agent-based computational process approach. This is partly the side effect of its special feature that "no analytical functions are required". Among the rapidly growing literature devoted to the departure from rational behavior assumptions, this dissertation makes effort to embed a sound theoretical foundation for computational process approach and agent-based microsimulations for transportation system modeling and analyses. The theoretical contribution is three-fold: (1) It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modeled and integrated in a unified and coherent approach. (2) Procedural and dynamic agent-based decision-making is modeled. Within the model, agents make decisions. They also make decisions on how and when to make those decisions. (3) Replace conventional user equilibrium with a dynamic behavioral user equilibrium (BUE). Search start/stop criteria is defined in the way that the modeling process should eventually lead to a steady state that is structurally different to user equilibrium (UE) or dynamic user equilibrium (DUE). The theory is supported by empirical observations and the derived quantitative models are tested by agent-based simulation on a demonstration network. The model in its current form incorporates short-term behavioral dimensions: travel mode, departure time, pre-trip routing, and en-route diversion. Based on research needs and data availability, other dimensions can be added to the framework. The proposed model is successfully integrated with a dynamic traffic simulator (i.e. DTALite, a light-weight dynamic traffic assignment and simulation engine) and then applied to a mid-size study area in White Flint, Maryland. Results obtained from the integration corroborate the behavioral richness, computational efficiency, and convergence property of the proposed theoretical framework. The model is then applied to a number of applications in transportation planning, operations, and optimization, which highlights the capabilities of the proposed theory in estimating rich behavioral dynamics and the potential of large-scale implementation. Future research should experiment the integration with activity-based models, land-use development, energy consumption estimators, etc. to fully develop the potential of the agent-based model

Impacts of Advanced Travel Information Systems on Travel Behaviour: Smartmoov’ case study

What are the effects of multimodal real-time information on travel behaviour? Large amounts of money have been invested to implement in the cities such systems, but until now few assessments have been done to verify if it contributes to a real modal shift and, in the higher end, to a more sustainable mobility. This research aims at thoroughly assessing the effectiveness of multimodal real-time information systems, pointing out the limitations before their use and recording the changes induced on the travel behaviour. Two wave questionnaires were designed and administered to a sample of 46 persons before and after a five months experimentation where a multimodal real-time information application for Smartphones (Smartmoov’) was tested after its implemented in the city of Lyon, in 2013. Besides the questionnaires twelve focus groups were conducted with the same sample, six before and six after the experimentation. The survey was aimed at investigating the potential changes of travel behaviour of the sample. Descriptive analysis, parametric and non-parametric tests, factor analysis and binary logistic regression were used as statistical approaches to analyse the collected data and evaluate the effectiviness of Smartmoov’. Before the experimentation, it was understood that participants had no constrains towards the use of the Smartmoov’, being its use under a positive outlook: almost everyone was expert in the technology and was familiar with the concept of Smartmoov’. The travellers’ assessment of the travel planner was initially modestly positive, but it decreased over time and, after the experimentation, the use of the different modes remained stable while a small increase of the car for the most frequent trip was observed. The perceived behaviour control and the intentions to change mode did not show variations after the experimentation; this fact points out that the behaviour is not completely reasoned, being partly under the influence of the habitual performance. The stability of the mode used, of the perceived behavioural control and of the intentions show that mobility is strongly influenced by the high frequency of the past behaviour. In fact, the mobility habits are a heavy burden on the process of modal choice. Nevertheless, information can play a role on modal shift, but only if it is strong enough to interrupt the patterns of routine commutes. The results of the experimentation were in line with previous studies; few people used this app on a daily basis or for planning daily commuting, but they most often used Smartmoov’ to plan occasional travels. Furthermore, people did not show any willingness to pay to use Smartmoov’ neither before or after the experimentation

Development of a dynamic traffic assignment system for short-term planning applications

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 141-147).Evaluation of Intelligent Transportation Systems (ITS) at the planning level, as well as various short-term planning projects, require the use of appropriate tools that can capture the dynamic and stochastic interactions between demand and supply. The objective of this thesis is to develop a methodological framework for such applications and implement it in the context of an existing dynamic traffic assignment system, DynaMIT. The methodological framework captures the day-to-day evolution of traffic. Furthermore, it models traveler behavior and network performance, in response to special events and situations such as incidents, weather emergencies, sport events etc. The new planning tool DynaMIT-P, consists of a supply (network performance) simulator, a demand simulator and algorithms that capture their interactions. The supply simulator captures traffic dynamics in terms of evolution and dissipation of queues, spill-backs etc. The demand simulator estimates OD flows that best match current measurements of them in the network, and models travel behavior in terms of route choice, departure time choice and response to information. DynaMIT-P is particularly suited to evaluate Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS) at various levels of sophistication. The results of a case study, focusing on the evaluation of alternative designs of Variable Message Signs (VMS) using a network in Irvine, California, illustrate the functionality and potential of the system.by Srinivasan Sundaram.S.M

e-Sanctuary: open multi-physics framework for modelling wildfire urban evacuation

The number of evacuees worldwide during wildfire keep rising, year after year. Fire evacuations at the wildland-urban interfaces (WUI) pose a serious challenge to fire and emergency services and are a global issue affecting thousands of communities around the world. But to date, there is a lack of comprehensive tools able to inform, train or aid the evacuation response and the decision making in case of wildfire. The present work describes a novel framework for modelling wildfire urban evacuations. The framework is based on multi-physics simulations that can quantify the evacuation performance. The work argues that an integrated approached requires considering and integrating all three important components of WUI evacuation, namely: fire spread, pedestrian movement, and traffic movement. The report includes a systematic review of each model component, and the key features needed for the integration into a comprehensive toolkit

Optimizing Transportation Systems with Information Provision, Personalized Incentives and Driver Cooperation

Poor performance of the transportation systems has many detrimental effects such as higher travel times, increased travel costs, higher energy consumption, and greenhouse gas emissions, etc. This thesis optimizes the transportation systems by addressing the traffic congestion problem and climate change impact resulting from the inefficient operation of these systems. I first focus on the key player of the transportation systems e.g., human being/traveler, and model travelers\u27 route choice behavior with real-time information. In this study, I define looking-ahead behavior in route choice as a traveler\u27s taking into account future diversion possibilities enabled by real-time information in a network with random travel times. Subjects participated in route-choice experiments in a driving simulator as well a PC-based environment. Three types of maps in increasing levels of complexity and information availability are used. Aggregate data analysis shows that network complexity negatively affects subjects\u27 ratio of choosing the risky route given an experiment environment. Higher cognitive load in the driving simulator results in a higher level of risk aversion than in the PC-based environment for the simplest map. I specify and estimate a mixed logit model with two latent classes, looking-ahead and myopic, taking into account the panel effect. The estimated latent class membership function suggests that some subjects can look ahead while others are myopic in making their route choices, and drivers learn to look ahead over time. The experiment environment plays a role in the risk attitude of myopic subjects. A bias against information is found for subjects who look ahead, however, is not significant among myopic subjects. I then shift my focus to influencing the travel patterns of individual travelers to reduce the energy and environmental impacts of the transportation sector. I present the system optimization (SO) framework of Tripod, an integrated bi-level transportation management system aimed at maximizing energy savings of the multi-modal transportation systems. From the user\u27s perspective, Tripod is a smartphone app, accessed before performing trips. The app proposes a series of alternatives each with an amount of tokens which the user can later redeem for goods or services. The role of SO is to compute the optimized set of tokens associated to the available alternatives, in order to minimize the system-wide energy consumption, under a limited token budget. I present a method to solve this complex optimization problem and describe the system architecture, the multimodal simulation-based optimization model and the heuristic method for the on-line computation of the optimized token allocation. I then present the framework with the simulation results. Finally, I optimize the systems travel time by addressing the equity issue of congestion pricing. I propose an alternative approach to an equitable and Pareto-improving transportation systems based on cooperation among travelers assisted by defector penalty. Theoretical analysis shows the existence condition of the cooperative scheme for heterogeneous value of time (VOT) of travelers. I formulate a mathematical programming problem for the optimal cooperative scheme problem in a general network with Pareto-improving constraints and practical considerations on the length the cooperation cycle. I then conduct computational tests on a simple network and evaluate the solutions in terms of efficiency improvement (total system travel time) and equitability (Gini index)

Impacts of Advanced Travel Information Systems on Travel Behaviour: Smartmoov' case study

What are the effects of multimodal real-time information on travel behaviour? Large amounts of money have been invested to implement in the cities such systems, but until now few assessments have been done to verify if it contributes to a real modal shift and, in the higher end, to a more sustainable mobility. This research aims at thoroughly assessing the effectiveness of multimodal real-time information systems, pointing out the limitations before their use and recording the changes induced on the travel behaviour. Two wave questionnaires were designed and administered to a sample of 46 persons before and after a five months experimentation where a multimodal real-time information application for Smartphones (Smartmoov') was tested after its implemented in the city of Lyon, in 2013. Besides the questionnaires twelve focus groups were conducted with the same sample, six before and six after the experimentation. The survey was aimed at investigating the potential changes of travel behaviour of the sample. Descriptive analysis, parametric and non-parametric tests, factor analysis and binary logistic regression were used as statistical approaches to analyse the collected data and evaluate the effectiviness of Smartmoov'. Before the experimentation, it was understood that participants had no constrains towards the use of the Smartmoov', being its use under a positive outlook: almost everyone was expert in the technology and was familiar with the concept of Smartmoov'. The travellers' assessment of the travel planner was initially modestly positive, but it decreased over time and, after the experimentation, the use of the different modes remained stable while a small increase of the car for the most frequent trip was observed. The perceived behaviour control and the intentions to change mode did not show variations after the experimentation; this fact points out that the behaviour is not completely reasoned, being partly under the influence of the habitual performance. The stability of the mode used, of the perceived behavioural control and of the intentions show that mobility is strongly influenced by the high frequency of the past behaviour. In fact, the mobility habits are a heavy burden on the process of modal choice. Nevertheless, information can play a role on modal shift, but only if it is strong enough to interrupt the patterns of routine commutes. The results of the experimentation were in line with previous studies; few people used this app on a daily basis or for planning daily commuting, but they most often used Smartmoov' to plan occasional travels. Furthermore, people did not show any willingness to pay to use Smartmoov' neither before or after the experimentatio

Traveler Responses to Real-Time Transit Passenger Information Systems

In recent years, a considerable amount of money has been spent on Real-time Transit Passenger Information Systems (RTPISs), which provide timely and accurate transit information to current and potential riders to enable them to make better pre-trip and en-route decisions. Understanding traveler responses to real-time transit information is critical for designing such services and evaluating their effectiveness. To answer this question, an effort is made in this dissertation to systematically conceptualize a variety of behavioral and psychological responses travelers may undertake to real-time transit information and empirically examine the causal effects of real-time information on traveler behavior and psychology. This research takes ShuttleTrac, a newly implemented real-time bus arrival information system for UMD's Shuttle-UM service, as a case for empirical study. In Part 1 analysis, using panel datasets derived from three-waved online campus transportation surveys, fixed-effects OLS models and random-effects ordered probit models are estimated to sort out causal relations between ShuttleTrac information use and general/cumulative behavioral and psychological outcomes. In addition, a two-stage instrumental variable model was estimated to examine the potential change in habitual mode choices due to real-time transit information use. The results show that with a few months of adjustment, travelers may increase their trip-making frequency as a result of real-time transit information use, and positive psychological outcomes are more prominent in both short and longer terms. In Part 2 analyses, using the cross-sectional dataset derived from the onboard survey, OLS models and ordered logit models were estimated to examine the trip-specific psychological effects of real-time transit information. The results show that these trip-specific psychological effects of real-time transit information do exist in expected directions and they vary among user groups and in different scenarios. A finding consistent across two parts of analyses is that accuracy of information plays a greater role in determining traveler behavior and psychology than the mere presence. This research contributes to the general discussion on traveler behavior under advanced information by 1) developing an integrative conceptual framework; and 2) providing useful insights into the issue with much empirical evidences obtained with revealed-preference data and sophisticated modeling techniques

Road network recovery from concurrent capacity-reducing incidents : model development and optimisation

Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions.Local and regional economies are highly dependent on the road network. The concurrent closure of multiple sections of the network following a hazardous event is likely to have significant negative consequences for those using the network. In situations such as these, infrastructure managers must decide how best to restore the network to protect users, maximise connectivity and minimise overall disruption. Furthermore, many hazardous events are forecast to become more frequent and extreme in the future as a result of climate change. Extensive research has been undertaken to understand how to improve the resilience of degraded transport networks. Whilst network robustness (that is, the ability of a network to withstand stress) has been considered in numerous studies, the recovery of the network has captured less attention among researchers. Methodologies developed to date are overly simplistic, especially when simulating the dynamics of traffic demand and drivers’ decision-making in multi-day situations where there is considerable interplay between actual and perceived network states and behaviour. This thesis presents a decision-support tool that optimises the recovery of road transport networks after major day-to-day disruptions, maximising network connectivity and minimising total travel costs. This work expands upon previous efforts by introducing a new approach that models the damage-capacity-time relationship and improves the existing reinforcement-learning traffic-assignment models to be applicable to disrupted scenarios. An efficient metaheuristic approach (NSGA-II) is proposed to find optimal solutions for the recovery problem. The model is also applied to a real-world scenario based on the Scottish road network. Results from this case study clearly highlight the potential applicability of this model to evaluate different recovery strategies and optimise the recovery of road networks after multi-day major disruptions

Estrategias multi-mapa para el enrutamiento dinámico de tráfico urbano

La Directiva «Clean Transport, Urban Transport» de la Unión Europea identifica que la congestión en áreas urbanas tiene un coste anual acumulado de 100 billones de euros. El 60% de la población europea se ubica en áreas urbanas de más de 10,000 habitantes. De igual manera, se estima que la movilidad urbana es causante del 40% de emisiones de CO2 y hasta el 70% de otros contaminantes. Pero el problema es global y generalizado. La tesis aborda la problemática de optimizar tanto la planificación del tráfico urbano como su enrutamiento dinámico mediante una nueva técnica denominada Traffic Weighted Multi-Maps (TWM) con el fin de mitigar la congestión y sus efectos en los entornos urbanos. TWM propone la entrega selectiva de mapas de tráfico a los diferentes conjuntos de vehículos presentes en la red tenido en cuenta sus especificidades, el momento temporal, las situaciones de la via y el contexto. Para ello, recoge la colección de artículos científicos publicados en revistas indexadas respecto a TWM. La tesis analiza el uso de TWM para diversos casos de uso: mejora de la congestión en redes urbanas complejas mediante mapas de red aleatorizados, el encaminamiento selectivo de flotas, la reducción de la congestión ante incidentes aleatorios o planificados, y se plantean otros muchos escenarios. Asimismo, la tesis profundiza en cómo obtener distribuciones de mapas TWM óptimos para una cierta demanda de tráfico conocida por medio de datos históricos, proponiendo un conjunto de algoritmos de optimización basado en algoritmos evolutivos. El éxito de la implantación de un sistema de gestión inteligente de tráfico (ITS) depende de la adherencia de los conductores al mismo, dependiendo ésta de la percepción de la utilidad por los conductores. La tesis propone un modelo de experiencia de usuario-conductor para analizar el caso complejo de una red de tráfico que emplee diversos ITS de forma simultánea y no coordinada, con el objetivo de analizar la evolución en el tiempo de la adherencia de los conductores a TWM y así validar las hipótesis de partida respecto a su efectividad. La parte experimental de la tesis describe cómo se han empleado simulaciones de tráfico de diferente tipología: microscópicas y macroscópicas, desarrollando componentes de simulación específicos sobre plataformas abiertas de simulación de tráfico. Los resultados obtenidos son muy prometedores, obteniendo mejoras en la congestión global que oscilan entre el 20% y el 45%, con impacto en el resto de indicadores de emisiones y movilidad. Los estudios de simulación del comportamiento de los conductores en base a la utilidad percibida de TWM, muestran cómo la adherencia al mismo crece y se estabiliza garantizando el comportamiento global. Por último, se indican las posibles líneas futuras de investigación identificadas