An activity-based spatial-temporal community electricity vulnerability assessment framework

The power system is among the most important critical infrastructures in urban cities and is getting increasingly essential in supporting people s daily activities. However, it is also susceptible to most natural disasters such as tsunamis, floods, or earthquakes. Electricity vulnerability, therefore, forms a crucial basis for community resilience. This paper aims to present an assessment framework of spatial-temporal electricity vulnerability to support the building of community resilience against power outages. The framework includes vulnerability indexes in terms of occupant demographics, occupant activity patterns, and urban building characteristics. To integrate factors in these aspects, we also proposed a process as activity simulation-mapping-evaluation-visualization to apply the framework and visualize results. This framework can help planners make an effective first-time response by identifying the most vulnerable areas when a massive power outage happens during natural disasters. It can also be integrated into community resilience analysis models and potentially contributes to effective disaster risk managementComment: to be published in Proceedings of the 5th International Conference on Building Energy and Environmen

Community Time-Activity Trajectory Modelling based on Markov Chain Simulation and Dirichlet Regression

Accurate modeling of human time-activity trajectory is essential to support community resilience and emergency response strategies such as daily energy planning and urban seismic vulnerability assessment. However, existing modeling of time-activity trajectory is only driven by socio-demographic information with identical activity trajectories shared among the same group of people and neglects the influence of the environment. To further improve human time-activity trajectory modeling, this paper constructs community time-activity trajectory and analyzes how social-demographic and built environment influence people s activity trajectory based on Markov Chains and Dirichlet Regression. We use the New York area as a case study and gather data from American Time Use Survey, Policy Map, and the New York City Energy & Water Performance Map to evaluate the proposed method. To validate the regression model, Box s M Test and T-test are performed with 80% data training the model and the left 20% as the test sample. The modeling results align well with the actual human behavior trajectories, demonstrating the effectiveness of the proposed method. It also shows that both social-demographic and built environment factors will significantly impact a community's time-activity trajectory. Specifically, 1) Diversity and median age both have a significant influence on the proportion of time people assign to education activity. 2) Transportation condition affects people s activity trajectory in the way that longer commute time decreases the proportion of biological activity (eg. sleeping and eating) and increases people s working time. 3) Residential density affects almost all activities with a significant p-value for all biological needs, household management, working, education, and personal preference.Comment: to be published in Computers, Environment and Urban Syste

Truck Activity Pattern Classification Using Anonymous Mobile Sensor Data

To construct, operate, and maintain a transportation system that supports the efficient movement of freight, transportation agencies must understand economic drivers of freight flow. This is a challenge since freight movement data available to transportation agencies is typically void of commodity and industry information, factors that tie freight movements to underlying economic conditions. With recent advances in the resolution and availability of big data from Global Positioning Systems (GPS), it may be possible to fill this critical freight data gap. However, there is a need for methodological approaches to enable usage of this data for freight planning and operations. To address this methodological need, we use advanced machine-learning techniques and spatial analyses to classify trucks by industry based on activity patterns derived from large streams of truck GPS data. The major components are: (1) derivation of truck activity patterns from anonymous GPS traces, (2) development of a classification model to distinguish trucks by industry, and (3) estimation of a spatio-temporal regression model to capture rerouting behavior of trucks. First, we developed a K-means unsupervised clustering algorithm to find unique and representative daily activity patterns from GPS data. For a statewide GPS data sample, we are able to reduce over 300,000 daily patterns to a representative six patterns, thus enabling easier calibration and validation of the travel forecasting models that rely on detailed activity patterns. Next, we developed a Random Forest supervised machine learning model to classify truck daily activity patterns by industry served. The model predicts five distinct industry classes, i.e., farm products, manufacturing, chemicals, mining, and miscellaneous mixed, with 90% accuracy, filling a critical gap in our ability to tie truck movements to industry served. This ultimately allows us to build travel demand forecasting models with behavioral sensitivity. Finally, we developed a spatio-temporal model to capture truck rerouting behaviors due to weather events. The ability to model re-routing behaviors allows transportation agencies to identify operational and planning solutions that mitigate the impacts of weather on truck traffic. For freight industries, the prediction of weather impacts on truck driver’s route choices can inform a more accurate estimation of billable miles

Characterizing activity sequences using Profile Hidden Markov Models

In literature, activity sequences, generated from activity-travel diaries, have been analyzed and classified into clusters based on the composition and ordering of the activities using Sequence Alignment Methods (SAM). However, using these methods, only the frequent activities in each cluster are extracted and qualitatively described; the infrequent activities and their related travel episodes are disregarded. Thus, to quantify the occurrence probabilities of all the daily activities as well as their sequential orders, we develop a novel process to build multiple alignments of the sequences and subsequently derive profile Hidden Markov Models (pHMMs). This process consists of 4 major steps. First, activity sequences are clustered based on a pre-defined scheme. The frequent activities along with their sequential orders are then identified in each cluster, and they are subsequently used as a template to guide the construction of a multiple alignment of the cluster of sequences. Finally, a pHMM is employed to convert the multiple alignment into a position-specific scoring system, representing the probability of each frequent activity at each important position of the alignment as well as the probabilities of both insertion and deletion of infrequent activities. By applying the derived pHMMs to a set of activity-travel diaries collected in Belgium as well as a group of mobile phone call location data recorded in Switzerland, the potential and effectiveness of the models in capturing the sequential features of each cluster and distinguishing them from those of other clusters, are demonstrated. The proposed method can also be utilized to improve activity-based transportation model validation and travel survey designs. Furthermore, it offers a wide application in characterizing a group of any related sequences, particularly sequences varying in length and with a high frequency of short sequences that are typically present in human behavior

Modélisation des systèmes d’activités pour fins de prévision de la demande de transport

«RÉSUMÉ:Ce mémoire vise à enrichir le processus de prévision de la demande en transport du ministère des Transports du Québec. Tout comme les premiers modèles de prévision apparus dans les années 1950, celui du ministère a comme principal objet de modéliser les déplacements. En revanche, ce qui incite une personne à effectuer un déplacement sont les activités qu’elle veut réaliser à l’extérieur de son domicile. C’est pourquoi la volonté de concevoir des modèles basés sur le désir de réaliser des activités a émergé au courant des années 1970. Toutefois, l’implantation de tels modèles dans la pratique tarde à se matérialiser. L’objectif poursuivi par ce mémoire consiste donc à contribuer à la modernisation des pratiques de planification des transports au Québec. Cette contribution vise à prendre en compte le système d’activités des individus dans le processus de prévision de la demande de transport au Québec en développant une typologie de patrons typiques d’activités. Plus particulièrement, le but ultime de ce projet de recherche est de développer une méthode permettant de générer une séquence d’activités pour chaque individu.» et «----------ABSTRACT: The purpose of this research project is to contribute to the enhancement of the ministère des Transports du Québec's transportation demand forecasting process. Like the first forecasting models that appeared in the 1950s, this model is based on trips. However, what drives people to make these trips are the activities they want to carry out outside their homes. For this reason, the desire to develop models based on the desire to carry out these activities emerged in the 1970s. However, the implementation of such models in practice has been slow to materialize. The goal of this project is therefore to contribute to the modernization of transportation planning practices in Quebec. This contribution aims to consider the activity system of individuals in the MTQ’s process by developing a typology of typical activity patterns. More specifically, the goal of this research project is to develop a method for generating a sequence of activities for each individual.