Activity Scheduling Models Research Articles

Missing Typical Weekdays in Travel Surveys: A Pseudo-Panel Approach to Explore Weekly Travel Patterns

Travel surveys generally rely on single-day travel diaries where respondents report their travel information for a typical weekday. However, the concept of a typical weekday does not represent the current reality, as travel behavior has been largely altered in the post-pandemic period. Besides, the conclusions based on analyzing single-day travel diaries lack the ability to capture daily variations in travel behavior. In response to these concerns, this research proposed a framework to expand single-day travel diaries into longitudinal multi-day travel data using a pseudo-panel approach. Leveraging the constructed longitudinal data, the study evaluated the determinants of people’s daily participation in work–school, routine, and discretionary activities. Fixed and random effects panel data estimation models were used for this purpose. Results showed that activity participation is largely attributed to vehicle ownership, income, education, driving license, and household structure. Noticeable daily trends were observed in work–school and discretionary activities. A negative association between transit pass ownership and activity participation was noticed, suggesting social exclusion faced by transit users. In addition, teleworkers were found to be relatively more engaged in discretionary activities. Suburban residents were found to travel longer to participate in activities compared to urban dwellers. The proposed research framework can support future activity-based modeling aspects, such as activity participation, scheduling, mode choice, shared travel, and destination choice models, specifically addressing the “typical weekday” barrier.

Using pedestrian modelling to inform virus transmission mitigation policies: A novel activity scheduling model to enable virus transmission risk assessment in a restaurant environment

The Covid-19 pandemic has had a large impact on the world. The virus spreads especially easily among people in indoor spaces such as restaurants. Hence, tools that can assess how different restaurant settings can impact the potential spread of an airborne virus and that can assess the effectiveness of mitigation policies are of high value. Microscopic pedestrian models provide the tools necessary to assess the detailed movements of people in a restaurant and with that the risk of virus transmission. This paper presents the application of a microscopic pedestrian model, including a novel activity choice and scheduling model, to assess virus transmission risks in restaurants. Simulation experiments identify that different factors impact virus transmission risks in a restaurant. Contacts between restaurant staff and customers are the driving factor for virus transmission in a restaurant whereby especially staff presents a big risk. Hence, mitigation policies focussing on these interactions and on preventing staff from transmitting the virus can be highly effective. The results also show that different restaurant layouts and setups lead to distinctly different transmission risks. Therefore, insights obtained from simulating one restaurant cannot be just transferred to any other restaurant. Together, these results show the added value of including pedestrian models in disease transmission risk modelling exercises to mitigate the impact of a pandemic caused by an airborne virus. However, the research also shows that, to better utilize the potential of pedestrian models for disease transmission risk modelling, future research of pedestrian activity scheduling behaviour in indoor spaces is necessary.

An optimization approach for managing electric vehicle and reused battery charging in a vehicle to grid system under an electricity rate with demand charge

Recently, migration from internal combustion cars to electric vehicles (EVs) has received increasing attention from both public and private stakeholders as one viable solution towards energy sustainability and security. US DOE projects over 120 million Light-duty ground vehicles owned by commercial & industrial electric ratepayers (e.g., forklifts in manufacturers and warehouses, shuttles on university campuses) to be electrified. On the other hand, the short life cycle of EV batteries, where current practices require lithium-ion (Li-ion) battery packs to be replaced when capacity drops to 70%–80%, poses a serious concern over the sustainability of a large-scale EV fleet system. These batteries, however, can provide abundant source of stationary energy. This paper considers a commercial & industrial electricity ratepayer’s electric fleet system consisting of reused batteries renewable energy sources, and a supporting vehicle to grid infrastructure, and optimizes the total electricity bill that includes “demand charge”, a substantial amount of charge unique to commercial & industrial electric ratepayers compared to residential users. We propose a novel optimal EV, battery and solar activity scheduling (OEVBSAS) model to coordinate various energy sources and usage. The OEVBSAS model is tested with a case study that involves an electricity subsystem, consisting of electric campus shuttle fleet, solar panels and reused EV battery systems, at a Turkish university that is subject to “demand charge”. Computational results indicate that the integrated system, coupled with the optimal activity scheduling solution, is able to decrease the total electricity cost by more than 5% and improve the peak to average ratio by 4%.

Activity Scheduling Behavior of the Visitors to an Outdoor Recreational Facility Using GPS Data

Understanding the decision-making behavior of pedestrians is essential for urban designers and developers in enhancing the commercial and aesthetic value of streets and other urban spaces. However, limited research has been conducted on the activity scheduling behavior of pedestrians. The majority of the studies conducted on outdoor facilities utilize spatial representations by links and are unable to sufficiently represent the highly flexible behavior of pedestrians. This study proposes a new method to discretize data from the global positioning system (GPS) into a two-dimensional grid-based spatial representation with a high spatial resolution. The information regarding the stay at the point of interests (POIs) is extracted from the discretized data, and the activity scheduling model is estimated. The estimation results indicate that the visitors’ attributes, such as the age of the representative and number of children, affect the probability of the activity choice and the time spent at the POI. The probability of choosing the main gate increases in the latter half of the stay, confirming the existence of time pressure. The information on the decision-making behavior of the visitors to a facility, obtained from the GPS data, can be applied to the data-oriented spatial design process to create attractive and lively spaces.

Job shop scheduling model for a sustainable manufacturing

Technological development was not always environmental-friendly but it led to problems that affect the entire population, such as climate change and the depletion of non-renewable resources. In recent years, we are trying to change the concept of industrialization, with a more prudent and focused view to environmental issues. In this perspective, the setting of the fourth industrial revolution, called Industry 4.0, seeks to make technological development sustainable with the crucial aim of resource saving firstly.Starting by this issue, this study aims at introducing a smart tool, based on the application of a mathematical model, already proposed in literature, for the job scheduling from an energy saving perspective into a real company IT system. After a preliminary introduction on the concepts of sustainability and industry 4.0, and how they are connected to each other, and a second part that will deal with Smart Factory, smart factories oriented to customization and flexibility, the mathematical model of activity scheduling will be discussed and than applied to a specific IT system. This model, in fact, properly implemented into a Manufacturing Execution System (MES) will represent a powerful tool oriented to the efficient use of energy resources.

Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns

Two dynamic, gap-based activity scheduling models are tested by applying a short-run microsimulation approach to replicate workers’ travel/activity patterns over a 1-week time period. In the first model, a two-level work episode scheduling model is applied to schedule weekly work episodes (Dianat et al. in Transp Res Rec 2664:59–68, 2017. https://doi.org/10.3141/2664-07 ). This includes joint choices of working or not on each day and work episode duration and start time in case of choosing to work. Assigning higher priority to scheduling work episodes, and assuming night sleep to be pre-determined, provides a weekly “skeleton schedule”. Non-work/school (NWS) episodes are then generated and scheduled in the available gaps as a joint choice of activity type and destination followed by a continuous time expenditure choice. The second model applies the same mathematical framework as the NWS model for scheduling all activity types including work/school, considering only night sleep as the pre-determined skeleton schedule. This exercise allows us to study the impact of assigning a higher priority to scheduling work/school activities on complete out-of-home travel/activity pattern prediction, compared to the alternative hypothesis, which is scheduling all the activities simultaneously. Comparing the simulation outcomes of the two models with the observed dataset reveals that organizing NWS episodes around the schedule skeleton not only is behaviorally more representative but also increases the accuracy of the predicted NWS episodes’ patterns. Moreover, applying the work scheduling model results in a more accurate prediction of the weekly work schedule compared to the second model.

Use of repeated cross-sectional travel surveys for developing meta models of activity-travel scheduling processes

The paper presents an investigation of the temporal transferability of activity scheduling process models and a Meta model of activity scheduling processed by using repeated cross-sectional datasets. Three repeated cross-sectional household travel survey datasets collected in the greater Toronto and Hamilton Area in the years 2001, 2006, and 2011 are used for the investigation. A random utility maximization based dynamic activity scheduling model is utilized to develop activity-travel scheduling models for non-workers and workers separately. Individual year-specific models are compared to identify the temporal stability of the effects of different variables in the model. Results are used to define temporal evolution components in the Meta models. Estimated models are tested for temporal transferability. Different transferability measures are used to test the temporal transferability of cross-sectional year-specific and the Meta models. Results demonstrate an approach of effectively using multiple repeated cross-sectional datasets as pseudo panel data to develop Meta models to improve the temporal transferability of activity scheduling models.

ANT COLONY OPTIMIZATION (ACO) IN SCHEDULING OVERLAPPING ARCHITECTURAL DESIGN ACTIVITIES

The increasing complexity of architectural design works refers to the need for high quality design solu­tions for overlapping activities through a shorter time period. Conventional network analysis techniques such as CPM could only represent sequential processes yet it is unable to handle a process which contains iterations so that it leads to the occurrence of unwanted omission of logic or information links between design activities. Ant Colony Optimiza­tion emerged as an efficient metaheuristic technique for solving computational problems in finding good paths through graphs. This research aims to develop an ACO based Design Activity Scheduling model (ACO-DAS) for the scheduling of overlapping architectural design activities and to test the workability of ACO-DAS through a hypothetical run. From the computational results of both CPM and ACO methods, the determination of critical path using ACO-DAS model resulted in a design duration at 50 while that for CPM was as long as 78. The durations of architectural design activi­ties have been significantly shortened by ACO-DAS. ACO-DAS results in shorter design completion time thus it deems more advanced than CPM.

Scheduling decision styles on leisure and social activities

This paper investigates scheduling decisions associated with different types of leisure and social activities. Correlations among decisions and self-selection biases are explicitly investigated by using a sample selection model with a bivariate probit selection rule. A dataset collected in the first wave of a recent activity-travel scheduling panel survey carried out in Valencia (Spain) was used for empirical investigation. Significant differences are revealed in the empirical models for leisure and social activities in planning decisions, including different effects of temporal, companionship and demographic factors. The findings of the empirical model have important implications to travel behavior and activity-travel scheduling model developments. These results confirm the existence of different mechanisms underlying the activity-travel decision processes when leisure and social activities are of concerns. Results provide significant insights into enhancing the performances of an activity scheduling model by capturing accurate activity-travel scheduling tradeoffs in flexible activity types e.g. leisure and social activities.

Application of Travel Activity Scheduler for Household Agents in a Chinese City

The travel activity scheduler for household agents (TASHA) is an operational rule-based model that generates activity schedules and travel patterns for a 24-h typical weekday for all persons in a household. This paper reports the application and validation of the model in Changzhou, China. The data cleaning procedure of the traditional household survey and the verification results of the rules used in the TASHA are presented. After the results of the activity generation and scheduling models were analyzed, an iterative approach was applied to inflating the observed activity rates to calibrate the results. The model was shown to replicate observed activity frequency, tour frequency, and trip start time fairly accurately at the regional level. The activity duration of the model was underestimated by 13.1%, but this underestimate did not add much bias to the shape of the duration distribution by start time. The final model results show that the TASHA is an attractive alternative to conventional modeling systems currently used in Changzhou.

Investigating the nonlinear relationship between transportation system performance and daily activity–travel scheduling behaviour

The paper presents an econometric investigation of the behavioural relationship between transportation system performance in terms of travel time changes and daily activity–travel scheduling processes. Innovative survey data on the complete daily activity-scheduling adaptation process is used jointly with revealed scheduling information. The survey, conducted in Zurich, Switzerland, collected daily scheduling information together with stated adaptation responses corresponding to four adaptation scenarios. The four scenarios are defined by applying hypothetical increases in travel time of 50%, 100%, and 200% and a 50% decrease in travel time. Stated adaptation responses are collected in the context of 24-h activity scheduling. Data are used to estimate RUM based daily travel activity scheduling models. Models are estimated for one revealed schedule and four stated scheduling datasets. In addition, a joint model is estimated for pooled revealed and stated scheduling data. In the joint model, separate scale/variance parameters are estimated for revealed and stated information. Results clearly identify the nonlinear responses of activity–travel scheduling to the changes in travel time. Asymmetric responses are shown for travel time increases and decreases. People become more conservative with time expenditures when scheduling activities subject to increased travel times. However, beyond a certain limit of travel time increase, scheduling behaviour becomes more unpredictable. The lessons learned from this investigation have implications in the application of activity-based models for forecasting and policy analyses. Models developed using only a revealed preference dataset should not be used to extrapolate to situations where travel times changes by large margins. The results also prove that significant improvements in capturing behavioural responses in the activity scheduling process are possible by pooling revealed preference and stated preference data sets and jointly modelling with an explicit representation of RP scale/variance differences.

Activation of Shopping Pedestrian Agents—Empirical Estimation Results

This paper reports progress in the development of a multi-agent model which simulates pedestrian destination, route choice and scheduling behavior. In this model, the simulation of movement is embedded in a more comprehensive model of activity scheduling and choice behavior. We assume that pedestrians will be activated to visit a store if the store is suited to conduct any of the activities that are still scheduled, and that they are aware of this store. When pedestrians are aware of a store and the store is included in their consideration set, they will be activated and gradually move to a store. We assume that activation level is a stochastic variable. To estimate the activation function, survey data were collected. Visitors were interviewed about various aspects of their behavior such as familiarity with stores, motivation, and expectations about store aspects like assortment, sphere, quality and service. In this paper, we will discuss some of the findings of this data collection.

On the engineering of agent-based simulations of social activities with social networks

ContextModels of how people move around cities play a role in making decisions about urban and land-use planning. Previous models have been based on space and time, and have neglected the social aspect of travel. Recent work on agent-based modelling shows promise as a new approach, especially for models with both social and spatial elements. ObjectiveThis paper demonstrates the design and implementation of an agent-based model of social activity generation and scheduling for experimental purposes to explore the effects of social space in addition to physical space. As a side-effect, the paper discusses the need for and requirements on structured design of agent-based models and simulations. MethodModel design was based on the MASQ meta-model and implemented in Python. The model was then tested against several hypotheses with several initial networks. ResultsThe model allowed us to investigate the effects of social networks. We found that the model was most sensitive to the pair attributes of the network, rather than the global or personal attributes. ConclusionAs demonstrated, a structured approach to model development is important in order to be able to understand and apply the results, and for the model to be extensible in the future. Agent-based modelling approaches allow for inclusion of social elements. For models incorporating social networks, testing the sensitivity to the initial network is important to ensure the model performs as expected.

Modelling Complex Activity‐Travel Scheduling Decisions: Procedure for the Simultaneous Estimation of Activity Generation and Duration Functions

Activity generation is a key factor in individual's choices of trip frequency and trip purpose. This paper describes the results of an experiment conducted to estimate functions of several temporal factors on individuals' propensity to schedule a given activity on a given day. The theory on which the experimental design is based states that the probability of scheduling an activity is a complex and continuous function of how long ago the activity was lastly performed, the duration constraints for the activity and the amount of available time in the activity schedule of the day considered. Aurora, an existing model of activity scheduling, assumes S‐shaped utility functions for the history as well as the duration functions, whereas most time‐use studies assume monotonically decreasing marginal utilities. The stated‐choice experiment involves a range of flexible activities and a large sample of individuals to measure the utility effects of a set of carefully chosen levels for the factors and tests these specific assumptions. The results suggest that the amount of discretionary time on a day has no significant impact on the scheduling decisions provided that enough time is available for the activity. The effects of other factors are as expected and show diminishing marginal utilities. We find mixed evidence for an initial phase of increasing marginal returns as assumed in an S‐shaped function.

The Effects of Different Interaction Protocols in Agent-Based Simulation of Social Activities

Decision making in models of activity and travel behaviour is usually individual-based and focuses on outcomes rather than the decision process. Using agent-based modelling techniques and incorporating interaction protocols into the model can assist in modelling decision-making in more detail. This paper describes an agent-based model of social activity generation and scheduling, in which utility-based agents interact with each other to schedule activities. Six different protocols are tested. The authors show that the model outcomes reflect minor changes in the protocol, while changing the order of the protocol leads to significantly different outcomes, hence the protocol plays a large role in the simulation results and should be studied in more detail.

Traveler delay costs and value of time with trip chains, flexible activity scheduling and information

The delay costs of traffic disruptions and congestion and the value of travel time reliability are typically evaluated using single trip scheduling models, which treat the trip in isolation of previous and subsequent trips and activities. In practice, however, when activity scheduling to some extent is flexible, the impact of delay on one trip will depend on the actual and predicted travel time on itself as well as other trips, which is important to consider for long-lasting disturbances and when assessing the value of travel information. In this paper we extend the single trip approach into a two trips chain and activity scheduling model. Preferences are represented as marginal activity utility functions that take scheduling flexibility into account. We analytically derive trip timing optimality conditions, the value of travel time and schedule adjustments in response to travel time increases. We show how the single trip models are special cases of the present model and can be generalized to a setting with trip chains and flexible scheduling. We investigate numerically how the delay cost depends on the delay duration and its distribution on different trips during the day, the accuracy of delay prediction and travel information, and the scheduling flexibility of work hours. The extension of the model framework to more complex schedules is discussed.

Modeling households activity participation decisions in a rule-based system of travel demand

This paper describes an empirical derivation of an activity participation choice model at the household level decisions taking into account the allocated activities and joint activity participation of household heads in discretionary activities. The households that we consider here are two-heads households; each is either a worker or non-worker. Attributes of households, such as, for example the presence of young children, attributes of the work activities and space-time settings are considered as explanatory variables. To deal with this large set of attributes and account for non-linear relationships between the variables, a decision tree induction method - CHAID - is used to derive a decision tree model. We show how the decision tree model can be used as a component in an activity-scheduling model, ALBATROSS, to predict travel demand in an activity-based-micro-simulation system. The model shows a satisfactory performance as indicated by its goodness-of-fit on validation data.

A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.

Framework for the development of the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model

This paper describes a new framework for dynamically simulating activity planning and scheduling within an activity-based microsimulation model. The dynamic activity planning framework is a process model which attempts to replicate time-dependent activity scheduling. By modeling the actual underlying activity and travel planning and scheduling processes rather than revealed activity-travel patterns, the model can represent a much wider range of travel demand management policies, especially policies which are expected to impact the planning process of individuals. In contrast with previous activity scheduling models, the proposed model considers activity scheduling steps as discrete events within the overall activity-travel simulation, and furthermore considers each attribute decision as a separate event. The paper develops a framework for modeling each activity and its attributes, and allows for a non-fixed attribute planning order, so that there is no pre-determined planning order assumed in the model. Various stages of the model that are implemented in an overall simulation framework are discussed. In addition, some initial data results from a pilot test of a new GPS-based prompted recall survey used to capture the underlying activity attribute planning process are presented and discussed in the context of the overall model framework. The initial data tend to support the hypothesis that significant variation exists in the manner in which activities are actually planned.

Modeling activity conflict resolution strategies using scheduling process data

Developments in activity scheduling process data have recently allowed for much improvement and validation of rule-based activity scheduling models. The use of actual scheduling process data allows for a potentially more realistic model in terms of how the travel episodes are actually derived. Since these types of models are simulations of the scheduling process, scheduling conflicts naturally arise in the creation of the final schedule. The handling of these scheduling conflicts is a critical component of all rule-based activity models. Many of these models rely on the concept of priority of the activities to resolve conflicts. However, recent research has shown that this is often not how conflicts are actually resolved. In order to more accurately model actual conflict resolution behavior, scheduling process data was used to estimate conflict resolution rules using decision trees. The use of a conflict resolution model allows the strategy chosen to depend on the attributes of the individual and the conflicting activities, rather than assuming a prior definition of precedence based on the activity types. The conflict resolution decision showed reasonable ability to predict the resolution strategy chosen in actual scheduling process survey data, and represents an important advance in developing a functional computational process model of activity scheduling.