Daily Activity Chain Research Articles

An Activity-Based Travel Personalization Tool Driven by the Genetic Algorithm

The necessity for an external control mechanism that optimizes daily urban trips becomes evident when considering numerous factors at play within a complex environment. This research introduces an activity-based travel personalization tool that incorporates 10 travel decision-making factors driven by the genetic algorithm. To evaluate the framework, a complex artificial scenario is created comprising six activities in a daily plan. Afterwards, the scenario is simulated for predefined user profiles, and the results of the simulation are compared based on the users’ characteristics. The simulations of the scenario successfully demonstrate the appropriate utilization of activity constraints and the efficient implementation of users’ spatiotemporal priorities. In comparison to the base case, significant time savings ranging from 31.2% to 70.2% are observed in the daily activity chains of the simulations. These results indicate that the magnitude of time savings in daily activity simulations depends on how users assign values to the travel decision-making parameters, reflecting the attitudinal differences among the predefined users in this study. This tool holds promise for advancing longitudinal travel behavior research, particularly in gaining a more profound understanding of travel patterns.

Linking utility maximization models with decision making models to support activity chain optimization

The study aims to contribute to developing and optimizing the decision-making mechanism regarding activity chains by integrating a utility model with the decision-making by considering the most influential factors on activity chains. The data used are based on the survey during 2020 in Budapest. The parameters contained traveller factors, trip factors, weather factors, travel comfort, travel quality and travel safety. The study used Excel, NLOGIT and MATLAB to analyze and identify the interaction between parameters and create the optimum way to conduct the activity chain. The results found that travel time, travel cost, marital status, and trip distance significantly affect the activity chains. So, these variables are considered when the travellers assign the optimum daily activity chains. This study provides good insights into the activity behaviour of travellers in Budapest.

Heuristic time-dependent personal scheduling problem with electric vehicles

In this paper, a heuristic method which contributes to the solution of the Daily Activity Chains Optimization problem with the use of Electric Vehicles (DACO-EV) is presented. The DACO-EV is a time-dependent activity-scheduling problem of individual travelers in urban environments. The heuristic method is comprised of a genetic algorithm that considers as its parameters a set of preferences of the travelers regarding their initial activity chains as well as parameters concerning the transportation network and the urban environment. The objective of the algorithm is to calculate the traveler’s optimized activity chains within a single day as they emerge from the improved combinations of the available options for each individual traveler based on their flexibility preferences. Special emphasis is laid on the underlying speed-up techniques of the GA and the mechanisms that account for specific characteristics of EVs, such as consumption according to the EV model and international standards, charging station locations, and the types of charging plugs. From the results of this study, it is proven that the method is suitable for efficiently aiding travelers in the meaningful planning of their daily activity schedules and that the algorithm can serve as a tool for the analysis and derivation of the insights into the transportation network itself.

Activity Chains Modelling of Travellers by Using Logit Models Based on the Utility Function

Transportation planning plays an essential role in improving the transportation system. Therefore, planners should have the ability to forecast the response of transportation demand to changes in the characteristics of the travellers. This has led researchers to work on more effective behavioural models by updating conventional models and replacing them with activity-based modelling to describe the daily activity chains performed by travellers. So, this study uses the activity model to model and analyse daily activity to identify the factors affecting the activity chain. This study aims to use logit models based on the utility function for modelling the activity chains of travellers in Budapest city. At the same time, we identify the effects of various characteristics related to the traveller, trip and location in the activity chains. This paper presents the relationships between the two aspects of travel behaviour and activity chains by providing two different causal structures. The results showed that the location attribute, activity duration and activity purpose were most influential on the activity chains. This study provides good insights into activity chains behaviour of travellers. It also extends the need to incorporate activity model behaviour within these complicated processes and household and individual decision making of daily activity.

Synthetic population and travel demand for Paris and Île-de-France based on open and publicly available data

Synthetic populations of travelers and their detailed mobility behavior are an important basis for agent-based transport simulations, which are increasingly used in transport planning and research today. To date, research based on such simulations is rarely replicable as it is based on proprietary data and tools. To foster the discussion and steer research towards reproducible transport simulations, this paper introduces a process for generating a synthetic travel demand with individual households, persons, and their daily activity chains for Paris and its surrounding region Île-de-France — entirely based on open data and open software and replicable by any researcher. The resulting travel demand is published for others to use as a comprehensive data basis for agent-based transport simulations and as a test bed for population and demand synthesis algorithms. Furthermore, it is discussed how implicit correlation structures impact the potential use cases of the synthetic travel demand for simulation and analysis purposes and how the common practice of using population samples for downstream simulations affects the results.

Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data

Understanding residents' daily activity chains provides critical support for various applications in transportation, public health and many other related fields. Recently, mobile phone location datasets have been suggested for mining activity patterns because of their utility and large sample sizes. Although recently machine learning-based models seem to perform well in activity purpose inference using mobile phone location data, most of these models work as black boxes. To address these challenges, this study proposes a flexible white box method to mine human activity chains from large-scale mobile phone location data by integrating both the spatial and temporal features of daily activities with varying weights. We find that the frequency distribution of major activity chain patterns agrees well with the patterns derived based on a travel survey of Shenzhen and a state-of-the-art method. Moreover, a dataset covering over 16.5% of the city population can yield a reasonable outcome of the major activity patterns. The contributions of this study not only lie in offering an effective approach to mining daily activity chains from mobile phone location data but also involve investigating the impact of different data conditions on the model performance, which make using big trajectory data more practical for domain experts.

A Method for the Optimization of Daily Activity Chains Including Electric Vehicles

The focus of this article is to introduce a method for the optimization of daily activity chains of travelers who use Electric Vehicles (EVs) in an urban environment. An approach has been developed based on activity-based modeling and the Genetic Algorithm (GA) framework to calculate a suitable schedule of activities, taking into account the locations of activities, modes of transport, and the time of attendance to each activity. The priorities of the travelers concerning the spatial and temporal flexibility were considered, as well as the constraints that are related to the limited range of the EVs, the availability of Charging Stations (CS), and the elevation of the road network. In order to model real travel behavior, two charging scenarios were realized. In the first case, the traveler stays in the EV at the CS, and in the second case, the traveler leaves the EV to charge at the CS while conducting another activity at a nearby location. Through a series of tests on synthetic activity chain data, we proved the suitability of the method elaborated for addressing the needs of travelers and being utilized as an optimization method for a modern Intelligent Transportation System (ITS).

An activity chain optimization method with comparison of test cases for different transportation modes

ABSTRACTIn order to provide optimal choice of activity locations and travel time reduction, a daily activity chain optimization method has been elaborated, which includes temporal and spatial flexibility of the activities. In the course of the optimization process, possible alternatives are searched using a modified version of the Traveling Salesman Problem with Time Window. The method is extended with a genetic algorithm to provide an optimal order of activities as the minimum of the predefined cost function (i.e. travel time). The multiobjective optimization can efficiently mitigate the travel time of the users. In order to provide some insight regarding the performance of the optimization algorithm, application-oriented simulations were performed on a real-world transportation network using real travel time information. Three transportation modes were considered: car, public transport, and combined (public transport with car-sharing). The simulation results demonstrate that the optimization is able to reduce travel time by 20–30%.

Simulation Results for a Daily Activity Chain Optimization Method based on Ant Colony Algorithm with Time Windows

In this paper, a new approach is presented based on ant colony algorithm with time windows in order to optimize daily activity chains with flexible mobility solutions. This flexibility is realized by temporal and spatial change of activities achieved by travellers during one day. With the injection of flexibility concept of time and locations, the requirements for such a transport system are high. However, our method has shown promising results by decreasing 10 to 20% the total travel time of travellers based on combining and comparing different transport modes including the private transport as well as the public transport and by choosing the optimal set of activities using our method.

El uso del tiempo en espacios próximos. Una caracterización de la ciudad de Barcelona (2006)

La ciudad ofrece variadas posibilidades para interactuar con actividades y personas. El binomio acceso-actividad resulta ser relevante de estudiar, en función del tiempo que los ciudadanos dedican a actividades próximas y no próximas. Este es el objetivo del estudio, cuya metodología aplica geografía del tiempo y cadena de actividades. La novedad del método es reflejar de forma más real el comportamiento tanto de los tiempos como de los espacios utilizados en la ciudad. El método se aplicó a la encuesta de viajes basadas en hogares de Barcelona. Los resultados muestran que las duraciones de las actividades próximas son menores que las actividades no próximas, lo que induce a pensar que la proximidad genera mayor número de actividades cotidianas, cambiando el paradigma de sustitución entre tiempo de acceso y duración, al de tiempo de acceso y número de actividades. La conclusión final es que la proximidad presenta un valor relativo, que va de una clara sustentabilidad ambiental, a una probable des-socialización del desarrollo de las actividades.

Accessibility in a Post-Apartheid City: Comparison of Two Approaches for Accessibility Computations

Many authors argue that issues related to interpretability, lack of data availability, and limited applicability in terms of policy analysis have hindered a more widespread use of accessibility indicators. Aiming to address these aspects, this paper presents two accessibility computation approaches applied to Nelson Mandela Bay in South Africa. The first approach, a household-based accessibility indicator, is designed to account for the high diversity both among the South African society and in terms of settlement patterns. Besides OpenStreetMap (OSM) as its main data source, this indicator uses a census and a travel survey to create a synthetic population of the study area. Accessibilities are computed based on people’s daily activity chains. The second approach, an econometric accessibility indicator, relies exclusively on OSM and computes the accessibility of a given location as the weighted sum over the utilities of all opportunities reachable from that location including the costs of overcoming the distance. Neither a synthetic population nor travel information is used. It is found that the econometric indicator, although associated with much lower input data requirements, yields the same quality of insights regarding the identification of areas with low levels of accessibility. It also possesses advantages in terms of interpretability and policy sensitivity. In particular, its exclusive reliance on standardized and freely available input data and its easy portability are a novelty that can support the more widespread application of accessibility measures.

Definition and classification of parameters for daily activity chain optimization

Abstract A growing trend is observable towards flexible mobility solutions, especially regarding the flexibility in times and locations of the activities. This indicates a complex optimization problem, where many parameters have to be considered. Our paper deals with the optimization of daily activity chains, which is the optimization of series of activities during a certain time period for several parameters. In order to build the optimization framework a roadmap was elaborated. First the optimization parameters have to be defined and ideal target groups have to be identified. Then an optimization algorithm has to be implemented, where different transportation modes and personal preferences have to be taken into account. This results in a personalized and optimized activity chain for urban citizens. As the first point of the roadmap, the parameters were defined, which are connected to the user, to the chosen transportation mode or to the location type of the activity. Then a general utility function was defined to provide a calculation method for the optimization of activity chains.

Schedule Consistency for Daily Activity Chains in Integrated Activity-Based Dynamic Multimodal Network Assignment

The dynamic multimodal network assignment problem at the daily schedule level is addressed by integrating an activity-based model and a dynamic traffic assignment tool through a unified framework. The framework achieves this integration while retaining disaggregated individualized information. The problem is formulated as a fixed-point problem, and equilibrium is achieved by minimizing the gap between the expected travel time, which is used by the activity-based model to generate the travelers’ individual and household activity schedules, and their experienced travel times, simulated by the dynamic traffic assignment tool. The schedule adjustment problem for individuals and households is formulated as a linear optimization problem. Two measures—inconsistent-schedule penalty and number of households with unrealistic schedules—are defined to monitor the status of the equilibrium and convergence gap of the integrated system. To ensure convergence of the applied integration, heuristic strategies for selecting individuals for schedule adjustment and path swap are tested in a subarea network of Chicago, Illinois. Selecting individuals for schedule adjustment based on their inconsistent-schedule penalty reduces both defined measures significantly and leads to the convergence of the planned schedule and the experienced (i.e., simulated) schedule.

The Formal Development of Patterns of Daily Travel Activity Chains of the Urban Population

The Formal Development of Patterns of Daily Travel Activity Chains of the Urban Population

Effects of increased occlusal vertical dimension on daily activity and myosin heavy chain composition in rat jaw muscle

Mammalian skeletal muscles change their contractile-protein phenotype in response to mechanical loading and/or chronic electrical stimulation, implying that the phenotypic changes in masticatory muscles might result from new masticatory-loading conditions. To analyze the effects of increased occlusal vertical dimension (OVD) on daily activities and fibre-type compositions in jaw muscles, we measured the total duration of daily activity (duty time) and the myosin heavy chain (MyHC) compositions in the masseter and digastric muscles of freely moving control and bite-opened rats. In the control state, the duty time of the digastric muscle was higher than that of the masseter muscle at activity levels exceeding 5 and 20% of the day's peak activity. The opposite was true at activity levels exceeding 50 and 80% of the day's peak activity. The MyHCs consisted of a mixture of fast and slow types in the digastric muscle. The masseter consisted of mostly fast-type MyHC. The increment of OVD increased not only the duty time at activity levels exceeding 5, 20, 50 and 80% of the day’ peak activity in both muscles but also the proportion of MyHC IIa in the masseter muscle and MyHC I in the digastric muscle at the expense of that of MyHC IIb. These results suggest that the increment of OVD changes masseter and digastric muscles towards slower phenotypes by an increase in their daily activities.