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

. Te necessity for an external control mechanism that optimizes daily urban trips becomes evident when considering numerous factors at play within a complex environment. Tis 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 artifcial scenario is created comprising six activities in a daily plan. Afterwards, the scenario is simulated for predefned user profles, and the results of the simulation are compared based on the users’ characteristics. Te simulations of the scenario successfully demonstrate the appropriate utilization of activity constraints and the efcient implementation of users’ spatiotemporal priorities. In comparison to the base case, signifcant time savings ranging from 31.2% to 70.2% are observed in the daily activity chains of the simulations. Tese results indicate that the magnitude of time savings in daily activity simulations depends on how users assign values to the travel decision-making parameters, refecting the attitudinal diferences among the predefned users in this study. Tis tool holds promise for advancing longitudinal travel behavior research, particularly in gaining a more profound understanding of travel patterns.


Introduction
An average individual spends approximately 2.65 years, spanning from 12 to 65 years old, engaged in urban mobility [1,2].Te rapid growth of the global population coupled with car-centric mobility planning has led to a signifcant disparity between the travel demand and the availability of transportation infrastructure in urban areas [3].In addition, this situation is exacerbated by the swift pace of urbanization, posing a challenge meeting escalating travel demands within cities.To address these issues, the concepts of mobility management (MM) and transport demand management (TDM) have emerged aiming to alleviate the inefcient utilization of transportation capacity [4,5].MM focuses on the reallocating space to favor sustainable modes of transportation, while TDM is centered around modifying the travel behavior to manage the car demand and encourage users to transition to sustainable modes [6].Te advent of the digital age has witnessed the rise of information and communication technology (ICT) in the management of travel demand aligning with transport-related concepts.Consequently, over the past two decades, travel information, planning, and routing services have evolved signifcantly.Prominent examples include Google Maps, Moovit (i.e., specializing in public transport (PT) planning), and Bike-Citizens (i.e., focusing on cycling planning).Tese services provide comprehensive layouts of the transportation infrastructure and furnish users with pertinent information regarding locations within urban areas, thus facilitating efcient trip planning processes.
Moreover, the impact of preinformation on travel behavior is examined, highlighting the capacity of ICTs to guide and infuence individuals' travel choices [7].In relation to this, another concept of the consideration in travel information, planning, and routing services is the behavior change support system [8].Te primary objective of integrating the behavior change concept into travel planning services is to shape, manipulate, and transform users' behavior towards more sustainable patterns [9].Such ICT services can serve as strategic tools across various stages of travel decision-making by providing information and recommendations.An exemplary application, i.e., UbiGo, offers mobility as a service, enabling the testing of novel travel solutions.Based on users' reports, the UbiGo application has successfully reduced the users' reliance on private car usage by 50% through a transition from trial to adoption [10].
On the other hand, modern travel apps come with more advanced personalization features that allow users to tailor their experiences based on preferences, such as avoiding toll roads, choosing specifc bike paths, and prioritizing public transportation options.Given the intricate set of variables that characterize today's urban environments, the necessity for an external system to optimize travel is increasingly evident.Tis study aims to address this need by introducing an activity-based travel personalization tool.Te tool considers the ten travel decision-making factors outlined in the subsequent section, while it employs the genetic algorithm (GA) to optimize daily activity sequences.
Following the introduction, the next section provides a thorough literature overview of travel personalization, considering an activity-based approach and examining the key factors infuencing travel decision-making.Section 3 elucidates the methodological rationale and outlines the application of the GA in this study.Subsequently, the travel optimization formulation is expounded, encompassing the ftness function for the GA.Section 4 presents the case scenario considering built environment dynamics along with the requisite data requirements and processing steps.In Section 5, the efectiveness of the algorithm is evaluated by simulating an artifcial scenario that encompasses three predefned users' profles.Te simulation results are compared and assessed based on these distinct user profles.Section 6 deals with the main limitations of the study and directions with future work, while in the concluding section, a brief synthesis of the study's key fndings is presented.

Literature Review
Each individual lives in a distinct built environment and possesses unique needs and preferences, leading to variations in the perceived importance of decision-making factors [11].Consequently, everyone employs a cognitive control mechanism to optimize their daily trips.However, the need for an external control mechanism to optimize daily urban trips becomes apparent due to the multitude of factors at play within a complex environment.Terefore, travel personalization must be in focus.A travel personalization tool can be defned as a means of adjusting and tailoring travel services to incorporate contextual information and specifc preferences, as well as to generate user-centric outputs [12].Several application trials have attempted to support personalization in urban travel planning by considering certain user's mobility preferences and inputs, such as location, age, and gender [9], thus providing customized travel plans.However, these services have not attained the desired level of comprehensive consideration of factors and resultant measures yet.Te literature identifes numerous urban travel decision-making factors that should be taken into account during the decision-making process.Primary factors infuencing travel decision-making include travel time, travel time reliability (TTR), and travel cost, as highlighted by numerous studies [13][14][15][16].Additional persuasive factors infuencing travel decision-making encompass travel comfort [17][18][19], travel safety [19,20], travel quality [21][22][23], ownership [24][25][26], weather conditions [27][28][29][30], environmental friendliness [17,31], and healthcare [32,33], with the latter two assuming greater signifcance in the light of recent changes in travel behavior due to the COVID-19 pandemic [34].
Te literature ofers numerous mathematical models that support rational travel planning, among which activitybased travel demand models (ABMs) are particularly a compatible approach with travel personalization tools.In addition, the longitudinal data generated by these tools can play a crucial role in enhancing the realism and accuracy of ABMs.ABMs take into account decision-making processes that underlie participation in a variety of daily activities, including work, education, leisure, errands, and shopping.Tis comprehensive lens makes ABMs a standard in transportation modeling, ofering a more holistic understanding of urban travel behavior [35].One recent article [36] critically reviews the evolution and gaps in ABMs, assesses the strengths and weaknesses of various modeling techniques, and traces the integration of spatial-temporal and behavioral factors into ABMs.Te study emphasizes that current frameworks often neglect to model implicit activity participation decisions and focus mainly on activity scheduling, while there is the need for standardizing activity categories and balancing data requirements with behavioral realism.As stated above, decision-making parameters play a pivotal role in enhancing the modeling of travel behavior.Accordingly, we ofer a detailed examination of these decision-making processes presented in the next section, providing a more advanced perspective on the subject.
On the other hand, travel planning tools can supply robust and reliable longitudinal travel behavior datasets, which serve as the cornerstone for developing accurate ABMs.One study [37] introduces a dynamic structural equation model (SEM) focusing on longitudinal data from the Puget Sound transportation panel to explore complex causal connections between sociodemographics, activity engagement, and travel behavior.Te study fndings ofer valuable insights into the activity-based approach, enhancing its utility in transportation planning and policy development.Also, the authors in [38] using longitudinal data from the transportation tomorrow surveys conducted an analysis of activity generation behavior in the Greater Toronto and Hamilton Area (GTHA) with year-specifc and joint models to investigate study area dynamics and activity types.Leveraging social media platforms as data collection tools for activity behavior presents an alternative yet promising avenue.For example, the authors in [39] used geotagged tweets as longitudinal observations for the activity pattern estimation.Similarly, another study conducted by [40] emphasizes the importance of utilizing longitudinal geolocation data obtained from social media platforms, where users freely share their activity-related preferences.Te study illuminates how these emerging data sources substantially enhance our understanding of human activity patterns, providing insights for the realms of transportation planning and policy.Moreover, the study demonstrates the potential for augmenting conventional activity-based diaries by integrating them with the wealth of longitudinal geospatial information available through social media tools.
A recent study [41] discusses the present status of activity-based travel demand modeling.Global researchers are investigating how to optimally leverage the growing abundance of passive location data, such as cellphone and GPS traces or transit smartcard data to model activity patterns.Te study highlights that while these large, dynamic datasets are promising, their anonymized nature and varying spatial-temporal accuracy present hurdles for activity-based modeling.In addition, most traditional household travel surveys lack comprehensive data for all members, complicating the shift towards a householdcentric approach in an era of individualized, anonymous tracking data.As information and communication technology (ICT)-based tools proliferate, they contribute a rich content of mobility data that plays a pivotal role in understanding activity patterns and shaping sustainable transport planning.However, these tools are generally not designed to capture activity-based travel patterns, which is the focal point of our current study.Our work presents and tests an activity-based travel personalization tool, driven by the genetic algorithm (GA) considering built environment dynamics.Te subsequent section will elaborate on the methodological framework and demonstrate how the GA can optimize activity chains within the context of travel personalization.

Methodology
Tis section provides a detailed explanation of the methodological framework, specifcally focusing on the application of the genetic algorithm (GA) in our study.It then elaborates on the travel optimization formulation, including the design of the GA's ftness function.

Te Applied Algorithm.
In this section, we present the employment of the genetic algorithm (GA) framework for orchestrating daily travel activities.Te GA's optimization capabilities make it a preferred choice for tackling complex issues in transportation planning.Its strengths lie in its ability to efciently navigate expansive solution spaces, manage nonlinear objective functions, accommodate a range of goals, and fexibly integrate constraints.Te versatility of the GA in transportation planning is well-documented across many academic reviews, and the GA is the most typically used optimization algorithm for optimal route recommendations [42][43][44][45].For example, one study [46] utilized the GA to fne-tune the arrival and departure times of trains to better align with passenger schedules, while another application of the GA [47] was used to optimize the establishment of cordon sanitaire for controlling epidemics, taking intricate transportation systems into account.In the context of our current research, we employ the GA framework to streamline daily travel sequences based on predetermined activities.Prior studies have validated the efcacy of the GA in solving similar transportation planning problems rooted in an activity-based approach.For example, one such research efort [48] explored the optimization of individualistic daily activity chains in relation to the built environment, while another investigation [49] used a coevolutionary approach to optimize daily activity sequences for large populations.Tese preceding studies not only underscore the broad applicability of the GA framework but also highlight its proven efectiveness in addressing the complex challenges found in activity-based transportation planning optimization.Primarily, adopting the methodology outlined in [48], the present GA concentrates on individualized planning, while it aims to closely mirror the dynamics of the built environment.
Figure 1 depicts the GA framework applied in current study.Te GA comprises fve distinct phases aimed at identifying the most optimal solution from the population of daily plan solutions.Tese phases involve the initial population, ftness function, selection, crossover, and mutation.Te initial population includes a collection of individual daily plan solutions, i.e., referred to as chromosomes.Tese chromosomes are generated by mutating a set of activity variables, a.k.a.genes, which are derived from the user inputs.Te initial genes encompass the user's location, the type of activities used to flter the locations in the facility database, the sequence of daily activities, and the designated time windows for each activity in the sequence (i.e., arrival and departure times).
In certain situations, the location or timing of our activities, such as work, education, or errands, can be fxed, whereas other activities such as shopping and leisure ofer more fexibility.To account for these variations, we incorporate predefned constraints within the GA.Prior to the execution of the GA, a preconstraint phase is implemented to establish the initial pool of daily plan solutions, considering personal preferences.Tis process also aims to reduce the high number of chromosomes in the solution pool, thereby enhancing the efciency of the GA and expediting the processing time of the application.Te preoptimization procedure relies on the spatiotemporal priorities specifed by the users.Four spatiotemporal priority options are defned based on the userprovided information for each daily activity in the chain.Te frst option is spatiotemporally fxed; thus, it is ensured that the selected facility and the requested time windows remain unaltered.Te second option allows spatial fexibility, meaning that the selected activity locations can vary, while the demanded time windows remain fxed.Te third option provides temporal fexibility, allowing changes in demanded time windows while maintaining fxed activity locations.Lastly, the fourth option, i.e., spatiotemporally fexible, permits adjustments to both the locations of the selected activities and the demanded time windows within the chain.
For instance, if a user sets the third priority option for any activity in the chain, all other spatial options in the facility database for that activity are disregarded.However, users may not possess accurate real-time information regarding the time windows of facilities.Terefore, all chromosomes are subjected to certain constraints, such as verifying the validity of the selected facilities based on real-time information.Te demanded time windows are compared with the real-time information stored in the facility database, and the algorithm generates an error message if demanded time windows do not align with the actual time windows of the facilities.
Once the initial phase is completed, the initial daily plan solution pool undergoes evolution to generate a new population comprising additional potential plans.Te ftness function plays a crucial role in determining the direction of this evolution by serving as a metric for assessing the ftness level of each individual daily plan solution.Te ftness function utilizes the travel optimization formula, discussed in the subsequent subsection, to evaluate diferent daily trip chains for evolutionary purposes.Each plan solution is assigned a ftness score calculated by the algorithm taking the 10 parameters introduced in the optimization function into account.Tese parameters, involving both minimization and maximization objectives, guide the evolution of the solutions.Te weights assigned to these parameters are predetermined by the users prior to the execution of the algorithm.Te algorithm defnes the recognition and calculation methods for these parameters, which are elucidated in the subsequent subsection.While certain technical values and user experience valuations are incorporated as default values in the algorithm, as explained in the next section, users have the fexibility to customize these parameters based on their individual experiences.
Following the evaluation of the plan ftness by using travel utility scores, the algorithm proceeds to generate a new generation of daily plan solutions.Selection is performed in the selection phase, thus favoring the individual plan solutions with higher ftness scores for reproduction.Two pairs of parents consisting of daily plan solutions are randomly selected for reproduction, while   Journal of Advanced Transportation some plan solutions in the population are discarded during this process.Te selected parents serve as the basis for the new generation, and the crossover phase involves the mating process between each pair of parents.Ofspring are created in the same positions as their respective parents, enhancing the mating process.Te recombination of the chromosomes from the parent pool involves the exchange of genes between each pair of parents potentially altering the sequence of daily activities within the chain based on the initially set spatiotemporal priority.Te mutation phase aims to introduce diversity among candidate daily plan solutions.A low random probability triggers gene changes in some newly formed ofspring.Tis process randomly swaps the facilities with their alternatives and modifes the order of the activities according to the spatiotemporal priority.For the simulation, the following parameters are set: an initial population size of 30 daily plans, a 10% crossover probability, a 20% mutation probability, and 20 generations.Upon the completion of all phases, the algorithm terminates yielding the best-ft solution from the fnal generation as the output.

Fitness Function.
Section 2 highlights the factors infuencing the travel decision-making process.Building upon this, the formulation of the ftness function based on 10 identifed travel decision-making parameters is conducted.
Prior to initiating any daily activity chains, individuals engage in self-cognitive evaluation based on utility factors.Tis evaluation takes the specifc needs, desires, and benefts associated with transportation modes within a given built environment and at a particular time into account.When selecting from the available alternatives, individuals make decisions that aim to maximize their daily travel utility.To achieve the maximum overall travel utility, certain parameters must be maximized while considering the weight of an individual's attitude; at the same time, other parameters need to be minimized.Te general travel utility function, a.k.a. the ftness function, is formulated for use in the GA as follows: Te dependent variable V iσ is the utility value of mode choice "σ" for trip maker "i".Te independent variable X ij presents the maximization attribute of trip maker "i" for parameter "j", while Z ik is the minimization attribute of trip maker "i" for parameter "k".β ij and μ ik are independent parameter weights for trip maker "i".To harmonize with the travel decision-making factors, the utility function of mode choice "σ" for trip maker "i" is formulated as follows: (2) In this study, regarding minimization, the focus is on the following three main attributes: absolute travel time, relative ratio of total travel time, and environmental impact.Te absolute travel time T vi represents the expected in-vehicle travel time without considering congestion delays, while congestion delays are separately accounted for as T ci .Te metric for travel time reliability is quantifed by the relative ratio of total travel time to absolute travel time, denoted as TTR i .Tis measure serves as an indicator of reliability, with a lower ratio yielding utility gains in the context of mode choice, represented by the variable "σ." Te total travel time variable T ti is calculated as the sum of the absolute in-vehicle travel time, delay time, out-vehicle travel time (i.e., including waiting and transfer time), and parking time.Te outvehicle travel time is calculated for the PT mode, while parking time is considered for the car mode.In addition, the environmental impact parameter E i , which represents CO 2 emissions per passenger-kilometer in logarithmic form for both car and PT travel modes, is incorporated.Logarithmic transformation was employed during the computation of certain variables exhibiting diminishing returns as depicted in the equation.
Te maximization attributes in this study encompass various factors that contribute to the travel decision-making process.Tese factors include the purchasing power PP i , travel comfort C t , travel quality Q i , travel safety S i , availability A i , travel mode performance under weather conditions W i , and health contribution H i .Te purchasing power is determined by the relative ratio of the daily income to the total trip costs C t in the logarithmic form.Te trip costs for the diferent modes can be calculated based on the running costs per kilometer.For the car mode, this includes such expenses as gasoline, maintenance, tires, cleaning, congestion, parking, and road toll costs.Te costs of PT are represented by the ticket costs or the equivalent cost of a monthly pass per day.Te costs of cycling are based on the maintenance costs per kilometer, while the costs of walking can be estimated by using the lifetime cost of shoes per Journal of Advanced Transportation kilometer walked.Te health contribution refects how many calories in the logarithmic form are burned during the travel time in the case of mode choice "σ." Te availability parameter indicates whether the individual owns or holds a monthly pass for the chosen mode.Te travel comfort, travel safety, and travel quality parameters represent the average performance ranking of mode choice "σ" in a given city.In addition, the parameter W i captures the transportation mode performance under specifc weather conditions.It considers the average performance ranking of the mode choice "σ" under various weather conditions including rain, snow, temperature, humidity, and wind.Tese factors collectively contribute to the assessment of the travel decision-making process and aid in selecting the optimal mode of transportation.

Case Study
In this section, we present the case scenario, primarily detailing the necessary datasets required and also various processing steps involved to prepare and integrate the data into the algorithm.Following this, we elaborate on a simulation scenario featuring three predefned user profles, each with unique characteristics, to further illustrate the study's applications.

Data Requirements and Processing.
In this study, for the simulation, the built environment of Budapest serves as the test environment.To ensure the functionality of the GA, several data requirements need to be fulflled including the real transportation infrastructure map, the spatiotemporal data of the facilities, and the spatiotemporal information of the PT network.To obtain data on the real transportation infrastructure and the spatiotemporal information of the facilities in Budapest, OpenStreetMap (OSM) is applied.OSM relies on volunteer contributions for geocoding, which can lead to some potential errors, particularly concerning PT stops and routes.To mitigate these errors, the spatiotemporal data of the PT system are sourced from the local authority BKK (Centre for Budapest Transport) in the general transit feed specifcation (GTFS) format.Te GTFS provides comprehensive information on the time schedules and associated geographical data.In order to simplify the location search and facilitate the identifcation of the facility locations, the spatiotemporal data of the facilities are clustered based on the similarity of facilities.Te resulted spatiotemporal database comprises 57,350 locations encompassing 935 types of facilities.Following the simplifcation process, the database is streamlined to include 84 main types of facilities.Some examples of these clusters include bar and pub, beauty and cosmetics, cinema and theater, fast food, shopping center, and baby care.By leveraging these data sources and applying appropriate clustering techniques, the study ensures a comprehensive representation of the transportation infrastructure, facilities, and their spatiotemporal characteristics in Budapest, thus facilitating simulations and analyses within the GA framework.
To provide default travel parameter values specifc to Budapest for the tool, a travel survey conducted by the Budapest University of Technology is utilized in this study.Te survey consists of 285 samples and focuses on participants who reside in Budapest.Te survey data were collected within a specifc timeline, from October 15 to November 15, 2020.Te survey employs a Likert-7 scale to inquire about transportation mode parameters.Participants are asked to rate various factors related to travel modes, such as travel comfort, travel safety, travel quality, and travel mode performance under diferent weather conditions (i.e., rainy, snowy, hot, cold, windy, and humid).In addition, the survey collects information on the average time spent by the participants on fnding a car parking place and the average out-vehicle travel time when using PT.Te mean values obtained from the survey responses for each transportation mode are used as default values within the algorithm.Tese values include travel comfort, travel safety, travel quality parameters, travel mode performance under weather conditions, out-vehicle travel time (i.e., for PT-related planning solutions), and parking time (i.e., for car-related planning solutions).Utilizing average values from the survey data for these parameters does not pose any challenges in testing the algorithm.Furthermore, the algorithm calculates the TTR parameter incorporating data from the survey.With these parameter values derived from the travel survey, the algorithm is set to the default mode for the daily travel planning solutions, ensuring better refection of the representation of the travel preferences and experiences in the city.
Te ownership parameter, which indicates whether the user owns a monthly pass for a particular transportation mode, is determined by the user and can be set as either 0 or 1 depending on the mode of transportation.Te algorithm calculates the purchasing power based on the user's income inputs, which are specifed at the beginning of the process.Furthermore, travel costs are calculated by the algorithm, taking the chosen transportation mode into account as outlined in the formulation.For such modes as car, bike, and walking, running costs are computed based on the distance covered during the activity chain.In contrast, the costs associated with PT remain constant and are determined by the ticket or monthly pass prices.To determine the average running costs per kilometer for diferent transportation modes, a comparative study [50] provides reliable information for the algorithm.
To facilitate the functioning of the mechanism, a routing algorithm is essential.In this case, OpenTripPlanner 1.4 (OTP), i.e., an open-source multimodal routing algorithm, is employed.OTP utilizes OSM infrastructure and GTFS data [51] to generate routes and calculate travel distances and times.Te OTP router is responsible for determining the absolute in-vehicle travel time and travel distance to facilities within the activity chain.By utilizing travel routes obtained from OTP, these metrics are accurately computed.It is important to note that the OTP router calculates the travel time without considering the impact of road congestion, which is particularly signifcant for car and bus users who rely on TTR.To address this concern and incorporate the infuence of road congestion, the algorithm integrates 6 Journal of Advanced Transportation TomTom API, and to the best of our knowledge, this is the frst attempt of its kind.Te TomTom trafc-monitoring service leverages data from millions of mobile phones, government-owned cameras, road sensors, and millions of connected GPS devices to monitor trafc conditions [3].By utilizing TomTom API, the algorithm estimates the trafc conditions and incorporates the date-based travel time increase, thus ofering insights into trafc conditions relevant to activity chains.Te historical database provided by TomTom API includes the percentage increase in travel time on an hourly basis for each day of the year.In addition, the algorithm has the capability to provide real-time trafc information by using TomTom API.However, for predicting the percentage increase in travel time for activity chains, the historical database is utilized.By utilizing both OTP and TomTom API, the algorithm ensures accurate routing calculations and incorporates real-time and historical trafc information to enhance better precision of the travel time estimates for users' activity chains.
Te framework is integrated with OpenWeatherMap API to retrieve real-time weather information.OpenWeatherMap is an open-source online service that ofers comprehensive data on current weather conditions, such as precipitation, humidity, wind, and temperature.In the algorithm, provisions are made to automatically identify the type of precipitation, whether it is rain or snow, if any is expected prior to commencing the journey.To enable the algorithm to interpret the weather conditions, specifc thresholds have been defned.Tese thresholds include categorizing temperatures above 29 °C as hot, temperatures below 15 °C as cold, humidity levels above 49% as humid, and wind speeds exceeding 10 m/s as windy.By incorporating OpenWeatherMap API and utilizing these defned thresholds, the algorithm can accurately assess the prevailing weather situation.
Te framework utilizes the Harris-Benedict equation to calculate the health contribution, which is determined by the total number of calories burned during the in-vehicle time.Tis calculation takes the basal metabolic rate (BMR), travel time, and activity level into account.Te BMR is calculated by using the personal inputs provided by users including age, gender, weight, and height, which are collected prior to running the algorithm.Te activity level is determined by the transportation mode; with each level (i.e., light, moderate, and heavy), diferent weights are assigned.Te default activity levels are set to light for driving and PT, moderate for walking, and heavy for biking.Tese activity levels play a role in estimating calories burned during the travel time.To assess environmental friendliness, the framework calculates total CO 2 emissions associated with the transportation mode and travel distance throughout the activity chain.Tis calculation relies on an average value of CO 2 emissions per kilometer per passenger extracted from a dataset [52].By incorporating these data, the environmental impacts of transportation modes can be evaluated in terms of CO 2 emissions.

Simulation Scenario.
In this section, an overview of the simulation scenario is provided.To observe how the algorithm output varies under diferent conditions, three predefned user profles with distinct characteristics are created.Table 1 presents the predefned users' attitudes towards the travel decision parameters along with their sociodemographic information and urban weather conditions.Te users share the same age (29), weight (72 kg), and height (1.74 m).However, several assumptions about the urban weather conditions are made for each user as follows: (i) User A plans daily activities under favorable weather conditions, i.e., a temperature of around 20 °C, dry conditions (i.e., humidity below 45%), low wind speed (i.e., below 10 m/s), and no precipitation expected.(ii) User B plans daily activities under cold, snowy, and windy weather conditions.Te temperature is approximately −5 °C with dry conditions (i.e., humidity below 45%) and a wind speed of 13 m/s.(iii) User C plans daily activities under hot weather conditions with a temperature of around 35 °C, high humidity (i.e., 70%), low wind speed (i.e., below 10 m/s), and no precipitation expected.
Table 2 illustrates a detailed input of a complex daily activity scenario.Te initial activity chain, which remains the same for all predefned users, is presented.Te table provides information about the six activities in the chain and the corresponding seven travel routes required to complete them.For each daily activity, the table includes the activity ID, geographic location, type of activity, processing time, the time windows of the selected activity locations (i.e., indicating the opening and closing time), the spatiotemporal priority of each activity in the chain, and the desired time windows for each activity (i.e., indicating the starting and closing time).In addition, Figure 2 visually represents the geographic locations of the input activities, where each activity is labeled by its corresponding activity ID (i.e., the order ID).Te map serves as a visual aid for better understanding activity locations and their arrangement.Te subsequent section focuses on presenting and evaluating the results of the simulation derived from these input data.

Simulation Results
In this section, the daily activity chain presented above is simulated by using the GA framework.Tis simulation incorporates predefned user inputs and takes into account the infuence of the built environment dynamics.Te optimization results, including the order of the activities based on the transportation modes, are displayed for each predefned user in Tables 3-5.Furthermore, the optimized activity locations for each best-ft solution along with their corresponding order numbers are visualized in Figures 3-5.Please note that the numbers displayed in the fgures do not represent the ID of the activity.Instead, they indicate the order of the activities in the chain for the respective solutions.
Table 3 presents the results of the optimization for user A's daily activity chain, showcasing the best-ft solution based on alternative modes.User A prioritizes factors such Journal of Advanced Transportation as travel costs, environmental friendliness, burnt calories, cold and rainy weather conditions, and TTR.Te other travel decision-making factors are considered moderately important by user A, except for the following parameters which are given low importance: travel comfort, travel quality, and hot weather conditions.Taking these preferences into account, the algorithm suggests a cycling-based daily activity chain as the best-ft solution for user A, as shown in Table 3.As depicted in Figure 3, the algorithm aims to increase the burnt calories throughout the day while moderately considering a reduction in travel time.Te total absolute travel time for user A is 42 minutes, which is 31.2%less than the base scenario when cycling is chosen.To optimize the activity chain, the algorithm creates two clusters of nearby activities for user A by leveraging priority options to minimize the travel time and maximize burnt calories by increasing the distance between the activity zones.Initially, the user is directed to activity ID 1, which is the farthest point from the home location.Subsequently, the user is guided to a cluster of nearby activities near the home location comprising activity IDs 2, 6, and 5. Afterwards, the user is advised to proceed with another activity cluster consisting of activity IDs 3 and 4.
Te algorithm ofers a midft daily activity scheduling solution for user A, which is based on PT and walking.In this case, the total absolute travel time is reduced to 24.2 minutes, representing a 68% reduction compared to the base case of using PT alone.Te algorithm guides the user to take the metro line for activity ID 6 and continue using the same mode for the spatiotemporally fxed activity (i.e., ID 1).A cluster of nearby activities is formed consisting of activity IDs 5, 2, and 4, which are all within a few

Users Attitude
User A User A is a male from the low middle class with a monthly income of 900€.He owns a bike, a car, and a PT monthly pass.When it comes to his attitude towards the optimization parameters, he assigns a high importance degree to the following factors: travel costs, TTR, CO 2 emissions, burnt calories, and specifc weather conditions such as cold and rainy.For the parameters of travel time, travel safety, ownership, and other weather conditions such as snowy, humid, and windy, he has a moderate importance degree.Lastly, he assigns a low importance degree to travel comfort, travel quality, and the weather condition of hot weather User B User B is a female from the middle class with a monthly income of 1300€.She owns a bike and a PT monthly pass.When considering her attitude towards the optimization parameters, she assigns a high importance degree to specifc weather conditions such as hot and humid.For the parameters of travel time, travel costs, TTR, ownership, travel comfort, travel quality, travel safety, and other weather conditions such as rainy and windy, she has a moderate importance degree.Lastly, she assigns a low importance degree to the parameters of CO 2 emissions, burnt calories, and weather conditions of cold and snowy weather User C User C is a male from the upper middle class with a monthly income of 1750€.He owns a bike, a car, and a PT monthly pass.When considering his attitude towards the optimization parameters, he assigns a high importance degree to travel comfort, travel quality, travel safety, and specifc weather conditions such as cold, snowy, and windy.For the parameters of travel time, TTR, ownership, and the weather condition of rainy weather, he has a moderate importance degree.Lastly, he assigns a low importance degree to the parameters of CO 2 emissions, burnt calories, travel costs, and weather conditions of hot and humid weather emissions and enhance TTR.In addition, the algorithm identifes nearby home activity locations for the frst and last activities (i.e., IDs 5 and 6).Table 4 presents the results of the daily activity chain optimization for user B including the best-ft solution by using alternative modes.Te algorithm suggests a PT and walking-based solution for user B. User B's main concerns revolve around adverse weather conditions, specifcally hot and humid conditions.Other travel decision-making factors are considered at a moderate level, except for CO 2 emissions, cold and snowy weather conditions, and burnt calories.Te total absolute travel time for user B is 23.3 minutes, which is a signifcant 70.2% reduction compared to the base scenario when using PTalone.Tis reduction is slightly higher than in the case of user A, demonstrating the efectiveness of the algorithm in minimizing the travel time for user B. In the recommended solution, the algorithm utilizes the metro mode for reaching activity locations with IDs 1, 2, 5, and 6.Activities 3 and 6, which serve as the frst and last activities in the chain, are conveniently located near the user's home.In addition, the algorithm clusters nearby activities with IDs 2, 4, and 5.
As an alternative solution, cycling is considered for user B, resulting in an expected total absolute travel time of 34.6 minutes.Tis represents a 43.3% reduction compared to the base scenario where using cycling alone.In comparison to user A, the algorithm achieves a greater reduction in the  travel time of cycling for user B. Tis diference is mainly due to user B assigning a lower importance level to burnt calories during daily activities.Furthermore, the algorithm optimizes the cycling mode by prioritizing other parameters that user B considers moderately important.Te algorithm guides the user to a location between the home and the spatiotemporally fxed activity location (i.e., ID 1) for the frst activity.
After completing the frst activity, the user proceeds to activity location ID 1. Considering priority options, the algorithm creates a cluster of nearby activities, which include activities with IDs 2, 4, 3, and 5. Table 5 presents the results of the daily activity chain optimization for user C, demonstrating the best-ft solution when using alternative modes.Te algorithm suggests a carbased planning solution as the optimal choice for user C. User C's primary concerns revolve around travel comfort, travel quality, travel safety, and adverse weather conditions such as cold, snowy, and windy.Other optimization factors are considered at a moderate level, except for travel costs, CO 2 emissions, hot and humid weather conditions, and burnt calories.Te total absolute travel time for user C is 31.5 minutes, representing a signifcant 48% reduction compared to the base car scenario.To minimize the travel time of car and enhance TTR, the algorithm creates a cluster of nearby activities in the following order: activity IDs 6, 5, 3, and 4. Te midft solution for user C involves the PT and walking modes.Te total absolute travel time is 27.8 minutes, which is a substantial 64.4% reduction compared to the base scenario of using PT and walking alone.Te algorithm identifes a cluster of nearby activities, including activity IDs 2,  4, 6, and 3, utilizing spatiotemporal priorities to minimize the travel time in case of PT and increase TTR.Te low-ft solution recommended by the algorithm is cycling based.Te total absolute travel time is 32.4 minutes, thus representing a 47% reduction compared to the base cycling scenario.Tis reduction is higher than the simulated cycling scenarios for users A and B. User C assigns the least importance to the parameters that give prominence to the bike mode.Terefore, the algorithm focuses on optimizing the travel time to a greater extent.Initially, the algorithm guides the user to activity ID 4 on the way to spatiotemporally fxed activity ID 1. Subsequently, two clusters are suggested to complete the activity chain: the frst cluster comprises nearby activities with activity IDs 2 and 5, and the last cluster includes activity IDs 3 and 6, which are conveniently located near the user's home.
Te optimization process leads to noticeable changes in the users' initial daily activity plans, as evident in the output tables.Tese changes encompass the activity sequence, preferred facilities, and demanded time windows for each activity.Te extent of these changes is determined by spatiotemporal priorities set prior to running the algorithm.In the output tables, it is observed that when a priority value of four is assigned, the algorithm is granted the fexibility to modify both the spatial and temporal aspects of the activities to achieve a more optimal solution.Conversely, when a spatiotemporally fxed priority (e.g., priority 1) is set, no changes are observed, as seen in the preferred facilities and the demanded time windows for the college activity.Moreover, the algorithm robustly applies other onedimensional fxed priorities (e.g., priorities 2 and 3) for specifc aspects, such as demanded time windows for the

Discussion
Overall, the optimization of daily activity chains for users proves to be highly promising.While heuristic algorithms like the GA may not always yield the global optimum, they consistently deliver satisfactory results in terms of travel personalization.Moreover, these algorithms operate within a reasonable timeframe, making them highly benefcial for practical applications.
Although OTP is an open-source router that is actively under development, it has certain limitations that need to be considered.One notable limitation is the incomplete functionality of certain mode options within the OTP router.For instance, when selecting the car option, walking as a mode of transportation is not properly accounted for.Tis means that even if the optimization algorithm identifes locations in close proximity, the router still treats the scenario as if the car were used.Another issue arises when dealing with PT modes.For instance, when selecting a specifc PT option, such as "Bus + Walk" or "Metro + Walk," the router tends to default to the closest available PT option, potentially disregarding the user's preferences.Tese limitations in the mode options of OTP can impact the accuracy and fexibility of routing calculations.It is important to be aware of these issues when utilizing OTP as part of the optimization algorithm.
Te present tool holds potential for longitudinal research ofering valuable insights into the in-depth understanding of travel patterns; however, it is important to note that the existing framework does not currently gather users' feedback.As such, future research could concentrate on long-term travel behavior studies, utilizing data collected from users once the web-based version of the tool becomes available.Tis additional layer of user insight would not only enrich the feld of travel behavior research but also facilitate ongoing enhancements to the tool itself.
In terms of real-time implications, the tool can be realized as an application with a user-friendly design to collect real data from real users.Deployment could start with the university staf using the application to provide a longitudinal dataset within a specifc timeframe.After that, the collected data could be used for evidence-based transport planning to model activity-based travel behavior of specifc user groups, deriving some suggestions related to transport planning and policymaking within the urban context.

Conclusion
Tis study introduces an activity-based travel personalization tool that incorporates 10 travel decision-making factors driven by the GA considering built environment dynamics.To improve both the level of personalization and algorithmic efciency, the tool takes into account the spatiotemporal priorities of users and the real-time location of facilities, which are served as the constraints within the GA.A complex artifcial scenario involving six activities in a specifc order and seven route requirements is presented to simulate travel diaries of three predefned users by using the presented framework.Te scenario simulations demonstrate the successful application of activity constraints and the efcient implementation of the users' spatiotemporal priorities.Compared to the base case, the simulations show signifcant travel time savings ranging from 31.2% to 70.2% during daily activity chains.Tese variations in travel time savings refect the attitudinal diferences among the predefned users, highlighting the infuence of individual preferences on travel decisionmaking parameters.
xi (t) be the ith individual in P (t) Let f (xi (t)) be the fitness of xi (t) An individual xi (t) is selected with a probability p (xi (t)) proportional to f (xi (t)) Let x' and x" be the offspring produced from parents x1 and x2 with probability pc Terminate if f (xbest)≥ftmax xi [j] = {1-xi [j] with probability pm otherwise xi [j] ity Lo ca tio n Ac tiv ity Ty pe O rd er of Ac tiv ity De m an de d Ti m e W in do w

Figure 1 :
Figure 1: Genetic framework for the organization of daily plans.

Figure 2 :
Figure 2: Te input locations and their sequence in the daily activity chain.

Figure 3 :
Figure 3: Best-ft solution for the activity locations and their sequence in user A's daily activity chain.

Figure 4 :
Figure 4: Best-ft solution for the activity locations and their sequence in user B's daily activity chain.

Figure 5 :
Figure 5: Best-ft solution for the activity locations and their sequence in user C's daily activity chain.

Table 2 :
Te input information of the daily activities.

Table 3 :
Te results of user A's daily activity chain optimization.

Table 4 :
Te results of user B's daily activity chain optimization.

Table 5 :
Te results of user C's daily activity chain optimization.