Trip Characteristics Research Articles

Study on the characteristics of weekend trips to three types of non-work destinations based on multi-source data: a case study of Shanghai, China

The proportion of Shanghai residents' non-commuting trips in the total number of urban trips has been increasing year by year. Non-commuting trips, as an important symbol of modernized lifestyles, reflect the use of urban resources by people in production and life, and their dynamic distribution and changes have extremely important guiding roles in the city's fine-tuned management and resource allocation. Taking Shanghai as an example, this paper extracts three types of urban function grids, namely, urban park green space, commercial consumption and public service through the establishment of Shanghai urban grid function identification system based on multi-source data such as Baidu LBS (Location-BasedServices), data, POI (Point of Interest) data, etc. Then 3 types of non-commuting trips, namely, urban park, commercial and public service are further classified and identified, and their characteristics are analyzed. The results show that: ①Urban park travel frequency is low, and the proportion of long-distance trip is high. Zhujiajiao Town, Nanjing East Road Street and Lujiazui Street are three main travel gathering nodes in the city. There is a close relationship between the main urban area and the suburbs and the main urban area is more dependent on the suburbs. ②The travel frequency of commercial is the highest and the average travel distance is the shortest. The trend of trip agglomeration to the main urban area is significant and the trip distribution shows an obvious multi-center trend. ③The travel frequency of public service is high and the average travel distance is short. The trip distribution shows an obvious hierarchical structure, gathering in the main urban area and suburban new towns. ④Songjiang, Jiading and Qingpu in the suburban new towns have stronger attraction. The study suggests that non-commuting trips have an important impact on the urban spatial network structure, which needs to be further analyzed and compared with the spatial network structure formed by commuting trips to find the links and differences, so as to provide research support for the optimization of urban spatial structure.

Traveling for Safety: Price and Income Elasticities of Hurricane Evacuation Behavior

Using multiple hurricane surveys collected from different areas in the U.S., this study estimates how much distance and how much time were traveled by evacuees to reach safer destinations during a hurricane event. Regression results indicate that flood risk, respondents’ age, income, and education levels are correlated with both the distance and travel time of hurricane evacuation trips. Moreover, to gain deeper insights, we estimated the price and income elasticities of hurricane evacuation trip characteristics. The estimated elasticities of travel distance reveal that travel distance is a necessary and ordinary good, implying that safety is essential and less responsive to price changes. In addition, the estimated elasticities of travel time suggest that travel time is an inferior good, indicating that as income goes up, people tend to spend less time traveling for evacuation. This finding provides logistic implications for emergency management agencies to analyze the evacuation travel demand and ensure safety in vulnerable communities.

Research on temperature rise prediction model for circuit breaker based on numerical Laplace transform

Abstract The temperature rise in circuit breakers’ thermal paths is a critical indicator influencing their tripping characteristics, directly affecting response speed, reliability, and safety. Existing models for predicting circuit breaker temperature rise are often structurally limited, lack generality, and exhibit low predictive accuracy. Addressing this issue, this paper investigates an efficient, precise, and widely applicable theoretical model for predicting circuit breaker temperature rise. This model focuses on the thermal conduction mechanisms within small-scale circuit breakers, establishing a fundamental numerical heat transfer model based on Newton’s cooling formula and Fourier’s heat conduction law. By applying numerical Laplace transformation, the model is simplified into the complex frequency domain, and employing residue theorem for solving, it is transformed back into the time domain using Laplace inverse transformation to derive the predictive model. Experimental validation using a 32 A rated current circuit breaker demonstrates the model’s high predictive accuracy, suitability across various circuit breaker structures, with an average deviation below the permissible deviation in industry standards. This research holds significant implications for enhancing the thermal tripping characteristics of circuit breakers.

Factors Influencing Transportation Mode Preferences for Educational Trips Among Dormitory Resident University Students in Kütahya, Türkiye

This study explores the transportation behaviors of university students residing in dormitories in Kütahya, Türkiye, emphasizing their preferred modes for educational trips. Utilizing a Multinomial Logit model, the research analyzes the influence of socio-demographic factors, trip characteristics, and environmental perceptions on mode choice. The results indicate that public transport and walking are the predominant modes, with significant negative associations being observed between car ownership and the likelihood of choosing these sustainable options. Key findings reveal that, as trip distances increase, students are more likely to use public transport, while higher income levels decrease reliance on both public transport and walking. Male students demonstrate a higher preference for these modes compared to female students. Environmental perceptions, including feelings of safety and satisfaction with infrastructure, play a critical role in shaping transportation choices, highlighting the need for improved lighting, walkability, and public transport quality. These insights have important implications for transportation policy, suggesting that reducing private vehicle reliance and enhancing public transport services can significantly promote sustainable travel behaviors. Overall, the study underscores the importance of comprehensive transportation policies that not only enhance infrastructure and service quality but also consider environmental perceptions and safety to promote sustainable travel behaviors among university students.

Estimation of Trip-based Generation Models and Calibration of Mode Choice Models for the American Travel Behavior

Background The National Household Travel Survey (NHTS) is the primary national dataset for analyzing personal and household travel trends. It covers daily non-commercial travel across all modes and details information on travelers, their households, and their vehicles. Materials and Methods The NHTS includes critical data for calibrating travel demand models, such as vehicle details (number and type), individual demographic characteristics (age, gender, employment), and household demographics (income, ownership status, size, race). In this study, we utilized the 2017 NHTS summary statistics to estimate individual trip generation models, differentiating between weekdays and weekends. By comparing factors influencing daily person trips during these periods, we aimed to discern distinctions between the two models. Additionally, we calibrated a mode choice model to understand the impact of trip purpose, duration, household income, and the ratio of available vehicles to household size on the chosen mode. Our analysis focused on identifying and quantifying the factors influencing travel behavior, providing insights into how various variables affect the number of trips and mode choices. Results The results indicated variations between weekday and weekend models, with the presence of non-workers and individuals' education levels emerging as crucial factors for weekday travel. Conversely, the existence of children, household income level, and personal yearly miles driven were identified as significant factors affecting weekend travel. Additionally, common characteristics such as household size and urban residence were substantial in both models. The Multinomial regression analysis investigated the correlations between individual, household, activity, and trip characteristics and the modes of transportation selected by travelers. The most significant factors influencing an individual's mode choice are household income, the ratio of available vehicles to household size, and activity purpose. Discussion The study compared weekday and weekend travel behavior using trip-based generation models. Weekday travel was significantly influenced by non-workers and education levels, while weekend travel was more affected by factors like the presence of children, household income, and annual kilometers traveled. Both models emphasized the role of household size and urban residence in shaping travel patterns. The research also examined transportation mode choice, with validation confirming the high accuracy and robustness of the mode choice model in predicting travel behavior. Conclusions The study findings are valuable to transportation planners, policymakers, and urban mobility experts aiming to enhance the efficiency and effectiveness of transportation systems. By offering a detailed understanding of individual and household travel patterns, the research enables data-driven interventions that support policy decisions, such as optimizing transit routes, enhancing infrastructure for active transportation, and managing congestion.

Handheld Cellphone Use and Risky Driving in Adolescents

Handheld cellphone use while driving is associated with increased motor vehicle crash risk among adolescents. To examine the association of handheld cellphone use while driving with kinematic risky driving (KRD) events-hard braking and rapid acceleration-in adolescent drivers. Adolescents aged 16.50 to 17.99 years licensed 365 days or less in Pennsylvania were eligible to participate in this cross-sectional study. Enrollment occurred from July 29, 2021, to June 6, 2022. Participants downloaded a smartphone telematics cellphone app to record driving data for 60 days. Trip characteristics, including frequency, length, and duration; presence of speeding; frequency and duration of handheld cellphone use; time of day; and presence of precipitation. Kinematic risky driving events per 100 miles driven. Zero-inflated Poisson regression models examined whether individual characteristics and trip characteristics were associated with KRD. Incidence rate ratios were computed. Of 405 adolescents who responded to recruitment, 151 enrolled, 140 completed study procedures, and 119 with 12 360 trips were included in the analytic sample (60 female participants [50.4%]; mean [SD] age, 17.2 [0.4] years). Adolescents drove a mean (SD) of 103.8 (65.7) trips, 565.0 (487.3) miles, and 25.1 (19.3) hours. Adolescents had minimal night trips (1.5% [192]), and few trips with precipitation present (9.0% [1097]). Speeding occurred in 43.9% (5428) of the trips and handheld cellphone use occurred in 34.1% (4214) of the trips. Kinematic risky driving events occurred in 10.9% (1358) of the trips at a rate of 2.65 per 100 miles. In adjusted models, increased KRD events were associated with handheld cellphone use (incidence rate ratio [IRR], 2.62; 95% CI, 1.53-4.48), speeding (IRR, 2.12; 95% CI, 1.06-4.26), and minutes driving (IRR, 1.02; 95% CI, 1.01-1.02). Trips at night, precipitation presence, licensure for less than 6 months, and sex were not associated with increased KRD events. In this cross-sectional study of adolescent drivers, trips with handheld cellphone use and speeding were associated with higher rates of KRD, while individual characteristics were not. The findings suggest that smartphone telematics apps provide an opportunity to observe behaviors as well as surveil changes due to intervention efforts.

Northern Gannet foraging trip length increases with colony size and decreases with latitude

Density-dependent competition for food influences the foraging behaviour and demography of colonial animals, but how this influence varies across a species’ latitudinal range is poorly understood. Here we used satellite tracking from 21 Northern Gannet Morus bassanus colonies (39% of colonies worldwide, supporting 73% of the global population) during chick-rearing to test how foraging trip characteristics (distance and duration) covary with colony size (138–60 953 breeding pairs) and latitude across 89% of their latitudinal range (46.81–71.23° N). Tracking data for 1118 individuals showed that foraging trip duration and maximum distance both increased with square-root colony size. Foraging effort also varied between years for the same colony, consistent with a link to environmental variability. Trip duration and maximum distance also decreased with latitude, after controlling for colony size. Our results are consistent with density-dependent reduction in prey availability influencing colony size and reveal reduced competition at the poleward range margin. This provides a mechanism for rapid population growth at northern colonies and, therefore, a poleward shift in response to environmental change. Further work is required to understand when and how colonial animals deplete nearby prey, along with the positive and negative effects of social foraging behaviour.

Measuring heterogeneity in pedestrians’ perceived importance towards crossing facilities: An experience in Roorkee

Although several studies in the past discussed the effects of socio-demographic and trip characteristics on users’ preferences towards crossing facilities in urban areas, they were mostly confined to metro cities or million-plus cities. Adequate investigation has not been performed for the same in the small-sized cities where pedestrian activities are expected to be more frequent because of the higher share of shorter and walkable trip lengths. Accordingly, the present study aimed to investigate the heterogeneity in the perceived importance of crossing facility attributes among different pedestrian groups based on gender, age group, and walking preference. The study was conducted in a small-sized city, Roorkee, in the state of Uttarakhand, India. A face-to-face questionnaire survey of 554 pedestrians was conducted to collect their perceived importance towards 12 crossing facility attributes under study using a 5-point Likert-type ordinal scale. Collected data was analyzed using the non-parametric tests to measure the heterogeneity in the perceived importance of crossing facility attributes among aforementioned pedestrian groups. Subsequently, the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to derive relative importance scores of attributes found statistically significant in heterogeneity tests for different pedestrian groups. The findings of the present study reveal that perceived importance towards crossing facility attributes is heterogeneous based on the pedestrians’ age group and walking preferences; however, it remains unaffected by gender. These findings would help policymakers, planners, and government authorities frame improvement strategies for the existing crossing facilities in the city.

Study of an Impedance Function for Mixed Traffic Flows Considering the Travel Time–Cost Characteristics of Long-Distance Electric Vehicle Trips

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems.

Data-driven approach based on hidden Markov model for detecting the status of bikes in Bike-Sharing systems

Detecting unusable bikes remains a significant challenge within the supply chain of bike-sharing systems (BSS). While bad riding experiences negatively affect users’ satisfaction, uncollected broken bikes harm the environment. Although some studies have addressed the issue of detecting unusable bikes, the methods used have specific assumptions (or limitations) that make them inapplicable in some other cases. This is the first study to apply the Hidden Markov Model (HMM) in detecting the bikes’ status. The applied method tracks the gradual changes in bike status and takes into account that the maintenance process could improve the status of the bike. The proposed method demonstrated its ability to detect the hidden state of bikes in both dock-less and station-based BSSs, where it predicted the status of more than 94% of the bikes and detected most of the unusable bikes in a timely manner. The study also analyzed the relationship between trip features (speed, distance, and waiting duration) and the status of bikes and offered a better understanding of users’ behavior toward unusable bikes. Based on the quantitative results, this research proposed a paradigm for improving user-company communication. Finally, the study recommends using HMM as an explainable AI model for detecting the hidden status of bikes.

Service Quality and Personal Attitudes as Predictors of Overall Satisfaction with Public Buses: A Case Study in Kathmandu, Nepal

This study analyzes the effects of certain variables on bus service quality (SQ) and how SQ and personal attitudes affect satisfaction with public buses. A total of 552 responses were collected using a questionnaire that captured socioeconomic and trip characteristics, satisfaction ratings for SQ attributes, and the personal attitudes of consumers. Factor analysis was used to uncover unobserved latent features and to generate two measurement models: for SQ and for personal attitudes of bus users. Three latent variables (“information, safety and security”, “comfort”, and “accessibility/availability”) were observed that signify SQ. Simultaneously, two latent variables (perceived value, and behavioral intention and involvement) were obtained representing the attitude of customers. A structural equation modeling method was employed to compute interconnections among these variables. Information and safety and security had a major influence on SQ followed by comfort and accessibility/availability. Similarly, perceived value had a greater impact on personal attitudes than that on behavioral intention and involvement. Findings also show that evaluating overall SQ is better explained when consumers rate the service quality of buses after knowing about the various attributes. Findings revealed that the overall satisfaction of customers with bus services was influenced more by SQ-related attributes than by personal attitudes. The study also provided insights into public bus service quality improvements that must be emphasized and enhanced to increase ridership. This understanding of connections among SQ, personal attitudes, and overall satisfaction can assist transit officials in developing effective strategies and investment plans to meet the needs of passengers and boost customer satisfaction with public buses.

Shared e-scooters: A last-minute mode?

In this paper we assess whether shared e-scooters are being used in hurried situations to avoid arriving late, making trips we define as last-minute trips. We identify trip characteristics for last-minute trips using data from shared e-scooters in Stockholm. Trip arrival time patterns, distances, durations, and speeds are analyzed in relation to desired arrival times. We observe a peak in the number of trips ending before the full hour and a smaller peak of trips ending before the half hour of weekday mornings. These trips have lower average trip durations and higher average speeds, indicating a larger share of last-minute trips. Arrival patterns shift when the assumed preferred arrival time changes, and when the risk associated with arriving late increases. Furthermore, there is an increased share of trips that started close to public transportation around the full hour, indicating that a portion of last-minute trips may also be last-mile trips. Our results show that while last-minute trips are infrequent per user, they do make up a relatively large share of weekday morning trips. These results highlight an important market segment for shared e-scooter services and may also explain why some research reports e-scooter usage replacing walking.

A framework to include socio-demographic characteristics in potential job accessibility levels in low-car and car-free development areas in the Netherlands

Car-free development has become popular in recent years due to concerns regarding transport-related health issues in urban areas as well as a growing trend toward sustainability and environmentally friendly living. Although car-free development is regarded as progress to promote active transport modes and healthier cities, the accessibility impacts for its residents remain unclear. To address this knowledge gap, this paper proposes a job accessibility assessment framework that integrates individual and household socio-demographic characteristics into a job accessibility assessment, making it possible to account for commuting preferences of different population groups in accessibility analyses. For this purpose, a stated choice survey was conducted in existing low-car areas in the Netherlands to determine transport use and perception of public transport trip characteristics. Then, the influence of socio-demographic characteristics on trip perceptions was analyzed using a Latent Class Logit (LCL) regression model and Monte Carlo simulations. Finally, a multi-modal transport network combining walking and public transport trips was used to assess potential job accessibility levels of different population groups in a car-free development area. The proposed framework was implemented in a case study in the province of Utrecht (the Netherlands). Results show notable differences between the job accessibility levels within different population groups, reflecting distinct perceptions toward commuting trip characteristics based on socio-demographic characteristics and demonstrating the suitability of the applied approach to assess accessibility levels in car-free development areas. Compared to the sample average distribution, more than 15% lower accessibility levels were observed for starters (age 18-35) in some urban areas, indicating the aversion to longer and more expensive commuting trips. Contrarily, increased accessibility levels for families (>2 persons in household) were observed, demonstrating the acceptance to experience longer commuting travel times and additional costs. No differences were observed between accessibility levels of the sample average and senior adults (age >50).

Modeling choice determinants for bicycle-bus integration in developing countries: Case study in Fortaleza, Brazil

In the context of first/last mile, the bicycle-transit intermodality could lessen accessibility problems of stations and stops, reducing the need for feeder services. Although extensively addressed in developed countries, bicycle-transit is less studied in Global South metropolises, where distinct usage patterns are observed. Thus, this research aimed to model the choice behavior of the low-income population for bicycle-transit integration in Fortaleza - Brazil, a large Latin American city with a deployed bike-sharing system focused mainly on bike-bus integration. A Stated Preference (SP) survey was conducted addressing socioeconomic and trip characteristics, as well as policy variables such as bicycle parking and cycleways/lanes. SP data allowed the estimation of nested and mixed logit models representing choice behavior, and sample segmentation was used to identify heterogeneity among different groups of individuals. The results show that investing in bicycle infrastructure could stimulate bike-bus integration in Fortaleza; however, their relative importance depends on the analyzed individual strata. Findings also indicate the influence of public and road safety issues, as well as gender, income, and trip characteristics such as purpose and access distance.

Examining ride sourcing services as an emerging mode in Metro Vancouver: Insights into trip characteristics and impacts on multimodal competitions

The availability and utilization of ride-sourcing services have the potential to transform how people travel. While these services could improve mobility and accessibility, they could also attract users away from active modes and public transit and increase congestion and emissions. Understanding the impacts of transportation network companies (TNCs) on the transportation system is critical to ensure that the benefits of ride-sourcing are captured, and its negative externalities are minimized. This study uses web-based survey data administered to Metro Vancouver residents to explore the characteristics of ride-sourcing trips and the early impacts of ride-sourcing use on mode choice, given that TNCs are new to the study area. Additionally, this study utilizes stated preference experiments and error-components mixed logit models to examine the influence of sociodemographic characteristics and attitudinal factors on mode choice decisions for commuting and non-commuting trips. The results offer insights into the relationship between ride-sourcing and private vehicles, local and regional transit, taxi, and active modes (such as walking and cycling). Furthermore, model results highlight the heterogeneity in mode substitution behavior across population segments. This study can help planners and agencies capitalize on the advantages of TNCs and better integrate ride-sourcing into the transportation system.

Transportation Nature during Mega-Events: Investigating Trip Characteristics of Umrah Performers during Umrah Peak-Seasons

Transportation Nature during Mega-Events: Investigating Trip Characteristics of Umrah Performers during Umrah Peak-Seasons

Shared mobility - Novel insights on mode substitution patterns, trip and user characteristics

With a growing number of shared mobility options, choosing not to own a car may become a new norm in the future. Shared mobility offers users a flexible range of low or zero-emission alternatives, potentially reducing the use of private cars, as well as noise and air pollution. However, to date, there is little evidence of shared mobility acting as an alternative to private car use. Indeed, recent research suggests that shared vehicles not only substitute trips by private car, but also trips by active travel and public transport, thus not resulting in emission savings. Therefore, as part of the eHUBS project focusing on shared electric mobility hubs, the mode substitution patterns of 602 self-reported shared mobility users across Europe were investigated. More specifically, respondents of an online survey were asked to recall their last trip using shared mobility and to report on both their chosen and substituted travel mode in addition to common trip characteristics including trip distance and frequency. Chosen shared modes considered were both conventional or electric alternatives including cars, bicycles, cargobikes, and scooters. Overall, the results suggested that shared vehicles were about as or more likely to substitute public transport, cycling and walking, as private car trips, thus providing a mixed picture with respect to their potential contribution to reducing private car use and associated carbon emissions. Local authorities and policy makers are urged to actively continue to improve active travel facilities and to be proactive in better integration of shared mobility and public transport services.

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

More detailed and precise mobility patterns are needed for policies to reduce monomodal automotive dependency and promote multimodality in travel behaviors. Yet, empirical evidence from an integrated view of a complete door-to-door trip mode chain with daily mobility for pattern identification is still lacking. As an improvement and a solution on this issue, a multi-layer cluster model was designed and proposed for distinguishing 20 mobility pattern clusters, including six monomodal traveler groups, two non-transit multimodal traveler groups, and 12 transit multimodal based on big data mining. Statistical analysis with seven indicator measurements and a spatial distribution analysis with the Kernel density GIS maps of travelers’ residential location were carried out to reveal significant disparities across pattern clusters concerning spatial, social, and trip characteristics, based on which more precise and target policies for each group were discussed. This research may help provide more detailed information in establishing traveler mobility pattern profiles and solutions in filling the planning–implementation gap from the perspective of planners, policymakers, and travelers.

Exploring Trip Chain Pattern Choices at Urban Relaxation Facilities: A Case Study of Lego-Lego in Central Point of Indonesia

Lego-Lego at Central Point of Indonesia (CPI) is one of the famous relaxation facility areas currently hosting numerous visitors, specifically on weekends. People tend to spend their time at the first multi-purpose facilities in the morning and afternoon, causing traffic congestion and long queues at the entrance and exit. Therefore, this study explored the trip chain activity pattern and behavior of visitors using the facilities provided in Lego-Lego. This study was considered necessary because the government and stakeholders need to understand the trip chain pattern choices preferred by the people to give infrastructure, manage traffic problems, and formulate relevant regulations. The data were collected through interview-based questionnaires randomly distributed to visitors and analyzed using a multinomial logistic regression model to determine individual and trip characteristics. The results showed the variables with significant impacts on the trip chain and the trip chain models for the morning and afternoon were similar. It was also discovered that the probability of selecting trip chain pattern 1 reduced as the time activity increased while the probability of selecting patterns 2, 3, and 4 increased. This indicated that time activity influenced the trip chain activity pattern, but the cost was not. Another important observation was that the greater the diversity of facilities influencing the activity-travel patterns, the more time was required to engage in the activities.

A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY

Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.