Trip Attraction Research Articles

Trip Attraction Rate Estimation Using Open Data Sources for Smart Transportation Planning

Trip Attraction Rate (TAR) serves as a load factor utilized in aggregate transportation modeling. Conventional estimation consumes a lot of time and manpower for field surveys. Meanwhile, open data sources, such as Google Maps, offer an alternative approach to conducting a survey. The popular times feature from Google Maps can estimate coffee shop TAR in the city of Bandung. This study focuses on coffee shops in Bandung City. Data was collected using web scraping techniques on Google Maps and produced a sample size of 377 data points after the validity and reliability processes. Subsequently, multiple linear regression was employed to estimate TAR. The findings reveal that variables like building area and distance to public transportation hubs influence TAR simultaneously. Moreover, our approach could also distinguish TAR between weekdays (0.13 people/m2/hour) and weekends (0.14 people/m2/hour) in Bandung City. This result challenges the usage of Institute of Transportation Engineers (ITE) standards, which is often due to limitations of time and workforce in conducting surveys for TAR estimation. The difference in values indicates the need to estimate specific TAR for each city in Indonesia rather than relying on ITE values, and the proposed approach to using open data sources will shorten the estimation process.

Analysis of External Trip Attraction for Proving Ground at ITS

The development of research in the automotive field is characterized by an increasing number of automotive competitions involving college students. The results of this research have been tested around the campus environment without specialized testing facilities such as a Proving Ground, thus prompting ITS to initiated to build a Proving Ground. The construction of a Proving Ground at ITS will also be utilized as a go-kart activity. The purpose of this research is to develop a trip attraction model and to determine the factors that influence trip atrraction decisions to the Proving Ground to be built at ITS. The method used is Multiple Linier Regression Analysis. The independent variables used in this research are Circuit Length (X1), Travel Distance (X2), Travel Time (X3) and Cost (X4). And the independent variables used to determine the gokart sport are Travel Distance (X1), Travel Time (X2) and Cost (X3). Meanwhile, the dependent variable is the trip attraction to visit the ITS Proving ground, which can be used as an exercise for automotive researchers or visitor to gokart sports. The results of the regression equation model of trip attraction to the Proving Ground are Y = 36,589 + 6,412 X1 - 0,584 X3. In this study, the most influential variables are Circuit Length and Travel Time with R² value in the calculation obtained at 0,297. Furthermore, the results of the regression equation model of trip attraction for gokart sport visitors are Y = 20,569 – 0,08X1 – 0,011X2 – 0,00009X3. In this study, the most influential variables are Travel Distance and Travel Time with the R² value in the calculation obtained at 0,161.

Predicting cycling flows in cities without cycling data

Cycling is a potential tool to mitigate many of the problems faced by urban populations today. Encouraging the use of bicycles as a legitimate mobility tool, however, demands adequate knowledge of current mobility patterns, such as locations of trip generation and attraction. Unfortunately, cities usually do not gather enough data to adequately understand cycling demand. We propose models based on spatial econometrics and gradient boosted regression trees which can be trained with data from cities with mature cycling cultures and then applied to cities still in their cycling infancy to supply city officials with a better estimate of potential future OD matrices. We perform a case study in the Boston Metropolitan Area and show results comparing both types of models.

Analysis of Trip Production and Attraction Model In Serang District

Analysis of Trip Production and Attraction Model In Serang District

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.

Developing Trip Generation and Attraction Models Using High-Frequency Proxy Data

The attraction and generation of a trip play a crucial role in transport planning and forecasting of trips. Multiple linear regression (MLR) is the most popular method of calculating trip attractions (TA) and trip generations (TG) to produce a distribution that can be used to forecast trips with updated values of independent variables such as electricity consumption, no of households, area of the land use etc. Literature shows that the surveys require independent variables to be updated; making them expensive and time consuming. This study aims to develop an MLR model for TA and TG based on available survey data from 2013 for Western Province, Sri Lanka and to update those independent variables using High Frequency (HF) proxy data for the predicted year (2019). Data from HF proxy sources such as electricity consumption, GPS customer points, and Landsat satellite imagery data have been used to update the independent variables of TG and TA for 2019. In the initial stage of this research, data from a home visit survey conducted in 2013 and land use data from the Western Province of Sri Lanka were used to develop the MLR model for TG and TA. A correlation and a regression analysis were performed using these surveyed data. According to the study, the MLR model for TG for home-based work trips has an r2 value of 0.79 and TA for general purposes (including shops and businesses) and industrial has an r2 value of 0.74 and 0.79, respectively, indicates the strong relationship between the considered variables to predict TG and TA. The model is validated with 2013 survey data and would be helpful for real-time estimation of the TG and TA of each zone.

Multiscalar trip resilience and metro station-area characteristics: A case study of Hong Kong amid the pandemic

A resilient urban system should cope with the dynamic human activities in its subsystems. Since travel allows people to have their needs satisfied in scattered locales, the dynamics of travel and its effects on people's well-being can vary across different spatial scales (including different subsystems) and over time. However, little empirical research has been done regarding these spatiotemporal variations. In this article, we introduced a trip resilience (TR) index to measure the time-varying travel characteristics, especially trip attraction changes, across different spatial scales. Using empirical data from Hong Kong, we quantified the TR indices for metro trips at a station across three spatial scales: local, neighborhood, and citywide. Then, we examined the spatial distribution of the TR and investigated which station (area) characteristics could explain the stations' TR indices for trips in different scales. We found that the TR indices and their predictors varied across the scales. The diversity of points of interest significantly predicted the TR indices across all the scales. Yet, other characteristics, such as the median age of residents, street density, working population, and provisions of parking spaces, only predicted the TR indices for trips at one or two of the scales. These findings shed light on more refined urban and transport planning strategies and policies concerning travel demand management across spatial scales in the post-COVID-19 era.

Trip Attraction Model at Elementary Schools in Suradita Valley Cisauk District Tangerang Municipal 2022

Activity in schools area located in the Suradita Village area has the potential to cause traffic congestion because the schools start time coincides with the rush hour for work. This is due to the fact that the schools do not have a designated area for dropping off passengers, which forces vehicles transporting students to stop or park on the road, reducing the road’s capacity. The purpose of this research to determine trip attraction model, totals of trip attraction, factors that influence of trip attraction, and trip rate on SDN Rahayu, SDN Suradita, MIS Nurul Falah area. The research method used is interview to students, teachers, and staffs about trip data, also secondary data collection were amount of students, teachers, and staffs, land area, floor space, amount of classrooms, parking area of SDN Rahayu, SDN Suradita, MIS Nurul Falah area. Data that has been collected then analyzed with multiple linear regression method used SPSS 25.0 application with significant level 95%. Trip attraction model of SDN Rahayu, Suradita, and MIS Nurul Falah can be attained YSMDS = 0,998 + 0,199 JS + 0,067 JGK – 0,071 JRK with R2 (R square) = 0,636. Total of trip attraction that is generated by SDN Rahayu, SDN Suradita, MIS Nurul Falah area = 463 trip attraction/day. Based on t Test with significant level <0,05, therefore an influential factors are amount of students, teachers, and staffs, also amount of classrooms. Trip rate of SDN Rahayu, SDN Suradita, and MIS Nurul Falah = 0,9 people/m2 or 9 people/100m2.

Analisis Model Tarikan Pergerakan Sekolah Di Kota Palangka Raya

The school zone is a land use that can create the trip attraction. The purpose of this study is to model the trip attraction and to analysis the factors that most influence attraction to the Catholic school area in Palangka Raya. The study was conducted at four schools in one zone. Primary data, i.e. the number of vehicles entering the school premises, was obtained through direct observation in the target schools. Secondary data was obtained through documentation and interviews with the operators of the schools. The collected data was analysed using the linear regression statistical method assisted by SPSS software version 25. All variables involved, i.e. number of students, number of teachers, number of school benches, class capacity and class area, are strongly correlated with the variable 'traffic volume'. From the linear regression analysis, the number of students is the independent variable that is highly correlated and significantly influence the trip attraction, as shown by the value of r=0.99 and R2 = 0.998. The model of physical activity attractiveness in this study is Y = -10.686 + 0.934X1. The results of this study contribute to education field especially in transportation, as they support the theory that the number of students significantly influences the trip attraction in school land use

Travel pattern analysis and freight trip generation modeling of textile commodities. Case study: Thamrin City Shopping Center, Jakarta

One of the sectors supporting the country’s economic development is the commercial sector. With the increasing population, the demand for consumer goods will increase, impacting trip generation. Thus, it is necessary to conduct a study to predict the trip generation of freight transport in shopping centers, especially in DKI Jakarta, with textile stores as trip generators. This research aims to determine the travel pattern of freight transport and freight trip generation models produced and attracted at the Thamrin City shopping center and to analyze the factors influencing freight trip generation. The research was conducted using a quantitative research method with a cross-sectional. Data collection used a questionnaire given to 105 merchants. Data analysis used linear regression methods with the number of freight trip production and attraction as the dependent variable. The independent variables used are the number of employees and gross floor area. The study results show that the delivery of goods using expedition services is sent to the distribution center before being sent to the destination. While shipping goods using private vehicles, sent directly to the destination location. The gross floor area significantly influences the trips generation. Transportation planners can use this research to estimate the number of trip generation in shopping center areas in the future.

The Implementation of Autocad® Civil 3D for Road Geometric Redesign on Educational Areas: A Case Leumah Neundet Bandung

Infrastructures, especially those directly related to the transportation system and has a crucial role in society is the road. “Jalan Leumah Neundeut” is a road transportation infrastructure in Sukawarna Village, Sukajadi District, Bandung City, West Java, Indonesia. It has the potential for trip attraction because it very close to the education area and passed by public transportation. This paper’s problem formulation arises based on the need for a well-road transportation infrastructure on “Jalan Leumah Neundeut”. It will be geometrically redesigned in its horizontal alignment to be as ideal as possible to fit the design criteria using AutoCAD® Civil 3D because it does not meet the design criteria, especially regarding the road spaces and lane configuration. The geometric redesign of the “Jalan Leumah Neundeut” horizontal alignment resulted in 450 meters of the total road length, 7 meters of carriageway width with 2/2 UD (undivided with two lanes and two ways) type, 60 km/hour of velocity design, S-C-S type of road curve, 47 meters of LS value, 130 meters of curve design radius, 2% of normal super-elevation, and 8% of maximum super-elevation. With the results obtained, Jalan Leumah Neundeut can maximize its potential.

Freight trip attraction estimation considering informal trade for a commercial area in Mexico City

The knowledge of the freight trips attracted by a commercial area allows understanding the requirements of urban freight transportation and proposing initiatives to enhance its distribution. Commercial areas in developing countries usually include informal trade activities, which are not specifically considered when estimating Freight Trip Attraction (FTA). This paper describes a procedure for estimating FTA, based on sampling, and applies it to a commercial area in Mexico City. The procedure considers employment as explanatory variable, for both formal and informal establishments with three industrial sectors. The results emphasize the importance of including informal establishments in FTA estimations. An underestimation of FTA equal to 27% is obtained if informal trade is not taken into account, for the study area. For improving the estimates, additional explanatory variables must be considered.

Effect of the measurement period and spatial dependence on the accuracy of urban freight trip generation models

Despite recent advancements in the Freight Trip Generation (FTG) modelling literature, there is a lack of understanding on the effect of the choice of a regression model, measurement period (daily/weekly FTG), and spatial dependence on model fit and freight-related policies. This study addresses these research gaps by developing non-spatial and spatial autoregressive multiple linear regression and count models for daily and weekly Freight Trip Production (FTP) and Freight Trip Attraction (FTA). We model Freight Shipments (FS) as FTP and Freight Deliveries (FD) as FTA. The results show that the best model for daily and weekly FTP is the spatial Zero-Inflated Negative Binomial (ZINB) model. The best daily and weekly FTA model is the non-spatial Negative Binomial (NB) model. The findings indicate the presence of spatial dependence in the best FTP model, while it is absent in the best FTA model. The elasticity analysis shows that daily models may lead to bias and inaccurate prediction of policy impacts. The study recommends using count models that capture more FTG characteristics with a week as the measurement period and consider spatial dependence, if present.

Development of spatial econometric models for estimating the bicycle sharing trip activity

In developing countries, the implementation of bicycle sharing programs is in a nascent stage; this paper provides a preliminary understanding of bicycle sharing trip activity introduced in Kerala, India. Fundamentally, a framework is utilized to incorporate spatial interaction effects and estimate the bicycle sharing trip activity using spatial econometric techniques. MYBYK is the associated bicycle rental company, with 67 stations in the various major locations at Cochin, Kalamasery, Choornikkarra, and Aluva cities in India. Information such as socio-demographic, bicycle sharing trip activity, weather, and built environment information have been collected in the study regions. Initially, spatiotemporal variation is analyzed to understand the bicycle-sharing trip activity. Then, spatial indicators are determined using Moran's I technique, and the study area's locational information is transmitted to the spatial weight matrix to illustrate the spatial neighbourhood structure. Finally, Trip Production and Trip Attraction models were developed for estimating the bicycle sharing trip activity using five spatial econometric techniques as Spatial Autoregressive (SAR) model, Spatially Lagged X model (SLX), Spatial Error Model (SEM), Spatial Durbin Error model (SDEM), and Spatial Durbin Model (SDM). The models are evaluated, and an appropriate model is found using the Bayesian approach. The findings indicate that SDM effectively estimates the bicycle sharing trip activity, and its spillover effects are also addressed for each spatial indicator variable.

A flexible and scalable single-level framework for OD matrix inference using IoT data

This study proposes a flexible and scalable single-level framework for origin–destination matrix (ODM) inference using data from IoT (Internet of Things) and other sources. The framework allows the analyst to integrate information from multiple data sources, while controlling for differences in data quality across sources. We assess the effectiveness of the framework through a real-world experiment in Greater Adelaide (GA), Australia. We infer car OD flows within the region using four separate data sources: site-level traffic counts from loop detectors, vehicle trajectories recorded by roadside Bluetooth sensors, partial OD flows based on data from in to vehicle navigation systems, and journey-to-work OD data collected by the Australian Census. We compare our OD inferences with those from the current version of the Strategic Adelaide Model (SAM), calibrated using data from traditional household travel surveys. We find remarkable consistency between our inferences and those from SAM, despite differences in input data and methodologies. For example, for the morning peak period, we predict the total number of trips made within GA to be equal to 556,000, while the corresponding prediction from SAM is equal to 484,000. The two predictions are within 15 per cent of each other. When we compare the spatial distribution of trips, in terms of origins and destinations, we find that our inferred OD matrix has an 86 per cent cosine similarity to the corresponding SAM matrix. Travel time predictions based on our OD inferences correspond closely to those predicted by the Google Directions API, lending further confidence in our inferences. In summary, our results show that the proposed framework can produce highly comparable ODM, trip production and trip attraction patterns to those inferred from traditional household travel survey-based transportation demand modelling methods.

Estimating urban spatial structure based on remote sensing data

Understanding the spatial structure of a city is essential for formulating a spatial strategy for that city. In this study, we propose a method for analyzing the functional spatial structure of cities based on satellite remote sensing data. In this method, we first assume that urban functions consist of residential and central functions, and that these functions are measured by trip attraction by purpose. Next, we develop a model to explain trip attraction using remote sensing data, and estimate trip attraction on a grid basis. Using the estimated trip attraction, we created a contour tree to identify the spatial extent of the city and the hierarchical structure of the central functions of the city. As a result of applying this method to the Tokyo metropolitan area, we found that (1) our method reproduced 84% of urban areas and 94% of non-urban areas defined by the government, (2) our method extracted 848 urban centers, and their size distribution followed a Pareto distribution, and (3) the top-ranking urban centers were consistent with the districts defined in the master plans for the metropolitan area. Based on the results, we discussed the applicability of our method to urban structure analysis.

Modelling the effect of spatial determinants on freight (trip) attraction: A spatially autoregressive geographically weighted regression approach

This paper investigates the effect of spatial and locational characteristics of establishments on freight (trip) attraction (FA/FTA) models. The authors estimated econometric models of FA and FTA as a function of the establishment attributes as well as the spatial and locational determinant variables, using establishment-level data collected from Addis Ababa City, Ethiopia. The interconnected issues of spatial dependency and spatial heterogeneity, together with nonlinear specifications, were incorporated with the application of spatial techniques, including spatial error models (SEM), spatial autoregressive model (SAR), geographically weighted regression (GWR), multiscale-GWR (MGWR), and the combination GWR-SAR/MGWR-SAR. Regarding the explanatory variables, the empirical results revealed that firms in the manufacturing, wholesale and retail sectors located on the wider streets tend to receive more FA and FTA. The closeness to the primary road network and the city entry gate influences the FTA of manufacturing and construction firms. Moreover, retail establishments near the major market tend to receive more tonnage. The models also confirm that FA and FTA are the results of two different processes. Overall, the use of spatial regression techniques improves the accuracy of both FA and FTA models. MGWR-SAR exhibits superior performance by jointly addressing spatial dependency and heterogeneity. The MGWR-SAR model also uncovers the local variability of the variables representing the spatial and locational effects on freight attraction. The methodological analysis and empirical findings of the study could provide useful insights to support urban freight modelling, planning, and decision-making.

Modeling The Generation And Attractiveness Of Passenger Demand Trips On The Madiun-Slahung Railway Reactivation Line

The reactivation plan of the Madiun-Slahung railway line in the National Railway Master Plan is the main program for the development of the railway network and service stage IV in 2026-2030, therefore it is necessary to conduct research on the demand for traction and generation as well as passenger interest in the future Madiun- This shelters are reactivated. Based on the results of data analysis and discussion of the calculation of the Madiun-Slahung area, it can be concluded that the model of the reactivation area of the Madiun-Slahung railway covers 7 districts, consisting of 45 sub-districts/villages. The method used is a household survey (Household Interview) which is intended to produce data on travel patterns and characteristics that affect the trip. The number of trip generation occurs in the range of numbers 6939-22594 motorbikes/hour for trip production and 1123-52265 motorbikes/hour for trip attraction, where zone 2, Kartoharjo sub-district has the largest number of trip production and zone 4, Ponorogo sub-district has the largest number of trip attractions. This is due to the high population, number of vehicle ownership and the strength of attraction due to the large number of shops and campuses and schools in the zone. The largest travel distribution value occurs in zone 4, because the zone is land use for shops, offices, campuses and schools.

Trip Attraction Rates of Banking Services in Developing Countries' Cities

There is a lack of studies on trip generation rates related to land use activities in developing countries. The absence of trip rates makes transportation planners use rates listed in the Institute of Transportation Engineers (ITE) Trip Generation Manual, which may lead to unrealistic results. The study aims to determine trip attraction rates for banking services in Zagazig city, Egypt, and evaluate the factors affecting the attracted trips to the banks. The study has designed and implemented a comprehensive data collection and analysis program. The study sample contained nine banks. The study has conducted field surveys that counted the total number of persons entering/exiting the bank under investigation and the generated traffic volume of the adjacent street. The trip rates have been established in terms of independent variables, i.e., gross floor area, the total number of employees, and the number of tellers for different types of banks. Findings showed that average trip attraction rates for public banks were higher than for private banks. There was a strong relationship between trip attractions and the total number of employees at public banks. The most representative factor of private banks' trip attractions was the number of tellers. Doi: 10.28991/CEJ-2023-09-02-07 Full Text: PDF

Assessing The Intersected Relationship Between Land Use And Transportation Planning

This research analyses the causes of traffic congestion in urban corridors. It specifically studies the intersected relationship between land-use and transportation planning by examining Al-Madina Al Monawara street in Amman, the capital of Jordan, as a case study. Techniques implemented in data collection are personal observations, traffic counts, land use maps and questionnaires. The methods applied in analysing the collected traffic counts are: Level of Service (LOS), Automatic Traffic Counters (ATC) and Trip Attraction Analysis (TAA). The outcome of the research verified the relationship between the high level of traffic, where the traffic exceeds the actual capacity of the street by 43%, and the unregulated land use planning where 85% of the existing buildings along the street are commercial. The findings of this research will help to establish an assessment tool for testing the impacts of transportation and land use in congested developing cities. This study is important since transportation solutions receive little attention by planner’s development agendas in Jordan and the neighbouring countries.