Trip Production Research Articles

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.

Analyzing the effect of depopulation on the spatial structure of the city of Łódź, Poland: Development and application of an integrated land use and transportation model

This article evaluates the effect of depopulation, as a common trend in European countries and post-industrial cities, on the selected components of urban spatial structure, as an example of Łódź (Poland). An integrated land use and transportation model was adapted and calibrated based on the available data in the study area and modeled the evolution of the city structure from 2020 to 2040. For the land use part, the spatial input-output concept and economic base theory are combined to compute the number of activities in the study area. First, the economic sectors (i.e., land, population, base, and service classes) are defined. Second, the production and consumption of each sector in Łódź is calculated. Third, productions and consumptions are allocated to spatial units for determining the level of activities and flow between them. This process, which was programmed in Python, iterates until it reaches equilibrium in the activity system. For the transportation part, the PTV Visum simulator does the tasks of network representation and traffic assignment. Following the existing literature, changes in the distribution of employment, population, and land consumption within the activity system, along with trip length, trip production, and traffic loading in the mobility system, are regarded as urban structure factors and used for assessing the integrated model outputs. Getis-Ord and Theil's indices are also calculated to obtain the auxiliary results. Decentralization and dispersion in the spatial distribution of activities were identified as the chief characteristics of the city form. As the population reduces in Łódź, the average land consumption and service activities per spatial analysis units drop in the simulation period, which lies in the correlation between population decline and the economic fall. In addition, depopulation leads to flattened spatial structure and lesser development intensity due to the scattered and de-intensified pattern of land consumption. Besides, the population shrinkage causes a decrease in inequality level by 60% and 25% from 2020 to 2030 and 2030 to 2040, respectively, which might root in the reduced and lower difference between population and service simultaneously. Moreover, computations showed that while the number of trips decreases, the trip length increases, which means after depopulation, people travel less between spatial units to fulfill their needs because the city shifts to the borderlines. Findings also assist decision- and policy-makers in recognizing the effect of population loss on Łódź’s spatial structure and offer implications of integrated models for addressing such phenomena.

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.

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.

Applied Techniques for Twitter Data Retrieval in an Urban Area: Insight for Trip Production Modeling

This paper presents methods of retrieving Twitter data, both streaming and archive data, using Application Programming Interfaces. Twitter data are a kind of Location Based Social Network Data that, nowadays, is emerging in transportation demand modeling. Data regarding the locations of trip makers represent the most crucial step in the modeling. No research article has specifically addressed this topic with an up-to-date method; hence, this paper aims to refresh methods for retrieving Twitter data that can capture relevant data. The method is unique as the data are gathered for trip production modeling in zonal urban areas. Python script programs were built for both data retrieving methods. The programs were run for streaming data from May 2020 to April 2021 and archive data from 2018. The data were collected within Serang City, which is the nearest provincial city to Jakarta, the capital of Indonesia. In order to gather streaming data with no loss, the program has been run with referencing on sub-district office coordinate locations. Retrieving the intended data produces 1,090,623 documents, of which 54,103 are geotagged data from 2495 users. The study concluded that streaming data produce more geolocation data, while historical data capture more Twitter user data with relatively very little geotagged data and greater textual data than the period covered in this research. Thus, both techniques of retrieving Twitter data for urban personal trip modeling are necessary. Obtaining sufficient data collection using data streaming retrieval resulted in the most effective data preprocessing. This research contributes to Location Based Social Network data mining knowledge, both geolocation and text mining, and is useful for insight into developing trip production modeling in passenger transportation demand modeling using Machine Learning. This study also aims to provide useful methods for transportation system researchers and data scientists in utilizing Location Based Social Network data.

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.

Exploring Daily Activity Pattern Using Spatio-Temporal Statistics with R for Predicting Trip Production

Spatio-temporal data modelling is one of the methods in data analysis that uses space (spatial) and time (temporal) approaches. This study used Spatio-temporal statistical modelling to observe the daily activity patterns of people. Spatio-temporal modelling selected for support activity-based transportation demand. This research identifies community mobility patterns that will provide trip production data for transportation demand prediction. Using Spatio-temporal statistical modelling benefit this study because statistical this model can make model components in a physical system appearing to be random. Even if they are not, the models are helpful as they have accurate and precise predictions. In this study, descriptive analysis was used. Incorporating statistical distributions into the model is a natural way to solve the problem. This research collects daily activity data from 400 respondents recorded every 15 minutes. From this data, a pattern of respondents’ daily activities was formed, which was analyzed using R. Software R also visualizes data on daily activities of the community in Spatio-temporal modelling. This research aims to depict the daily activity patterns to predict trip production. This research found three clusters of trip production patterns with specific groups member and specific patterns between workdays and holidays.

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.

A Cluster Analysis of Temporal Patterns of Travel Production in the Netherlands: Dominant within-day and day-to-day patterns and their association with Urbanization Levels

This paper explores temporal patterns in travel production using a full month of production data from traffic analysis zones (TAZ) in the (entire) Netherlands. The mentioned data is a processed aggregated derivative (due to privacy concerns) from GSM traces of a Dutch telecommunication company. This research thus also sheds light on whether such a processed data source is representative of both regular and non-regular patterns in travel production and how such data can be used for planning purposes. To this end, we construct normalized matrix (heatmap) representations of weekly hour-by-hour travel production patterns of over 1200 TAZs, which we cluster using K-means combined with deep convolutional neural networks (inception V3) to extract relevant features. A silhouette score shows that three dominant clusters of temporal patterns can be discerned (K=3). These three clusters have distinctly different within-day and day-to-day production patterns in terms of peak period intensity over different days of the week. Subsequently, a spatial analysis of these clusters reveals that the differences can be related to (easily observable) land-use features such as urbanization levels (i.e., Urban, Rural, and mixed-level). To substantiate this hypothesis and the usefulness of this clustering result, we apply an OVR-SMOTE-XGBoost ensemble classification model on the land-use features of the TAZs (i.e., to identify their cluster). The results of our clustering analysis show that given the land-use features, the overall production patterns are identifiable. Further analysis of the mixed-level areas shows a more complex relationship between temporal heterogeneity and spatial characteristics. Population density seems to impose additional uncertainty on the temporal patterns. All in all, feature selection and spatial and temporal discretization play essential roles in identifying the dominant trip production patterns. These findings are directly useful for data-driven estimation and prediction of demand time series. Furthermore, this study provides further insights into people's mobility, relevant for transportation analysis and policies.

Trips Production in Khartoum State

The rapid increase in population, fast urbanization and change, high demand for public transport in Khartoum State necessitates development of a proper transportation plan for the state, to enable predicting the impacts that various policies and programs will have on travel. Travel demand forecasting is an essential part of the transportation planning process and trip production is the first step in the concept of four steps travel demand model. In this paper a model for trip production in Khartoum State is proposed. The trip generation model is based on data collected by the Ministry of Infrastructures and Transportation and a consultant in the period 2008-2011. The exclusion of incomplete data and outliers and the consideration of the car ownership in the development of the model, has resulted in a more realistic estimate of the number of trips produced than those predicted by the currently adopted model of Khartoum State. The results of this study predict the number of daily trips in the year 2035 at 9.56 million person-trips, creating a demand of 6.69 million person-trip/days to be covered by public transport. This will necessitate huge investments in mass transit systems to accommodate the uprising demand.

IMPACT ANALYSIS OF URBAN FREIGHT TRIPS GENERATED FROM WHOLESALE MARKET, AHMEDABAD

Freight transport plays an important role in meeting the domestic needs of the city's inhabitants. But when freight vehicles come into contact with city traffic, it has a negative impact on urban routes, such as extra traffic congestion, noise pollution, air pollution, etc. The aim of the study is to assess the impact of freight transport on the overall traffic of the city. A case study of the wholesale market of Ahmedabad has been considered as a study area. The freight trip models based on the trips attracted and produced by the market are developed on the basis of data collected from establishment surveys and freight vehicle driver surveys. Both the models developed in this study, FTA (Freight Trip Attraction) and FTP (Freight Trip Production), have R Square values of 0.799 and 0.715, respectively. The volume of freight vehicles contributing to the overall traffic flow is also measured by the traffic volume study. Through the data analysis and identification of the impact of freight transportation on city traffic, remedial measures are discussed to reduce the impact of freight transportation.

Land use and trip production model in central and peri-urban Semarang to anticipate land use conversion in post pandemic era

The development of core and peri-urban Semarang has land use conversion that increases human mobility. If this phenomenon is not well anticipated, it will cause transportation problems such as inefficiency of energy, and pollution. After the pandemic, the urban activity will be normal, and land use conversion will be more extensive again. A transportation model is needed to understand the land use that most influences the movement. This article aims to formulate a mathematical model that can identify land uses that affect trips or movement. To build a model, data on trip production in each village in core and peri-urban Semarang as the dependent variable and the various land use as independent variables. The regression model obtained by D = 0.009 residence + 1432.529 with R2 0.597. in the core of Semarang and D = 0.004 residence – 991.223 with R2 0.791 in peri-urban. The results of this model show that the most influential type in causing trip attraction is residential land use both in peri-urban and in core Semarang with a different coefficient. According to these findings, it requires more attention from the Semarang City Government to regulate land use in anticipation of transportation problems.

Modeling Trip-generation and Distribution using Census, Partially Correct Household Data, and GIS

The efficiencies of urban transport systems in several cities are drastically affected due to difficulties imposed by rapid urbanization and the proliferation of private modes of transport. The conventional four-stage travel demand modeling approach provides an ideal platform to formulate strategies to rectify problems in urban transport. Trip generation is the first stage in this exercise (where trip production and trip attractions are modelled), followed by trip distribution in the second stage. The present work related to the development of models for trip generation and trip distribution necessitated the use of census data related to the number of households in each zone since the available revealed preference (RP) data compiled based on household interview surveys was partially incorrect. A review of the literature indicated that studies on the use of sparsely available and partially inaccurate data such as revealed preference and zone-specific secondary data on trip generation and trip distribution were limited. In the present study, the use of the initial trip generation regression models developed based on existing household survey data resulted in prediction errors ranging between 26% and 32%. Modeling efforts after applying corrections to zone-specific characteristics based on secondary data and the use of trip rate per household later resulted in prediction errors of less than ±5%. In the latter phase of work related to trip distribution modeling, a log-linear regression model was developed based on a smaller refined set of the revealed preference data obtained by eliminating erroneous data in a stage-wise manner. The use of the calibrated and validated model ensured that the errors in predicted trip frequencies were less than 0.6%. Here, the information on the inter-zonal aerial distances that formed part of the trip distribution model was obtained using GIS approaches that employed the moment area method, which considered the intensity of land use at the sub-zone level. The combined strategy incorporates the use of GIS-based approaches to determine inter-zonal aerial distances, and the use of the refined relationship between trip interchanges and the inter-zonal aerial distances in the development of a reliable log-linear regression model for trip distribution contributed towards attaining higher accuracies in travel demand estimation. The modeling approaches described herein do not rely on the use of sophisticated technology, and time-consuming data processing. The study will provide the basic framework for transport planners to formulate better strategies for travel demand modeling where available data is noisy and less reliable. Doi: 10.28991/CEJ-2022-08-09-013 Full Text: PDF

EXPLORING IMPACTS OF URBAN DENSITY ON TRAVEL BEHAVIOR IN KHULNA CITY, BANGLADESH USING SPATIAL ANALYSIS

Urban density not only represents the intensity of travel demand, but also serves as a useful tool for quantifying the elements that influence travel behavior and demand. In this study, Khulna City of Bangladesh has been selected as a case study to explore the impacts of urban density on travel behavior in a city of a developing country. The average characteristics of urban density, as well as their impacts on travel behavior and demand, have been studied using geospatial analysis techniques. Urban densities have been categorized into Single Density (Population Density, Employment Density, and Building Density) and Composite Density (Household to Work trip Density, Household to School trip Density and Household to Shop trip Density) based on characteristics of travel behavior and demand. Descriptive statistics have been used to explore the impacts of urban densities on the various trip characteristics such as trip production, trip distance, trip cost, trip time, and trip mode. Diversity of trips and overall trip attraction areas have been identified using the Shannon diversity index and other spatial analyses. Trip production, trip distance, trip time, trip cost, and mode choice all show a positive linear relationship with urban single-density, according to this study. The everyday activities of residents are closely related to urban composite densities, and trip production increase in tandem with composite densities. It has been concluded that different urban densities have significant impacts on travel demand and travel behavior.

Investigating Factors Related to Criminal Trips of Residential Burglars Using Spatial Interaction Modeling

This study used spatial interaction modeling to examine whether origin-specific and destination-specific factors, distance decay effects, and spatial structures explain the criminal trips of residential burglars. In total, 4041 criminal trips committed by 892 individual offenders who lived and committed residential burglary in Tokyo were analyzed. Each criminal trip was allocated to an origin–destination pair created from the combination of potential departure and arrival zones. The following explanatory variables were created from an external dataset and used: residential population, density of residential burglaries, and mobility patterns of the general population. The origin-specific factors served as indices of not only the production of criminal trips, but also the opportunity to commit crimes in the origin zones. Moreover, the criminal trips were related to the mobility patterns of the general population representing daily leisure (noncriminal) trips, and relatively large origin- and destination-based spatial spillover effects were estimated. It was shown that considering not only destination-specific but also origin-specific factors, spatial structures are important for investigating the criminal trips of residential burglars. The current findings could be applicable to future research on geographical profiling by incorporating neighborhood-level factors into existing models.

A Feasibility Study of Public Transport of Panna City Madhya Pradesh

Public transport refers to the transport that is used by the public like bus, auto, van, etc. for travelling at various locations. Public transport makes the travel very safe and comfortable and easily a number of the public can travel at the same time which helps to reduce traffic congestion in the city. This research paper consists of a study and analysis of public transport in the Panna city Madhya Pradesh. The analysis is done with the help of a questionnaire made for road interview survey and after that modeling is done for trip attraction and production and trips produced by each household by regression method and gravity model of trip generation and the public route is proposed for travelling inside the city after analysis with this a feasibility of public transport is made for Panna city.

Service trip generation modeling in urban areas

Using modeling techniques as Ordinary Least Squares-OLS, this paper studies the main variables that affect service trip generation (STG) (production and attraction) by the service sector (e.g., maintenance, repairs) in commercial establishments located in urban areas of developing countries. Data obtained from Medellin Metropolitan Area (Colombia) origin–destination freight surveys conducted in 2017–2018 were used to analyze service trips for the first time in the region. This helped determine the number of produced and attracted service trips, average duration per service, most used modes of transport in service trips, and other characteristics that improve the understanding of how service trips work in urban areas This information might help in analyses of variables that mainly influence STG. To this effect, the authors estimated more than 30 service trip production and attraction models for establishments; these models depend on independent variables, such as employment, and establishment’s area. Finally, the freight community will benefit from these models because transport planners would analyze service trips as part of the freight and service activities in urban areas to contribute to improve the mobility using the models to plan better the transportation in the areas.

Trip generation modeling for a selected sector in Baghdad city using the artificial neural network

Abstract This study is planned with the aim of constructing models that can be used to forecast trip production in the Al-Karada region in Baghdad city incorporating the socioeconomic features, through the use of various statistical approaches to the modeling of trip generation, such as artificial neural network (ANN) and multiple linear regression (MLR). The research region was split into 11 zones to accomplish the study aim. Forms were issued based on the needed sample size of 1,170. Only 1,050 forms with responses were received, giving a response rate of 89.74% for the research region. The collected data were processed using the ANN technique in MATLAB v20. The same database was utilized to develop the model of multiple linear regression (MLR) with the stepwise regression technique in the SPSS v25 software. The results indicate that the model of trip generation is related to family size and composition, gender, students’ number in the family, workers’ number in the family, and car ownership. The ANN prediction model is more accurate than the MLR predicted model: the average accuracy (AA) was 83.72% in the ANN model but only 72.46% in the MLR model.