Trip Production Models Research Articles

Analysis of Trip Production and Attraction Model In Serang District

Analysis of Trip Production and Attraction Model In Serang District

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.

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.

Spatial Seemingly Unrelated Regression Models for Freight Trip Generation by Vehicle Type: Application to the Chennai Metropolitan Area in India

During the last two decades, there has been substantial interest in developing freight trip generation (FTG) models. Most studies consider only truck trips or convert all freight trips into equivalent truck trips. Freight in several large cities is increasingly being moved by smaller vehicles. This calls for modeling FTG by vehicle type. The present research identifies and compares establishment characteristics affecting FTG by different vehicle types. In this context, spatial correlations among nearby establishments and the error-term correlations between independent models by vehicle type become relevant. Based on the Lagrange-Multiplier (LM) tests, we develop non-spatial seemingly unrelated regression (SUR) models for freight trip production (FTP) and spatial SUR models with a spatial lag in the dependent variable to account for both spatial and error-term correlations for freight trip attraction (FTA). The results show that establishment type and size affect FTG by different vehicle types.

Interpretable data-driven demand modelling for on-demand transit services

In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services. However, due to their infancy, there is a strong need to understand and model the demand for these services. In this study, we developed trip production and distribution models for ODT services at Dissemination areas (DA) level using four machine learning algorithms: Random Forest (RF), Bagging, Artificial Neural Network (ANN) and Deep Neural Network (DNN). The data used in the modelling process were acquired from Belleville’s ODT operational data and 2016 census data. Bayesian optimalization approach was used to find the optimal architecture of the adopted algorithms. Moreover, post-hoc model was employed to interpret the predictions and examine the importance of the explanatory variables. The results showed that the land-use type was the most important variable in the trip production model. On the other hand, the demographic characteristics of the trip destination were the most important variables in the trip distribution model. Moreover, the results revealed that higher trip distribution levels are expected between dissemination areas with commercial/industrial land-use type and dissemination areas with high-density residential land-use. Our findings suggest that the performance of ODT services can be further enhanced by (a) locating idle vehicles in the neighbourhoods with commercial/industrial land-use and (b) using the spatio-temporal demand models obtained in this work to continuously update the operating fleet size.

Hospital Trip Production and Attraction Modeling for Future Predictions

The demand for healthcare facilities in the world is growing daily, and this growth in demand exceeds general population growth. This situation has made hospital trips a vital issue during urban transportation planning. In this context, the aim of this study is twofold: (1) to investigate the hospital trip behavior as home-based hospital trips within the scope of trip production and attraction (trip generation) models and (2) to evaluate the predictive power of some robust and regularization methods in trip generation modeling. Within this scope, approximately 49,000 valid Household Survey Data (HSD) from 2001 (base year) and 2015 (target year) were used to develop comprehensive trip generation models for the home-based hospital trips. Multiple linear regression (MLR), ridge regression (RR), Liu, least trimmed squares (LTS), least trimmed squares–ridge (LTS–R), and least trimmed square–Liu (LTS–L) methods were used for the analysis, and results were evaluated in terms of specialized mean squared error (MSE) and root mean square error (RMSE). As a result, LTS–R and LTS–L increased future predictive power by approximately 55% compared with OLS. This study fills an important gap in the literature in terms of not only the use of regularization and robust methods in trip demand modeling but also examining characteristics that affect hospital trip behaviors. In addition, the paper proposes a “Hospital Mobility Coefficient” projection that could be useful in demand estimation for future hospital trips.

MODELING PUBLIC TRANSPORT USERS’ TRIP PRODUCTION IN HAWASSA CITY, ETHIOPIA

Today, overcrowded public transport demand, resulting in huge costs in an urban area. Similarly, there are a lot of people who use public transport in Hawassa city. This study aimed to develop public transport users' trip production models at the household level. Some socio-economic characteristics and trip detail of the public transport users were collected randomly from the different households through a questionnaire survey. The data gathered was fed into IBM SPSS package version 20 to develop linear regression models. The developed models are associated with trips for purpose and time intervals of trips made. The developed linear regression models, general trips, work trips, educational trips, and trips made before 8:00 AM and after 4:00 PM had good explanatory power. The value of explanatory power comprised of 0.656, 0.722, 0.549, 0.610 and 0.510. These values indicated the explanation power of the socio-economic characteristics on the trips made. It means the daily trips production was significantly affected by the number of working individuals, the different age brackets, cars and motorcycles, and the monthly income per household. The most frequent public transport users’ trips production regarding the trip purpose and time are work trips and occurred after 4:00 PM. This scenario represented a good model developed in this study. Hence, it is suggested that Hawassa city’s traffic management office use the developed models to predict the future trips demand to provide a proper scheme to avoid congestion during the peak hour of the day.

Calibration and validation of person-based trip production models of optional trip purposes

ABSTRACT Trip generation models are typically calibrated using zone-based aggregated data, despite some theoretical drawbacks pertaining to the use of aggregated data. This study demonstrates that models calibrated with disaggregated data not only have a better theoretical basis, but also forecast more accurate trip productions. In particular, optional trip purposes are focused on the calibration of trip production models, of which trips tend to be irregularly generated, rendering it difficult to build more reliable models. There are three models calibrated: an aggregate model with zone-level data, a disaggregate model with household-level data, and with household and person-level data using household survey data for two years. The prediction accuracy and stability of the models are compared and verified using statistical methods. It is discovered that calibrating a trip production model using household and person-level data jointly yields a better calibration method.

Estimation of hospital trip characteristics in terms of transportation planning

IntroductionIncreasing urban population and traffic density on one side and rising hospital demands on the other has underlined the importance of transportation modeling. In this study, Four-Step Transportation Model (FSTM), which focuses primarily on home-based work and school trips, is used to evaluate the increasing share of home-based hospital trips among all from a transportation planning perspective. The aims of this study are to 1) examine the hospital trip behaviors and the parameters affecting it within the framework of ‘home-based hospital trips’, 2) evaluate the effectiveness of different robust and biased estimation techniques to be used which could be an alternative to Ordinary Least Square (OLS) in FSTM. MethodOLS, Ridge Regression (RR), Least Trimmed Squares (LTS), and Least Trimmed Squares-Ridge (LTS-Ridge) techniques were used for the comprehensive evaluation of home-based hospital trip production models for the Eskişehir City. In this context, the five characteristics affecting hospital trips were used as independent variables. Approximately 20000 valid household survey data (HSD) for 2001 (Training data) and 29000 valid HSD for 2015 (Testing data) were used and results were evaluated in terms of Mean Squared Error (MSE). ResultsAs a result of the analysis, the MSE values of LTS-Ridge, LTS, RR, and OLS models are 127484, 169060, 274211, 434164, respectively. The most consistent and successful results were obtained from LTS-Ridge according to MSE and direction of the coefficients. Hospital demand coefficient proposed in this study increased the success of future estimations. ConclusionWhen data have multicollinearity or contain outliers, LTS-Ridge makes more successful predictions than OLS. This study fills a large gap in the literature by examining the home-based hospital trips in terms of socio-economic and demographic characteristics from a transportation planning perspective.

ANALISIS TARIKKAN PERGERAKAN PENUMPANG ANGKUTAN PENYEBERANGAN PELABUHAN ULEE LHEUE KE ZONA PERDAGANGAN DAN JASA DAN ZONA PERKANTORAN DAN PENDIDIKAN

The city of Sabang is one of the most iconic tourism spots in Aceh Province. The Ulee Lheue Port is one of the most important accesses to Sabang, which is located in Banda Aceh City, the capital of Aceh Province. So that crossing activities carried out by both local residents and tourists will always pass through Banda Aceh. Along with the developments in each of these regions, the travel demand in a form of increased movement activity has been rising. The purpose of this study is to get a trip attraction model of Ulee Lheue Port ferry boat passenger that attracted to two destination zones in Banda Aceh by using multiple linear regression analysis methods and calculated by using SPSS software based on socio-economic and travel behavior data as independent variables (X) and the movement frequency as dependent variable (Y). Based on the analysis results, the model with each trip destination zone has a different travel behavior. The most influential variables for trip attraction model of the Ulee Lheue Port ferry passengers trip to trade and service zone are work activity (X12D), gender (X1), and income (X3). The most influential variables for the trip production model of the Ulee Lheue Port ferry passengers trip to office and educational zone are government workers (a type of work) (X6C), age (X2), gender (X1), crossing time (X11), private employees (a type of work) (X6D).

The effects of traffic zoning with regular geometric shapes on the precision of trip production models

One of the first and most critical steps involved in transportation studies is to determine the traffic zoning scheme. A traffic analysis zone (TAZ) is the main unit of the transportation analysis. In most transportation planning studies, the focus is only on the modeling process, while ignoring aspects such as TAZ definition, configuration, and design. Although the definition of TAZs is one of the key stages in transportation planning studies, a proper approach for the definition is still one of the unsolved issues.In this research, a TAZ with regular geometric shapes, called regular traffic analysis zones (RTAZs), is presented as a new approach to traffic zoning. On the other hand, the accuracy of calculating trip production, as the first step of well-known 4-step transportation demand modeling system, is of high necessity, because this step determines the total number of trips. In the current study, the effects of RTAZs on the results of trip production models (TPMs) for the city of Khoy in Iran were investigated. The results indicated that TPMs based on regular zoning system acted almost similar to traditional zoning system in estimating produced trips and, in some cases, even presented relatively better estimations. Regarding the difficulties encountered in the topic of the traditional traffic zoning system, the present work showed that RTAZs facilitated the modeling process and led to time and cost savings.

Paired-line hybrid transit design considering spatial heterogeneity

This study attempts to incorporate spatial heterogeneity into the optimal design of paired-line hybrid transit systems, which aims to strike a better balance between accessibility and efficiency by leveraging the flexibility of a demand adaptive service. A simple trip production and distribution model is introduced to differentiate the central business district (CBD) of a city from its periphery. To cope with the heterogeneous demand pattern, the transit system is also configured differently inside and outside the CBD, for both its fixed route and demand adaptive services. Allowing the supply heterogeneity complicates transit users’ route choice modeling considerably. As a result, user costs must be estimated separately for six subregions that constitute the feasible set of the fixed route headway. Each subregion corresponds to a unique route choice behavior, hence leading to a distinctive design model that is formulated as a mixed integer program and solved by a commercial solver. Results of numerical experiments show that concentrating demand in the CBD significantly reduces the average system cost, and this benefit increases as the average demand density becomes larger. Also, recognizing demand heterogeneity and responding to it with a tailored design can be highly beneficial. However, this benefit diminishes as the average demand density increases.

Paired-line hybrid transit design considering spatial heterogeneity

Abstract This study attempts to incorporate spatial heterogeneity into the optimal design of paired-line hybrid transit systems, which aims to strike a better balance between accessibility and efficiency by leveraging the flexibility of a demand adaptive service. A simple trip production and distribution model is introduced to differentiate the central business district (CBD) of a city from its periphery. To cope with the heterogeneous demand pattern, the transit system is also configured differently inside and outside the CBD, for both its fixed route and demand adaptive services. Allowing the supply heterogeneity complicates transit users’ route choice modeling considerably. As a result, user costs must be estimated separately for six subregions that constitute the feasible set of the fixed route headway. Each subregion corresponds to a unique route choice behavior, hence leading to a distinctive design model that is formulated as a mixed integer program and solved by a commercial solver. Results of numerical experiments show that concentrating demand in CBD significantly reduces the average system cost, and this benefit increases as the average demand density becomes larger. Also, recognizing demand heterogeneity and responding to it with a tailored design could be highly beneficial. However, this benefit diminishes as the average demand density increases.

Empirical models of transit demand with walk access/egress for planning transit oriented developments around commuter rail stations in the Greater Toronto and Hamilton Area

This paper explores the relationship between characteristics of Transit Oriented Development (TOD) and passenger transit demand with walk access/egress. Two separate Log-linear Cobb- Douglas regression models are estimated for predicting transit ridership with walk access/egress as a function of specific land-use and station-level attributes around commuter rail stations in the Greater Toronto and Hamilton Area (GTHA). The empirical models are estimated using passenger survey data collected by the regional transit authority in the GTHA. The regional transit system considered in this research is GO Transit which has stations distributed across the GTHA. The first estimated model is a trip production model that predicts the number of daily trips produced per GO Transit station with walk as the access mode to the station. A trip attraction model is also estimated for predicting the daily trips attracted to a GO Transit station with walk as the egress mode from the station. The empirical models reveal that the station-level trip production via walk access is significantly associated with population density and walkability in the vicinity of the station, as well as the station’s frequency of trains, number of bus feeder lines and utilization of parking spaces. Trip attraction by rail via walk egress at a GO Transit station is shown to be highly related to employment density, frequency of trains, and the relative network distance from the station to the central business district. A noteworthy finding with policy implications is that the frequency and number of bus feeder lines to a station is negatively associated with trip production via walk access. The study results can form the basis of transportation and land use policies aimed at implementing successful TODs.

BOOSTING CATEGORISATION SYSTEM IN THE UK TRICS DATABASE AND DEVELOPING NEW TRIP PRODUCTION MODELS

In the UK, TRICS is a national trip generation databases and system. It is recommended to be used in quantifying traffic impacts of new residential developments. Thus, any enhancement in its methodology will positively influence the transport assessment process. This paper has three objectives; first, to examine the possibility of proposing new statistically-proven grouping for the TRICS residential land use subcategories. The second objective is to use the proposed grouping to construct parsimonious models for home-based trip production that can aid in quantifying traffic impact assessment. The third objective is to examine the validity of the developed models against two of the most internationally recognised trip information systems; the UK TRICS and the US ITE Trip Generation Manual. The statistical analysis ended up with new more accurate grouping where based on it eight trip production model were built. The developed models were found to be practical and rigorous.

A Freight Modal Shift Model for Slovakia

This paper presents the multimodal transport model done for Feasibility Study Update of Railway Corridor Žilina – Košice – Čierna nad Tisou – state border, in the Slovak Republic. Model includes entire area of Slovakia in different levels of detail and also border and remote European external zones.The data of freight preference in Slovakia we obtained from adaptive state preference survey on main freighters in Slovakia. Each of participants was asked to deal with maximally three logistic tasks. The entire freight task was defined by transported commodity, length of transport path and transport mode used in the present. For each of the tasks respondent passed adaptive state preference experiment where mode, cost, travel time and reliability were attributes of alternatives. 51 freighters participated on the survey and they were asked about 71 freight tasks.We find out that railroad or road mode was not preferred significantly but freighters are resistant to change routine freight traffic mode. In general, specific constant of present usage mode was positive and almost thirty times larger than value of time. This is complication for standard modal shift traffic model because alternative specific constant could not be assigned to specific traffic mode equation. Moreover, actual data of freight transport in Slovakia have different level of accuracy. Very accurate and detailed information (commodity and values in origin-destination pairs) were available for railway transport. On the other hand, there was lack of information about road freight transport. This difficulty leads us to build double modal split model for road and railway freight transport.Trip generation process is comprised of two components which represents two (railway and road) currently used modes. First part of trip generation process contains matrix of volumes of freight currently transported by rail. The second part of the process estimate origin-destination matrix by trip production and attraction model and represented road freight transport. This model is based on knowledge about location of big companies and population density.The two products of trip generation processes enter modal split model which is doubled again. One of the modal split models is for freighters who currently use railroad and second of them is model for current road freight users. Each of the two models prefers” own traffic mode. Four origin-destination matrices are product of the entire modal split process. One pair of the OD matrices is for railway freight transport and the other pair is for road freight transport. Corresponding matrices are put together for the next step of the modelling – assignment of mode specific freight demand matrices to model transport network, by the generalized costs method.In the part of transport model supply, freight trains and their attributes (type, capacity, speed, and time intervals) were put in the model. The model can simulate both direct cargo transport from origins to destinations and cargo movement by trucks, manipulation trains and relational trains in order to optimize transport of carried goods.

Forecasting Net Induced Travel Demand for Optional Trip Purpose by Trip Production Model with Accessibility

Predicting net induced demand can be helpful for better feasibility study before investigating transportation infrastructure. The net induced demand can be defined as newly generated trips, but not shifted trips, by some improvements in transportation systems. This study only focused on the net induced demand rather than any other concepts belong to the general term “induced demand”. The net induced demand is quite related to the trip generation model because it refers to purely increased trips in frequency due to transportation improvement that would not be occurred without the improvement. The study analyzed trip production models with accessibility measure for each traffic analysis zone. Multiple linear regressions were adopted as the production models in the study. The study’s basic hypothesis on trip generation was that number of generated trips per day will be statistically different at different level of accessibility for each production zone. In the estimated regression model, the study considered that the parameter for accessibility, as an independent variable, can be interpreted as unit change of the net induced demand by changes of a unit of accessibility measure. This study tried to show empirically that the number of production trips was varied depending on level of accessibility for a specific traffic analysis zone with real data of household survey. The study used the 2010 household travel survey data of Daegu Metropolitan Area in South Korea with 68,398 traveler’s data. As the results, the regression analysis showed that accessibility for a specific zone significantly affected net induced demand especially for optional trip purpose. The study also showed the way how estimating quantitatively the increased net induced demand with an example.

FORMULATING A TRIP PRODUCTION PREDICTION MODEL FOR THE RESIDENTIAL LAND USE IN MEDIUM SIZED TOWNS OF KERALA

Formulating of suitable travel demand forecasting m odels are the key elements for the development of a long-range transportation plan. This paper focuses its study on the formulati on of a trip production model using multiple regres sion technique for the residential land use in medium sized towns of Kerala. The trip production model estimated the number of trips that will be produced from the residential land use of these medium sized towns. T he Perinthalmanna, Tirur, and Ponnani towns of Kera la were selected as the study area based on certain criteria. The data on demogra phic and socio-economic characteristics these areas were collected through the administration household interviews. The quantitati vely and qualitatively analysis of the results were done using the correlation and multiple regression analysis. The study showed that the regression model with the independent variable s such as the percentage of automobile availability, percentage of persons empl oyed, percentage of students and percentage of pucc a type of dwelling with R 2 and Adjusted R 2 value of 0.878 and 0.859 respectively gives a bett er estimate of the trips produced. The model accura cy was also tested by checking the validity of the assumptions employed imultiple regression technique. Since most of the work related to traffic and transportation planning requires an effective frame work for the analysis of the present and future tra vel demand pattern, a model forecasting the trip produced based on the above me ntioned characteristics shall be advantageous for a speedy travel demand forecast.

Multiple Classification Analysis for Trip Production Models Using Household Data: Case Study of Patna, India

Trip production models in India have traditionally been developed using simple regression analysis with population at census ward level as the independent variable. The current study developed multiple classification analysis (MCA) tables at the household unit level for trip production considering several other important variables that affect trip production. The city of Patna in India is taken as the case study, and its household data is considered for analysis. Households are further disaggregated into slum (low income) and nonslum households, and scenarios within them are considered for analysis. Nomograms are developed based on MCA tables and can be used to estimate trip rate values for other cities with similar socioeconomic characteristics. Slums and nonslum households revealed similar trip rate patterns, with the household size having the maximum impact on trips produced. Income and vehicle ownership show little effect on trip production rates. Also, MCA and linear regression models resulted in similar trip rates and accuracy. Hence, MCA is recommended to be adopted because it gives a more disaggregated output that is more stable over time and is easier to use because the values are readily available without further analysis.

Trip generation production model for North zone of Fallujah city

Fallujah is one of Iraqi cities which need a comprehensive transport plan for its increased urban development and have increasing in umber of moving vehicles, caused an increasing in population with changes in the standard of living and daily trips especially at the central area, which causes jamming in transportation network. For the purpose of study, the study area in the north sector of Fallujah city is considered as one sector consists of (10) zones containing about (11954) dwelling units. Home interview survey was made of random sample which represent 5% of the study area population, and data collected through this zone. Data was related to socioeconomic characteristics of the population. The collected information solve by using the method multi – linear regression by a package(SPSS). The total persons trips per dwelling unit and various purposes were investigated. A trip production model in Fallujah city, through find relationship between socioeconomic characteristics (car ownership, family income, employee, family no., population, family size, time and cost of trip, distance to the center and area of household unit). It was found that the number of families is the most influential variable to trip production model.