Predicting bus trip rates across Indian cities: a generalised ANN–MLR comparative study

Trip generation is the first step of transportation planning and trip distribution, stochastic separation, and assignment of traffic are done at the end of the finding of the values. In the scope of this study, the three provinces were selected from developed, developing, and non-developed provinces in Turkey and the field of the study was determined. In the determined provinces, trip generation models were generated according to the household characteristics by making household transportation surveys in the determined provinces. The aim of this study is to determine the trip generation of provinces of different categories according to the household characteristics related to the size and development situation of the provinces and to determine the factors affecting trip generation in these provinces. Besides, it is decided which one of the multiple linear, poisson, and negative binomial regression models is more appropriate for trip generation and Artificial Neural Networks model is compared with the most significant regression model. At the end of the analyses, Artificial Neural Network models have shown better performance among three different data sets. As a result, Artificial Neural Networks was proposed as an alternative method in the trip generation of the provinces.

Application of adaptive neuro-fuzzy inference system in modelling home-based trip generation

Most trip generation models are insensitive to the effects of transportation demand management (TDM) strategies. To evaluate the potential effectiveness of TDM solutions, transportation professionals must rely largely on the results of case studies, which cannot be generalized for all urban areas. To evaluate TDM strategies in a context that is sensitive to the unique characteristics of each urban area, TDM strategies should be incorporated into regional travel demand models. Five TDM strategies affecting trip generation rates are examined: telecommunications, alternative work schedules, on-site amenities, pricing strategies, and land use strategies. To analyze these strategies, data from the Puget Sound Transportation Panel (PSTP) were used. Variables derived from the PSTP data that may help explain the impacts of these strategies were evaluated for significance in trip generation models for several home-based and non-home-based trip purposes. The trip generation models were specified using Poisson and negative binomial regression techniques. After the models were estimated, the significance of the variables representing the impacts of TDM strategies was analyzed. Many of the TDM variables were indeed significant in the trip generation models; however, in some cases, the significance of the variables can be attributed to factors such as trip chaining, which does not describe the effects of TDM strategies. Additional research is needed to fully determine the effects of trip chaining on the variables examined in this study, and additional data could enable the development of variables that more accurately describe the effects of TDM strategies.

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.

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.

Trip generation is the first step model in the transportation forecasting process. In this study, there are six developed models. Such as the general trip generation model, work trip generation model, educational trip model, shopping trip model, recreational trip generation model, and social trip generation model by using an artificial neural network. The mail-back survey method was adapted to collect pilot data, and household interview surveys were adapted to collect main trips and socio-economic household data. A sample size selected by stratified random distribution method from each TAK of Wolayta-Soddo city is 718 households. And the essential data is collected from all the households that enable to development of different models. The results of the developed models are to identify the possible modifications required in the study area to improve the transportation facilities and policy decisions furthermore for the economic development of the country in general and state in particular.

Many public transport drivers in City of Dili do not apply various regulations from the government. Located on Becora-Baidi, Becora-Bidau, and Tasi Tolu-Bidau Road. This causes congestion and traffic accidents, and impact on economy. The purpose of study is to determine the trip generation model and its factors, city transportation service performance, and how to formulate TDM concept in public transport. The survey conducted was daily volume of public transport, questionnaires, and interviews 2020. Looking for value of angkot generation with instrument test and multiple regression (IBM SPSS 22). To analyze services used parameters and Severy Index. Then, concept of Transportation Demand Management (TDM) with Guttman Scale. After analyzing, the trip generation value, Y = -1,3920 + 0,0275.X1 + 0,4958.X2 + 0,1734.X3 – 0,0601. X4 - 0,0657.X5 - 0,0001.X6 - 0,0193.X7 - 0,7670.X8 + 0,8801.X9 + 0,6721.X10 + 0,1058.X11, positive value is factor that most influences the respondent's decision to trips using public transportation: gender (X1), age (X2), job (X3), duration of trip (X9), number of passengers (X10), and waiting time for public transportation (X11). However, in service level is still “low” category. Then, for TDM concept gets 87% in 51%-99% range. Means that angkot users agree, if TDM concept is propose to the government of Dili City, and public transportation will be better in the future. Keywords: Trip Generation, Public Transportation Mode, Public Transportation Services, Transportation Demand Management, TDM.

Advancing smartness of traditional settlements-case analysis of Indian and Arab old cities

Determinants of urban mobility in India: Lessons for promoting sustainable and inclusive urban transportation in developing countries

The trip generation model (TGM) is the first step in transportation forecasting, this is useful for estimating travel demand because it can predict travel from or to a particular land use. Typically, the analysis focuses in residential trip generation as a function of the social and economic attributes of households, but nonresidential land use suggests others variables. Travel generator poles such as: Private school, Semi-private and Public, have not been studied in Venezuela. The TGMs that shows the Institute of Transportation Engineers (ITE), EE.UU, are used typically and could be not appropriate. By using stepwise regression and transformation of data, high correlation coefficients and substantial improvements in the variability of data from several schools they were found. The trip generation rates (TGRs) by transportation mode: walking, motorcycle, public transport and cars, can be compared and be included in the Ibero-American Network of travel attractors poles.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3410

India's Smart Cities Mission: The Beginning of Corporate Takeover of the Nation State

Predicting the concrete breakout strength of an expansion anchor embedded in short carbon fiber-reinforced concrete (SCFRC) is challenging due to the nonlinear and heterogeneous nature of fiber–matrix interaction. This study develops an Artificial Neural Network (ANN) model to estimate the breakout capacity of a single expansion anchor installed in SCFRC. Experimental data from 48 cases covering variations in compressive strength, tensile strength, fiber volume fraction, and fiber length were used to train and validate multiple ANN architectures in MATLAB’s Regression Learner. A 4-4-1 trilayered ANN with Rectified Linear Unit (ReLU) activation and 5-fold cross-validation achieved the most reliable performance, yielding R2 values of 0.6726 (validation) and 0.9376 (test), with correspondingly low RMSE, MAE, and scatter index (SI < 0.1). SHAP-based sensitivity analysis identified tensile strength as the dominant predictor, contributing 70.78% to model output influence. ANN predictions were compared with the Concrete Capacity Design (CCD) model adopted by ACI and the National Structural Code of the Philippines (NSCP) and a multiple linear regression (MLR) model, showing that while the ANN is not the most precise model, it provides acceptable accuracy and captures nonlinear concrete breakout behavior more effectively than linear approaches. Results demonstrate that the ANN framework offers a viable data-driven tool for estimating concrete breakout strength in SCFRC anchorage systems.

This research investigates the influence of socio-demographic attributes on students’ trip generation on the campus of Kwame Nkrumah University of Science and Technology (KNUST). A cross-sectional and mixed-method approach were employed, collecting primary data from 113 students through an online questionnaire. Multiple linear regression analysis and ordinal logistic regression explored the relationships between generated trips and socio-demographic attributes, including age, household size, vehicle ownership, educational level, income, and trip purpose. Age and household size were significant influencers of trip generation in the multiple linear regression. The ordinal logistic regression showed that, males are 2.40 times more likely to generate higher trip counts than females. Individuals aged 16–21 are 0.11 times less likely to produce more trips compared to older age groups. Single-occupant households have a 0.09 times lower likelihood of generating more trips than households with two or more occupants. However, individuals attending morning and afternoon lectures, only morning lectures, and morning and evening lectures among other activities have substantially higher odds of producing more trips, with odds ratios of 22.14, 15, and 567, respectively, compared to those exclusively attending evening lectures. Recommendations include improving shuttle services, pedestrian infrastructure, and promoting sustainable mobility solutions tailored to student demographics.

It is key index of cotton yarn quality such as cotton yarn strength and so on. It can well control cotton yarn quality by predicting yarn strength and so on. Generally, it is normal used to predict yarn strength such as Multiple Linear Regression (MLR), Support Vector Regression (SVR) and shallow Artificial Neural Network (ANN). Because the processing of cotton yarn production has time sequence, the paper proposes a new deep neural network, it is artificial Recurrent Neural Network (RNN). It used 1800 sets of data to train RNN, SVR and ANN. It tested RNN, MLR, SVR and ANN with 200 sets of data. Experimental results show that the Recurrent Neural Network (RNN) is the best accuracy among these four algorithms.

Traditionally trip generation models have been estimated with linear-regression structures even though this methodology does not recognize the nonnegativity and integer nature of the trips. Although the theoretical superiority of count-data models as an alternative approach is well recognized, the empirical benefits of such models have not been well established. In that context, the intent of this study is to undertake a comparative analysis of four different econometric structures for trip generation models. The structures are compared across three different trip purposes with significantly different distribution patterns. The models are estimated by using 2001 U.S. National Household Travel Survey data and are applied to samples from the 2009 National Household Travel Survey data. Predictive validations indicate that the ordered probit models are able to replicate the trip generation patterns better than linear-regression, log-linear, and negative-binomial models for all three trip purposes. The negative-binomial model performs reasonably well in the case of the non-home-based trips, which have a monotonically decreasing distribution pattern. The negative-binomial and the log-linear models have comparable mean errors for disaggregate predictions. Overall, this study recommends the use of ordered probit models as a substitute for the traditional linear-regression models.