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.

Developing vehicular and non-vehicular trip generation models for mid-rise residential buildings in Kelowna, British Columbia: Assessing the impact of built environment, land use, and neighborhood characteristics

This study develops vehicular and non-vehicular trip generation models for mid-rise, multi-family residential developments. A comparative analysis of observed and Instiutue of Transportation Engineers (ITE) trip rates suggests that ITE rates consistently overestimate. A latent segmentation-based negative binomial (LSNB) model is developed to improve the methodology for estimating vehicular and non-vehicular trips. One of the key features of an LSNB model is to capture heterogeneity. Segment allocation results for the vehicular and non-vehicular models suggest that one segment includes suburban developments, whereas the other includes urban developments. Results reveal that a higher number of dwelling units is likely to be associated with increased vehicle trips. For non-vehicular trips, a higher number of dwelling units and increased recreational opportunities are more likely to increase trip generation. The LSNB model confirms the existence of significant heterogeneity. For instance, higher land-use mix has a higher probability to deter vehicular trips in urban areas, whereas trips in the suburban areas are likely to continue increasing. Higher density of bus routes and sidewalks are likely to be associated with increased non-vehicular trips in urban areas, yet such trips are likely to decrease in suburban areas. An interesting finding is that higher bikeability in suburban areas is more likely to increase non-vehicular trips. The findings of this study are expected to assist engineers and planners to predict vehicular and non-vehicular trips with higher accuracy.

A Trip Generation Model for a Petrol Station with a Convenience Store and a Fast-Food Restaurant

A trip generation manual and database are important for transportation planners and engineers to forecast new trip generation for any new development. Nowadays, many petrol stations have fast-food restaurant outlets. However, this land use category has yet to be included in the Malaysian Trip Generation Manual. Therefore, this study attempted to develop a new trip generation model for the new category of “petrol station with convenience store and fast-food restaurant”. Significant factors influencing the trip generation were also determined. Manual vehicle counts at the selected sites were conducted for 3 h during morning, afternoon and evening peak hours. Regression analysis was used in this study to develop the model. A simple trip generation model based on the independent variable number of restaurant seats showed a greater value for the coefficient of determination, R2, compared with the independent variables gross floor area in thousand square feet and number of pumps. The multivariable trip generation model using three independent variables generated the highest R2 among all of the models but was still below a satisfactory level. Further study is needed to improve the model for this new land use category. We must ensure more accuracy in trip generation estimation for future planning and development.

Transportation Network Spatial Analysis to Measure Pedestrian Suitability. The Case of Hilly Cities

The current climate and environmental emergency, together with the growing traffic congestion and pollution in urban areas, make mobility and its sustainability a priority in current transport policies. It is essential to change citizen’s behaviour in order to increase the use of less pollutant, economic and egalitarian transport modes, such as walking, combining it with other public transport modes. For this change to happen, it is necessary to provide feasible alternatives to private cars, namely through the offer of high-quality pedestrian infrastructures, adapted to the cities’ specific characteristics and their citizen’s needs. These aspects are particularly important in hilly cities, where traveling by foot requires an additional effort. The present study aims to contribute to the promotion of soft mobility in hilly cities by creating a support instrument to assess the potential of existing pedestrian infrastructures. Three variables are considered in the analysis: trip generation poles, population density and pedestrian network characteristics, with especial consideration of slopes. These variables were processed with spatial and network analysis tools available in Geographic Information Systems (GIS) and combined using a multi-criteria decision analysis to obtain a measure of the pedestrian infrastructure potential. The identification of areas with high pedestrian potential supports the definition of priority intervention programs on the public space and a better allocation of human and financial resources. The proposed instrument was validated through its application to a case study, the hilly city of Covilhã (Portugal). From the results obtained it is possible to conclude that the variable with more impact on the pedestrian infrastructure suitability value is the location of the trip generation poles, influenced by the footpaths’ longitudinal slopes. The instrument also allowed to identify the city’s main expansion areas, corresponding to places presenting a good pedestrian potential and relatively low values of population density.

Evaluasi Kinerja Simpang Bersinyal Jl. Arief Rahman Hakim-Jl. Klampis Anom-Jl. Klampis Jaya Surabaya

The intersection between Arief Rahman Hakim Street, Klampis Anom Street, Klampis Jaya Street is a signalized intersection with a high-density level due to trip generation and attraction. The low level of knowledge and culture of traffic is the phenomenon of vehicle congestion at the intersection involving many actors as the cause. This study aimed to determine the traffic performance of the signalized intersection at the Arief Rahman Hakim Street, Klampis Anom Street, Klampis Jaya Street at this point as well as to predict the traffic performance of the signalized intersection on the Arief Rahman Hakim Street,Klampis Anom Street, Klampis Jaya Street in 2024. The results of the performance evaluation of the signalized intersection on the Arief Rahman Hakim Street, Klampis Anom Street, Klampis Jaya Street was for the standard degree of saturation received in 2021; it was 4.33 in the north, 0.23 in the south, 3.17 in the east, and 2.98 in the west. In 2024 it will be 4.96 in the north, 0.36 in the south, 3.49 in the east, and 3.41 in the west. In 2024 the rate of population growth and the need for private vehicles will increase greatly.

Exploring Socio-Demographic and Urban Form Indices in Demand Forecasting Models to Reflect Spatial Variations: Case Study of Childcare Centres in Hobart, Australia

This study investigated whether indices for socioeconomic, demographic and urban form characteristics can reflect the overall effect of each category in a demand forecasting model. Regression equations were developed for trip generation of the land use of long day care centres (LDCC) in the metropolitan region of Hobart, Australia, to estimate the morning peak hourly private car trip generation of the centres. The independent variables for the model were functions of socioeconomic, demographic and urban form related indices, while the dependent variable was private car trip generation per number of staff or children. Findings show that using indices for socioeconomic, demographic and urban form characteristics enhances overall model performance, while the models based on the commonly used method for estimating trip generation present acceptable results in just some specific sites. The use of socioeconomic, demographic and urban form indices can reflect differences in these characteristics across suburbs when estimating trip generation.