A structural equation model based analysis of non-workers’ activity-travel behaviour from a city of a developing country

This paper presents an empirical analysis of non-workers’ activity-travel behaviour from Bangalore city, India. The paper builds a causal model—to describe the relationships among socio-demographics, activity-participation, and travel behaviour of non-workers—following structural equation modelling methodology. The results indicate that in-home maintenance activity-duration drives the time allocation decisions of non-workers. The model also shows the presence of ‘time-budget’ effects i.e., excess travel time cuts into in-hhome discretionary activity duration, implying the trade-off between daily travel time and in-home discretionary activity duration. The out-of-home activity durations of non-workers are found to be insensitive to travel time—an important finding of this research. The model also suggests that mixed residential development reduce travel distance and indirectly contribute to more trips. An indirect effect of mixed residential development on daily travel distance offsets the direct effect, which leads to a limited total effect of this variable on travel distance. The basic model was expanded further by separating the time spent on others’ activity (children and elders) from in-home maintenance activity duration. The stable model reveals that the time spent on others’ activity also influences in-home and out-of-home activities, and travel behaviour. This indicates that the time spent on others’ activity is an important time allocation of its own.     

A dynamic modeling approach to investigate impacts to protected and low-income populations in highway planning

Environmental justice (EJ) assessment has traditionally focused on identifying distributive effects to protected populations. Federal and State highway improvement programs have been established to stimulate economic development for these populations. While this issue has long been recognized as part of EJ initiatives, no quantitative comparisons of highway construction impacts on protected populations have been reported in the literature. This paper presents a dynamic modeling approach to investigate impacts to protected and low-Income populations in highway planning using an integrated Geographic Information System (GIS) and Genetic Algorithms (GAs) optimization framework. Using census and county level parcel data, the model integrates various socioeconomic factors into a GIS while generating highway alignments using GAs. Examples using county level census data from North Carolina are demonstrated to test the sensitivity of generated highway alignments with constrained distances from protected populations. The results indicate that it is important to consider local social and economic effects, in addition to regional planning objectives when measuring the effectiveness of feasibility studies associated with highway construction. Within the proposed modeling framework attention is directed on various EJ initiatives, such as environmental health and safety laws in minority and low-income areas. The model would help planners, designers, and policy-makers understand the intricate interrelationships among local communities, while facilitating more scientific and economically equitable planning for highway construction projects.