Estimating the HARA Land Use Model for Housing Planning based on Hedonic Price Analysis

HARA is a land-use model that uses a search algorithm to find the optimal spatial allocation of new housing demands in an urban plan area. In the model, the plan area is represented as a grid of cells. A core element of the algorithm is a function that is used to evaluate the value of a cell for each possible land-use given its location. The value function is specified as the net value of a (housing) development given the land costs, the construction costs, and the market value of the development at a location. Specified in that way, the solution generated represents an optimum as well as a market equilibrium (maximum net value for developers). A critical prerequisite for this is, however, that the value-function is specified such that it accurately represents buyers’ willingness-to-pay for dwelling and location characteristics in the housing market. In this paper, we show how the value function can be estimated using hedonic price analysis. The analysis is carried out based on a large housing transaction data set focusing on two medium-sized cities in The Netherlands combined with detailed land-use data of these areas. Although a full set of land-use types is taken into account, special attention is paid to the classification of urban green space, given the purpose to analyze scenarios for developing urban green space. The results indicate that land-use effects on housing prices differ considerably between housing types as well as city. We conclude therefore that it is important in the estimation of land-use models to take the specific local conditions of housing markets and housing segments into account

Multi-state supernetworks: recent progress and prospects

Abstract:Supernetworks have long been adopted to address multi-dimensional choice problems, which are thorny to solve for classic singular networks. Originated from combining transport mode and route choice into a multi-modal network, supernetworks have been extended into multi-state networks to include activity-travel scheduling, centered around activity-based models of travel demand. A key feature of the network extensions is that multiple choice facets pertaining to conducting a full activity program can be modeled in a consistent and integrative fashion. Thus, interdependencies and constraints between related choice facets can be readily captured. Given this advantage of integrity, the modeling of supernetwork has become an emerging topic in transportation research. This paper summarizes the recent progress in modeling multi-state supernetworks and discusses future prospects

Capturing preference heterogeneity of truck drivers’ route choice behavior with context effects using a latent class model

This paper investigates heterogeneity in truck drivers’ route choice preferences. A latent class model is estimated to identify heterogeneous segments of drivers. A stated choice experiment designed for identifying route choice behavior of truck drivers provides the data for model estimation. The effects of road pricing and environmental bonus are examined considering context dependency. Results reveal that size of truck is a significant segmentation variable of preferences for route attributes. Drivers of light trucks care more about congestion than drivers of heavy trucks, and are highly sensitive to road pricing and slightly sensitive to a road bonus. Drivers of heavy trucks are more sensitive to road category and urban area than drivers of light trucks, and are insensitive to bonus and slightly sensitive to pricing

How physical home workspace characteristics affect mental health:A systematic scoping review

BACKGROUND:During the 1990s, voluntary teleworking became more grounded, because of expected advantages as increased productivity and comfort. However, COVID-19 obliged employees to work from home (WFH), even in unsuitable houses, which might have reduced their mental health. A holistic overview of methods and measures of the physical home-workspace characteristics and mental health is currently lacking. Insights in the potential influence of the physical home-workspace on mental health are also not yet holistically examined.OBJECTIVE:The aim of this study is to provide insights in previously studied relationships between the physical home-workspace and mental health and to identify measures for both using a systematic scoping review.METHODS:This study used the PRISMA method to systematically review existing literature.RESULTS:Most studies focussed on noise, acoustics, and privacy, in relation to productivity, concentration, and sleep quality. Only a few studies used objective measures for physical home-workspace characteristics.CONCLUSION:The list of relevant measures can be used by academics to examine relationships between the home-workspace and mental health further. Workplace managers can use it to help employees in optimizing their home-workspace

Level-adaptive sound masking in the open-plan office:how does it influence noise distraction, coping, and mental health?

In the open-plan office, intelligible speech is a major distractor, reducing cognitive performance. Sound masking emits an electronic broadband sound to increase the background sound level in a controlled manner. To date, most studies on sound masking are short-term laboratory studies that do not consider aspects of mental health. The current study aims to evaluate, using a longitudinal field study with intervention (N=42) and control (N=41) floors at two organizations, whether leveladaptive sound masking could reduce intelligible speech and increase mental health, while being exposed to level-adaptive sound masking for two to three months. The study consists of two subjective measurements, prior- and post-intervention, using survey questions on coping strategies, noise distraction, and ten mental health indicators (short-and long-term consequences). The increase in background noise level (at company 1 from 28.7dB(A) to 41.9dB(A) and at company 2 from 32.4dB(A) to42.6dB(A)) at both organizations significantly reduced intelligible speech distraction. Short-term mental health aspects were rated more positively, and level-adaptive sound masking also reduced the frequency with which people put on radio or headphones to cope with noise

Car Allocation between Household Heads in Car Deficient Households: A Decision Model

This paper considers car allocation choice behaviour in car-deficient households explicitly in the context of an activity-scheduling process, focusing on work activities. A decision tree induction method is applied to derive a decision tree for the car allocation decision in automobile deficient households using a large travel-and-activity diary data set recently collected in the Netherlands. The results show a satisfactory improvement in goodness of fit of the decision tree model compared to a null model. Overall, the probability of males getting the car for work is considerably higher than that of female in many condition settings. However, activity schedule, spatial and socio-economic variables appear to have an influence as well. An analysis of impacts of condition variables on car allocation decisions reveals that socio-economic variables have only a limited impact, whereas attributes of the transportation and land-use system have a relatively big impact. The propensity of men driving a car to the work place is higher than that of women. However, the relative accessibility of the work location by bike compared to car appears to have a relatively large influence on who gets the car for work. Household income and presence of children also appear to have significant effects

Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment

In this paper, we report the results of a stated choice experiment, which was conducted to examine truck drivers’ route choice behavior. Of particular interest are the questions (i) what is the relative importance of road accessibility considerations via-a-vis traditional factors influencing route choice behavior, (ii) what are the influences of particular personal and situational variables on the evaluation of route attributes, (iii) how sensitive are truck drivers for possible pricing policies, and (iv) is there a difference in impact if environmental concerns are framed as a bonus or as a pricing instrument. The main findings indicate that road accessibility characteristics have a substantial impact on route preferences which is of the same order of magnitude as variation in travel times. This suggests that provision of adequate travel information in itself can be an effective instrument to prevent negative externalities of good transport associated with shortest routes. Furthermore, the results indicate that truck drivers/route planners when choosing a route are relatively sensitive to road pricing schemes and rather insensitive to environmental bonuses

Shopping Context and Consumers' Mental Representation of Complex Shopping Trip Decision Problems

Depending on the shopping context, consumers may develop different mental representations of complex shopping trip decision problems to help them interpret the decision situation that they face and evaluate alternative courses of action. To investigate these mental representations and how they vary across contexts, the authors propose a causal network structure that allows for a formal representation of how context-specific benefits requirements affect consumers’ evaluation of decision alternative attributes. They empirically test hypotheses derived from the framework, using data on consumers’ mental representations of a complex shopping trip decision problem across four shopping contexts that differ in terms of opening hour restrictions and shopping purpose, and find support for the proposed structure and hypotheses