Spatial Mismatch in the Toronto Census Metropolitan Area : Examining the Commuting Patterns of Immigrants

The spatial mismatch hypothesis argues that the geographic separation between jobs and housing has an adverse effect on the employment outcomes of ethnic minorities. This research paper tests this assumption for immigrant populations in the Toronto Census Metropolitan Area through mapping, cross tabulation and a generalized ordered logit model to determine whether immigrants are at a disadvantage in both the labour market and in terms of commuting distance when compared with Canadian born residents. The results of this study suggest that immigrants are more likely to live over 5 kilometres away from work and that they experience more difficulty in negotiating longer commutes due to higher unemployment rates, lower median household incomes and a greater reliance on transit. In contrast, Canadians are more likely to make daily commutes of over 15 kilometres, however, they are often more capable than immigrants of travelling these increased distances.

Spatial Mismatch in the Toronto Census Metropolitan Area : Examining the Commuting Patterns of Immigrants

The spatial mismatch hypothesis argues that the geographic separation between jobs and housing has an adverse effect on the employment outcomes of ethnic minorities. This research paper tests this assumption for immigrant populations in the Toronto Census Metropolitan Area through mapping, cross tabulation and a generalized ordered logit model to determine whether immigrants are at a disadvantage in both the labour market and in terms of commuting distance when compared with Canadian born residents. The results of this study suggest that immigrants are more likely to live over 5 kilometres away from work and that they experience more difficulty in negotiating longer commutes due to higher unemployment rates, lower median household incomes and a greater reliance on transit. In contrast, Canadians are more likely to make daily commutes of over 15 kilometres, however, they are often more capable than immigrants of travelling these increased distances.

Using a General Ordered Logit Model to Explain the Influence of Hotel Facilities, General and Sustainability-Related, on Customer Ratings

The hotel market has become extremely competitive over the past years. Hotels try to differentiate themselves through their services and facilities. To make the best choice when searching for accommodation, guests increasingly use rating systems of booking sites. Using an ordered logit model (OLM), we identify, in our study, a sample that comprises of 635 hotels from Romania. These are the hotel facilities that significantly influence customer review scores (as an expression of customer satisfaction) on booking.com, the most widespread rating system. We also identify whether their impact on intervals of satisfaction levels vary. Some explanatory variables invalidate the Brant test for proportional odds assumption. Thus, for the final estimates, we use a generalized ordered logit model (GOLOGIT). The results show that food-related facilities, restaurants, and complimentary breakfasts, are very significant for customer ratings. Relevant hotel common facilities are the pool and parking spaces, while for the room—the flat-screen TV. It is interesting to note the negative influence of pets, which seem to disturb other tourists. In the sustainability category, only facilities for disabled people and electric vehicle charging stations are relevant.

Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis

This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its strength is demonstrated by the existence of significant spatial correlation and its better model fit and more reasonable estimation results than the counterparts of a generalized ordered logit model. The estimation results show that an increase in the precipitation is associated with decreases in the probabilities of light and severe crashes, and an increase in the probability of medium crashes. Additionally, driver type, vehicle type, vehicle registered province, crash time, crash type, response time of emergency medical service, and horizontal curvature and vertical grade of the crash location, were also found to have significant effects on the crash severity. To alleviate the severity levels of crashes on rainy days, some engineering countermeasures are suggested, in addition to the implemented strategies.

Perceived happiness, perceived trust and perceived income levels: The case of the reunified Germany

This study explored the possible impact of perceived income on individual (perceived) happiness in Eastern and Western Germany in relation to perceived trust and four socio-economic variables, namely gender, age, marital status and employment status. To examine the relationship between these variables, a generalized ordered logit model was applied using the World Values Survey data. Bootstrapping and marginal effects were used to obtain a more robust model. The findings provided insights regarding the impact of perceived income and perceived trust on individual (perceived) happiness in both regions after reunification. Perceived income had a positive effect on all happiness categories in both regions. Perceived trust had a stronger positive impact on individual happiness than that of perceived income, although its significance varied across individual (perceived) happiness categories. Analysis of marginal effects revealed differences between the base models.

Understand e-bicyclist safety in China: Crash severity modeling using a generalized ordered logit model

A thorough crash modeling effort was made to examine e-bicyclists’ injury severity, using a generalized ordered logit model, which can capture the ordinal nature of injury severity and allow for heterogeneity across observations. A number of factors associated with injury severity of e-bicyclists are identified, including older e-bicyclists, heavy truck involved, e-bicyclist at fault, e-bicyclist turn left, e-bicyclist cross the road, driver turn right, industrial area, weekday, and tree separation. Safety countermeasures and interventions are thus proposed based on the modeling results, including developing educational programs for specific age groups (e.g. older e-bicyclists, female e-bicyclists, and inexperienced drivers), launching safety campaigns, improving geometric design and traffic control in low-developed area, curve roads, and signalized intersections. Moreover, some interesting research topics are also suggested, such as examining head-on e-bike crash mechanism, crash mechanism between e-bicyclists and heavy trucks and motorcycles, and safety effects of different separation treatments on e-bicyclists’ injury severity from kinetic and kinematic perspectives.