Assessing Socio-Demographic and Urban Form Changes of Sprawl Retrofitting Projects in the United States

Growing population and urbanization have escalated the inclination in today’s societies to live in the suburbs. In the United States, urban development has had a suburbanization pattern since World War II. People living in such areas must use their cars to reach their destination and commute to work. Sprawl retrofitting is a term introduced by planners and researchers to overcome urban sprawl\u27s negative impacts on mobility, transportation, and the environment. This approach is used to densify and change the built environment to make daily trips easier, shorten daily travels, and enhance pedestrian activity in places dealing with sprawl. Sprawl retrofitting has been more frequently researched over the past few decades. It has attracted a great deal of attention among planners to utilize different tools in urban design and city planning to overcome the fast-growing sprawl. However, there are not many studies examining the aftermath. This study attempts to analyze and compare the changes after sprawl retrofitting projects\u27 completion. By using national demographic data and built environment changes, such as population density, block size fluctuations, and green space development, this research examines the difference in changes before and after the projects. The results are based on 59 sprawl retrofitting case studies throughout the United States chosen by the criteria, including size and completion date of the projects and other built environment factors, such as land use, that defined each project site. Results show an increase in population, job density, and the density of intersections in the project sites. By comparing the results, this study will inform future research about the implications of sprawl retrofitting and the current impacts they can have on the population and the built environment

Estimating local car ownership models

Many studies in the transport demand literature have shown that income is an important factor in determining how many cars a household owns. When the models used to measure the strength of this relationship are estimated on cross-sectional data, they typically yield one overall value as the estimate. Local circumstances will, however, vary. This paper illustrates the use of the Geographically Weighted Regression technique to estimate the individual strength of this relationship for each of the United Kingdom electoral wards. Use of this type of model enables a wards’ income elasticity to be based on both the local estimate of the strength of this relationship and the current local level of car ownership. How the use of this local elasticity changes future forecasts of the size of the vehicle fleet is illustrated

Configuration of SafetyAnalyst Software for Efficient and Effective Safety Management Izadpanah Configuration of SafetyAnalyst Software for Efficient and Effective Safety Management Configuration of SafetyAnalyst Software for Efficient and Effective Safet

Abstract Many road authorities recognize the challenges associated with a reactive approach in road safety and have adopted a proactive and systematic approach in their road safety initiatives. However, automation is a key challenge road authorities face in implementing an efficient and effective traffic safety management program. In the summer of 2009, American Associated of State Highway and Transportation Officials released the first version of SafetyAnalyst software, and in 2010 published the Highway Safety Manual. The Highway Safety Manual provides road safety knowledge and tools in a practical form to facilitate improved decision making based on safety performance. The focus of the Highway Safety Manual is to provide quantitative information for decision making. SafetyAnalyst software incorporates methodologies set forth in the Highway Safety Manual for road safety management in computerized analytical tools. These tools support the identification of safety improvement needs and the decision making process for developing a system-wide program of safety improvement projects

Use of Advanced Techniques to Estimate Zonal Level Safety Planning Models and Examine their Temporal Transferability

Historically, the traditional planning process has not given much attention to the road safety evaluation of development plans. To make an informed, defensible, and proactive choice between alternative plans and their safety implications, it is necessary to have a procedure for estimating and evaluating safety performance. A procedure is required for examining the influence of the urban network development on road safety, and in particular, determining the effects of the many variables that affect safety in urban planning. Safety planning models can provide a decision-support tool that facilitates the assessment of the safety implications of alternative network plans. The first objective of this research study is to develop safety planning models that are consistent with the regional models commonly used for urban transportation planning. Geographically weighted Poisson regression (GWPR), full-Bayesian semiparametric additive (FBSA), and traditional generalized linear modelling (GLM) techniques are used to develop the models. The study evaluates how well each model is able to handle spatial variations in the relationship between collision explanatory variables and the number of collisions in a zone. The evaluation uses measures of goodness of fit (GOF) and finds that the GWPR and FBSA models perform much better than the conventional GLM approach. There is little difference between the GOF values for the FBSA and GWPR models. The second objective of this research study is to examine the temporal transferability of the safety planning models and alternative updating methods. The updating procedures examine the Bayesian approach and application of calibration factors. The results show that the models are not temporally transferable in a strict statistical sense. However, relative measures of transferability indicate that the transferred models yield useful information in the application context. The results also show that the updated safety planning models using the Bayesian approach predict the number of collisions better than the calibration factor procedure.Ph

Analysis of Transit Safety at Signalized Intersections in Toronto, Ontario, Canada

The objective of the research presented here was to capture the relationship between public transit service configurations and the overall safety performance of signalized intersections in Toronto, Ontario, Canada. Negative binomial regression models were developed for this purpose for three sets of dependent variables: transit-involved collisions at signalized intersections with both regular traffic and transit service operations; total collisions at the same signalized intersections; and total collisions at all signalized intersections, including those without transit service. The models showed that annual average daily traffic, public transit and pedestrian traffic volumes, turn movement treatments, and transit features (such as public transit stop location, mode technology, and availability of transit signal priority technology) all have significant associations with public transit–related collisions at signalized intersections. Intersections with public transit service also tend to experience more collisions than otherwise similar intersections. The research helps to address intersection safety from two perspectives: (a) it enables public transit providers to consider safety implications in the service planning process, and (b) it enables transportation departments to assess signalized intersection safety for various configurations of surface transit services by taking into consideration their interaction with the general traffic stream

Models for Safety Analysis of Road Surface Transit

A study was done to explore the development of zonal- and arterial-level collision prediction models that incorporate characteristics applicable to urban transit planning. A generalized linear modeling approach with a negative binomial regression error structure was employed by using a data set from Toronto, Ontario, Canada. The zonal-level models indicate that vehicle kilometers traveled, bus or streetcar kilometers traveled, arterial road kilometers, bus stop density, percentage of near-sided stops, and average posted speed have significant associations with occurrences of transit-involved collisions. The arterial-level models, which were developed for collisions involving all motor vehicles, suggest that average annual daily traffic, transit frequency, segment length, presence of on-street parking, and percentage of near-sided stops are all associated with increased frequency of these collisions, whereas percentage of far-sided stops and average stop spacing are linked with reduced collision frequency. It is evident that models such as those developed in this study can provide transit agencies with decision-support tools for considering safety implications in the strategic and service-planning processes. These models can also be used as a tool to predict future levels of transit-involved collisions for an existing and a new transportation network or arterial route

Investigation of road network features and safety performance

The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models\u27 results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety. © 2013 Elsevier Ltd. All rights reserved