Assessment of the Employment Accessibility Benefits of Shared Autonomous Mobility Services

The goal of this study is to assess and quantify the potential employment accessibility benefits of Shared Autonomous Mobility Service (SAMS) commute modes across a large diverse metropolitan region considering heterogeneity in the working population. To meet this goal, this study employs a welfare-based (i.e. logsum-based) measure of accessibility, obtained via estimating a hierarchical work destination-commute mode choice model. The employment accessibility logsum measure incorporates the spatial distribution of worker residences and employment opportunities, the attributes of the available commute modes, and the characteristics of individual workers. This research further captures heterogeneity of workers using latent class analysis (LCA). The LCA model inputs include the socio-demographic characteristics of workers to subsequently account for different worker clusters valuing different types of employment opportunities differently. The accessibility analysis results indicate: (i) the accessibility benefit differences across latent classes are modest but young workers and low-income workers do see higher benefits than high- and middle-income workers; (ii) there are substantial spatial differences in accessibility benefits with workers living in lower density areas benefiting more than workers living in high-density areas; (iii) nearly all the accessibility benefits come from the SAMS-only mode as opposed to the SAMS+Transit mode; and (iv) the SAMS cost per mile assumption significantly impacts the magnitude of the overall employment accessibility benefits

Street centrality and land use intensity in Baton Rouge, Louisiana

This paper examines the relationship between street centrality and land use intensity in Baton Rouge, Louisiana. Street centrality is calibrated in terms of a node's closeness, betweenness and straightness on the road network. Land use intensity is measured by population (residential) and employment (business) densities in census tracts, respectively and combined. Two CIS-based methods are used to transform data sets of centrality (at network nodes) and densities (in census tracts) to one unit for correlation analysis. The kernel density estimation (KDE) converts both measures to raster pixels, and the floating catchment area (FCA) method computes average centrality values around census tracts. Results indicate that population and employment densities are highly correlated with street centrality values. Among the three centrality indices, closeness exhibits the highest correlation with land use densities, straightness the next and betweenness the last. This confirms that street centrality captures location advantage in a city and plays a crucial role in shaping the intraurban variation of land use intensity. (C) 2010 Elsevier Ltd. All rights reserved

Integrated Modeling Approach for the Transportation Disadvantaged

Transportation models have not been adequate in addressing severe long-term urban transportation problems that transportation disadvantaged groups overwhelmingly encounter, and the negative impacts of transportation on the disadvantaged have not been effectively considered in the modeling studies. Therefore this paper aims to develop a transportation modeling approach in order to understand the travel patterns of the transportation disadvantaged, and help in developing policies to solve the problems of the disadvantaged. Effectiveness of this approach is tested in a pilot study in Aydin, Turkey. After determining disadvantaged groups by a series of spatial and statistical analyses, the approach is integrated with a travel demand model. The model is run for both disadvantaged and nondisadvantaged populations to examine the differences between their travel behaviors. The findings of the pilot study reveal that almost two thirds of the population is disadvantaged, and this modeling approach could be particularly useful in disadvantage-sensitive planning studies to deploy relevant land use and transportation policies for disadvantaged groups

European Region Types in EU-25

In this paper a categorization of 1,212 European NUTS 3 regions is conducted by means of factor and cluster analysis according to economic structure and spatial characteristics. Subsequently the hypothesis is tested that these region types do explain differences in level and growth of regional income. The resulting 14 region types (10 non-urban and 4 urban) do show differences in regional income per capita and some of them are expected to converge to different steady state levels. In particular, region types with low employment rates obtain lower per capita income on average than others, while those with productivity differentials in favour of industry obtain higher GDP p.c. when judged against region types comparable in their degree of accessibility. In estimating regressions on conditional beta-convergence, the inclusion of national dummies shows a reduced speed of convergence (in the total and the western sample) and even divergence in the sample of (former communist) new member states, while the additional inclusion of region type dummies points again to a still low but higher speed of convergence. The estimates indicate significant lower steady state incomes in the peripheral agrarian regions, peripheral industrial regions with a lower productivity differential in favour of industry, peripheral tertiary regions and both types of central regions with low employment rates. A higher steady state income is estimated for metropolitan areas and big agglomerations.Region Types; Cluster Analysis; European Regions; Convergence

Hybrid choice model for propensity to travel and tour complexity

During the last years cities around the world have invested important quantities of money in measures for reducing congestion and car-trips. Investments which are nothing but potential solutions for the well-known urban sprawl phenomenon, also called the “development trap” that leads to further congestion and a higher proportion of our time spent in slow moving cars. Over the path of this searching for solutions, the complex relationship between urban environment and travel behaviour has been studied in a number of cases. The main question on discussion is, how to encourage multi-stop tours? Thus, the objective of this paper is to verify whether unobserved factors influence tour complexity. For this purpose, we use a data-base from a survey conducted in 2006-2007 in Madrid, a suitable case study for analyzing urban sprawl due to new urban developments and substantial changes in mobility patterns in the last years. A total of 943 individuals were interviewed from 3 selected neighbourhoods (CBD, urban and suburban). We study the effect of unobserved factors on trip frequency. This paper present the estimation of an hybrid model where the latent variable is called propensity to travel and the discrete choice model is composed by 5 alternatives of tour type. The results show that characteristics of the neighbourhoods in Madrid are important to explain trip frequency. The influence of land use variables on trip generation is clear and in particular the presence of commercial retails. Through estimation of elasticities and forecasting we determine to what extent land-use policy measures modify travel demand. Comparing aggregate elasticities with percentage variations, it can be seen that percentage variations could lead to inconsistent results. The result shows that hybrid models better explain travel behavior than traditional discrete choice models

Choice f micro-mobility: Case studies of ta public bicycle sharing system in New Zealand

This study considers how to improve understanding of sustainable urban transport planning from the perspective of the Central Business District (CBD) redevelopment process for two cities, Hamilton and Christchurch in New Zealand (NZ). The most proportion of ‘Public Bicycle Share Schemes’ operate in densely populated cities as these are characterized by limited modal accessibility but high population density in the urban CBD. This situation is similar to NZ’s two medium-sized cities, in each of which the city’s population density is constantly increasing in the past years. In this study, Multinomial and Mixed Logistic regression models were used to determine the model specification, and subsequently, to test the mode choice cross-elasticities for promoting greater use of the bicycle sharing system in conjunction with public transport service. The data were gathered using stated preference surveys from 486 New Zealanders, and the modeling results indicate that the potential improvement in a modal shift towards micro-mobility, which can be enhanced by applying different policy options

Land use and mobility

This paper analyses the effects of land use characteristics on mode choice and carownership. The study is based on a large sample of individuals from the National TravelSurvey of Great Britain for the years 1989-91 and 1999-2001. Land use characteristics aredefined as population density, size of the municipality, accessibility to public transport andlocal amenities, such as shops and services. Mode choice (shares of total travel by car,public transport and walking) and car ownership are modelled using multinomial andbinomial logit models respectively, which include a large number of socio-economicfactors (income, age, gender, household structure and employment status) as well as landuse indicators. The estimation results strongly support the importance of the land usefactors considered on mode choice and car ownership