Urban built environment analysis: evidence from a mobility survey in Madrid

Applications involving travel behavior from the perspective of land use are dating from the 1990s. Usually, four important components are distinguished: density, diversity and design (3D?s of Cervero and Kockelman) and accessibility (introduced by Geurs and van Wee). But there is not a general agreement on how to measure each of those 4 components. Density is used to be measured as population and employment densities, but others authors separate population density between residential and building densities. A lot of measures have been developed to estimate diversity: among others, a dissimilarity index to indicate the degree to which different land uses lie within one another?s surrounding, an entropy index to quantify the degree of balance across various land use types or proximities to commercial-retail uses. Design has been characterized by site design, and dwelling and street characteristics. Lastly, accessibility has become a frequently used concept, but its meaning on travel behavior field always refers to the ability ?to reach activities or locations by means of a travel mode?, measured as accessibility to jobs, to leisure activities, and others. Furthermore, the previous evidence is mainly based on US data or on north European countries. Therefore, this paper adds some new evidence from a Spanish perspective to the research debate. Through a Madrid smartphone-based survey, factor analysis is used to linearly combine variables into the 3D?s and accessibility dimensions of the built environment. At a first step for future investigations, land use variables will be treated to define accurately the previous 4 components

Enhancing station level Direct-Demand models with Multi-Scalar accessibility indicators

Direct-demand models (DDM) are increasingly being used for a diversity of transit research and practice purposes. Yet few station-level DDM studies have explored the use of composite indicators of metropolitan accessibility in predicting demand. After all, provision of access to metropolitan destinations is one of the main goals of rapid-transit systems. Furthermore, to this author’s knowledge no study has explored potential interactions with local-level accessibility indicators that are typically included in station level transit DDMs. This study explores these possibilities and uses Los Angeles multimodal rapid-transit network as a representative case study of a system that operates in a dispersed agglomeration where multiple sub-centers are linked. Multi-level generalized linear models were implemented where key predictors, including stations\u27 metropolitan- and a local-accessibility indicators are regressed onto average weekday boardings. Furthermore, more general accessibility constructs were developed via EFA and implemented in models; and parameters non-stationarity was assessed via geographically weighted regressions. Results indicate that nodal metropolitan accessibility is a significant predictor of patronage in LA’s rapid-transit network, and that its interaction with local-accessibility amplifies boardings and improves DDM models’ explanatory power. More general constructs of accessibility at metropolitan and local-scale were derived via EFA and these resulted in a more parsimonious model with equal predictive power. Land-use and transit planners would benefit from including an accessibility lens in their DDM modeling. Practical applications of these type of models include TOD scenario planning, comparative route alignment studies, system expansion studies, and for didactic purposes given the ability of accessibility measures to capture land-use/transportation interactions

Reaching Non-Work Destinations: Accessibility and Its Impacts on Travel Behavior

As people’s daily activities are diverse, having access to various opportunities is important. However, the existing body of accessibility literature places great emphasis on job accessibility; research on non-work accessibility is limited. To fill the gap, this dissertation examines accessibility to four types of non-work opportunities (healthcare, retail, recreation, and food services) by three transportation modes (automobile, transit, and walking) as well as their impacts on travel in the Milwaukee region. This dissertation examines accessibility disparities across different racial/ethnic groups and income groups in Milwaukee County by comparing weighted average accessibility and overlaying spatial distribution of accessibility with population distributions. Results suggest that disparities in non-work accessibility across different sociodemographic groups exist, and the dissertation identifies the group in the most disadvantaged position. Using structural equation models, the second part of this dissertation investigates the relationship between accessibility and travel behavior of non-work trips while controlling for neighborhood built environment characteristics, psychological factors, and socioeconomic characteristics. Results provide empirical evidence on whether accessibility affects various non-work trips differently. This dissertation finds that accessibility has significant impacts on reducing trip distance for non-work trips, and the impacts are the largest for food services, followed by healthcare and retail, and the smallest for recreation. Additionally, improvements in accessibility to food services and recreational facilities encourage non-work travel for respective trips. Findings of this dissertation have policy implications. The multi-modal accessibility indicators contribute to a comprehensive understanding of disparities in accessibility and inform planning research and practice about spatial gaps in both goods/service supply and transportation services. Additionally, the empirical analysis of the accessibility effect on travel can inform targeted mobility or land use strategies

Location effects on trip generation: Evidence from Madrid metropolitan area.

The relationship between land use and travel patterns has been studied in a number of cases, using several methods - aggregate and disaggregate approaches - and different focuses – trip frequency, automobile use, vehicle miles travelled and so on. Definitely, travel is generated by the need to undertake activities and obtain services, and there is a general consensus that urban components affect travel behaviour. However researches are still needed to better understand which components of the travel behaviour are affected most and by which of the urban components. This paper studies the effect on trip frequency, public transport and private vehicle dependency of socio-economic, transport and land use characteristics. In particular the land use is defined in terms of type of neighbourhoods and types of dwellers. Methodological attributes are also included to test the effect of the type of survey, namely trip-based versus activity-based survey. Using a data-base from a survey conducted in 2006 and 2007 in Madrid, ordered probit models are estimated to analyse the effect of neighbourhood type and socio-economic characteristics on trip frequency, public transport and private vehicle use. Our results show that the characteristics of the neighbourhoods are important to explain the trip frequency but the effect is quite different depending on the mode used for the trips. Our results confirm that living in low density increases the propensity to use the private vehicles, while it does not seem to have an impact on the propensity to make internal trips, i.e. with origin and destination in the same area. We also found that there is a positive correlation between the number of trips and the number of stops but only if the trips are made with the private vehicles while are not significant for the public transport

How the design of Complete Streets affects mode choice: Understanding the behavioral responses to the level of traffic stress

Following a federal policy statement in 2010 supporting bicycle and pedestrian accommodation in federal-aid transportation projects, many cities across the US have implemented Complete Streets principles and invested in developing better-planned infrastructure that can be safely accessed by a diversity of modes of transportation by all types of users, in a mix of land uses. However, most of the travel demand forecasting models and planning tools used in practice are not sensitive to changes in demand for non-motorized modes such as walking and cycling in response to road infrastructure improvements. Hence, there is a need for models and tools that are capable of evaluating impacts of infrastructure changes that include Complete Streets implementations on the travel behavior, and estimate shifts in mode choices from motorized to non-motorized modes. This paper proposes a specific data collection plan, a multi-modal choice model, and strategies to update traditional trip-based transportation models to forecast rates of non-motorized trips for evaluating Complete Streets plans at a higher level. Concretely, we estimate elasticities to Level of Traffic Stress, which defines the comfort or discomfort experienced by walkers and bikers, segmented by income levels and trip purposes. We then use them to compute the new non-motorized mode shares that would be achieved by improving CS attributes leading to lower levels of traffic stress. The proposed modeling framework has been successfully applied to the Maryland Statewide Transportation Model, producing reliable non-motorized trip rates, and can be extended to other methodological frameworks used by public agenciesThis research was sponsored by the Maryland Department of Transportation State Highway Administration (Project No: MD-21- SHA/UM/5-25, Erdogan et al., 2021), and the Urban Mobility & Equity Center (UMEC), based at Morgan State Universit

Active Commuting and Active Transportation

This book focuses on active transport as a way to increase physical activity levels. Active commuting and active transportation on foot or by bicycle create opportunities for physical activity, provide transportation options for those without a car, encourage social cohesion, and reduce contributions to air pollution

Factors influencing the choice of shared bicycles and shared electric bikes in Beijing

AbstractChina leads the world in both public bikeshare and private electric bike (e-bike) growth. Current trajectories indicate the viability of deploying large-scale shared e-bike (e-bikeshare) systems in China. We employ a stated preference survey and multinomial logit to model the factors influencing the choice to switch from an existing transportation mode to bikeshare or e-bikeshare in Beijing. Demand is influenced by distinct sets of factors: the bikeshare choice is most sensitive to measures of effort and comfort while the e-bikeshare choice is more sensitive to user heterogeneities. Bikeshare demand is strongly negatively impacted by trip distance, temperature, precipitation, and poor air quality. User demographics however do not factor strongly on the bikeshare choice, indicating the mode will draw users from across the social spectrum. The e-bikeshare choice is much more tolerant of trip distance, high temperatures and poor air quality, though precipitation is also a highly negative factor. User demographics do play a significant role in e-bikeshare demand. Analysis of impact to the existing transportation system finds that both bikeshare and e-bikeshare will tend to draw users away from the “unsheltered modes”, walk, bike, and e-bike. Although it is unclear if shared bikes are an attractive “first-and-last-mile solution”, it is clear that e-bikeshare is attractive as a bus replacement

Updated models of passenger transport related energy consumption of urban areas

Introduction: As the global warming threat has become more concrete in recent years, there is a need to update transport energy consumptions of cities and to understand how they relate to population density and transport infrastructure. Transportation is one of the major sources of global warming and this update is an important warning for urban planners and policy makers to take action in a more consistent way. Analysis: This paper estimates and analyzes the passenger transport energy per person per year with a large and diverse sample set based on comparable, directly observable open-source data of 57 cities, distributed over 33 countries. The freight transport energy consumption, which accounts for a large portion of urban transport energy, is not considered. The main focus of the analysis is to establish a quantitative relation between population density, transport infrastructure and transport energy consumption. Results: In a first step, significant linear relations have been found between road length per inhabitant, the road infrastructure accessibility (RIA) and private car mode share as well as between RIA and public transport mode share. Results show further relation between travel distance, population density and RIA. In a second step, a simplified model has been developed that explains the non-linear relation between the population density and RIA. Finally, based on this relation and the above findings, a hyperbolic function between population density and transport energy has been calibrated, which explains the rapid increase of transport energy consumption of cities with low population density. Conclusions: The result of the this study has clearly identified the high private car mode share as main cause for the high transport energy usage of such cities, while the longer average commute distance in low-population density cities has a more modest influence on their transport energy consumption

Urban sprawl, socioeconomic features, and travel patterns in Middle East countries: a case study in Iran

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THE INFLUENCE OF URBAN FORM AT DIFFERENT GEOGRAPHICAL SCALES ON TRAVEL BEHAVIOR; EVIDENCE FROM U.S. CITIES

Suburban lifestyle is popular among American families, although it has been criticized for encouraging automobile use through longer commutes, causing heavy traffic congestion, and destroying open spaces (Handy, 2005). It is a serious concern that people living in low-density suburban areas suffer from high automobile dependency and lower rates of daily physical activity, both of which result in social, environmental and health-related costs. In response to such concerns, researchers have investigated the inter-relationships between urban land-use pattern and travel behavior within the last few decades and suggested that land-use planning can play a significant role in changing travel behavior in the long-term. However, debates regarding the magnitude and efficiency of the effects of land-use on travel patterns have been contentious over the years. Changes in built-environment patterns is potentially considered a long-term panacea for automobile dependency and traffic congestion, despite some researchers arguing that the effects of land-use on travel behavior are minor, if any. It is still not clear why the estimated impact is different in urban areas and how effective a proposed land-use change/policy is in changing certain travel behavior. This knowledge gap has made it difficult for decision-makers to evaluate land-use plans and policies. In addition, little is known about the influence of the large-scale built environment. In the present dissertation, advanced spatial-statistical tools have been employed to better understand and analyze these impacts at different scales, along with analyzing transit-oriented development policy at both small and large scales. The objective of this research is to: (1) develop scalable and consistent measures of the overall physical form of metropolitan areas; (2) re-examine the effects of built-environment factors at different hierarchical scales on travel behavior, and, in particular, on vehicle miles traveled (VMT) and car ownership; and (3) investigate the effects of transit-oriented development on travel behavior. The findings show that changes in built-environment at both local and regional levels could be very influential in changing travel behavior. Specifically, the promotion of compact, mixed-use built environment with well-connected street networks reduces VMT and car ownership, resulting in less traffic congestion, air pollution, and energy consumption