Shopping morphologies of urban transit station areas: A comparative study of central city station catchments in Toronto,San Francisco,and Melbourne

Synergies between shopping and public transit have long been noted, with main streets emerging along tram lines and shopping malls attached to train stations. The shopping-transit synergy is also at the core of transit-oriented development (TOD), a widespread planning approach to urban sustainability. However, there is a lack of morphological research investigating how shopping clusters around transit stations at a fine-geographic scale. This paper explores the shopping morphologies of three central-city subway station areas in Toronto, San Francisco, and Melbourne, mapping and measuring the extent of public and quasi-public shopfronts relative to the station. The morphological analysis of 200, 300, 400, and 500 m walking catchments shows that as the distance from the station increases, the proportion of shopfronts in the total catchment declines. Quasi-public shopping space can enhance the walkable catchment and permeability of a station area and may contribute to urbanity as long as it adds to diversity of access options. The findings highlight the role walkable shopping environments can play within urban transit station areas and show the importance of nuanced consideration being given to morphologies in the analysis and planning of TODs.     

Children's school commuting in the Netherlands: Does it matter how urban form is incorporated in mode choice models?

Walking and cycling to school is environmentally sustainable and increases children's physical activity. Although it is known that the built environment influences children's travel behavior, there is scant knowledge of how urban form along children's commuting routes affects walking and cycling separately, or of how to incorporate urban form exposures in transport mode choice models. This research investigated (a) the associations between children's transport mode choice and urban form correlates en route, and (b) the consequences of different urban form operationalizations (i.e., individual variables, composite measure, and principal components [PCs]). Global Positioning System devices were used to track 623 trips to and from school made by Dutch children aged 6–11 years. Urban form exposures were derived with geographic information systems, and their relationships with mode choice were tested with mixed multinomial logit models in a cross-sectional research design. Differences between the number of associated urban form variables, their magnitudes, and their significance levels were found for both walking and cycling, independent of the operationalization. Urban form was most influential for walking, whereas distance-related effects were absorbed when modeled as PCs. The highest model fit was achieved through PCs; the composite measure resulted in the lowest fit. To maximize the effectiveness of planning and health interventions, walking and cycling must be targeted separately. Policymakers should avoid “one-fits-all policies,” which are deemed to be inefficient. Because urban form affects model interpretation and fit, careful attention should be paid to how urban form is modeled, and sensitivity analyses should be performed.     

Beyond absolute space: An exploration of relative and relational space in Shanghai using taxi trajectory data

The nature of urban space has long-drawn geographers' interest and David Harvey's conceptual framework of multiple spaces (i.e., absolute, relative, and relational) within cities has been widely adopted and developed. With its high spatial and temporal resolution, geospatial big data plays an increasingly important role in our understanding of urban structure. Taxi trajectory data is particularly useful in travel purpose estimation and allows for more granular insights into urban mobility due to the door-to-door nature of these trips. This article utilizes taxi trajectory data and explores the interaction among absolute space, relative space, and relational space in Harvey's framework using Structural Equation Modeling (SEM). Through an empirical study of Shanghai's downtown area, this paper highlights the importance of Harvey's framework in understanding cities' dynamic structure and argues for changes in urban planning and development to better coordinate land use and travel demand. We find an insignificant relationship between relative and relational space in Shanghai due to a mismatch between urban mobility and the built environment. This mismatch concentrates the transportation flow near the city's core area, transforming the polycentric structure of Shanghai's built environment in absolute space to a single-node structure in relational space. After identifying the contributing factors to this problem in Shanghai, this article suggests combining Harvey's conceptual framework of multiple spaces with geospatial big data to inform planning strategies that address the challenges of rapid urbanization.     

Exploring the Implications of Virtual Reality Technology in Tourism Marketing: An Integrated Research Framework

The proliferation of the Internet and other technological innovations has transformed the structure of the tourism industry as well as affected how tourism destinations are perceived and consumed. The 3D virtual world provides opportunities for destination marketing organizations to communicate with targeted markets by offering a rich environment for potential visitors to explore tourism destinations. However, as of yet, there is little understanding about how to effectively market tourism destinations to virtual world participants who are technology users as well as potential consumers. The purpose of the present study is to develop a research framework that integrates the technology acceptance model (TAM) and self‐determination theory to understand how tourists use a 3D virtual world. Primary data were obtained for this study through self‐administered Web questionnaires. The data were conducted in a laboratory setting with 186 participants. This study contributes to the empirical TAM literature by applying the model to a 3D virtual tourism site. Additionally, this study provides a research framework to capture the entertainment nature of a 3D virtual world by extending the TAM to incorporate psychological elements of self‐determination theory to understand consumer experience. From the perspective of tourism professionals, this study contributes to an understanding of how best to construct informative and interactive tourist destinations in 3D virtual worlds to attract potential online and real‐world tourists. Copyright © 2015 John Wiley & Sons, Ltd.     

Mapping bicyclists’ experiences in Copenhagen

This paper presents an approach to the collection, mapping, and analysis of cyclists’ experiences. By spatially relating located experiences to the availability of bicycle facilities and other aspects of the urban environment, their influence on cyclists’ experiences can be analysed. 398 cyclists responded and sketched their most recent cycle route and a total of 890 points to locations along the route where they had had positive and negative cycling experiences. The survey was implemented as an online questionnaire built on Google Maps, and allowed up to three positive and three negative experience points to be mapped and classified.By relating the characteristics of the experience points and the routes to the traversed urban area in general, the significance of the preconditions for obtaining positive or negative experiences could be evaluated. Thereby urban spaces can be mapped according to the potential promotion of positive or negative experiences. Further, the method might be applied to assess the effect of proposed changes to the urban design in terms of cyclists’ experiences.Statistical analysis of the location attributes, traffic environments and conflicts, bicycle facilities, urban density, centrality, and environmental amenities indicates that positive experiences, or the absence of negative experiences, are clearly related to the presence of en-route cycling facilities, and attractive nature environments within a short distance of large water bodies or green edges along the route.     

Accessibility,mobility, and realized travel behavior: Assessing transport disadvantage from a policy perspective

Limited accessibility and mobility can result in decreased quality of life and well-being, as well as social exclusion. In the United States (U.S.), rural and small urban communities suffer from transport disadvantage due to a lack of transit and a low density of employment, education, recreation, and other opportunities. While the international literature has produced a number of methods and frameworks to assess transport disadvantage and its impacts, the U.S. has lagged behind in providing pertinent studies. The objective of this paper is to establish comprehensive measures that can support the identification, evaluation, and quantification of transport disadvantage in U.S. rural and small urban communities, considering both data availability and the unique characteristics of the U.S. The concept of transport disadvantage in this paper denotes the disadvantage of a specific population group or area that results from a difficulty accessing transportation and/or opportunities. To achieve this objective, this paper develops a spatial multi-perspective approach to account for the three essential elements of transport disadvantage: accessibility, mobility, and realized travel behavior.The developed approach provides an assessment of transport needs and need gaps that can be of benefit to small urban and rural communities and their planning practices, as well as to transport providers. The analysis in this paper suggests that—from a policy perspective—a combination of measures that account for all three essential elements of transport disadvantage should be considered, because the results of each measure complement those of the others. This paper illustrates the proposed approach using a case study of Indiana. The findings suggest that a great part of rural and small urban Indiana presents a low density of opportunities and that transport-disadvantaged residents of such areas might experience the impacts of low transit supply as well. In addition, the findings suggest that residents of rural and small urban areas travel longer distances on their day-to-day activities.This paper attempts to advance the national research pertaining to transport disadvantage and provide a framework that can support planning and policy decisions at the community as well as at the state level.     

The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S.

Vehicle ownership is an important determinant of the travel demand forecasting process. Vehicle ownership models are used by policy makers to identify factors that affect vehicle miles traveled, and therefore address problems related to energy consumption, air pollution, and traffic congestion. For the conventional travel demand forecasting, it logically follows land use forecasting, before trip generation, which is commonly treated as step one. The most critical limitation of the vehicle ownership models, especially in the conventional process, is that they are often related mainly to sociodemographic variables, not so much to built environmental variables. In this study, by pooling regional household travel survey data from 32 diverse regions (almost 92,000 households) of the U.S., and by controlling for socio-demographic and the built environmental variables, we estimated a vehicle ownership model that contributes to the understanding of vehicle ownership and improves the accuracy of travel demand forecasts. Two main findings of this research are: 1) The number of vehicles owned by a household increases with socio-demographic variables and decreases with almost all of the built environmental variables. For the urban planning and design practices, this finding suggests that car shedding occurs as built environments become more dense, mixed, connected, and transit-served. 2) We used both count regression and discrete choice models, and the results suggest that count regression models have better predictive accuracy. The model developed in this study can be directly used for travel demand modeling and forecasting by metropolitan planning organizations.     

Urban structure,commuting burden,and employment status of labour forces in an Australian city

Understanding the productivity effects of worker commuting burdens is essential for appraisals of urban planning and investment strategies designed to improve urban productivity. However, the extant urban planning literature lacks systematic analysis of the influence of commuting costs on labour mobility, employment engagement, and productivity. This paper provides the first ever detailed analysis of mobility and commuting burdens for workers in a range of industries and occupational groups in Melbourne, Australia. Linking commuting burdens and measures of employment status in urban areas, this paper finds that the labour force in areas experiencing high commuting burdens exhibit lower levels of engagement with job markets. This research further reveals important variations in such relationships across diverse industry and occupation groups. The results provide urban policy makers with better information about urban transport and employment patterns, thus enabling them to investigate alternative approaches to investing in housing and transport infrastructure to reduce worker commuting burdens and improve economic performance.     

The reflection of the fear of falls and risk of falling in walking activity spaces of older adults in various urban environments

Participation of older adults in daily activities has a major positive impact on health and contributes to a sense of accomplishment, satisfaction, self-efficacy, and well-being. Walking is considered to be one of the most influential activities promoting health and active living. Older adults are particularly vulnerable to their immediate local environment where age- related declined capabilities combined with barriers in the home neighborhood pose a risk of falling and fear of falls. Most research focusing on the built environment role in incidents of older adults' outdoor falls and fear of falls is focused on identifying the environmental features' risk factors. Effort is made to develop audit checklist tools to assess out-door falls risk. In contrast, this study focuses on the manifestation of fear of falls in older adults' walking activity spaces. We identify spatial walking patterns of outdoor daily activities in public urban spaces and examine the relations between fear of falls, risk of falling status, previous occurrence of fall incidents and number of medical diagnoses and walking activity spaces among older adults in different urban environments.The analysis included 271 older adults (age 60+ with 70% females and 30% males), and 483 walking routes in three cities. A psychological measure related to mobility self-efficacy was evaluated by the Falls Efficacy Scale examining the fear of falling through a functional perspective. Motor evaluation was conducted by functional mobility evaluation through the Timed Up and Go (TUG) screening test which assesses the level of risk of falling. GIS analysis was conducted for mapping and identifying walking activity spaces. The analysis shows different walking activity spaces of people with high risk of falling and fear of falls in comparison to those with no risk and no fear of falling across gender and age groups (60–65, 66–74, and 75+). In this approach we show the outcome reflection of barriers and enablers and their revealed cumulative effect through walking activity spaces among older adults.     

Analysis of rural activity spaces and transport disadvantage using a multi-method approach

Current knowledge about the relationship between transport disadvantage and activity space size is limited to urban areas, and as a result, very little is known about this link in a rural context. In addition, although research has identified transport disadvantaged groups based on their size of activity space, these studies have, however, not empirically explained such differences and the result is often a poor identification of the problems facing disadvantaged groups. Research has shown that transport disadvantage varies over time. The static nature of analysis using the activity space concept in previous research studies has lacked the ability to identify transport disadvantage in time. Activity space is a dynamic concept; and therefore possesses a great potential in capturing temporal variations in behaviour and access opportunities. This research derives measures of the size and fullness of activity spaces for 157 individuals for weekdays, weekends, and for a week using weekly activity-travel diary data from three case study areas located in rural Northern Ireland. Four focus groups were also conducted in order to triangulate quantitative findings and to explain the differences between different socio-spatial groups. The findings of this research show that despite having a smaller sized activity space, individuals were not disadvantaged because they were able to access their required activities locally. Car-ownership was found to be an important life line in rural areas. Temporal disaggregation of the data reveals that this is true only on weekends due to a lack of public transport services. In addition, despite activity spaces being at a similar size, the fullness of activity spaces of low-income individuals was found to be significantly lower compared to their high-income counterparts. Focus group data shows that financial constraint, poor connections both between public transport services and between transport routes and opportunities forced individuals to participate in activities located along the main transport corridors.     

Utilizing urban form characteristics in urban logistics analysis: a case study in Lisbon,Portugal

Within the realm of urban logistics, Macário (2013) developed a hypothesis, denominated the Logistics Profile (LP) concept that suggests homogeneous groups of urban zones with respect to three dimensions, which could be used to analyze freight movement policy: (1) the social and built environment; (2) characteristics of the goods/products being moved; (3) characteristics of the deliveries at the receiver establishment. The concept was expected to ease the transferability of best practices in city logistics, by analyzing similarities and differences between zones. This research uses a quantitative methodology to apply the LP concept, and assess its potential, using the city of Lisbon as a case study. The analysis is focused on: (a) the extrapolation of freight trip generation per establishment and delivery characteristics from a sample of commercial establishments to the population within the case study, (b) proposing a methodology to test the LPs, (c) testing the existence of proposed LPs. Freight trips have been extrapolated using a Multiple Classification Analysis (MCA) model. Freight delivery characteristics were attributed to establishments from a sample-based probability distribution. LPs were tested using a two-step cluster analysis. Some LPs have been matched with clusters of case-study zones, subject to case study particularities. Profile overlap was not an issue and occurrences were expected. The testing showed that Logistic Profiles have the potential for being used as a departure point for urban freight planning and policy analysis.     

Modeling urban commercial vehicle daily tour chaining

This paper presents a tour-chain-based approach to modeling urban commercial vehicle daily activity patterns. A daily tour chain refers to a sequence of daily tours made by a vehicle. Multinomial logit model results demonstrate that urban daily tour-chain choice is a result of collective decisions based on cost and shipment characteristics. This research has significant contributions to the current literature in filling the gap of understanding such critical logistics decisions as distribution channel and factors affecting tour chaining. Furthermore, the paper presents an innovative use of commercial vehicle travel survey data, and points to the urgent need for better quality data.     

Time-use data, analysis and modeling: toward the next generation of transportation planning methodologies

This paper argues that transportation planning methodologies must be built on the central thesis of the activity-based approach to travel demand modeling, namely, that travel is a derived demand that reflects people's desire and need to participate in activities. The paper discusses why this foundation for transportation planning methodologies is necessary to address contemporary planning and policy analysis issues. The paper also argues that the introduction of time-use data, analysis and modeling is a key element in the development of the next generation of transportation planning methodologies. Following a brief review of time-use studies, the paper discusses a number of planning and policy analysis areas in which time-use data will be of particular value, including the evaluation of induced or suppressed travel demand. The concepts advanced in the paper are illustrated with two brief numerical examples. These examples show how model systems based on time-use data can be used to (i) estimate the number of induced trips that would result from a reduction in commute travel time, and (ii) evaluate the impacts of alternative transportation improvement projects.     

Achieving sustainable urban transport mobility in post peak oil era

Peak oil is the term used to describe the point at which global oil production will peak and thereafter start to decline. Recognising that transport uses a significant portion of global energy, the shortage of fossil fuel in post peak oil era will pose a global challenge in the transport sector. The paper presents an assessment of international research to illustrate the possible time frame of peak oil. It investigates the key implications of the oil shortage that threaten to render the urban transport system of Australia ineffective. Synthesis of documented research evidence suggests three major implications in the urban transport sector: (1) a reduction of mobility for individuals, (2) an increase of transport disadvantage, and (3) a disruption of urban freight movement. In addition, the paper explores strategies to cope with the devastating effects of the shortage of the fossil fuel in the post peak oil era. A number of strategies to achieve sustainable mobility in the future urban transport system are presented. These strategies are summarised into three main themes: (1) a mode shift to alternate transport modes, (2) an integration of land use and transport planning, and (3) a global technical effort for alternate fuels and vehicles. It is expected that a concerted global effort in this regard can have a far-reaching effect in achieving sustainability in urban transport mobility.     

Representing pedestrian activity in travel demand models: Framework and application

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems.     

Retail Mobility Environments: A methodological framework for integrating retail activity and non-motorised accessibility in Zaragoza,Spain

While adequate integration of land use and transport is seen as crucial for achieving sustainable outcomes, the reciprocal interconnection between retail activity and non-motorised accessibility is yet to be adequately examined. To address this gap, this paper proposes the concept of Retail Mobility Environment (RME) and develops a methodological process for identifying and mapping RMEs, using the city of Zaragoza, Spain, as a case study. The concept of RME is developed through three methodological phases: (i) definition of non-motorised Accessibility Zones (AZs), using three indicators (walking accessibility, bicycling accessibility, betweenness); (ii) definition of Retail Zones (RZs), also using three indicators (retail density, retail diversity, retail contiguity); and (iii) definition of RMEs, where both retail activity and non-motorised indicators were weighted and combined using multi-criteria analysis. In total, four RMEs were identified and mapped: short-distance environments, motorised environments, non-motorised environments, and long-distance environments. The paper concludes with a discussion on the need to unravel the relationships between retail activity and non-motorised accessibility, in order to reach sustainable planning goals, as well as the potential usefulness of RMEs for transport policy-making.     

Transit-oriented development,integration of land use and transport,and pedestrian accessibility: Combining node-place model with pedestrian shed ratio to evaluate and classify station areas in Lisbon

Transit-oriented development is being actively promoted as an urban design model for areas around transit stations. In addition, planning for accessibility is being promoted, which requires integrating land use with transportation planning, and to match the transportation features with the intensity and diversity of land use of the station areas. Nevertheless, and despite the evident similarities between the two approaches, an integrated evaluation tool of a station area in terms of its transportation, land use, and urban design features is missing. In this paper, we bring into the literature on integration of land use and transport a key feature of the transit-oriented development literature: the urban design features of the station areas, in particular their pedestrian friendliness. By complementing the node-place model with an evaluation of the pedestrian connectivity of station areas of Lisbon, we combine these two perspectives in order to evaluate and classify station areas in three different aspects: land use, transportation, and walkability conditions. Our results show that a balanced node-place is not necessarily a transit-oriented development, and vice versa, and so a complementary analysis of both is useful to identify and classify a station area. Therefore, we suggest a typology of station areas based on the three components, which might be used as a planning tool for the development of the station areas into balanced transit-oriented development areas.     

Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.     

Identification of land-use characteristics using bicycle sharing data: A deep learning approach

Extensive research has shown that urban land-use characteristics, including resident, work, consumption, transit, etc., are significantly interrelated with travel behaviors and travel demands. Many research efforts have been made to evaluate the impact of land use planning or policies on travel behavior, however, few studies are able to quantitatively measure the land-use characteristics based on the data of travel behaviors or travel demand. In this paper, a new hybrid model that combines time series feature extraction and deep neural network is proposed to identify regional land use characteristics and quantify land use intensity using ridership data of bicycle sharing. This method consists of four main parts: (i) A set of land-use characteristic labels are evaluated based on planning and Geographic Information System (GIS) data. (ii) An ensemble clustering method is used to determine the segmentation points of ridership time series. (iii) The statistical characteristics of the segmented time series are extracted and used as input to the neural network. (iv) A deep neural network is established and trained based on the processed ridership features and land-use labels. In terms of data collection, ridership data of the bicycle-sharing parking spots and land-use planning data are obtained from bicycle-sharing system and planning department in San Francisco Bay Area, California U.S.A., respectively. The test results show that this approach has high accuracy for identifying land-use characteristics based on several standard evaluation measures and that the identification distribution can be well explained. The extension results further prove that the model can be applied to effectively analyze the main land-use characteristics of the region although the identification results may become unstable after 3–4 months.