Journal of Urban Planning and Development

The main purpose of conventional transit systems is to serve concentrated travel patterns in urban areas, where they usually have high levels of success. Unconventional transit modes have also found success in specific conditions, fuelled by the need for transit modes that handle different demand levels, urban environment patterns, and even natural constraints and barriers. In many urban contexts, geographical and topographical barriers such as mountains, valleys, and bodies of water, and the very large infrastructure costs associated with overcoming these barriers, may not permit the implementation of conventional public transportation systems. In such cases, transit agencies may look to unconventional modes of travel to serve the needs of the residents of these areas. Aerial ropeway transit (ART), a type of aerial transportation in which passengers are transported in cabins that are suspended and pulled by cables, is one of the solutions that has shown its implementation rise in the past decade. This paper attempts to shed some light on ART technology by presenting experiences with this technology from both the United States as well as other parts of the world including the reasons for building these systems and their service and operational characteristics as well as other case-specific information. The paper concludes with an assessment of experiences with these systems including their benefits and limitations as well as a discussion of the advancements needed for ART technologies to be a fully recognized transit mode.

The protection of natural areas is one of the key factors considered in the development of urban areas, which also provides significant recreational opportunities for citizens. Furthermore, these protected natural areas are also important because they are habitats for living beings. The integration of a city with the natural environment will support social and economic development and improve the ecological structure for sustainable urban development. In this regard, greenways and ecological networks are habitats of living beings. Greenways also limit urban development. In this study, greenways were created for Malta by using Euclidean distance mapping (EDM) so that sites in Malta would be protected from urban development. The protected natural areas of Malta were integrated with cities by the EDM method. EDM is an application of a geographic information system (GIS) and was used to restrict urban development with natural areas. In this study, an ecological network was established with the EDM method among the protected areas of Malta. One of the most important results of this study is the connection of landscape areas of Malta that are very highly sensitive and highly sensitive with highly valued landscape areas through a greenway network. Another important result was that sensitive landscape area crossing over the island in the southwest-northeast direction overlapped the buffer zone formed by the EDM. The EDM method revealed that highly sensitive landscape areas of Malta can be preserved. EDM will make it possible to holistically approach the protected areas of Malta. It is concluded that greenways created by the EDM method contributed to sustainability of the protected areas and preserve the sensitive landscape areas of Malta. As a consequence, this method created a series of connected greenways that will both limit urban expansion and offer Malta a more livable environment.

As climate change mitigation becomes pervasive on all spatial scales, mitigation options related to urban spatial planning and behavioral change become increasingly important. Because transport energy consumption seems to scale inversely with population density, increased attention focuses on the role of urban form. This study specifically analyzes the importance of population density for the reduction of urban greenhouse gas emissions in Europe. For this, drivers of both carbon dioxide (${\mathrm{CO}}_2$) emissions from transport (for 134 cities) and total urban greenhouse gas emissions (${\mathrm{CO}}_2$eq emissions) of 62 cities across Europe are investigated. Results indicate that population density is not, per se, a strong determinant of greenhouse gas emissions in European cities. Crucially, the spatial scale of the analysis matters and national influences modulate ${\mathrm{CO}}_2$eq emissions in the analyzed urban areas. Results show that greenhouse gas emissions of European urbanites increase significantly with decreasing household sizes and increasing personal wealth. Although the results are bound by data quality, it is assumed that the relative similarity of European cities is also leading to a lesser degree of importance of population density with respect to climate change mitigation. The results further encourage more thorough analyses of the role of household size and personal wealth for effective mitigation of climate change, additional spatially explicit econometric studies, and detailed, city-specific causal models of urban areas.

This paper describes the development and the main components of a simulation framework aimed to model the bus transit network evolution. The prediction model of transit network evolution (P-TRANE) is a geographic information system (GIS)-based model that develops future predictions of service changes in the bus transit spatial network and service functionality at future time steps. These changes happen as a result of the periodic service review process and are influenced by a group of variables, including the changes in the future transit ridership pattern and future land use distribution changes. The main objective for P-TRANE is to function as a transit supply prediction component for the transportation/land use modeling simulation frameworks, such as the integrated land use, transportation, and environment (ILUTE), by generating the future bus network (supply) information for ILUTE. However, P-TRANE could be an aid to transit agencies and urban planners by means of providing a decision support system prediction tool. Preliminary model results on the case study of the Toronto Transit Commission bus network show that it is successful in identifying transit routes prone to frequency changes (poor performance and/or overcrowding). Furthermore, results show that the P-TRANE GIS module is a promising tool capable of proposing new bus lines, including feeder lines serving rapid transit projects/stations.

This paper looks at a new innovative form of social public–private partnership (PPP) introduced by the Hong Kong Special Administrative Region government, where service providers are invited to revitalize historic buildings for new functions. Under this scheme the government will provide all the initial financial assistance required unlike traditional PPP projects. Focus-group meetings were conducted with visitors of the first revitalized project under this scheme, that is, the Savannah College of Art and Design Hong Kong, formerly known as the North Kowloon Magistracy. The participants were asked to share their views regarding the effectiveness of the scheme, the impacts of the scheme, and also their views regarding the general public participation. The findings found that, in general, the revitalization scheme was well received by the participants. Although they did not feel that the project benefited the local community or Hong Kong in terms of uplifting economies or providing suitable services, the project was praised for being a good example of preserving historic buildings for new usages effectively. The revitalization scheme presented in this paper has demonstrated how projects can benefit from the private sector’s expertise but at the same time be funded by the government. This model is particularly attractive for those governments where money is not their greatest concern, but to tap in the expertise from the private sector is their priority. The findings in this paper have shown positive support toward this scheme, showing that there is a large possibility for further future developments in both Hong Kong and other similar jurisdictions.

Data and models of nonmotorized traffic on multiuse urban trails are needed to improve planning and management of urban transportation systems. Negative binomial regression models are appropriate and useful when dependent variables are nonnegative integers with overdispersion like traffic counts. This paper presents eight negative binomial models for estimating urban trail traffic using 1,898 daily mixed-mode traffic counts from active infrared monitors at six locations in Minneapolis, Minnesota. These models include up to 10 independent variables that represent sociodemographic, built environment, weather, and temporal characteristics. A general model can be used to estimate traffic at locations where traffic has not been monitored. A six-location model with dummy variables for each monitoring site rather than neighborhood-specific variables can be used to estimate traffic at existing locations when counts from monitors are not available. Six trail-specific models are appropriate for estimating variation in traffic in response to variations in weather and day of week. Validation results indicate that negative binomial models outperform models estimated by ordinary least squares regression. These new models estimate traffic within approximately 16.3% error, on average, which is reasonable for planning and management purposes.

Worsening highway congestion is a challenge to megaregional competitiveness; changing regional geographies and development location decisions, among other factors, demand that public policy responses go beyond traditional demand-management approaches. Congestion-pricing has been suggested as a remedy. In this paper, the writers analyze the outcomes of multiple congestion-pricing approaches for the capital megaregion that spans the following five metropolitan planning organization regions: (1) Washington (District of Columbia, Maryland, and Virginia), (2) Baltimore (Maryland), (3) Wilmington (Delaware), (4) Fredericksburg (Virginia), and (5) Frederick (Maryland), in addition to counties in the adjoining states of New Jersey, Pennsylvania, and West Virginia in the United States. Using a megaregional travel-demand model, the writers incorporate different values-of-time (VOTs) for travelers under different conditions. However, unlike past approaches, the writers’ VOT estimates go beyond income categories and include trip purposes across a number of scenarios. The writers demonstrate that adding the trip purpose to congestion-price determination provides added insight both at the megaregional level and for individual subregions. The results of this paper show how highway users respond differently and (in some cases) in unexpected ways to congestion charges when trips in accordance with income and purpose are assigned a unique VOT. The findings reported in this paper provide engineers, planners, and policymakers with a new analytical approach to measuring traveler elasticity to tolls while demonstrating the complexity involved with road-pricing mechanisms. The writers conclude with implications for adopting this approach and ideas for implementing them in a complex institutional setup.

Urban sprawl has resulted in numerous land-lost farmers who must relocate from villages to residential quarters. The spatial landscapes of new homelands have profoundly changed, which is necessary for the succession of settlements. This study explored the spatial patterns of trees adjacent to buildings from traditional villages to relocated farmers’ quarters in Shanghai, China. The stratified systematic method was adopted for 24 samples of 10 traditional villages, six relocated quarters in the late 1990s, and eight relocated quarters in the mid-2000s. Chessboard sampling was used to obtain 258 sample plots. A sketch map of every plot was premade for the investigation, which was conducted in 2009 and 2010. The four factors of tree height, defoliation, direction, and spacing were encoded for quantified analysis. The primary research results are as follows: the quarters had higher defoliation ratios than the villages (1.069 and 0.749, respectively), which means that there were more evergreens in the quarters and more deciduous plants in the villages. Most individual trees were to the north in the villages, but to the south in the quarters. The percentages of trees showed a nearly monotone decreasing trend for spacing in the quarters and near homogenization in the villages. For the villages, a valuable model is given of layered greening with upper deciduous and lower evergreen plants. More trees should be planted to the north as a barrier against cold wind in winter, and there should be open spaces in front of windows for sunshine, ventilation, and views of the outward landscape.

Trip production models in India have traditionally been developed using simple regression analysis with population at census ward level as the independent variable. The current study developed multiple classification analysis (MCA) tables at the household unit level for trip production considering several other important variables that affect trip production. The city of Patna in India is taken as the case study, and its household data is considered for analysis. Households are further disaggregated into slum (low income) and nonslum households, and scenarios within them are considered for analysis. Nomograms are developed based on MCA tables and can be used to estimate trip rate values for other cities with similar socioeconomic characteristics. Slums and nonslum households revealed similar trip rate patterns, with the household size having the maximum impact on trips produced. Income and vehicle ownership show little effect on trip production rates. Also, MCA and linear regression models resulted in similar trip rates and accuracy. Hence, MCA is recommended to be adopted because it gives a more disaggregated output that is more stable over time and is easier to use because the values are readily available without further analysis.

Urban spaces are comprised of different locations that offer varying levels of attractiveness to users. The users may be the residents of the space itself or may travel from outside the space to visit the locations of attractions. Such travelers, also known as tourists, decide on the destination based on its values and plan their tour either by themselves or through a travel agency. This paper examines whether or not differences exist between the travel decisions and behavior of these two segments of travelers and the way they take decisions. The research reported in this paper was performed in six cities of India, which offered different values like historical heritage, religious, and waterfront, and the travelers were categorized based on type of planning, i.e., self-planned or agency-based. Exploratory factor analysis was used to identify the factors that combine the effect of influencing attributes and logit analysis was used to arrive at a decision structure. Exploratory factor analysis indicated that safety and comfort, information, and destination-specific factors commonly affect the two planning segments, whereas predecision affects the self-planning travelers exclusively. Social and psychological attributes mainly govern the decisions as compared to travel attributes. Destinations with specific values were short of the aspirations of the travelers, which also were differing. The choice decision was found to be a two-level decision, making a destination-value decision at the first level, and making a tour-planning decision at the second level. The findings are of significance in terms of developing urban spaces.