Assessing the experience of mandated collaborative inter-jurisdictional transport planning in the United States

This paper explores collaborative transport planning among governmental authorities where jurisdictions overlap and the lines of authority are ambiguous or unclear–an increasingly common situation in this era of waning trade and travel restrictions. We do this by examining the experience of mandated collaborative transportation planning among state departments of transportation (DOTs) and metropolitan planning organizations (MPOs) in the USA following a significant change to national surface transportation policy in the early 1990s. To understand how state transport planning and plans changed following the inter-jurisdictional collaboration mandated by passage of the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA), we examine recent statewide transport planning in 14 of the largest US states and conducted interviews with 66 state and regional planners. We find that, despite the myriad topics that state DOTs tried to include within their comprehensive statewide plans, these plans have had, at best, a limited influence on metropolitan transport planning and activities. Despite this, we find that the mandated collaborative planning did help to increase inter-agency coordination on issues (1) where network or environmental externalities transcend regional boundaries, (2) that require the political clout of a higher-level governmental authority to enforce locally unpopular decisions, or (3) that take advantage of institutional economies of scale.     

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.     

Regional transportation planning in the US: An examination of changes in technical aspects of the planning process in response to changing goals

This paper presents a framework for examining the ways in which technical aspects of the regional transportation planning process are changing in response to a broadening of goals and strategies, driven in part by an increased emphasis on public involvement. As an illustration of this framework, the long-range plans of four Metropolitan Planning Organizations (MPOs) in the US are analyzed with respect to goals, performance measures, and use of forecasts. These examples suggest that changes in the technical aspects of the process are lagging changes in planning goals. As a result, plans may implicitly emphasize congestion relief even if they put forth a much broader range of goals. This paper provides a basis for further analysis of the current evolution of transportation planning in the US.     

Are we keeping up? Accessibility,equity and air quality in regional planning

Many US metropolitan areas have undergone dramatic shifts in socioeconomic organization.. As urban areas gentrify, many low-income residents and communities of color have transitioned towards the exurban periphery. These suburban neighborhoods tend to have fewer employment opportunities and are fairly disconnected from public transportation networks serving the urban core. Using regional transportation plans (RTPs) for three California MPOs, we show that the transportation accessibility and environmental health issues affecting these exurban communities are unique and inadequately captured by the MPOs' current equity metrics. MPOs performance evaluation is regional and achieving equity within the urban core communities will not address emerging equity, accessibility and air quality concerns for exurban communities. With a brief history and a focused case study of RTPs for the San Francisco Bay Area, San Diego, and Fresno, we examine how air pollution, equity, and transportation interact in three different types of 21st century cities. We find that when allocating limited transportation funds, California metropolitan planning organizations (MPOs) prioritize the improvement of existing public transportation in urban core areas over expansion of transit networks towards disconnected exurbs. This approach is an effective way to reduce vehicle miles traveled (and thus, air pollution) at the regional level due to high population concentrations in urban cores. However, this approach also concentrates the air quality benefits of VMT reduction in these same urban core areas. Exurban residents' on-road and near-road exposure to Traffic-Related Air Pollution (TRAP) will not be reduced by improving public transit within the urban core. We argue that although these suburban and exurban communities are a small percentage of the regional population, they have a right to share in the benefits of transportation investments, particularly given the historical and ongoing patterns of displacement and economic exclusion from urban core areas.     

A scenario planning approach for the flood emergency logistics preparation problem under uncertainty

This paper aims to develop a decision-making tool that can be used by government agencies in planning for flood emergency logistics. In this article, the flood emergency logistics problem with uncertainty is formulated as two stochastic programming models that allow for the determination of a rescue resource distribution system for urban flood disasters. The decision variables include the structure of rescue organizations, locations of rescue resource storehouses, allocations of rescue resources under capacity restrictions, and distributions of rescue resources. By applying the data processing and network analysis functions of the geographic information system, flooding potential maps can estimate the possible locations of rescue demand points and the required amount of rescue equipment. The proposed models are solved using a sample average approximation scheme. Finally, a real example of planning for flood emergency logistics is presented to highlight the significance of the proposed model as well as the efficacy of the proposed solution strategy.     

Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data

Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions.     

Sustainability evaluation of rapid routes for buses with a network DEA model

This study establishes a network data envelopment analysis (DEA) model to evaluate the sustainability of public transportation services targeting rapid routes for buses in the Seoul metropolitan area. A network DEA-based optimization model is formulated to evaluate the sustainability of the public transportation service. By considering public transportation services from both the operators’ and users’ perspectives, this model produces results that reflect the interaction of three sustainable transport service properties, i.e., efficiency, equity, and environmental impacts. It is identified that the expansion of median bus lanes and the conversion of conventional buses into compressed natural gas vehicles could improve the sustainability of the public transportation services in the Seoul metropolitan area. Some limitations and future research agenda also are presented.     

Unveiling large-scale commuting patterns based on mobile phone cellular network data

In this study, with Estonia as an example,we established an approach based on Hidden Markov Model to extract large-scale commuting patterns at different geographical levels using a massive amount of mobile phone cellular network data, which is referred to as Call detail record (CDR). The proposed model is designed for reconstructing and transforming the trajectories extracted from the CDR data. This step allowed us to perform origin-destination matrix extraction among different geographical levels, which helped in depicting the commuting patterns. Besides, we introduced different techniques for analyzing the commuting at the urban level. Our results unveiled that there is great potential behind mobile data of the cellular networks after transforming it into meaningful mobility patterns. That can easily be used for understanding urban dynamics, large-scale daily commuting and mobility. The aggressive development and growth of ubiquitous mobile sensing have generated valuable data that can be used with our approach for providing answers and solutions to the growing problems of transportation, urbanization and sustainability.     

Spatial interpolation of traffic counts based on origin–destination centrality

This paper presents a new method to estimate Annual Average Daily Traffic. Often traffic volumes are estimated based on roadway characteristics, such as number of lanes, speed limit, and adjacent land use. However, for many communities, especially small communities, these attributes are uniform across roadway types and therefore unable to adequately explain observed variation in traffic volumes. The new method uses novel explanatory variables that are intrinsically derived through a modified form of centrality, a network analysis metric that quantifies the topological importance of a link in a network. The new approach requires minimal data collection and is easily executed using a geographic information system. The case study showed high quality results (out-of-sample validation R² = 0.95). The new approach can be used for various activities related to transportation planning and investment decision making.     

Long haul freight network design using shipper–carrier freight flow prediction: A California network improvement case study

Transportation network design is a method for analyzing the interactive benefits of transportation projects applied to a network. In this paper, a network design model is developed for long haul freight movements which are represented by relationships between shippers and carriers. Additionally, an explicit capacity constraint is used to divert traffic volume from congested links. A case study based on the California transportation network is implemented to examine the effectiveness of this model when applied to a large network. A geographic information system is used to facilitate data management and analysis of the results.     

The case of Paratransit - ‘Trotro’ service data as a credible location addressing of road networks in Ghana

Road networks are by far the largest mobility infrastructure in Sub-Sahara Africa, and are key to providing access to economic and social opportunities. Yet the majority of road networks in Ghana and other countries in Sub-Sahara Africa are not geographically referenced, to facilitate their uses for road maintenance and management, transport planning, emergency services, disaster cases, logistics, tourism and other location based services. Consequently, this study takes advantage of paratransit service and the location of landmarks to provide location addressing of road networks in Ghana. The paratransit service, popularly known as Trotro in Ghana is a local transit system that uses an automobile to move people and goods along a prescribed travel route on a road network, with locally known stops where people get on and off the vehicle. The Trotro service is popular in Ghana, yet there is no critical investigation demonstrating its spatial coverage, relevance as a credible location addressing for road networks, or as a comprehensive location data for location based services. This study provides evidence for this case, and investigates the spatial coverage of road networks used by the Trotro vehicles, and the mapping of their service stops together with landmarks in the Asokore Mampong Municipality (AMM) of Ashanti region in Ghana. It was found that the location data from both Trotro service and landmarks covered about 86% of the entire road network in AMM; providing a large coverage of the road network and greater geographic detail. A service area estimation undertaken using the generated location data shows a high geographic accessibility, with travel distances as low as 160 m from any location to the nearest Trotro service or landmark covering about 80% of the road networks. These results show the capability of the generated location data to tackle the problem of spatially unreferenced road networks, and to significantly improve their effective uses. The popularity of paratransit in Sub-Sahara Africa means that this study can be adapted to other countries where the majority of the road networks are not spatially referenced.     

Developing a geocollaboratory for Indigenous tourism research

Collaboratories have been defined as virtual places where collaborative research can be undertaken. As part of the Aboriginal Tourism Network (ABORINET), a geocollaboratory was developed to support Indigenous tourism research. Indigenous communities are culturally distinct and remotely located and this presents geographic and sociocultural constraints when conducting research on issues affecting these communities. ABORINET's development focused on the specific goal of enabling collaboration between researchers and Indigenous peoples on issues related to Indigenous tourism planning and management, and the general issue of enabling the sharing of differing knowledge and management approaches among research and Indigenous communities. The purpose was to develop a multi-scale and multi-method data collection and analysis protocol for better understanding Indigenous tourism in a way that supports multi-site and longitudinal comparisons, for connecting Indigenous communities across the world, and for sharing the results in ways that are meaningful to stakeholders within and beyond Indigenous communities. This paper outlines the development of the geocollaboratory and describes the lessons learned with specific attention afforded the geographical nature of the collaboratory. Recommendations for mitigating challenges are proposed and future research opportunities are identified.     

A methodology to develop a geospatial transportation typology

We introduce a methodology to develop a geo-typology (geotype) that categorizes each location in the United States in terms of their main drivers of transportation demand and supply. We develop the first comprehensive set of geotypes for both urban and rural areas across the entire United States. This typology is designed to facilitate national level modeling of multi-modal transportation system's response to alternative investment strategies differentiated across different types of locations. We develop a two-stage clustering procedure to systematically and quantitatively characterize the ways in which locations across the nation are similar or different with respect to their potential response to investment strategies of interest. First, we cluster all 73,057 census tracts, using factor analysis and the CLARA clustering algorithm into “microtypes” based on their street network and economic characteristics. Then we cluster regions (core-basic statistical areas and counties) into “geotypes” using PAM clustering according to their commute configurations, polycentricity and density. The resulting set captures both local and regional variation. These microtypes and geotypes are comparable across all locations, enabling a national level perspective, while maintaining sufficient heterogeneity to support a variety of transportation analyses capturing critical geographic variation.     

Long-term impact of network access to bike facilities and public transit stations on housing sales prices in Portland,Oregon

Planners and economists generally accept that housing market values increase with proximity to transportation facilities through the provision of improved access to activity locations. While the market benefits of rail station access are well-documented, inconsistent and insufficient methods have led to limited agreement on the true value associated with this locational amenity. Far fewer hedonic price studies have assessed the influence of bike facility access on housing sales prices, and those that have generally analyze cross-sectional data. In this study, we estimated a spatial hedonic model using a bootstrapped pseudo panel to determine the joint impact of network proximity to bike lanes and off-street multi-use paths, as well as light rail and streetcar stations, on housing sales in Portland, Oregon, from 2002 to 2013. Our findings revealed housing sales prices increased as network distance to the nearest light rail transit and streetcar station decreased. Likewise, owner-occupied single-family and multifamily housing sales rose in conjunction with reduced street network access to regional multi-use bike paths; however, improved proximity to on-street bike lanes negatively affected housing values. In sum, we believe these findings may help to inform non-automotive transportation infrastructure financing mechanisms that rely on rising property values.     

Analysis of active school transportation in hilly urban environments: A case study of Dresden

This paper analyses the way students travel to school and examines the influence of environmental conditions on travel patterns. More specifically, it studies how topographic changes affect the likelihood of choosing cycling as a transport mode. We use mode choice data on students' home-to-school commuting trips from a previous study by Müller et al. (2008). The results show that models perform better when they account for the topographic conditions of the urban environment. We included this information in the model by introducing the “energy exerted” variable, which significantly improves the model and the results. The implications of this study are manifold; it guides the consolidation or expansion of school-based transportation network planning in Germany and prompts further analysis of active transportation systems, such as bike, pedelec and e-bike sharing systems. Overall, transportation policy should seek to foster active transportation, as it provides the greatest benefits for society and has a direct impact on people's well-being, while notably reducing the negative environmental and socioeconomic impacts of motorized transport.     

Divided we stand: institutional collaboration in tourism planning and development in the Central Region of Ghana

This research is an exploratory study that examines collaboration at the institutional level in the tourism sector of the Central Region, Ghana. The research begins with a review of the key issues related to collaboration in tourism planning and development followed by an extensive exploration of three main issues related to institutional collaboration in tourism in the Central Region. The three main issues are the vision of tourism development shared among stakeholders, collaboration and coordination within the public sector and between the public and private sectors and the factors that constrain and facilitate collaboration and coordination. Using extensive interviews with key stakeholders and reviewing policy documents, the research indicates low levels of collaboration between tourism institutions both within the public sector and across the public–private sectors. This is notwithstanding a shared awareness of the benefits of collaboration among all actors. The research thus contributes interesting insights into the politics of collaboration in tourism destinations. Given tourism's contribution to the Ghanaian economy, it is imperative that efforts are made towards improving the levels of collaboration and coordination between tourism agencies and institutions.     

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.     

Spatial context and the complexity of daily travel patterns: an international comparison

The analysis of travel patterns is an important research topic in transportation research and urban planning. It provides the background information necessary to better understand the complex relationship between urban structure, the transportation system and household travel patterns. To what extent do travel behaviour reflect the properties of the urban structure and the transportation network, or do these patterns largely follow their own regularities? Can different patterns be observed across different space-time settings, or can common patterns be observed, largely independent from such contexts? To better understand these relationships, this paper reports on some of the findings of analyses, conducted to identify underlying structures in various aspects of travel patterns. Travel patterns, derived from activity and travel diary data collected in Portland (USA), Midlands (UK), Fukuoka (Japan), Canadian metropolitan areas, and the South-Rotterdam region (The Netherlands) are compared. The results indicate that travel patterns are largely independent from spatial setting, except for some extreme cases.     

Unobserved heterogeneity in transportation equity analysis: Evidence from a bike-sharing system in southern Tampa

Assessing the equity impacts of transportation systems/policies has become a crucial component in transportation planning. Existing statistical modeling approaches for transportation equity analysis have typically assumed that parameter estimates are constant across all observations and used data aggregated to certain geographic units for the analysis. Such methods cannot capture unobserved factors that are not contained in the dataset, i.e., unobserved heterogeneity, which is likely to be present in the increasingly popular disaggregated datasets. To investigate whether there is unobserved heterogeneity in transportation equity impacts, this study carries out an empirical study focusing on the distribution of individual accessibility to activity locations via bike-sharing in southern Tampa. A disaggregated dataset containing information on individual bike-sharing accessibility and socio-economic factors is modeled with a random parameters logit model that allows for the investigation of possible unobserved heterogeneity. Further, models are estimated using data aggregated to parcel- and TAZ-levels to explore the impacts of data aggregation on model estimation results. The models unveil the unobserved heterogeneity in bike-sharing accessibility among populations in different groups defined by different sociodemographic factors in southern Tampa. These results shed insights into how the inconsistent disparity direction of transportation outcomes across individuals in a population group can be measured from the heterogeneity effects. Finally, a comparison between different models show that to capture such inconsistency, the use of disaggregated data with heterogeneity models is highly recommended for transportation equity analysis.     

Traveling in the three-dimensional city: applications in route planning, accessibility assessment, location analysis and beyond

Indoor environments are increasingly important spaces in contemporary three-dimensional cities. Handling these micro-scale spaces within the broader context of outdoor - urban environments is a significant challenge in transportation analysis and geographic information science. This complexity stems from the greater computational requirements, the specificity of indoor network data structures, as well as the inherent perceptual intricacies of 3D visualization. As built environments grow in size and in complexity, two-dimensional urban geography research reveals its limits. This paper advocates for 3D network-based urban research and showcases the feasibility of this approach for three specific types of urban analytical functionalities, namely route planning, spatial accessibility assessment, and facility location planning. The latter modeling functionality can be viewed as foundational elements for comprehensive network-based three-dimensional analytics of urban environments. The effectiveness of the data modeling scheme and of the integrated software application 3DCityNet is discussed through the three coupled geospatial modeling functionalities.