Conference Agenda

The concept of “accessibility” encompasses three key drivers of urban mobility patterns and therefore mobility energy use: people (who is traveling?), places (where do people want to go to?), and the mobility infrastructure (how can they get there?). As such, accessibility metrics play an important role in many urban and land use planning applications. This includes efforts to design and develop urban areas for more sustainable mobility without sacrificing the quality of service that those mobility systems provide. Such accessibility metrics range from simple counts (such as the number of supermarkets within a 500m radius) to far more complex options based on gravity decay functions or so-called ‘logsum’ appraisals. While there are inherent trade-offs between how powerful, intuitive, and context-specific different accessibility metrics are, systematic comparisons have been rare. In this work, we evaluate how well simple accessibility metrics perform compared to complex ones that require more data and that are more difficult to interpret. Based on these observations, we investigate what these metrics can tell us about how to design more sustainable urban systems that maintain or even improve the levels of accessibility. We evaluate accessibility metrics of different complexities and based on travel distance, travel time, and generalized travel cost (utility) by how well they correlate with mobility greenhouse gas emissions—an indicator that reflects both modal choices and the typical daily distance covered with those modes. We do so for different types of trips: work, education, groceries, other errands, and leisure. We find that easily interpretable accessibility metrics perform 80-90% as well as more complex ones. However, the meaningfulness of any accessibility metrics heavily depends on correctly specified hyperparameters, and the ideal parameters can depend on the trip purpose and the type of travel costs (distance, time, or utility) used. We also highlight that accessibility operates at different scales: the local scale (< 500 m) is important to accurately capturing active mobility (walking and cycling) trips, while the general urban scale is important to capture the impact of sprawl on typical trip distances for motorized travel. Based on these observations, we identify specific urban planning levers that may lead to changes in mobility behavior and promote more sustainable mobility.

Cities evolves through the time. Time series analysis of the urban form, that map the dynamics of urban morphological elements across chronologial eras has been widely applied in demonstrating the evolutionary process of cities world-wide. It is logical to assume that the small urban centers gradually eveolved into matured cities during this long-term process. In the global-north, a rapid and unprecedented urbanization was evident with the immediate effect of industrial revolution, i.e. a century ago. Whereas in the Global South, the rapid urbanization is a living reality, hence measuring the level of maturity or comprehending the maturization process is particuallrly important for shaping sustainable trajectories. This study introduces a composite spatial methodology to assess the urban maturation processes in rapidly urbanizing localities with refering to a set of small and medium towns in Sri Lanka. Drawing on the conceptual foundations of Natural Urban Maturation and network analysis, the research seeks to identify the spatial logic of the evolution of urban form and its implications on contemporary urban planning practices.

The proposed framework classifies urban areas along a continuum from fragmented sprawl to consolidated urban form. Three core spatial domains structure the analysis are Network Centrality, Built Density and Land Use Mix. The Spatial Maturity Index is composed of built from-normalized indicators including the integration of steet network, road capacity, Floor Area Ratio (FAR), building height and land use entropy. The methodology was tested across ten Sri Lankan towns through GIS-based morphological analysis. Statistical correlation and spatial autocorrelation methods are applied to validate relationships and detect clustering of development patterns.

Findings revealed that early-stage towns tend to exhibit high integration levels leading to high accessability yet low density and land use diversity, resulting in a state of ‘incomplete urbanity’. Transitional towns also display spatial mismatch, where infrastructural investment precedes morphological consolidation—leading to inconsistent land use and risks such as inefficient land consumption. Whereas more-matured towns those have been historically-evolved demonstrate a highly compact built forms, and diversified land-use arrangement and highly connected street networks, reflecting a convergence toward a functionally integrated urban structure. Nonetheless there were no circumstances in the studied towns where diversified-land use mix or densified built forms resulted without high level of integration. Further the study examined how road capacity and network accessibility interact with urban density and land-use diversity, showing that enhanced accessibility often fuels incoherent or low-density growth when not guided by supportive land policy, and adequate infrastrucure supply.

The study developed a decision-support tool to model the interdependencies among land use, accessibility, and urban densities—offering critical insights for managing development in emerging urban regions. This research offers a replicable, data-driven methodology for diagnosing spatial transformations in non-metropolitan settings. It contributes to the Planning practice and Policy by introducing a robust, interdisciplinary framework for evaluating the spatial dynamics of urbanization.

Many cities in Germany are highly engaged in promoting walking, but hardly any data on street-level pedestrian volumes exist. This lack of data hinders evidence-based planning, the formulation of measurable goals and their monitoring. The goal of this study is to develop a method for estimating pedestrian volumes based on openly accessible spatial data.

Pedestrian count data, the study’s dependent variable, were collected using cameras installed on both sides of 56 street sections in 14 cities in Germany. The data were automatically processed using OpenTrafficCam (OTC), a tool to extract pedestrian volumes from video footage.

The independent variables were primarily sourced from open platforms such as OpenStreetMap (OSM) and Google Map’s Street View. They include infrastructure data (e.g., sidewalk width, building characteristics), weather data (e.g., temperature, precipitation), demographic data (e.g., population density), and POIs (e.g., shops, educational institutions). Two types of POIs were considered: (1) the number and type of POIs located on each street section (On-street POIs), (2) Values based on the influence area of these POIs in generating pedestrian traffic (Buffer POIs). For the analysis of the Buffer POIs, POIs were categorized by type; each was assigned a unique sphere of influence and the degree of overlap with each street sections was calculated.

Given the count nature of the dependent variable, a negative binomial model was chosen. The counts were considered twofold: for the whole cross-section and each street side of the street section. The modeling followed a stepwise process, based on multiple model configurations, considering combinations of uncorrelated variables, using On-street and/or Buffer POIs. Finally, models were ranked based on their statistical performance and plausibility of the variables.

Before modelling, a correlation analysis of the independent variables was performed. Correlations indicate that Buffer POIs of different categories tend to emerge together, as if the presence of one encourages the presence of others along the nearby street sections. Also, Buffer POIs are significantly correlated with the architectural characteristics of its streetscape (e.g. average number of floors, number of different building colors, ratio of closed buildings to section length, and ratio of glass frontage to section length). In addition, streets with a high concentration of Buffer POIs are mainly located in the city center, reflecting the area’s significance as a core location for multiple facilities.

As a result of the modeling process 24 best-performing models were selected (12 for entire street sections and 12 for individual street sides). The following insights could be derived: Pedestrian volumes tend to be higher on streets with wider pavements, taller buildings, and less speed for motorized vehicles, highlighting the critical role of key street characteristics in design and operation. Among all types of POIs, service, retail, and gastronomy venues are associated with higher pedestrian volumes than any other facility. Interestingly, sports facilities negatively impact pedestrian volumes, challenging the common assumption that all POIs contribute positively to pedestrian volumes.

Walking is the most common mode of transport that enhances well-being and livability in cities and has been promoted by European planning authorities via Sustainable Urban Mobility Plans (SUMP) to reduce car dependency and foster accessible neighborhoods. In Turkey, the development of SUMPs has recently gained momentum, with the completion of one plan yet. However, many cities, including Canakkale, have not established SUMPs, resulting in urbanized areas lacking active transportation options. Despite strategic recognition, many mid and small-sized cities struggle to implement walking-oriented interventions due to institutional limitations, which results in inadequate human-friendly streets and pedestrian safety concerns. As one of the representatives of this, Canakkale was selected as a pilot case study based on its non-metropolitan character, population, urban land coverage, landscape and street variations, diversity of pedestrianization, and speed regulation practices.

The objective of this study is twofold: developing an objective walkability assessment framework and defining walking-related problem typologies in the built environment to serve as a guide for the walkability phase of SUMP processes and pedestrian-friendly interventions in the future. With this aim, this research adopts a multi-method approach to score objective walkability in seven neighborhoods in the Canakkale city center. By focusing on parameters such as land use, accessibility, street network, pedestrian comfort, safety, and street design, AHP procedures were employed with urban design professionals to formulate a hierarchical framework. GIS and space syntax analyses assessed land use diversity, density, accessibility, slope, and street networks. Fieldwork was conducted to document streetscape features via an online mapping tool. The final stage of the research involved the calculation of overall walkability scores for each street, with these scores being based on built-environmental elements using municipal and collected data.

The preliminary findings indicate inequalities in spatial walkability scores across neighborhoods due to sub-parameters. Safety-related issues in zones with high concentrations of derelict buildings, neighborhood characteristics based on land-use diversity, accessibility to points of interest and public facilities, and street connectivity play a critical role in these disparities. Additionally, the presence of public spaces and the quality of urban design elements and street furniture significantly influence spatial aesthetic scores. The research concludes by identifying typologies of walkability issues through a comparative analysis of neighborhoods in the city center of Canakkale. Thus, this study is envisioned to support urban planners and policymakers in reforming future action strategies for SUMPs and to provide an analytical base for developing pedestrian-friendly micro-interventions.