Purpose: The purpose of this paper is to explore the actual walking distance to public transport (PuT) stations and to report passenger perceptions on route choice. Design/methodology/approach: A systematic case study has been conducted after administrating a tailor-made paper-based intercept survey in a German city (Munich). It can determine the interrelation between the accessibility of the transit service and evaluation on walking distance acceptance. Statistical analysis and geo-spatial approach were completed for obtaining major findings. Findings: Statistical and geo-spatial analysis shows that respondents living in low-density areas walk longer than residents living in nearby inner city areas. In terms of PuT modes, residents walk longer for suburban train and subway/metro (U-Bahn) than for bus/tram services. Transit users accept a longer walking distance to reach a train station than other PuT modes and they choose the most direct and quickest route to reach PuT stations. Research limitations/implications: Findings of this study would help to formulate future strategies and standards for the sustainable planning of public transportation systems in the context of Munich and many other cities around the globe with similar conditions. However, future research should be conducted using a large-scale survey for evaluating the comprehensive picture of walking patterns to PuT stations. Accessibility to PuT stations can also be modeled and evaluated by adopting open data and voluntary social media information. Unfortunately, this study only presents a partial evaluation of walking focused on accessibility at selected PuT stations in different settings of the urban fabric. Social implications: This empirical study can be considered as an initial finding in the favor of the city transport authority to provide a design scale for improved accessibility of transit users; however, further investigation should be conducted using a large-scale survey for evaluating the comprehensive walking patterns. Originality/value: A systematic case study has been conducted after administrating a tailor-made paper-based intercept survey in a German city (Munich). Findings of this study would help to formulate future strategies and standard for the sustainable planning of the public transportation system in the context of Munich and many other cities in the globe with similar conditions.

Data for Policy (dataforpolicy.org), a global community, focuses on policy–data interactions by exploring how data can be used for policy in an ethical, responsible, and efficient manner. Within its journal, six focus areas, including Data for Policy Area 1: Digital & Data-driven Transformations in Governance, were established to delineate the evolving research landscape from the Data for Policy Conference series. This review addresses the absence of a formal conceptualization of digital and data-driven transformations in governance within this focus area. The paper achieves this by providing a working definition, mapping current research trends, and proposing a future research agenda centered on three core transformations: (1) public participation and collective intelligence; (2) relationships and organizations; and (3) open data and government. The paper outlines research questions and connects these transformations to related areas such as artificial intelligence (AI), sustainable smart cities, digital divide, data governance, co-production, and service quality. This contribution forms the foundational development of a research agenda for academics and practitioners engaged in or impacted by digital and data-driven transformations in policy and governance.

The creation of an accurate simulation of future urban growth is considered to be one of the most important challenges of the last five decades that involves spatial modeling within a GIS environment. Even though built-up densification processes, or transitions from low to high density, are critical for policymakers concerned with limiting sprawl, the literature on models for urban study reveals that most of them focus solely on the expansion process. Although the majority of these models have similar goals, they differ in terms of implementation and theoretical assumptions. Cellular automata (CA) models have been proven to be successful at simulating urban growth dynamics and projecting future scenarios at multiple scales. This paper aims to revisit urban CA models to determine the various approaches for a realistic simulation and prediction of urban densification. The general characteristics of CA models are described with respect to analysis of various driving factors that influence urban scenarios. This paper also critically analyzes various hybrid models based on CA such as the Markov chain, artificial neural network (ANN), and logistic regression (LR). Limitation and uncertainties of CA models, namely, neighborhood cell size, may be minimized when integrated with empirical and statistical models. The result of this review suggests that it is useful to use CA models with multinomial logistic regression (MLR) in order to analyze and model the effects of various driving factors related to urban densification. Realistic simulations can be achieved when multidensity class labels are integrated in the modeling process.