Aims
Railway station areas can play a crucial role in promoting sustainable development if integrated with cities and be fluctuation-responsive through effective urban design. However, during the design stage, assessing the station areas' performance, of which user satisfaction is indicative, is challenging due to methodological limitations. Agent-based simulation (ABS) is promising as it can link spatial features with agents' behavior features. This research questions to what extent ABS can help assess the urban design of station areas.

Methods
This paper adopts the user pyramid as the theoretical framework, which outlines five types of user needs: safety, speed, ease, comfort, and experience. The paper selects indicators linking satisfaction and spatial features at the district and building levels. These indicators are measured in the simulation of the station system using digital tools, including MassMotion and Python scripts. The theory, indicators, and tools, in combination, serve as an assessment framework. Rotterdam Central Station is used as a case to demonstrate how the framework works.

Results
The framework is capable of assessing design alternatives by identifying changes in user satisfaction. It can be applied on the district level (at a scale of 250 m) with substantial details to inform design decision-making, and it is useful during the design stage when only limited data is available. This paper strengthens the scientific knowledge of railway station areas through the multidisciplinary literature review that translates user needs for urban design use, and it advances the digital means to visualize user satisfaction affected by design.@en

Mobility is vital for societal wellbeing, economic growth, social inclusion, and access to essential amenities. However, the current system faces significant challenges, including environmental impact, unequal access, and safety concerns. […]@en

Post-war urban neighbourhoods in industrialised countries have been shown to negatively affect the lifestyles of their residents due to their design. This study aims at developing an empirical procedure to select locations to be redesigned and the determinants of health at stake in these locations, with involvement of residents’ perspectives as core issue. We addressed a post-war neighbourhood in the city of Groningen, the Netherlands. We collected data from three perspectives: spatial analyses by urban designers, interviews with experts in local health and social care (n = 11) and online questionnaires filled in by residents (n = 99). These data provided input for the selection of locations to be redesigned by a multidisciplinary team (n = 16). The procedure yielded the following types of locations (and determinants): An area adjacent to a central shopping mall (social interaction, traffic safety, physical activity), a park (experiencing green, physical activity, social safety, social interaction) and a block of low-rise row houses around a public square (social safety, social interaction, traffic safety). We developed an empirical procedure for the selection of locations and determinants to be addressed, with addressing residents’ perspectives. This procedure is potentially applicable to similar neighbourhoods internationally.

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Physical activity is good for people’s health. The relationship between the built environment and physical activity has been well documented. However, evidence is both scarce and scattered on specific urban interventions, i.e., intentional redesigns of the built environment that promote physical activity accompanied by pre- and post-effect measurement. This umbrella review aims to synthesize the findings of systematic reviews focused on these urban interventions. We followed the PRISMA 2020 and JBI umbrella review protocol guidelines and searched seven databases covering the period between Jan 2010 and April 2022 using keywords relating to the built environment, health, physical activity, and interventions. This yielded seven systematic reviews, in which we identified several urban interventions that can promote physical activity. We found positive effects of urban interventions on physical activity regarding park renovations, adding exercise equipment, introducing a (new) pocket park, improving cycling environments, improving walking & cycling environments, as well as multi-component initiatives for active travel and enhancing the availability & accessibility of destinations. The findings suggest that the urban environment can effectively promote physical activity, especially by adding various facilities and destinations and by making the environment better suitable for active use.

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Introduction: In China, Station-City Integration is proposed by the design academy in China to solve problems and add value. This research focuses on urban design for the redevelopment of Old Major Railway Stations in Megacities in China.

Problems & Goals: From our focused cases, two problems are discovered: Stuckness and congestion on peak days, Space underuse on normal days. Two goals are proposed correspondingly: Mobility Resilience and Public Space Flexibility.

Knowledge Gaps: Theoretical and practical knowledge are lacking for the proposed goals. The component words of the goals all have rich meanings. Some of them have related assessment tools and design recommendations.

Research Questions: For urban design research and practice, how can Mobility Resilience and Public Space Flexibility be defined, assessed, and designed?

Deliverables: The deliverables will be Concept Definitions, Assessment Frameworks, and Design Principles. The Concept Definitions offer researchers a new way to see the Station City problems. The Assessment Frameworks offer researchers a new tool to assess the Station City problems through different dimensions. It can also be used for evaluations during the iterative design process. The Design Principles can be used for the scheme establishment.

Methods: For Concept Definitions, papers about mobility, resilience, public space, and flexibility were examined to propose definitions. For Assessment Framework, available technologies & data will be tested. For Design Principles, case studies of the best practices will be conducted.

Relevance: Scientifically, the Problem Identifications show a creative way of framing research problems between specification and generalization. These Concept Definitions show a solid way of transferring knowledge from other disciplines to urban design fields. Societally, the Problems Identifications and Concept Definitions set a starting point for practitioners to take action. The Assessment Frameworks and Design Principles are practical tools for designers.@en

This study seeks answers to the research question ‘How can walkability of urban spaces be analysed with a multidimensional approach by using mobile methods?’ The research consists of a literature review, a field study in the city centre of Delft conducted as Go-Along walks, which provide better insight in capturing the experience of walking in situ, and evaluation of design scenarios that were developed according to the outcomes of the field study. As a result, the study emphasizes the strong inter-relations between metrics for a walkable place and the necessity to discuss walkability multi-dimensionally.

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First/last mile transport is essential for transit but is often found to be the weakest link in a trip. Moreover, as a result of multiple developments (e.g. demographic shifts, urbanization, climate change, technology advancement) first/last mile transport will likely change rapidly. The literature review of this study shows six different categories of factors affecting first/last mile mode choice: (1) traveller, (2) psychological, (3) first/last mile trip, (4) first/last mile modes, (5) built environment, and (6) main stage. We used this framework to understand and predict the complex process of mode choice, specifically given the emerge of new modes. The performed mode choice experiment shows varying results regarding the preferences of travellers for existing and new means of first/last mile transport. Four future scenarios (varying in level of sharing and flexibility of rides) are investigated. Traditional means of transport such as private vehicles and traditional ride services remain preferred over shared vehicles and on-demand ride services. For instance, 21% of the travellers chooses a private but no shared vehicle, and 12% chooses a traditional but no on-demand ride service. On the other hand, 21% of the travellers prefer a shared vehicle and 23% prefer an on-demand ride service whenever these vehicles/services are available. These results illustrate that when mode choice factors are absent and there are no restrictions taken into account (for example the possession of a car and driving license when choosing car), the actual chosen means of transport in the current situation differs from the preferred means of transport in the future. The results also show potential for new, emerging, means of first/last mile transport. According to the ‘preferred situation’ by travellers, transit nodes and first/last mile systems require a different design regarding first/last mile facilities, dependent on the scenario(s) that will develop. The challenge for decision makers and planners is to steer mode choice decisions in the direction according to their policy objectives, where our insights support the corresponding design choices and policy interventions.@en

Research on moving objects and analysis of movement patterns in urban networks can help us evaluate urban land-use types. With the help of technologies such as global positioning systems, spatial information systems and spatial data the study of movement patterns is possible. By understanding and quantifying the patterns of pedestrian trajectories, we can find effects of and relations between urban land-use types and movements of pedestrians. Understanding urban land-use and their relationships with human activities has great implications for smart and sustainable urban development. In this study, we use the data of various urban land-use types and the trajectory of pedestrians in an urban environment. This paper presents a new approach for identifying busy urban land-use by semantic spatial trajectory in which urban land-uses are assessed according to the pedestrian trajectories. Undoubtedly, the extraction of popular urban land-uses and analysis of the association between popular places and the spatial and semantic movement allow us to improve the urban structure and city marketing system. In this regard, for semantic analysis of urban land-use, all stop points are extracted by a time threshold and they are enriched according to semantic information such as age, occupation, and gender. We examine if and how habits of using land-use types depend on qualities such as age, gender and occupation. For analysis of effects of various urban land-use types, all stop points near each urban land-use are detected. Determining what type of urban land-use cause pedestrian traffic and high absorption coefficient and what relation such high traffic has with semantic information such as age, occupation and gender. By clustering the stop points, the results indicate that stop at urban networks for each gender have a spatial correlation. Also, the results show that some urban land-use types have high traffic and we have a correlation with some semantic information such as age, gender and occupation.

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Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an addition to static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy, measuring temperature and humidity. The emphasis of the research is on the localisation of the sensor platforms. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. In this method the GPS module is only used for the composition of the radiomap. The quality of the radiomap methodology was assessed by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size.@en

Purpose
The purpose of this paper is to provide local environmental information to raise community’s environmental awareness, as a cornerstone to improve the quality of the built environment. Next to that, it provides environmental information to professionals and academia in the fields of urbanism and urban microclimate, making it available for reuse.

Design/methodology/approach
The wireless sensor network (WSN) consists of sensor platforms deployed at fixed locations in the urban environment, measuring temperature, humidity, noise and air quality. Measurements are transferred to a server via long range wide area network (LoRaWAN). Data are also processed and publicly disseminated via the server. The WSN is made interactive as to increase user involvement, i.e. people who pass by a physical sensor in the city can interact with the sensor platform and request specific environmental data in near real time.

Findings
Microclimate phenomena such as temperature, humidity and air quality can be successfully measured with a WSN. Noise measurements are less suitable to send over LoRaWAN due to high temporal variations.

Research limitations/implications
Further testing and development of the sensor modules is needed to ensure consistent measurements and data quality.

Practical implications
Due to time and budget limitations for the project group, it was not possible to gather reliable data for noise and air quality. Therefore, conclusions on the effect of the measurements on the built environment cannot currently be drawn.

Originality/value
An autonomously working low-cost low-energy WSN gathering near real-time environmental data is successfully deployed. Ensuring data quality of the measurement results is subject for upcoming research.@en

Managing urban areas has become one of the most important development challenges of the 21st century. Building sustainable cities is a major factor nowadays. In this context, the advent of technologies such as GPS (Global Positioning System) and GIS (Geographical Information Systems) enables to better address the relationship between urban form and people travel behaviour. Spatial and temporal data can be collected at once, giving an insight into the actual movement pattern of people. In this paper we make a contribution to the existing literature in mobility and urban studies by comparing a series of GIS-based neighbourhood indicators with the actual people travel behaviour detected by GPS survey. Information about built environment characteristics is retrieved by OpenStreetMap and other datasets. 10 different neighbourhoods in The Netherlands are compared and in the end the main features that characterize efficient neighbourhoods in terms of sustainable mobility patterns are identified.@en

Knowledge of people's locations and related mobility patterns are important for many decision-making processes. How to efficiently use the available space, is a common problem in many fields. Wireless Local Area Networks (WLAN) are widely used for locating mobile devices within this network. This study attempts to identify movement from Wi-Fi log data on the Delft University of Technology campus. The proposed method automatically explores people’s movement by firstly, extract stay places, secondly discover movement and finally, identify movement patterns. This method is studied for two spatial levels: (1) at building level, movement between, from and to buildings can be detected, (2) at building-part level, movement between, from and to large indoor regions can be detected. For indoor analysis, the travelled path is estimated using a network graph of the underlying floorplan. This paper shows promising results for mining people’s movement patterns between buildings and indoor building-parts.@en

There are basically two kinds of city centres: historic and modern. Historic city centres, built in an era when no cars existed, tend to be very walkable environments. Modern cities, however, are much more car-dominated, leading to a less attractive walking environment. Rotterdam is one of the four main cities in the Netherlands and its historic city centre was destroyed during the Second World War. The post-war reconstruction mainly caters to cars, like many other cities throughout the world, resulting in a somewhat pedestrianunfriendly walking environment. We set out to analyse this environment and its users, and hopefully with our results, make recommendations for car-dominated cities worldwide. We wanted to find out which paths people really took and to gain insight about personal preferences in a modern, cardominated city. We wanted to extract lessons for improving the city centre based on both real experiences and objectively measured behaviour.@en

The distribution of people in buildings, the occupancy of lecture-, work- and study places and the accessibility of facilities are essential information at university campuses who have to cope with limited and even shrinking budgets and huge, rising real estate costs. Only little insight is gained in both occupancy and movement patterns with traditional counting techniques and user-based questionnaires. Management teams state that rooms and facilities are hardly used, though staff and students complain about overcrowded facilities and limited flexibility. Actual and accurate data on a 24/7 scale with high-granularity is missing. In general Facility- and Asset Management lacks efficient methods for realtime, comprehensive and high-granularity information of location, capacity and use of tangible and intangible assets. Asset management could benefit from more detailed, more accurate and longitudinal data on assets, providing more insight into efficiency and effectiveness on different levels of scale through time. Existing technologies could provide a platform delivering those required insights. Navigation- and communication technologies such as GNSS, Wi-Fi, Bluetooth, RFID can be used to ‘locate’ users, estimate intensities and reveal patterns of movement and patterns of use. For Asset management indoor localisation is essential.@en

With the help of technologies such as GPS tracks, GIS and open data is now possible to study people travel behaviour in a new way. Nowadays, large datasets can be easily handled thanks to databases and better visualized using GIS. Moreover, the availability of GPS data, open data and VGI makes accessible a lot of new information, which was not obtainable before. This research is about the analysis of mobility patterns in different neighbourhoods in three cities in the Netherlands. The study is based on the validation of mobility theoretical performances by the actual performances measured through the analysis of GPS logs of households, who live in the neighbourhoods. A series of spatial indicators based on proximity, density and accessibility are computed in order to assess the theoretical performances of the neighbourhoods. To achieve this task, information about built environment characteristics and infrastructure networks is retrieved from OpenStreetMap, and other datasets. In the end the neighbourhoods are classified into five classes, according to the different levels of performances in terms of sustainable mobility. In such a way, it is possible to better understand the key factors that influence actual people travel patterns, providing policy makers with accurate information about the real movement of people.@en

Global navigation satellite systems such as the Global Positioning System (GPS) is one of the most important sensors for movement analysis. GPS is widely used to record the trajectories of vehicles, animals and human beings. However, all GPS movement data are affected by both measurement and interpolation errors. In this article we show that measurement error causes a systematic bias in distances recorded with a GPS; the distance between two points recorded with a GPS is – on average – bigger than the true distance between these points. This systematic ‘overestimation of distance’ becomes relevant if the influence of interpolation error can be neglected, which in practice is the case for movement sampled at high frequencies. We provide a mathematical explanation of this phenomenon and illustrate that it functionally depends on the autocorrelation of GPS measurement error (C). We argue that C can be interpreted as a quality measure for movement data recorded with a GPS. If there is a strong autocorrelation between any two consecutive position estimates, they have very similar error. This error cancels out when average speed, distance or direction is calculated along the trajectory. Based on our theoretical findings we introduce a novel approach to determine C in real-world GPS movement data sampled at high frequencies. We apply our approach to pedestrian trajectories and car trajectories. We found that the measurement error in the data was strongly spatially and temporally autocorrelated and give a quality estimate of the data. Most importantly, our findings are not limited to GPS alone. The systematic bias and its implications are bound to occur in any movement data collected with absolute positioning if interpolation error can be neglected.@en

Despite advances in cartography, mapping is still a costly process which involves a substantial amount of manual work. This article presents a method for automatically deriving road attributes by analyzing and mining movement trajectories (e.g. GPS tracks). We have investigated the automatic extraction of eight road attributes: directionality, speed limit, number of lanes, access, average speed, congestion, importance, and geometric offset; and we have developed a supervised classification method (decision tree) to infer them. The extraction of most of these attributes has not been investigated previously. We have implemented our method in a software prototype and we automatically update the OpenStreetMap (OSM) dataset of the Netherlands, increasing its level of completeness. The validation of the classification shows variable levels of accuracy, e.g. whether a road is a one- or a two-way road is classified with an accuracy of 99%, and the accuracy for the speed limit is 69%. When taking into account speed limits that are one step away (e.g. 60 km/h instead of the classified 50 km/h) the classification increases to 95%, which might be acceptable in some use-cases. We mitigate this with a hierarchical code list of attributes.@en

The distribution of people in buildings, the occupancy of lecture-, work- and study places and the accessibility of facilities are essential information at university campuses who have to cope with limited and even shrinking budgets and huge, rising real estate costs. Only little insight is gained in both occupancy and movement patterns with traditional counting techniques and user-based questionnaires. Management teams state that rooms and facilities are hardly used, though staff and students complain about overcrowded facilities and limited flexibility. Actual and accurate data on a 24/7 scale with high-granularity is missing. In general Facility- and Asset Management lacks efficient methods for realtime, comprehensive and high-granularity information of location, capacity and use of tangible and intangible assets. Asset management could benefit from more detailed, more accurate and longitudinal data on assets, providing more insight into efficiency and effectiveness on different levels of scale through time. Existing technologies could provide a platform delivering those required insights. Navigation- and communication technologies such as GNSS, Wi-Fi, Bluetooth, RFID can be used to ‘locate’ users, estimate intensities and reveal patterns of movement and patterns of use. For Asset management indoor localisation is essential.@en

Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.@en

The paper presents a workflow for collecting, structuring and processing geo-referenced recreational mobility data from a sports tracking application as to monitor recreational usage of urban spaces. The data collected include GPS trajectories of people walking, jogging, and running for recreational purposes in European cities. The presented workflow includes systematic steps for aggregating the trajectories and attributing them to a spatial network model called Urban Space Network. The nodes of this network are the navigable spaces or streets and its links are the connections between them. A method is proposed to find a fuzzy notion of recreational space usage, using the number of distinct application users whose trajectories have been accounted for the space in question. The fuzzified space usage values are then attributed to the nodes of the network. This model can be used primarily used to observe actual patterns of space usage and has the potential to be used as ground truth data for validating and calibrating network-based models of recreational mobility. Patterns revealed by the workflow can be used to study where outdoor physically active mobility happens and where it is absent. Thus the proposed workflow can provide spatial and objective insight useful in planning, management and governance of cities in promoting active mobility that is already a rather global trend in urbanism.@en