Stefan van der Spek

Sensing Human Activity: GPS Tracking

The enhancement of GPS technology enables the use of GPS devices not only as navigation and orientation tools, but also as instruments used to capture travelled routes: as sensors that measure activity on a city scale or the regional scale. TU Delft developed a process and database architecture for collecting data on pedestrian movement in three European city centres, Norwich, Rouen and Koblenz, and in another experiment for collecting activity data of 13 families in Almere (The Netherlands) for one week. The question posed in this paper is: what is the value of GPS as ‘sensor technology’ measuring activities of people? The conclusion is that GPS offers a widely useable instrument to collect invaluable spatial-temporal data on different scales and in different settings adding new layers of knowledge to urban studies, but the use of GPS-technology and deployment of GPS-devices still offers significant challenges for future research.

Why GPS makes distances bigger than they are

ABSTRACT 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.

Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns.

What is an Appropriate Temporal Sampling Rate to Record Floating Car Data with a GPS

Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car’s dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.

Tracking Tourists in Historic City Centres

According to the initiators of the’ spatial Metro project’ the first impression when visiting a city centre is the most important one (Hoeven, Smit & Spek, 2008). Unfortunately, mostly cities are chaotic and confusing places. The Spatial Metro project addresses the topic of improving city centres for pedestrians, especially for shoppers and tourists. The question HOW and with what MEANS can only be answered if the cities have insight in the ISSUES, the VARIABLES or optional INTERVENTIONS and influence of specific SOLUTIONS. Questionnaires or street-interviews can provide information about the expectations and experience of visitors. But, more important is the information on spatial behaviour of visitors (Shoval & Isaacson, 2007), indicating destinations, routes and duration and thus also notvisited locations (Schaick & Spek, 2008). TU Delft developed a tool to collect spatio-temporal data and applied this tool in three cities: Norwich, Rouen and Koblenz (Hoeven, Smit & Spek, 2008). This chapter will focus on a method for tracking tourists in city centres.

Automatic update of road attributes by mining GPS tracks

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.

Mapping Pedestrian Movement: Using Tracking Technologies in Koblenz

The enhancement of GPS technology enables the use of GPS devices not only as navigation tools, but also instruments used to capture a travelled route. In the Spatial Metro project, this ability has been used to develop a method to track pedestrians in city centres. Where have you been, and what have you been doing?

Interpreting pedestrian behaviour by visualising and clustering movement data

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic

Abstract 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 cars movement along the road. Then, we compared these to fuel consumption data from the cars controller area network (CAN) bus, normalized to the cars 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.

Extraction of Usage Patterns for Land-Use Types by Pedestrian Trajectory Analysis

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.