David Gundlegård

Enabling reliable and secure IoT-based smart city applications

Smart Cities are considered recently as a promising solution for providing efficient services to citizens with the use of Information and Communication Technologies. With the latest advances on the Internet of Things, a new era has emerged in the Smart City domain, opening new opportunities for the development of efficient and low-cost applications that aim to improve the Quality of Life in cities. Although there is much research in this area, which has resulted in the development of many commercial products, significant parameters like reliability, security and privacy have not been considered as very important up until now. The newly launched FP7-SmartCities-2013 project RERUM aims to build upon the advances in the area of Internet of Things in Smart Cities and develop a framework to enhance reliability and security of smart city applications, with the citizen at the center of attention. This work presents four applications that will be developed within RERUM, gives a general description of the open reliability and security issues that have to be taken into account and gives an overall view of the solutions that RERUM will develop to address these issues.

Generating Road Traffic Information from Cellular Networks - New Possibilities in UMTS

This paper summarizes different approaches to collecting road traffic information from second-generation cellular systems (GSM) and point out the possibilities that arise when third generation systems (UMTS) are used. Cell breathing is a potential problem, but smaller cells, soft handover and flexible measurements have the potential to increase the usage area and information quality when road traffic information is extracted from the UMTS network compared to using the GSM network

Travel demand estimation and network assignment based on cellular network data

Abstract Cellular networks’ signaling data provide means for analyzing the efficiency of an underlying transportation system and assisting the formulation of models to predict its future use. This paper describes how signaling data can be processed and used in order to act as means for generating input for traditional transportation analysis models. Specifically, we propose a tailored set of mobility metrics and a computational pipeline including trip extraction, travel demand estimation as well as route and link travel flow estimation based on Call Detail Records (CDR) from mobile phones. The results are based on the analysis of data from the Data for development “D4D” challenge and include data from Cote dIvoire and Senegal.

Route classification in travel time estimation based on cellular network signaling

Travel time estimation based on cellular network signaling is a promising technology for delivery of wide area travel times in real-time. The technology has received much attention recently, but few academic research reports has so far been published in the area, which together with uncertain location estimates and environmental dependent performance makes it difficult to assess the potential of the technology. This paper aims to investigate the route classification task in a cellular travel time estimation context in detail. In order to estimate the magnitude of the problem, two classification algorithms are developed, one based on nearest neighbor classification and one based on Bayesian classification. These are then evaluated using field measurements from the GSM network. A conclusion from the results is that the route classification problem is not trivial even in a highway environment, due to effects of multipath propagation and changing radio environment. In a highway environment the classification problem can be solved rather efficiently using e.g., one of the methods described in this paper, keeping the effect on travel time accuracy low. However, in order to solve the route classification task in urban environments more research is required.

Hybrid Approach for Short-Term Traffic State and Travel Time Prediction on Highways

Traffic management and traffic information are essential in urban areas and require reliable knowledge about the current and future traffic state. Parametric and nonparametric traffic state prediction techniques have previously been developed with different advantages and shortcomings. While nonparametric prediction has shown good results for predicting the traffic state during recurrent traffic conditions, parametric traffic state prediction can be used during nonrecurring traffic conditions, such as incidents and events. Hybrid approaches have previously been proposed; these approaches combine the two prediction paradigms by using nonparametric methods for predicting boundary conditions used in a parametric method. In this paper, parametric and nonparametric traffic state prediction techniques are instead combined through assimilation in an ensemble Kalman filter. For nonparametric prediction, a neural network method is adopted; the parametric prediction is carried out with a cell transmission model with velocity as state. The results show that the hybrid approach can improve travel time prediction of journeys planned to commence 15 to 30 min into the future, with a prediction horizon of up to 50 min ahead in time to allow the journey to be completed.

Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering

The number and heterogeneity of traffic sensors are steadily increasing. A large part of the emerging sensors are measuring point speeds or travel times and in order to make efficient use of this data, it is important to develop methods and frameworks for fusion of point speed and travel time measurements in real-time. The proposed method combines a macroscopic traffic model and a non-linear filter with a new measurement model for fusion of travel time observations in a system that uses the velocity of cells in the network as state vector. The method aims to improve the fusion efficiency, especially when travel time observations are relatively long compared to the spatial resolution of the estimation framework. The method is implemented using the Cell Transmission Model for velocity (CTM-v) and the Ensemble Kalman Filter (EnKF) and evaluated with promising results in a test site in Stockholm, Sweden, using point speed observations from radar and travel time observations from taxis.

The smartphone as enabler for road traffic information based on cellular network signalling

The higher penetration rate of GPS-enabled smartphones together with their improved processing power and battery life makes them suitable for a number of participatory sensing applications. The purpose of this paper is to analyse how GPS-enabled smartphones can be used in a participatory sensing context to build a radio map for RSS-based positioning, with a special focus on road traffic information based on cellular network signalling. The CEP-67 location accuracy achieved is 75 meters for both GSM and UMTS using Bayesian classification. For this test site, the accuracy is similar for GSM and UMTS, with slightly better results for UMTS in the CEP-95 error metric. The location accuracy achieved is good enough to avoid large errors in travel time estimation for highway environments, especially considering the possibility to filter out estimates with low accuracy using for example the posterior bin probability in Bayesian classification. For urban environments more research is required to determine how the location accuracy will affect the path inference problem in a dense road network. The location accuracy achieved in this paper is also sufficient for other traffic information types, for example origin-destination estimation based on location area updates.

Traffic management for smart cities

Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management . This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example, short-term prediction and control.

Cellpath Routing and Route Traffic Flow Estimation Based on Cellular Network Data

ABSTRACT The signaling data in cellular networks provide means for analyzing the use of transportation systems. We propose methods that aim to reconstruct the used route through a transportation network from call detail records (CDRs) which are spatially and temporally sparse. The route estimation methods are compared based on the individual routes estimated. We also investigate the effect of different route estimation methods when employed in a complete network assignment for a larger city. Using an available CDR dataset for Dakar, Senegal, we show that the choice of the route estimation method can have a significant impact on resulting link flows.

Trip extraction for traffic analysis using cellular network data

To get a better understanding of peoples mobility, cellular network signalling data including location information, is a promising large-scale data source. In order to estimate travel demand and infrastructure usage from the data, it is necessary to identify the trips users make. We present two trip extraction methods and compare their performance using a small dataset collected in Sweden. The trips extracted are compared with GPS tracks collected on the same mobiles. Despite the much lower location sampling rate in the cellular network signalling data, we are able to detect most of the trips found from GPS data. This is promising, given the relative simplicity of the algorithms. However, further investigation is necessary using a larger dataset and more types of algorithms. By applying the same methods to a second dataset for Senegal with much lower sampling rate than the Sweden dataset, we show that the choice of the trip extraction method tends to be even more important when the sampling rate is low.