Ove Andersen

A Location Privacy Aware Friend Locator

A location-based service called friend-locator notifies a user if the user is geographically close to any of the users friends. Services of this kind are getting increasingly popular due to the penetration of GPS in mobile phones, but existing commercial friend-locator services require users to trade their location privacy for quality of service, limiting the attractiveness of the services. The challenge is to develop a communication-efficient solution such that (i) it detects proximity between a user and the users friends, (ii) any other party is not allowed to infer the location of the user, and (iii) users have flexible choices of their proximity detection distances. To address this challenge, we develop a client-server solution for proximity detection based on an encrypted, grid-based mapping of locations. Experimental results show that our solution is indeed efficient and scalable to a large number of users.

EcoMark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data

Eco-routing is a simple yet effective approach to substantially reducing the environmental impact, e.g., fuel consumption and greenhouse gas (GHG) emissions, of vehicular transportation. Eco-routing relies on the ability to reliably quantify the environmental impact of vehicles as they travel in a spatial network. The procedure of quantifying such vehicular impact for road segments of a spatial network is called eco-weight assignment. EcoMark 2.0 proposes a general framework for eco-weight assignment to enable eco-routing. It studies the abilities of six instantaneous and five aggregated models to estimating vehicular environmental impact. In doing so, it utilizes travel information derived from GPS trajectories (i.e., velocities and accelerations) and actual fuel consumption data obtained from vehicles. The framework covers analyses of actual fuel consumption, impact model calibration, and experiments for assessing the utility of the impact models in assigning eco-weights. The application of EcoMark 2.0 indicates that the instantaneous model EMIT and the aggregated model SIDRA-Running are suitable for assigning eco-weights under varying circumstances. In contrast, other instantaneous models should not be used for assigning eco-weights, and other aggregated models can be used for assigning eco-weights under certain circumstances.

EcoTour: Reducing the Environmental Footprint of Vehicles Using Eco-routes

Reduction in greenhouse gas emissions from transportation is essential in combating global warming and climate change. Eco-routing enables drivers to use the most eco-friendly routes and is effective in reducing vehicle emissions. The EcoTour system assigns eco-weights to a road network based on GPS and fuel consumption data collected from vehicles to enable ecorouting. Given an arbitrary source-destination pair in Denmark, EcoTour returns the shortest route, the fastest route, and the eco-route, along with statistics for the three routes. EcoTour also serves as a testbed for exploring advanced solutions to a range of challenges related to eco-routing.

An Open-Source Based ITS Platform

In this paper, a complete platform used to compute travel times from GPS data is described. Two approaches to computing travel time are proposed one based on points and one based on trips. Overall both approaches give reasonable results compared to existing manual estimated travel times. However, the trip-based approach requires more GPS data and of a higher quality than the point-based approach. The platform has been completely implemented using open-source software. The main conclusion is that large quantity of GPS data can be managed, with a limited budget and that GPS data is a good source for estimating travel times, if enough data is available.

Trajectories for novel and detailed traffic information

Trajectories based on GPS tracks have been studied for a number of years but only to a limited degree been used for analyzing and monitoring traffic. This paper shows how novel and important information about traffic can be computed from trajectories. Concretely the paper proposes to compute the central metric free-flow speed from trajectories, instead of using point-based measurements such as induction-loops. This free-flow speed is widely used to compute and monitor the congestion level. The paper argues that the actual travel-time is a more accurate metric. The paper suggests a novel approach to analyzing individual intersections that enables traffic analysts to compute queue lengths and estimated time to pass an intersection. Finally, the paper uses associative rule mining for evaluating green waves on road stretches. Such information can be used to verify that signalized intersections are correctly coordinated, and navigational device manufacturers to advice drivers in real-time on expected behavior of signalized intersections. The main conclusion is that trajectories can provide novel insight into the actual traffic situation that is not possible using existing approaches. Further, extracting this information requires no expensive changes to the road-network infrastructure, which is a problem with the technologies currently used.

Trajectory based traffic analysis

We present the INTRA system for interactive path-based traffic analysis. The analyses are developed in collaboration with traffic researchers and provide novel insights into conditions such as congestion, travel-time, choice of route, and traffic-flow. INTRA supports interactive point-and-click analysis, due to a novel and efficient indexing structure. With the web-site daisy.aau.dk/its/spqdemo/we will demonstrate several analyses, using a very large real-world data set consisting of 1.9 billion GPS records (1.5 million trajectories) recorded from more than 13 000 vehicles, and touching most of the road network in Denmark.

An Advanced Data Warehouse for Integrating Large Sets of GPS Data

GPS data recorded from driving vehicles is available from many sources and is a very good data foundation for answering traffic related queries. However, most approaches so far have not considered combining GPS data from many sources into a single data warehouse. Further, the integration of GPS data with fuel consumption data (from the so-called CAN bus in the vehicles) and weather data has not been done. In this paper, we propose a data warehouse design for handling GPS data, fuel consumption data, and weather data. The design is fully implemented in a running system using the PostgreSQL DBMS. The system has been in production since March 2011 and the main fact table contains today approximately 3.4 billion rows from 16 different data sources. We show that the system can be used for a number of novel traffic related analyses such as relating the fuel consumption of vehicles with the road network and road congestion.

Analyzing Electric Vehicle Energy Consumption using Very Large Data Sets

An electric vehicle (EV) is an interesting vehicle type because it has the potential of reducing the dependence on fossil fuels by using electricity from, e.g., wind turbines. A significant disadvantage of EVs is a very limited range, typically less than 200 km. This paper compares EVs to conventional vehicles (CVs) for private transportation using two very large data sets. The EV data set is collected from 164 vehicles (126 million rows) and the CV data set from 447 vehicles (206 million rows). Both data sets are collected in Denmark throughout 2012, with a logging frequency of 1 Hz. GPS data is collected from both vehicle types. In addition, EVs also log the actual energy consumption every second using the vehicle’s CAN bus. By comparing the two data sets, we observe that EVs are significantly slower on motorways, faster in cities, and drive shorter distances compared to CVs. Further, we study the effects of temperature, wind direction, wind speed, and road inclination. We conclude that the energy consumption (and range) of an EV is very sensitive to head wind, low temperatures, and steep road inclinations.

Efficient one-click browsing of large trajectory sets

Traffic researchers, planners, and analysts want a simple way to query the large quantities of GPS trajectories collected from vehicles. In addition, users expect the results to be presented immediately even when querying very large transportation networks with huge trajectory data sets. This paper presents a novel query type called sheaf, where users can browse trajectory data sets using a single mouse click. Sheaves are very versatile and can be used for location-based advertising, travel-time analysis, intersection analysis, and reachability analysis (isochrones). A novel in-memory trajectory index compresses the data by a factor of 12.4 and enables execution of sheaf queries in 40 ms. This is up to 2 orders of magnitude faster than existing work. We demonstrate the simplicity, versatility, and efficiency of sheaf queries using a real-world trajectory set consisting of 2.7 million trajectories (1.36 billion GPS records) and a network with 1.5 million edges.

Electric and conventional vehicle driving patterns

The electric vehicle (EV) is an interesting vehicle type that can reduce the dependence on fossil fuels, e.g., by using electricity from wind turbines. A significant disadvantage of EVs is a very limited range, typically less than 200 km. This paper compares EVs to conventional vehicles (CVs) for private transportation using two very large data sets. The EV data set is collected from 164 vehicles (126 million rows) and the CV data set from 447 vehicles (206 million rows). Both data sets are collected in Denmark throughout 2012, with a logging frequency of 1 Hz. By comparing the two data sets, we observe that EVs are significantly slower on motorways, faster in cities, and drive shorter distances compared to CVs.