Benjamin Bjerre Krogh

Path-based queries on trajectory data

In traffic research, management, and planning a number of path-based analyses are heavily used, e.g., for computing turn-times, evaluating green waves, or studying traffic flow. These analyses require retrieving the trajectories that follow the full path being analyzed. Existing path queries cannot sufficiently support such path-based analyses because they retrieve all trajectories that touch any edge in the path. In this paper, we define and formalize the strict path query. This is a novel query type tailored to support path-based analysis, where trajectories must follow all edges in the path. To efficiently support strict path queries, we present a novel NET work-constrained TRAjectory index (NETTRA). This index enables very efficient retrieval of trajectories that follow a specific path, i.e., strict path queries. NETTRA uses a new path encoding scheme that can determine if a trajectory follows a specific path by only retrieving data from the first and last edge in the path. To correctly answer strict path queries existing network-constrained trajectory indexes must retrieve data from all edges in the path. An extensive performance study of NETTRA using a very large real-world trajectory data set, consisting of 1.7 million trajectories (941 million GPS records) and a road network with 1.3 million edges, shows a speed-up of two orders of magnitude compared to state-of-the-art trajectory indexes.

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.

Efficient in-memory indexing of network-constrained trajectories

With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory data. To exploit the main-memory setting SPNET exploits efficient shortest-path compression of trajectories to achieve a compact index structure. SPNET is capable of exploiting the parallel computing capabilities of modern machines and supports both intra- and inter-query parallelism. The former improves response time, and the latter improves throughput. By design, SPNET supports a wider range of query types than any single existing index. An experimental study in a real-world setting with 1.94 billion GPS records and nearly 4 million trajectories in a road network with 1.8 million edges indicates that SPNET typically offers performance improvements over the best existing indexes of 1.5 to 2 orders of magnitude.

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.

CO 2 NNIE: personalized fuel consumption and CO 2 emissions

We propose a system for calculating the personalized annual fuel consumption and CO2 emissions from transportation. The system, named CO2NNIE, estimates the fuel consumption on the fastest route between the frequent destinations of the user. The travel time and fuel consumption estimated are based on 3.8 billion GPS records from 16 thousand cars and 198 million records from 218 cars annotated with fuel consumption data, respectively. The fuel consumption estimates from the system are validated using fuel-pump data. We find that estimates have good accuracy, i.e., are generally within 10% of the actual fuel consumption (4.6% deviation on average). We conclude, that the system provides new detailed information on CO2 emissions and fuel consumption for any make and model.

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.