Hairuo Xie

Privacy-Aware Traffic Monitoring

Traffic-monitoring systems (TMSs) are vital for safety and traffic optimization. However, these systems may compromise the privacy of drivers once they track the position of each driver with a high degree of temporal precision. In this paper, we argue that aggregated data can protect location privacy while providing accurate information for traffic monitoring. We identify a range of aggregate query types. Our proposed privacy-aware monitoring system (PAMS) works as an aggregate query processor that protects the location privacy of drivers as it anonymizes the IDs of cars. Our experiments show that PAMS answers queries with high accuracy and efficiency.

Privacy-aware collection of aggregate spatial data

Privacy concerns can be a major barrier to collecting aggregate data from the public. Recent research proposes negative surveys that collect negative data, which is complementary to the true data. This opens a new direction for privacy-aware data collection. However, the existing approach cannot avoid certain errors when applied to many spatial data collection tasks. The errors can make the data unusable in many real scenarios. We propose Gaussian negative surveys. We modulate data collection based on Gaussian distribution. The collected data can be used to compute accurate spatial distribution of participants and can be used to accurately answer range aggregate queries. Our approach avoids the errors that can occur with the existing approach. Our experiments show that we achieve an excellent balance between privacy and accuracy.

Distributed Histograms for Processing Aggregate Data from Moving Objects

For monitoring moving objects via wireless sensor networks, we introduce two aggregate query types: distinct entries to an area and the number of objects in that area. We present a new technique, Distributed Euler Histograms (DEHs), to store and query aggregated moving object data. Aggregate queries occur in a variety of applications ranging from wildlife monitoring to traffic management. We show that DEHs are significantly more efficient, in terms of communication and data storage costs, than techniques based on moving object identifiers and more accurate than techniques based on simple histograms.

Browsing large online data tables using generalized query previews

Companies, government agencies, and other organizations are making their data available to the world over the Internet. They often use large online relational tables for this purpose. Users query such tables with front-ends that typically use menus or form fillin interfaces, but these interfaces rarely give users information about the contents and distribution of the data. Such a situation leads users to waste time and network/server resources posing queries that have zero- or mega-hit results. Generalized query previews enable efficient browsing of large online data tables by supplying data distribution information to users. The data distribution information provides continuous feedback about the size of the result set as the query is being formed. Our paper presents a new user interface architecture and discusses three controlled experiments (with 12, 16, and 48 participants). Our prototype systems provide flexible user interfaces for research and testing of the ideas. The user studies show that for exploratory querying tasks, generalized query previews can speed user performance for certain user domains and can reduce network/server load.

SMARTS: Scalable Microscopic Adaptive Road Traffic Simulator

Microscopic traffic simulators are important tools for studying transportation systems as they describe the evolution of traffic to the highest level of detail. A major challenge to microscopic simulators is the slow simulation speed due to the complexity of traffic models. We have developed the Scalable Microscopic Adaptive Road Traffic Simulator (SMARTS), a distributed microscopic traffic simulator that can utilize multiple independent processes in parallel. SMARTS can perform fast large-scale simulations. For example, when simulating 1 million vehicles in an area the size of Melbourne, the system runs 1.14 times faster than real time with 30 computing nodes and 0.2s simulation timestep. SMARTS supports various driver models and traffic rules, such as the car-following model and lane-changing model, which can be driver dependent. It can simulate multiple vehicle types, including bus and tram. The simulator is equipped with a wide range of features that help to customize, calibrate, and monitor simulations. Simulations are accurate and confirm with real traffic behaviours. For example, it achieves 79.1% accuracy in predicting traffic on a 10km freeway 90 minutes into the future. The simulator can be used for predictive traffic advisories as well as traffic management decisions as simulations complete well ahead of real time. SMARTS can be easily deployed to different operating systems as it is developed with the standard Java libraries.

Euler histogram tree: a spatial data structure for aggregate range queries on vehicle trajectories

This work addresses the problem of aggregation of trajectories data. Specifically, we propose a tree-based data structure for counting vehicle trajectories by mapping them into a set of spatial histograms with different granularities. We also present an approach for processing spatio-temporal range queries by aggregating the histograms in the query rectangles. The proposed methodology can be used for preserving the privacy of vehicle drivers by maintaining aggregated trajectory data. In addition, as we show, it can be used to handle the well-known distinct counting problem. Experimental results show that the new data structure achieves a high level of accuracy in query results and consistently outperforms the leading histogram-based approach.

Discovering the Impact of Urban Traffic Interventions Using Contrast Mining on Vehicle Trajectory Data

There is growing interest in using trajectory data of moving vehicles to analyze urban traffic and improve city planning. This paper presents a framework to assess the impact of traffic intervention measures, such as road closures, on the traffic network. Connected road segments with significantly different traffic levels before and after the intervention are discovered by computing the growth rate. Frequent sub-networks of the overall traffic network are then discovered to reveal the region that is most affected. The effectiveness and robustness of this framework are shown by three experiments using real taxi trajectories and traffic simulations in two different cities.

Straggler Mitigation for Distributed Behavioral Simulation

Running large-scale behavioral simulations requires high computational power, which can be acquired by distributing computation workload to multiple computing nodes (i.e., workers) that run in parallel. The implementations of such systems commonly follow the Bulk Synchronous Parallel (BSP) model. However, implementations using BSP usually suffer from the straggler problem, where the delay of any worker slows down the entire simulation. The problem usually occurs due to communication delays or imbalanced workload among workers. To mitigate the straggler problem, we propose a novel parallel computational model, called Priority Synchronous Parallel (PSP) model. PSP exploits data dependencies of parallel processes to determine high priority data to be computed and synchronized while computing the remaining data. PSP is implemented and evaluated using traffic simulations for three large cities. The proposed technique shows significant performance improvements over the BSP model.

Is Euclidean Distance Really that Bad with Road Networks

Spatial queries play an important role in many transportation services. Existing solutions to spatial queries commonly rely on the measurement of road network distance, which is the length of the shortest path from one point to another in a road network. Due to the high computation cost of measuring road network distance, a service provider may not be able to handle all the queries in a timely manner. We are interested in Euclidean distance-based solutions to spatial queries as Euclidean distance is significantly cheaper to compute than road network distance. A common view is that Euclidean distance is not suitable for solving any spatial query in road networks as road network distance can be significantly different to Euclidean distance. We challenge this view by evaluating the performance of an Euclidean distance-based approach in solving Group Nearest Neighbor queries, which can be used in transportation applications. Our study shows that Euclidean distance can help to achieve an excellent level of accuracy in solving the associated query type. In return, this opens the door for other studies to check whether similar results could be found in other query types and that per query type a judgement should be made.

Using a Traffic Simulator for Navigation Service

Traffic congestion is a serious problem that is only expected to get worse in the future. Statistics shows that half of traffic congestion is caused by temporary disruptions like accidents. These events have dramatic impact on road network availability and cause huge delays for commuters. Also, they are usually unexpected and hard to manage by traffic authorities. State-of-the-art navigation systems started to provide real-time information about traffic conditions to help users make better routing decisions. However, traffic in the road network changes rapidly and the advice calculated now may not be valid after few minutes. This is especially critical in the presence of traffic incidents, where the impact of the incident could cause traffic to propagate to nearby roads. Thus, it is important for navigation systems to consider the evolution and future impact of traffic events. In this work, we present a navigation system that uses faster than realtime simulations to predict the evolution of traffic events and help drivers proactively avoid congestion caused by events. The system can subscribe to real-time traffic information and forecast the traffic conditions using fast simulations. We evaluate our approach through extensive experiments to test the performance and accuracy of the simulator with real data obtained from TomTom Traffic API. Also, we test the quality of navigation advice in realistic settings and show that our solution is able to help drivers avoid congested areas in cases where even real-time update methods lead drivers to congested routes.