Accurate and up-to-date digital road maps are the foundation of many mobile applications, such as navigation and autonomous driving. A manually-created map suffers from the high cost for creation and maintenance due to constant road network updating. Recently, the ubiquity of GPS devices in vehicular systems has led to an unprecedented amount of vehicle sensing data for map inference. Unfortunately, accurate map inference based on vehicle GPS is challenging for two reasons. First, it is challenging to infer complete road structures due to the sensing deviation, sparse coverage, and low sampling rate of GPS of a fleet of vehicles with similar mobility patterns, e.g., taxis. Second, a road map requires various road properties such as road categories, which is challenging to be inferred by just GPS locations of vehicles. In this paper, we design a map inference system called coMap by considering multiple fleets of vehicles with Complementary Mobility Features. coMap has two key components: a graph-based map sketching component, a learning-based map painting component. We implement coMap with the data from four type-aware vehicular sensing systems in one city, which consists of 18 thousand taxis, 10 thousand private vehicles, 6 thousand trucks, and 14 thousand buses. We conduct a comprehensive evaluation of coMap with two state-of-the-art baselines along with ground truth based on OpenStreetMap and a commercial map provider, i.e., Baidu Maps. The results show that (i) for the map sketching, our work improves the performance by 15.9%; (ii) for the map painting, our work achieves 74.58% of average accuracy on road category classification.
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RISC: Resource-Constrained Urban Sensing Task Scheduling Based on Commercial Fleets
With the trend of vehicles becoming increasingly connected and potentially autonomous, vehicles are being equipped with rich sensing and communication devices. Various vehicular services based on shared real-time sensor data of vehicles from a fleet have been proposed to improve the urban efficiency, e.g., HD-live map, and traffic accident recovery. However, due to the high cost of data uploading (e.g., monthly fees for a cellular network), it would be impractical to make all well-equipped vehicles to upload real-time sensor data constantly. To better utilize these limited uploading resources and achieve an optimal road segment sensing coverage, we present a real-time sensing task scheduling framework, i.e., RISC, for Resource-Constraint modeling for urban sensing by scheduling sensing tasks of commercial vehicles with sensors based on the predictability of vehicles' mobility patterns. In particular, we utilize the commercial vehicles, including taxicabs, buses, and logistics trucks as mobile sensors to sense urban phenomena, e.g., traffic, by using the equipped vehicular sensors, e.g., dash-cam, lidar, automotive radar, etc. We implement RISC on a Chinese city Shenzhen with one-month real-world data from (i) a taxi fleet with 14 thousand vehicles; (ii) a bus fleet with 13 thousand vehicles; (iii) a truck fleet with 4 thousand vehicles. Further, we design an application, i.e., track suspect vehicles (e.g., hit-and-run vehicles), to evaluate the performance of RISC on the urban sensing aspect based on the data from a regular vehicle (i.e., personal car) fleet with 11 thousand vehicles. The evaluation results show that compared to the state-of-the-art solutions, we improved sensing coverage (i.e., the number of road segments covered by sensing vehicles) by 10% on average.
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- NSF-PAR ID:
- 10436097
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 4
- Issue:
- 2
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 20
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Imputing missing data is a critical task in data-driven intelligent transportation systems. During recent decades there has been a considerable investment in developing various types of sensors and smart systems, including stationary devices (e.g., loop detectors) and floating vehicles equipped with global positioning system (GPS) trackers to collect large-scale traffic data. However, collected data may not include observations from all road segments in a traffic network for different reasons, including sensor failure, transmission error, and because GPS-equipped vehicles may not always travel through all road segments. The first step toward developing real-time traffic monitoring and disruption prediction models is to estimate missing values through a systematic data imputation process. Many of the existing data imputation methods are based on matrix completion techniques that utilize the inherent spatiotemporal characteristics of traffic data. However, these methods may not fully capture the clustered structure of the data. This paper addresses this issue by developing a novel data imputation method using PARATUCK2 decomposition. The proposed method captures both spatial and temporal information of traffic data and constructs a low-dimensional and clustered representation of traffic patterns. The identified spatiotemporal clusters are used to recover network traffic profiles and estimate missing values. The proposed method is implemented using traffic data in the road network of Manhattan in New York City. The performance of the proposed method is evaluated in comparison with two state-of-the-art benchmark methods. The outcomes indicate that the proposed method outperforms the existing state-of-the-art imputation methods in complex and large-scale traffic networks.
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Human mobility data may lead to privacy concerns because a resident can be re-identified from these data by malicious attacks even with anonymized user IDs. For an urban service collecting mobility data, an efficient privacy risk assessment is essential for the privacy protection of its users. The existing methods enable efficient privacy risk assessments for service operators to fast adjust the quality of sensing data to lower privacy risk by using prediction models. However, for these prediction models, most of them require massive training data, which has to be collected and stored first. Such a large-scale long-term training data collection contradicts the purpose of privacy risk prediction for new urban services, which is to ensure that the quality of high-risk human mobility data is adjusted to low privacy risk within a short time. To solve this problem, we present a privacy risk prediction model based on transfer learning, i.e., TransRisk, to predict the privacy risk for a new target urban service through (1) small-scale short-term data of its own, and (2) the knowledge learned from data from other existing urban services. We envision the application of TransRisk on the traffic camera surveillance system and evaluate it with real-world mobility datasets already collected in a Chinese city, Shenzhen, including four source datasets, i.e., (i) one call detail record dataset (CDR) with 1.2 million users; (ii) one cellphone connection data dataset (CONN) with 1.2 million users; (iii) a vehicular GPS dataset (Vehicles) with 10 thousand vehicles; (iv) an electronic toll collection transaction dataset (ETC) with 156 thousand users, and a target dataset, i.e., a camera dataset (Camera) with 248 cameras. The results show that our model outperforms the state-of-the-art methods in terms of RMSE and MAE. Our work also provides valuable insights and implications on mobility data privacy risk assessment for both current and future large-scale services.more » « less
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Vehicles are becoming more intelligent and automated. To achieve higher automation levels, vehicles are being equipped with more and more sensors. High data rate connectivity seems critical to allow vehicles and road infrastructure exchanging all these sensor data to enlarge their sensing range and make better safety related decisions. Connectivity also enables other applications such as infotainment or high levels of traffic coordination. Current solutions for vehicular communications though do not support the gigabit-per-second data rates. This presentation makes the case that millimeter wave communication is the only viable approach for high bandwidth connected vehicles. The motivation and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. Examples from recent work are provided including new theoretical results that enable mmWave communication in high mobility scenarios and innovative architectural concepts like position and radar-aided communication.more » « less
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Effective road traffic assessment and estimation is crucial not only for traffic management applications, but also for long-term trans- portation and, more generally, urban planning. Traditionally, this task has been achieved by using a network of stationary traffic count sensors. These costly and unreliable sensors have been replaced with so-called Probe Vehicle Data (PVD), which relies on sampling individual vehicles in traffic using for example smartphones to assess the overall traffic condition. While PVD provides uniform road network coverage, it does not capture the actual traffic flow. On the other hand, stationary sensors capture the absolute traffic flow only at discrete locations. Furthermore, these sensors are often unreliable; temporary mal- functions create gaps in their time-series of measurements. This work bridges the gap between these two data sources by learning the time-dependent fraction of vehicles captured by GPS-based probe data at discrete stationary sensor locations. We can then account for the gaps of the traffic-loop measurements by using the PVD data to estimate the actual total flow. In this work, we show that the PVD flow capture changes sig- nificantly over time in the Washington DC area. Exploiting this information, we are able to derive tight confidence intervals of the traffic volume for areas with no stationary sensor coverage.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3397337
