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Travelling by taxi is more convenient and effective. With an overcrowding population and a much terrible traffic, the traditional way of hailing a taxi encounters many challenges like where to pick-up/drop-off passengers reasonably and where to find potential passengers quickly. More cities have established taxi stands to advocate and to guide passengers to hail a taxi. However, most of them have...
Gliding robotic fish, a hybrid of underwater gliders and robotic fish, are energy-efficient and highly maneuverable, and hold strong promise for long-duration sampling of underwater environments. In this paper a novel systematic autonomous water-column-based sampling scheme for gliding robotic fish is proposed to measure the three-dimensional spatial distributions of variables of interest in aquatic...
Recent successful approaches to autonomous vehicle localisation and navigation typically involve 3D LIDAR scanners and a static, curated 3D map, both of which are expensive to acquire and maintain. In this paper we propose an experience-based approach to matching a local 3D swathe built using a push-broom 2D LIDAR to a number of prior 3D maps, each of which has been collected during normal driving...
Understanding the Origin-Destination (OD) patterns between different regions of a city is important in urban planning. In this work, based on taxi GPS data, we propose OD-Wheel, a novel visual design and associated analysis tool, to explore OD patterns. Once users define a region, all taxi trips starting from or ending to that region are selected and grouped into OD clusters. With a hybrid circular-linear...
In this paper, we propose a novel visual analysis method TrajRank to study the travel behaviour of vehicles along one route. We focus on the spatial-temporal distribution of travel time, i.e., the time spent on each road segment and the travel time variation in rush/non-rush hours. TrajRank first allows users to interactively select a route, and segment it into several road segments. Then trajectories...
Long-term location tracking, where trajectory compression is commonly used, has gained high interest for many applications in transport, ecology, and wearable computing. However, state-of-the-art compression methods involve high space-time complexity or achieve unsatisfactory compression rate, leading to rapid exhaustion of memory, computation, storage and energy resources. We propose a novel online...
When planning routes, drivers usually consider a multitude of different travel costs, e.g., distances, travel times, and fuel consumption. Different drivers may choose different routes between the same source and destination because they may have different driving preferences (e.g., time-efficient driving v.s. fuel-efficient driving). However, existing routing services support little in modeling multiple...
Electric vehicles (EVs) have undergone an explosive increase over recent years, due to the unparalleled advantages over gasoline cars in green transportation and cost efficiency. Such a drastic increase drives a growing need for widely deployed publicly accessible charging stations. Thus, how to strategically deploy the charging stations and charging points becomes an emerging and challenging question...
This paper extends the work in [4] by introducing the neighborhood querying process for reducing the effects of sensor observation errors. The objective of this work is to track moving objects by using one-bit information from sensor nodes that provide information whether an object is moving towards a group of sensors or moving away. In [3], they allowed that sensor nodes make observation errors but...
The urban road traffic flow condition prediction is a fundamental issue in the intelligent transportation management system. While extracting the high-dimensional, nonlinear and random features of the transportation network is a challenge, which is very useful to improve the accuracy of traffic prediction. In this paper, we propose DeepSense, a novel deep temporal-spatial traffic flow feature learning...
Mobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability...
Android smart phone can be used in ITS (Intelligent Transportation Systems) to obtain people and vehicle’s location since it is integrated with GPS, direction sensor and acceleration sensor. Because the GPS built in smart phone always has an error of dozens of meters, improving the positioning accuracy is necessary before introducing it into ITS. This paper proposed an approach to improve the accuracy...
Online and robust localization in a large scale outdoor environment is an essential component for self-driving vehicles. This paper addresses the theoretical and experimental development of a 6DOF localization approach with an online Unscented Rauch-Tung-Striebel (RTS) Smoother. This work focuses on the performance evaluation of the Unscented RTS smoother from a low-cost inertial sensor and consumer-grade...
Many factors can affect the predictability of public bus services such as traffic, weather, day of week, and hour of day. However, the exact nature of such relationships between travel times and predictor variables is, in most situations, not known. In this paper we develop a framework that allows for flexible modeling of bus travel times through the use of Additive Models. The proposed class of models...
In Vehicular Ad Hoc Networks (VANETs), moving vehicles are considered as mobile nodes in the network and they are connected to each other via wireless links when they are within the communication radius of each other. Efficient message delivery in VANETs is still a very challenging research issue. In this paper, a Q-learning based routing protocol (i.e., QGrid) is introduced to help to improve the...
Every day, thousands of videos are uploaded to the web, creating an ever-growing demand for methods to make them easier to retrieve, search, and index. These videos include both spatial and temporal geographic features captured via camera and embedded sensors, e.g., GPS and the digital compass. The current state-of-the-art video retrieval systems are based on content-based or concept-based techniques...
Taxicab demand discovering is one of the most fundamental issues of taxicab services. Most of the regions in one city suffer the demand and supply disequilibrium problem. It causes the difficulty in scheduling taxicabs for taxicab companies. It will be solved by modeling the regional demand of taxicabs by using trajectory data. In this paper, we propose a method to model regional taxicab demand. Firstly,...
Map Matching is a well-established problem which deals with mapping raw time stamped location traces to edges of road network graph. Location data traces may be from devices like GPS, Mobile Signals etc. It has applicability in mining travel patterns, route prediction, vehicle turn prediction and resource prediction in grid computing etc. Existing map matching algorithms are designed to run on vertical...
Roadside workers and emergency responders, such as police and emergency medical technicians, are at significant risk of being struck by vehicular traffic while performing their duties. While recent work has examined active and passive systems to reduce pedestrian collisions, current approaches require line of sight using either laser, infrared, or vision based systems. We address this problem by developing...
Map matching (MM), pins the drifting position data to the correct road link on which a vehicle is travelling, is a crucial step needed by many industrial or research ITS projects which rely on post-hoc analysis of trajectories. To address the unprecedented challenge of massive GPS data processing in urban transportation data center nowadays, this paper proposed an improved parallel topological map-matching...
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