Lai Tu

A Relay Assignment Algorithm With Interference Mitigation For Cooperative Communication

Recently, cooperative communication is shown to be a promising approach to achieve spatial diversity. The performance improvement by cooperative communication heavily depends on selecting suitable relay node. Therefore, designing effective relay assignment algorithm becomes critical in wireless cooperative networks. Although many studies focus on relay assignment problem, none of them concerns the interference problem, produced by relay nodes. In this paper, we investigate how the interference impacts the relay assignment problem. In addition, we give a relay assignment algorithm with interference mitigation for cooperative communication.

Understanding operational and charging patterns of Electric Vehicle taxis using GPS records

The major obstacle to the wide acceptance of Electric Vehicles (EV) is the lack of a wide spread charging infrastructure. To solve this, the Chinese government has promoted EVs in public transportation. The operational patterns of EV taxis should be different from Internal Combustion Engine Vehicles (ICEV) taxis: EVs can only travel a limited distance due to the limited capacity of the batteries and an EV taxi may re-charge several times throughout a day. Understanding the status (e.g., operational patterns, driver income and charging behaviours) of EV taxis can provide invaluable information to policy makers. To our best knowledge, this is the first paper to understand EV taxis behavior patterns. We use real taxi GPS records data from a fleet with about 600 EV taxis operating in Shenzhen, China. We study the patterns from two aspects: operational behaviors and charging behaviors. The most important finding is: based on the net profits of both EV and ICEV taxis, which are derived from data, we find that commercial operation of an EV taxi fleet can be profitable in metropolitan area, when specific policies give advantages to EV taxis.

Estimation of Passenger Route Choice Pattern Using Smart Card Data for Complex Metro Systems

Metro systems play an important role in meeting the demand for urban transportation in large cities. The understanding of passenger route choice is critical for public transit management. The wide deployment of automated fare collection (AFC) systems opens up a new opportunity. However, only each trips tap-in and tap-out time stamp and stations can be directly obtained from AFC system records; the train and route chosen by a passenger are unknown, information necessary to solve our problem. While existing methods work well in some specific situations, they hardly work for complicated situations. In this paper, we propose a solution that needs no additional equipment or human involvement than the AFC systems. We develop a probabilistic model that can estimate from empirical analysis how the passenger flows are dispatched to different routes and trains. We validate our approach using a large-scale data set collected from the Shenzhen Metro system. The measured results provide us with useful input when building the passenger path choice model.

Analyzing passenger density for public bus: Inference of crowdedness and evaluation of scheduling choices

Bus service is an important public transportation. Besides the major goal of carrying passengers around, providing a comfortable travel experience for passengers is also an important business consideration. The crowdedness inside a bus can directly affect the number of people choosing the bus. Traditional approaches to obtain passenger density rely on field investigations, which are both non-scalable and incomplete. The wide adoptions of smart card fare collection systems and GPS tracing systems in public transportation provide new opportunities. In this paper, we associate these two independent datasets to derive the passenger density, and evaluate the effectiveness of scheduling choices. To our best knowledge, this is the first paper which utilizes smart card data and GPS data to calculate the passenger density of bus service.

Congestion Avoidance Routing Based on Large-Scale Social Signals

The emergence of large-scale social signal data has provided unprecedented opportunities to develop techniques for improving transportation systems. In this paper, we use two types of social signal data, namely, mobile phone data and subway card data, to investigate congestion avoidance routing methodologies in the Beijing subway and San Francisco road networks. The social signal data were used to estimate detailed travel demand information and to target sources of congestion, in order to develop intelligent routing models. We study two fundamental routing scenarios, namely, the shortest path (SP) scenario and the minimum cost (MC) scenario, and propose a hybrid routing model that combines SP routing and MC routing. The hybrid model requires only a small fraction of travelers to take MC routes, but achieves nearly the same effect as MC routing. To apply the proposed routing methodologies in practical situations, we develop an information-releasing framework to suggest routes for a small group of travelers whose route adjustments can significantly improve the efficiency of the transportation networks.

Segmentation of Urban Areas Using Vector-Based Model

Urban areas are often segmented into sub-regions for indepth analysis and complexity reduction. This paper tries to use vector based model to segment urban areas into regions by adopting a graph theory approach. Vector-based model uses geometric primitives such as points, lines and polygons to denote spatial objects on the Cartesian coordinate system. Generally, we mainly store and analyze the vector data with Post GIS, in which a myriad of powerful functions are available. Here, we first find all intersections of road segments and turn vector data into a graph. Secondly, we simplify the graph by merging the redundant lines and removing the needless points. Lastly, dijkstra algorithm is applied to partition the areas into regions. In addition, we will present a case study of the Open Street Map data of Beijing to demonstrate the usability of the segmentation method.

Identifying Hot Lines of Urban Spatial Structure Using Cellphone Call Detail Record Data

The rapid growth of cell phone users in cities enable the cell phone towers spread all over urban area in past years. The user call logs, which refer to users movement trajectory in urban area, can provide an opportunity to understand urban spatial structure. As the extraction of more popular channel of human movement in urban area, the hot lines highlighted the spatial morphology of human flows in urban structure. In this paper, we propose popularity index that utilizes diversity and density index of channel to identify the hot lines based on cell phone call detail record dataset. The density of cell phone users that travel across one channel and the diversity of travel behaviors from different cell phone users refer to one channel has been combined to infer the level of popularity index for each channel. In the case study, a call detail record dataset that generated from the users of an anonymous telecom in Wuhan has been applied to identify the hot lines. The results showed the effectiveness of our approach and can be used as references for more explicitly representing urban dynamics to support urban plan applications.

What We Use to Predict a Mobile-Phone Users' Status in Campus?

Mobile phones are quickly becoming the primary source for social and behavioral sensing and data collection. A great deal of research effort in academia and industry is put into mining this data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In this work, we have an attempt to predict a users status in campus, such as teacher and student. We focus on comparing the difference among voice, message, and stream which we use to predict a user is a teacher or student. Result show that when we use voice, message or stream separately to predict, the results have obvious differences.

Synchronized Multi-Channel Cognitive MAC Protocol with Efficient Solutions for Second Spectrum Access

The MAC protocols for cognitive radio network should permit access to unused white spectrum without (or with minimal) interference to incumbent devices. With in mind the fact that channel availability can rapidly change over space and time, the coordination between the users is important. In this paper a synchronized cognitive MAC protocol for decentralized network is proposed, the protocol works in multiple channels so it can deal with resource availability and with the aide of time slots the scarcity of the dedicated common control channel had been resolved. With the enhancement of GPS receiver capabilities, a prominent neighboring discovery, transmission power estimation and synchronization have been achieved. Efficient sensing and updating information exchange mechanism enhance the primary detection and resolve the hidden terminal problem. The protocol enables the secondary users to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users, preserve the transmission energy, and improves the network performance as demonstrated by the analysis and simulation.

A Random Group Mobility Model for Mobile Networks

In this paper, we investigate mobility model in mobile networking and propose a novel random group mobility model for mobile ad hoc networks. The presented model named Charge Vector Group Mobility Model regards mobile nodes as particles carrying different kinds of charges. Reference points are also defined carrying only one kind of charge and moving in simulation area with some general rules. Mobile nodes then move according to several rules and transit among three states. This random group mobility model can illustrate the mobilities in real life, including aggregating, disaggregating, group movement and individual movement and show both randomness and some orderliness.