Fast handover algorithm for hierarchical mobile IPv6 macro-mobility management

Mobile lh.6 (MIPv6) has some limitations due to long delays and signaling load during handover operation. Hierarchical Mobile IPv6 (HMIPv6) is the extension of MIPv6 that is designed to reduce the signaling load and to improve handover speed of MIPv6 by splitting the mobility management into macro and micro mobility management schemes. However HhfIPv6 only improves micro mobility problem where the signifcant delay still occurs in the macro mobility management because the handover algorithm is similar with the MIPv6 environment, This paper proposes a new fast handover algorithm that overcomes the limitations in Mobile MIPv6 and its extension HMIPv6. Our design objective is to re-establish the communication traffic flow quickly and to minimize the service disruption delay that occnrs during handover process in a macro mobility environment. This handover algorithm is based on the modification of the HMIPv6 protocol using the multicast technique concept. This algorithm will enable the mobile node to receive packet faster than HMIPv6 protocol during handover, seamlessly and transparently. Keywords: Mobile lPv6, HMPv6, Hondover. Mufticart Schem

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models

The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model

Review of current study methods for VRU safety : Appendix 4 –Systematic literature review: Naturalistic driving studies

With the aim of assessing the extent and nature of naturalistic studies involving vulnerable road users, a systematic literature review was carried out. The purpose of this review was to identify studies based on naturalistic data from VRUs (pedestrians, cyclists, moped riders and motorcyclists) to provide an overview of how data was collected and how data has been used. In the literature review, special attention is given to the use of naturalistic studies as a tool for road safety evaluations to gain knowledge on methodological issues for the design of a naturalistic study involving VRUs within the InDeV project. The review covered the following types of studies: •Studies collecting naturalistic data from vulnerable road users (pedestrians, cyclists, moped riders, motorcyclists). •Studies collecting accidents or safety-critical situations via smartphones from vulnerable road users and motorized vehicles. •Studies collecting falls that have not occurred on roads via smartphones. Four databases were used in the search for publications: ScienceDirect, Transport Research International Documentation (TRID), IEEE Xplore and PubMed. In addition to these four databases, six databases were screened to check if they contained references to publications not already included in the review. These databases were: Web of Science, Scopus, Google Scholar, Springerlink, Taylor & Francis and Engineering Village.The findings revealed that naturalistic studies of vulnerable road users have mainly been carried out by collecting data from cyclists and pedestrians and to a smaller degree of motorcyclists. To collect data, most studies used the built-in sensors of smartphones, although equipped bicycles or motorcycles were used in some studies. Other types of portable equipment was used to a lesser degree, particularly for cycling studies. The naturalistic studies were carried out with various purposes: mode classification, travel surveys, measuring the distance and number of trips travelled and conducting traffic counts. Naturalistic data was also used for assessment of the safety based on accidents, safety-critical events or other safety-related aspect such as speed behaviour, head turning and obstacle detection. Only few studies detect incidents automatically based on indicators collected via special equipment such as accelerometers, gyroscopes, GPS receivers, switches, etc. for assessing the safety by identifying accidents or safety-critical events. Instead, they rely on self-reporting or manual review of video footage. Despite this, the review indicates that there is a large potential of detecting accidents from naturalistic data. A large number of studies focused on the detection of falls among elderly people. Using smartphone sensors, the movements of the participants were monitored continuously. Most studies used acceleration as indicator of falls. In some cases, the acceleration was supplemented by rotation measurements to indicate that a fall had occurred. Most studies of using kinematic triggers for detection of falls, accidents and safety-critical events were primarily used for demonstration of prototypes of detection algorithms. Few studies have been tested on real accidents or falls. Instead, simulated falls were used both in studies of vulnerable road users and for studies of falls among elderly people

A Handover Prediction Mechanism Based on LTE-A UE History Information

[[abstract]]In response to the rapidly developing of wireless communication technology, the deployed of eNB is denser and more complex. The research of how to handover accurately and fast in LTE-A are discussed much in recent years .In 3GPP Release 8, the UE History Information recorded by eNB was first proposed, it's proposed to provide eNB to judge the target eNB when handover. The history information includes the Cell ID and Time UE stayed in cell. We proposed an advance UE history information, reducing the handover failure rate and ping-pong handover rate by using the history information like Region-Domain, Time-Domain and Time To Trigger.[[sponsorship]]Tamkang University[[incitationindex]]EI[[conferencetype]]國際[[conferencetkucampus]]台北校園[[conferencedate]]20150902~20150903[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Taipei, Taiwa