The assessment of traffic livability, including local effects at home, during trips and at the destination, based on the individual activity pattern and trip behaviour

The environmental quality of the living environment is mainly linked to the direct and indirect impact of traffic in the neighborhood of the dwellings. In the Flemish mobility and urban planning, the term ‘livability’ is used focusing on the living conditions of people’s home location: what is the satisfaction about their living environment? The more specific term ‘traffic livability’ is used to describe the impact of all types of traffic on the livability of a dwelling location. Some methodologies were developed for an objective measurement of the traffic impact on quality of life. In Flanders the most commonly used methodologies are the ‘traffic livability index’ and the ‘bearing capacity’, which use a very narrow interpretation of the traffic livability, as they are highly based on the local road design (number of lanes, cycle path, …) and the local traffic characteristics (traffic flow, speed, traffic safety, …) of the street of the dwelling. The main critic is that these methods should measure over the complete living environment of a person, rather than just at the dwelling. For this reason, an alternative methodology was developed for an objective measurement of the impact of traffic on the local quality of the living environment. Compared to the current practice, this new methodology aims at the following objectives: • The evaluation is not done for the average person, but includes individual needs and travel patterns, based on personal characteristics, representing the large diversity of the mobility needs. • The methodology should reflect a daily activity pattern, including the traveled routes and destinations. The traffic livability of a specific household in a specific area will reflect the full extent of their needs at home, during the trips and at the destinations. • Traffic livability is measured by means of a broad set of indicators, representing different types of traffic impacts (accessibility, traffic noise, traffic emissions, …). The separate indicators are combined into an evaluation of the traffic livability, including an extensive set of secondary effects. This is mainly realized by a better simulation of the personal trip behavior, using the data from the Flemish Trip Behavior Survey. In order to evaluate the livability at a certain home location (a number of) households are sampled from this database, with the specific characteristics of the household (composition, car availability, children, …), the people in the household (age, employment, …) and their activities and trip pattern. With this information, the different indicators for traffic livability can be evaluated on the home location, as well as during the trip and at the destination

Probabilistic sampling of finite renewal processes

Consider a finite renewal process in the sense that interrenewal times are positive i.i.d. variables and the total number of renewals is a random variable, independent of interrenewal times. A finite point process can be obtained by probabilistic sampling of the finite renewal process, where each renewal is sampled with a fixed probability and independently of other renewals. The problem addressed in this work concerns statistical inference of the original distributions of the total number of renewals and interrenewal times from a sample of i.i.d. finite point processes obtained by sampling finite renewal processes. This problem is motivated by traffic measurements in the Internet in order to characterize flows of packets (which can be seen as finite renewal processes) and where the use of packet sampling is becoming prevalent due to increasing link speeds and limited storage and processing capacities.Comment: Published in at http://dx.doi.org/10.3150/10-BEJ321 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Towards Informative Statistical Flow Inversion

This is the accepted version of 'Towards Informative Statistical Flow Inversion', archived originally at arXiv:0705.1939v1 [cs.NI] 14 May 2007.A problem which has recently attracted research attention is that of estimating the distribution of flow sizes in internet traffic. On high traffic links it is sometimes impossible to record every packet. Researchers have approached the problem of estimating flow lengths from sampled packet data in two separate ways. Firstly, different sampling methodologies can be tried to more accurately measure the desired system parameters. One such method is the sample-and-hold method where, if a packet is sampled, all subsequent packets in that flow are sampled. Secondly, statistical methods can be used to ``invert'' the sampled data and produce an estimate of flow lengths from a sample. In this paper we propose, implement and test two variants on the sample-and-hold method. In addition we show how the sample-and-hold method can be inverted to get an estimation of the genuine distribution of flow sizes. Experiments are carried out on real network traces to compare standard packet sampling with three variants of sample-and-hold. The methods are compared for their ability to reconstruct the genuine distribution of flow sizes in the traffic