<p>Problems associated with traffic, such as traffic congestionand pollution, have occurred in major urban areas in particulardue to the increased use of cars. One possible way to reducethe use of cars is to replace commuter trips by car with othermodes of transport, such as a combination of car and publictransport called Park&Ride. The aim of this thesis was tounderstand more about factors influencing the choice of modeand to find measures that could attract car drivers to Park&Ride. A stated preference survey has been conducted inorder to quantify some standard factors. The factors used inthis stated choice experiment were: security at the lot,availability of spaces at the parking lot, costs at the parkinglot and walking distances between the parking space and thestation. The results indicate that security at the Park&Ride facility is important. Both sexes assign a high value tosecure parking, but women are more willing to pay for lights atan unguarded parking lot. The results reveal that parkingfacilities, such as free parking and short distance betweenparking place and work at work, influence people’s choiceof mode. A general conclusion is that there is potential forincreasing the use of Park&Ride facilities, especiallyamong women and respondents over 30 years.</p><p><b>Key words:</b>Mode choice, valuation, traffic reduction,stated preference, factors, Park&Ride</p>

Problems associated with traffic, such as traffic congestionand pollution, have occurred in major urban areas in particulardue to the increased use of cars. One possible way to reducethe use of cars is to replace commuter trips by car with othermodes of transport, such as a combination of car and publictransport called Park&amp;Ride. The aim of this thesis was tounderstand more about factors influencing the choice of modeand to find measures that could attract car drivers to Park&amp;Ride. A stated preference survey has been conducted inorder to quantify some standard factors. The factors used inthis stated choice experiment were: security at the lot,availability of spaces at the parking lot, costs at the parkinglot and walking distances between the parking space and thestation. The results indicate that security at the Park&amp;Ride facility is important. Both sexes assign a high value tosecure parking, but women are more willing to pay for lights atan unguarded parking lot. The results reveal that parkingfacilities, such as free parking and short distance betweenparking place and work at work, influence people’s choiceof mode. A general conclusion is that there is potential forincreasing the use of Park&amp;Ride facilities, especiallyamong women and respondents over 30 years. Key words:Mode choice, valuation, traffic reduction,stated preference, factors, Park&amp;Ride / <p>NR 20140805</p>

In 2012 the two biggest smartphone application markets - the Google Play store and the Apple App Store - each had close to 700 thousand applications with approximately 2 billion downloads happening every month. The introduction of new features and correction of bugs and security vulnerabilities make it usual for mobile application developers to release new version of an application every month. Combined with the great smartphone popularity, it leads to approximately 400 PB annual traffic generated by app updates in the U.S. wireless networks alone. Being partially transmitted through cellular networks, mobile application updates traffic accounts to up to 20% of the annual cellular traffic in the U.S. This thesis presents delta encoding based techniques that significantly reduce update traffic by transferring only the changes (or patches) between two versions of an application. Such network bandwidth reduction enables savings for smartphone users, mobile operators, and data centers that serve app updates. Two Android application update methods - called DELTA and DELTA++ - were developed, implemented, and evaluated. Both methods use delta encoding to transfer only the changes between application versions. DELTA++ improves on DELTA by exploiting the internal structure of APK packages, which are used to distribute Android applications. The APK file can be seen as a compressed archive of all the files contained in application. The DELTA++ algorithm unpackages APK and computes differences between decompressed application files, which allows it to produce much smaller patches. Our experimental results show that DELTA++ reduces app update size by 77% on average. DELTA++ patches are twice smaller than those produced by the Google Smart Application Update method, which is currently used in the Google Play store. This reduction has a trade-off - increased complexity of generated patches makes patch deployment process more sophisticated. Consequently, more time has to be spent to apply the received patch in smartphone. Such delay can be considered acceptable as application update is a delay-tolerant process and smartphone users do not need an update immediately after its release. In order to estimate how much savings can be achieved with DELTA++, a study of Android smartphone users was conducted. The results show that if DELTA++ is used in Google Play instead of the Google Smart Application Update method, then 32 PB or 1.7% of annual traffic can be saved every year in cellular networks in the U.S. The Apple App Store currently does not use any method based on delta encoding to reduce application updates traffic. Usage of methods similar to DELTA++ in the App Store can further increase the savings up to the 12% of yearly cellular traffic in the U.S., which equals to more than $2 billion cost savings a year.

Within Ericsson there is a continuous activity of traffic modeling. Traffic modeling is a practice to analyze traffic patterns and determine necessary resources to handle it optimally. This activity focuses on gathering and analyzing live network measurements, implementing and presenting traffic models. One example of concept in packet traffic modeling is transmission object log which is an aggregation of packet data traces from a measured network over a transmission period. These trace logs that are simple list of all transmission objects contain a vast number of data. When the amount of data increases in these logs several problems can occur such as expensive analysis time, costly data storage and even statistical analysis and data processing in software environments run out of memory. On the other hand, sophisticated and costly computing systems are required for analysis and storage of the data. Therefore, monitoring and analyzing these large traces motivate data reduction. The goal of this thesis is to reduce the number of traffic objects in large object trace logs while preserving the statistical characteristics of the original transmission objects.    Sampling techniques are wildly used to cope with the issues of large amount of data in network monitoring. First, this thesis aims to assess the impact of two sampling techniques as a reduction method. Second, to analyze traffic characteristics and showing the effects of sampling, some statistical properties of both original and sampled datasets as well as their distribution plots will be discussed. The distortion introduced by sampling as the distance between the distribution of properties for sampled and unsampled traffic is also presented by a statistical metric. One of the issues in sampling technique is the sampling size. In order to estimate the sampling size and reduce the logs to a certain level, the concept of offline marginal utility as a complementary method to sampling is proposed in this report. The thesis also makes some suggestions as further works to reduce the logs by having less impact on the object characteristics.