The Svealand line opened in 1997 and the services areoperated with regional high speed trains. While the Svealandline was being built, the slow trains that had been inoperation on the old railway between Eskilstuna and Stockholm(a distance of 115 km) were replaced by buses with a highfrequency of service. In a case study of the effects of regional high speed trainservices, field surveys were made of residents and publictransport passengers along the line, and in a reference centreof population, before and after the Svealand line opened.Changes in knowledge, valuations and travel behaviour have beenanalysed, as have changes in accessibility. The supply and thedemand for regional journeys by car, bus and train have alsobeen examined. The results show that the Svealand line has meant anincrease of up to seven times in regional travel by railcompared to the old railway between Eskilstuna and Stockholm,and the market share has risen from 6% to 30%. Those who travelmost are people who have access to a car at times. Habitualmotorists, on the other hand, account for the largest increasein travel by public transport. In areas close to the railwaystations in Strängnäs and Eskilstuna new patterns ofcar ownership, travel behaviour, choice of transport mode andchoice of destination have been found since the regional highspeed trains began operating on the Svealand line. Commuting towork has also shown a marked increase. Travelling times arevalued highly and especially motorists value the high speedtrain mode of transport highly. Poorer train services and busservices are not attractive to motorists other than as areserve alternative to their own cars. A general conclusion is that the regional high speed trainservices have had a major impact on the travel market, travelbehaviour and accessibility. The improved accessibility toStockholm in particular is especially noticeable amongresidents close to the railway stations. Keywords:The Svealand line, high speed trains, regionaltravel, travel behaviour, choice of transport mode,accessibility / QC 20100608

Transportation planners have traditionally focused on physical travel only, and disregarded the fact that other modes of communication may influence travel demand. However, modern telecommunications are rapidly increasing the accessibility to activities that previously only could be reached by physical transportation. This development calls for methods to analyse interactions between telecommunications and transport systems. The objective of this thesis is to accomplish a better understanding of if and how impacts of information technology could be implemented in travel demand models. An important part of this issue is to investigate what kind of data that is needed. This thesis also aims at investigating whether the Communication Survey, KOM, collected by Swedish Institute for Transport and Communications, SIKA, can be used to improve transport modelling with respect to how modern telecommunications influence travel demand. KOM is a one-day travel and communication diary survey, including information on the respondents telecommuting habits as well as socio-economic status. One problem was the small sample size in KOM, which made the analyses uncertain. Since KOM is collected on a yearly basis, it is still possible to apply similar analysis methods within a few years, using a larger data set, which might enable extended analyses. The small sample in KOM available to date is best suited for general descriptive analyses of communication patterns in Sweden. The main conclusions of the paper are therefore connected to the methods and future data collection. The thesis includes three papers. The first paper tested a model approach that assumes substitution between travel and non-travel based communication, using the KOM database. Travel demand models are in general constructed as nested logit models with frequency, mode and destination choice levels. In the paper, non-travel based modes of communication were included in the choice set of such a model. The non-travel based modes of communication considered were Internet (and e-mail), ordinary mail and telephone contacts. The second and third papers investigate telecommuting. As a first step to reach the goal of forecasting telecommuting, the second paper examined the characteristics of current telecommuters by use of KOM. This was mainly accomplished by estimating a telecommuting adoption model of logit type. However, only 122 employees out of 7578 actually telecommutes full days at home. These telecommuters work primarily in information- and service-based industrial sectors concerned with computers, finance or communication. The difficulties in describing the utility of the telecommunications based alternatives (representing ”no travel”) concerned also the telecommuting adoption model. The third paper used data collected from a working site within the company Ericsson, located in the office district of Nacka Strand in Stockholm during the autumn 2002. The telecommuting frequency was substantially higher at Ericsson than in the workforce as a whole. The propensity to adopt telecommuting was modelled as a function of socio-economic variables and access to technical equipment, work task suitability and management attitudes, as perceived by the employees. The focus was to identify tools that the company can use to promote telecommuting, and to find incentives for the company to promote telecommuting. Technical equipment, suitable work tasks and managers attitude were identified as constraints for telecommuting. / <p>NR 20140805</p>

Automation of travel diary collection, an essential input for transport planning, has been a fruitful line of research for the last years; in particular, concerning the problem of automatic inference of transport modes. Taking advantage of technological advance, several solutions based on the collection of mobile devices data, such as GPS locations and variables related to movement (such as speed) and motion (e.g. measurements from accelerometer), have been investigated. The literature shows that many of them rely on explicit initial segmentation of GPS trajectories into trip legs, followed by a segment-based classification problem. In some cases, GIS-related features are included in the classification instance, but usually in terms of distance to transport networks or to specific points of interest (POIs). The aim of this MSc Thesis is to investigate a novel transport mode inference procedure based on the generation of topological features from a multimodal map matching instance. We define topological features as the topological context of each point of a GPS trajectory. Further utilization of these features as part of a sequence classification problem leads to mode prediction and to the implicit definition of the trip legs. In addition to not depending on an explicit segmentation step, the proposed routine also has less requirements in terms of the complexity of the required GIS features: there is no need to consider distance features, and the proposed map matching implementation does not require the usage of one unified multimodal network —as other multimodal map matching approaches do. The procedure was tested with a travel diary data set collected in Stockholm, containing 4246 trips from 368 different commuters. The transport modes considered were walk, subway, commuter train, bus and tram. In order to assess the impact of the topological context, different feature set compositions were investigated, including topological and conventional movement and motion features. Three different classifiers —decision tree, support vector machine and conditional random field— were evaluated as well. The results show that the proposed procedure reached high accuracy, with a performance that is similar to the one offered by current approaches; and that the most performant feature set composition was the one that included both topological and movement and motion features. The best evaluation measures were obtained with decision tree and conditional random field classifiers, but with some differences: while both of the them presented similar recall, the former yielded better precision and the latter achieved a higher segmentation quality.

The fast development in telecommunication is producing a huge amount of data related to how people move and behave over time. Nowadays, travel data are mainly collected through Global Positioning Systems (GPS) and can be used to identify human mobility patterns and travel behaviors. Transport mode detection (TMD) aims to identify the means of transport used by an individual and is a field that has become more popular in recent years as it can be beneficial for various applications. However, developing travel models requires different types of information that can be extracted from raw travel data. Although many useful features like speed, acceleration and bearing rate can be extracted from raw GPS data, detecting transport modes requires further processing. Some previous studies have successfully applied machine learning algorithms for detecting the transport mode. Despite achieving high performance in their models, many of these studies have used rather small datasets generated from a limited number of users or identified a small number of different transport modes. Furthermore, in most of these studies more complex methodologies have been applied, where extra information like GIS layers or road and railway networks were required. The purpose of this study is to propose a simple supervised learning model to identify five common transport modes on large datasets by only using raw GPS data. In total, six commonly used supervised learning algorithms are tested on seven selected features (extracted from raw GPS data). The Random Forest (RF) algorithm proves to perform better in detecting five transport modes from the dataset utilized in this study, with an overall accuracy of 82.7%.

The main purpose of this diploma thesis is the analisis of the choice distribution chain for products quoted by form of the mail-order trade. It was selected the company TV Products and it was made the description of her suppliers, transport mode of goods, technique of stock-keeping and manner of sale. The part of the focus is found out an optimal supply and an optimum order amount by using predictive methods.

Understanding travel behavior and identifying the mode of transportation are essential for adequate urban devising and transportation planning. Global positioning systems (GPS) tracking data is mainly used to find human mobility patterns in cities. Some travel information, such as most visited location, temporal changes, and the trip speed, can be easily extracted from GPS raw tracking data. GPS trajectories can be used as a method to indicate the mobility modes of commuters. Most previous studies have applied traditional machine learning algorithms and manually computed data features, making the model error-prone. Thus, there is a demand for developing a new model to resolve these methods' weaknesses. The primary purpose of this study is to propose a semi-supervised model to identify transportation mode by using a contemporary machine learning algorithm and GPS tracking data. The model can accept GPS trajectory with adjustable length and extracts their latent information with LSTM Autoencoder. This study adopts a deep neural network architecture with three hidden layers to map the latent information to detect transportation mode. Moreover, different case studies are performed to evaluate the proposed model's efficiency. The model results in an accuracy of 93.6%, which significantly outperforms similar studies.

Ett sätt att informera smartphone-användare om deras klimatpåverkan är genom att automatiskt identifiera vilket transportmedel användaren nyttjat och använda informationen för att uppskatta användarens utsläpp av växthusgaser. Målet med det här projektet var att sammanställa en översikt av befintliga lösningar och metoder för att detektera smartphone-användares transportmedel och utvärdera hur ett system presterar då testdata är samlad i ett annat geografiskt område än datan som använts för att träna systemet. Utvärdering av systemet skedde via kvantitativa metoder där sensordata samlades in och användes för att testa systemet. Sensordata samlades vid gång, stilla, tåg, buss och bil. Resultatet är ett system som med varierande sannolikhet kan avgöra transportmedel i Sverige. Systemets totala precision var 29 procentenheter lägre då data som samlats i Sverige användes i testerna jämfört med data insamlad i samma geografiska område som träningsdatan. Slutsatsen är att det kan vara problematiskt att applicera en lösning i ett annat geografiskt område än lösningen utvecklats för. Genom testerna framkom att fordonstransport verkar särskilt känsligt vid byte av geografisk kontext. / A way to inform smartphone users about their climate impact is by automatically identifying their means of transport and use the information to estimate the user's emissions of greenhouse gases. The aim of this project was to create an overview of existing solutions and methods for detecting smartphone users' means of transport and evaluating how a system performs when test data is collected in a different geographical area than the data used to train the system. Evaluation of the system was done via quantitative methods where sensor data was collected and used to test the system. Sensor data was collected by walking, still, train, bus and car. The result is a system that, with varying probability, can determine the means of transport in Sweden. The system's total accuracy was 29 percentage points lower when data collected in Sweden was used in the tests compared to data collected in the same geographical area as the training data. The conclusion is that it can be problematic to apply a solution in a different geographical area than where the solution was developed for. The tests showed that vehicle detection seems particularly sensitive to changing geographical context.

With the beginning of the COVID-19 outbreak and the restrictions put in place to prevent an uncontrolled spread of the virus, the circumstances for daily activities changed. A remarkable shift in the modal split distribution was observed. Cycling was seen as a reliable and resilient option in pandemic times as it allowed social distancing and poses a low risk of contagion. There are detailed studies on the effect of the pandemic on cycling traffic all over the globe which used different data sources, like app data. counters or surveys [1] [2]. Apart from the citizens' behavioral responses to the corona pandemic, the municipalities also put up interventions that were meant to support a shift to cycling-based movements in cities. The question to discuss is what changes will be permanent and which changed circumstances, e.g. increased subjective safety, lead to a long-term change of mobility patterns. The changes in mobility during the COVID-19 pandemic bad different impacts on road traffic collisions and road deaths in different countries. While there was a reduction of both indicators in 32 out of 36 countries in April 2020 compared to April 2019, there was an increase in the other four countries [3]. Others also found a reduction of traffic fatalities in 23 out of 24 countries in 2020 compared to a baseline of the previous years (2017-2019), the only exception being Switzerland [4]. The subjective well-being has also changed differently for the different transport modes throughout the pandemic. For example, in April 2020, 9% of respondents said they would feel more comfortable or much more comfortable if they used or would use a bicycle compared to pre-pandemic times; in summer and autumn 2020, this figure was 11 %, in spring 2021, it was 13%. In autumn 2021, 15% of respondents said they would feel more comfortable or much more comfortable if they used or would use a bicycle than before the spread of the coronavirus [ 5]. [From: Introduction]

The transport sector is an extensive contributor to the total CO2 emissions, and private transports hold a vast share. This has implications on environmental and human health, which eventually have economic consequences for society. Equal access to opportunities is essential in a sustainable society and public transport is a crucial element. Apart from public transport, physical active transport modes are key components in a sustainable transport system. The aim of this thesis was through an intersectional perspective to gain deeper understanding about travel mode choices and to identify barriers to use of public transport. This thesis comprises an extensive literature review of 62 articles, reviews and publications on travel behavior and travel mode choice undertaken in different parts of Sweden, Germany, UK, Portugal and the USA. A limited case study shares through nine qualitative interviews the travel experiences of four men and five women in different ages in Nåntuna/Vilan and Sävja in Uppsala, Sweden. The influencing factors were categorized and later intersectionally analyzed with the respect to gender, age and socioeconomic class. The analysis revealed that travel mode choices are complex and can be made for various reasons. Access to a car, habits, travel pattern and time indicated to be the most influencing factors. Economic resources seemed to influence the availability of transport mode, and indications could be seen that economic resources might impair gender differences. Looking at preferences and actual mode choice, the study sample illustrates that men, older, and richer, are having more opportunities to take their preferable mode choice. Planning factors appeared to both promote and constrain the use of public transport. Public transport seemed to have hard to meet everyone’s need, and indicated to have low competitiveness to the car. It is suggested that future research focuses on how to meet more people’s need in order to increase the use of public transport by its own attractiveness. Further research is also suggested about the health perspective of physical active modes and public transport. The study revealed difficulties in studying experiences outside the white, majority Swedish norm. More time would have been needed to include ethnicity, as it is an important aspect and should be included in future research. / Transportsektorn bidrar till en omfattande del av det totala koldioxidutsläppet, och privata transporter utgör en ansenlig del av detta. De miljö- och hälsomässiga negativa effekterna är betydande, vilket följaktligen kommer att få sociala och ekonomiska konsekvenser. Det övergripande politiska målet är att öka användandet av hållbara transportmedel, så som fysiskt aktiva färdmedel och kollektivtrafik. Lika möjligheter att nå arbeten och service är en förutsättning för ett hållbart samhälle, och kollektivtrafiken är en viktig nyckel till detta. Förutom kollektivtrafiken är också fysiskt aktiva färdmedel, så som cykling och gång, en nyckelfaktor i ett hållbart transportsystem. Syfte: Syftet med den här masteruppsatsen är att få djupare kunskap om de faktorer som påverkar resebeteende och färdmedelsval, samt att identifiera barriärer för kollektivt resande. Uppsatsen har ett intersektionellt perspektiv och undersöker hur maktfaktorer som kön, ålder och socioekonomisk klass påverkar valet av färdmedel. Metod: En omfattande litteraturstudie om resvanor och resebeteende i Sverige, Tyskland, Storbritannien, Portugal och USA föregick en fallstudie. Med fokus på de två områdena Nåntuna/Vilan och Sävja, i Uppsala, Sverige, genomfördes en mindre fallstudie. Nio kvalitativa intervjuer belyser fyra män och fem kvinnors erfarenheter från sina dagliga färdmedelsval. Resultat: Av dessa framgår att färdmedelsval är komplexa; de kan göras av olika anledningar, samt olika anledningar kan leda till samma val. En mängd olika faktorer indikerade på att påverka valet av färdmedel, bland annat tillgången till bil, vanor, attityder, resmönster och restiden. Dessutom indikerar resultat att maktfaktorer som kön, ålder och socioekonomisk klass formar möjligheterna till att välja färdmedel. Indikationer tyder på att ekonomiska resurser styr tillgången på färdmedelsval samt kan minska könsskillnader. Det emellertid ringa urvalet exemplifierar att män, äldre och rikare har större möjligheter att välja sitt önskvärda färdmedelsval. Respondenternas erfarenheter visar att planeringsfaktorer kan både främja och försvåra användandet av kollektivtrafiken. Kollektivtrafiken verkade ha svårt att möta människors olika behov och därmed vara konkurrenskraftig i förhållande till bilen. Mer forskning om detta är nödvändig för att öka och behålla resenärer utifrån kollektivtrafikens egen attraktionskraft. Vidare så föreslås ytterligare studier om länken mellan hälsa, fysiskt aktiva färdmedelsval och kollektivtrafikanvändande. Studien innefattar inte erfarenheter från personer med annan bakgrund än vit, majoritetssvensk eftersom det hade krävt mer tid än vad denna studie medgav. Det är dock ett viktigt perspektiv för framtida forskning.

The rapid increase in car ownership has caused rigorous issues for people living in the major cities in China, which is observe from traffic pressure, the inconvenience of city travelling, and air pollution. While the fast development of digital service platforms based on the Internet provides an alternative approach to touch the problems, leading a researchable phenomenon, online car-sharing service in China. This paper strives to explore the impact of car sharing on millennial sustainability attitudes by using the daily service on apps to ‘drive less, share more’. The paper is conducted using mixed research methods in Suzhou, China. Principally, the researchers interviewed ten car- sharing consumers during shared ride. To ensure the creditability and reliability, the paper collected 326 online survey responses from local car-sharing platforms as comparable data. The results show that most millennials agree car-sharing service makes their traffic modes more convenient, and taking shared ride more compared to self-driving has a significant influence on social and environmental issues in cities. Also, some respondents present willingness or already take actions on giving up car ownerships. However, the result also emphasises the fundamental reasons for millennials to participate in car-sharing service, which is personalised service and reasonable price. The paper closes with three outcomes, sharing economy as ‘Development’, digital service as ‘Innovation’, and sustainability as ‘The future’. They not only enrich the current literature research between Millennials and sharing economy, but also promote further strategies for car-sharing companies with empirical data.