Level of Traffic Stress (LTS) is a bicycle quality of service measure originally developed by the Mineta Transportation Institute (MTI) that categorizes road infrastructure into four levels based on amount of traffic stress perceived by a bicyclist. The concept builds on research indicating that bicyclists can be grouped based on their comfort level. Riders identifying as strong and fearless as well as enthused and confident bicyclists represent most of the current users of the bicycle network across the US. However, there is a large group of cautious and concerned bicyclists that might be more likely to bike if the bicycle infrastructure were less stressful. This research applies the LTS methodology to quantify low stress bicycle access around the West End, Oakland City, and Lakewood/Ft. McPherson (Metropolitan Atlanta Rapid Transit Authority) MARTA rail stations. The Equitable Transit Oriented Development (TOD) typology analysis conducted by Reconnecting America identified these station areas as highly vulnerable with lagging markets. Additional analysis compares the existing low stress network, improved low stress networks, and the entire (low and high stress) bike network. Ultimately this work can serve as a model for both transportation planners interested in improving bike access both in general and specifically to transit.

Level of Traffic Stress (LTS) is a four-level system that classifies the stress experienced by cyclists on road segments and at intersections. While LTS has been used in past studies to assess cycling connectivity, accessibility, and safety, very little is known concerning its influence on cycling preferences. This study investigates this topic using a dataset containing 323,163 unique GPS trajectories of Hamilton Bike Share (HBS) users collected over a 12-month period (January 1st to December 31st, 2019). A GIS-based map-matching algorithm is used to generate users’ routes from these trajectories along with attributes such as route length, number of intersections, and number of turns. Unique routes and their use frequencies are then extracted from all routes. The most popular routes between bike share hub (station) pairs are then identified as dominant routes while shortest distance routes are derived by minimizing distance traveled. Weighted level of traffic stress (WLTS), a novel measure of impedance (travel cost) developed for this study, is used to derive the least stressful routes between hub pairs. The three types of routes are compared statistically. The comparison finds that HBS users tend to choose longer routes with bicycle infrastructure in an effort to reduce their traffic stress. However, they do not choose to minimize traffic stress in its entirety by choosing the lowest WLTS routes. In other words, dominant routes are not the least stressful routes in a bike share system. Likewise, minimizing distance is not the sole consideration of HBS users. The findings suggest that other factors also influence route choice. This study not only enhances our understanding of cyclist route preferences with respect to LTS, it also presents a novel measure of impedance – WLTS – that could be used when planning new cycling infrastructure or as an alternative means to route cyclists between origins and destinations. / Thesis / Master of Science (MSc)

Detta examensarbete syftar till att svara på ett av Trafikverket fastställt problem: en gemensam regional cykelplanerings process skulle göra dem billigare och mer jämförbara. De erbjuder för närvarande planerarna en modell som utvecklades av Kågeson 2007. Denna modell har formen av en rapport som ger råd om när man ska bygga en cykelväg mellan städer eller platser i en region. Ändå används den bara i endast 6 av de 21 svenska länen. Trafikverket kräver ett nytt planeringsstödverktyg, mer interaktivt och komplett än Kågeson-modellen. Några nya önskade funktioner är separationen av efterfrågan per syfte, införandet av e-cyklar, olika resesyfte och en prioritering av investeringarna.  Examensarbetet är att designa och implementera det här verktyget, även kallat Planning Support System (PSS), som syftar till att jämföra utbud och efterfrågan på cykelväg till prioritering av infrastrukturförbättringar. En huvudbegränsning för modellen är att den måste vara billig datavis, men så komplett och exakt som möjligt. Det baseras på flera öppna dataleverantörer, till exempel OpenStreetMap, den svenska nationella vägdatabasen (NVDB) eller reseundersökningar från Sverige och Nederländerna. Resultatet är en modell, uppdelad efter turändamål och typ av cykel.  Del för efterfrågeuppskattning anpassar en klassisk fyrsteg transportmodell till cykelplanering och begränsad data. För olika resändamål genereras och distribueras resor tack vare en ursprungs begränsad gravitationsmodell. Valet av cykelläge är anpassat till det faktiska resebeteendet genom logistisk regression med en binär logit-modell. Resorna tilldelas sedan nätverket med tilldelnings metoden "allt-eller-ingenting" genom Dijkstras algoritm. För att utvärdera cykelförsörjningen använde vi ett mått som heter Level of Traffic Stress (LTS), som uppskattar den potentiella användningen av en nätverkslänk för olika delar av befolkningen som en funktion av vägnätvariablerna. Prioriteringsrankningen är då förhållandet mellan mått på efterfrågan och utbud.  Detta nya verktyg implementeras med opensource Geographic Information System (GIS) som heter QGIS och med Python 3 och testas i Södermanlands län / This Master Thesis main objective is to answer a problem set by the Swedish Transport Administration: a common regional bicycle planning process would them cheaper and more comparable. They currently offer the planners a model developed by Kågeson in 2007. This model takes the form of a report which advises on when to build a bicycle path between cities or places of a region. Still, it is only used in only 6 of the 21 Swedish counties. Trafikverket requires a new planning support tool, more interactive and complete than the Kågeson model. Some new desired features are the separation of demand per purpose, the inclusion of e-bikes, different trip purposes, and a prioritization of the investments.  The Degree Project work is to design and implement this tool, also called Planning Support System (PSS), which compares supply and demand for bicycle path to prioritizing infrastructure improvements. A main constraint for the model is that it needs to be cheap data-wise, but as complete and precise as possible. It bases on several open data providers, such as OpenStreetMap, the Swedish National Road Database (NVDB), or Travel Surveys from Sweden and the Netherlands. The result is a model, disaggregated by trip purpose and type of bicycle.  The demand estimation part adapts a classic four-step transportation model to bicycle planning and limited data. For different trip purposes, trips are generated and distributed thanks to an origin-constrained gravity model. Bicycle mode choice is fit to actual travel behaviour through logistic regression with a binary logit model. The trips are then assigned to the network using the "all-or-nothing" assignment method through the Dijkstra algorithm. To evaluate bicycle supply, we used a metric called Level of Traffic Stress (LTS), which estimates the potential use of a network link by different parts of the population as a function of the road network variables. The prioritization ranking is then the ratio between demand and supply metrics.  This new tool is implemented with the opensource Geographic Information System (GIS) called QGIS and with Python 3, and it is tested on Södermanland County.