During the urbanization process, vehicles quantity increase with expansion in population. Under this situation, bus transportation system also suffers from bus delay. Bus delay could be caused by a series of factors, for instance, overload passengers, traffic jam, traffic accident and other unpredictable situations. Therefore, choosing crucial elements to efficiently evaluate bus delay is a complex problem in bus delay researches and operation management. The thesis propose an approach to evaluate and explain bus delay by two elements: traffic congestion and passengers’ waiting time. Those two elements would represent the action of external and internal factors on bus operation. This approach could be adaptive to explain the reasons for bus delays, thus to help the optimization of bus lines and give useful information for decision making of transportation company. To achieve the research aim, a GIS-T database was created by combining the GIS database and TIS database. Spatial data as well as attribute data are combined in the database to represent the crucial information for bus delay. Based on GIS-T the database, the impact of traffic congestion and passengers’ waiting time was calculated using the bus line simulation. By implementing the above steps, the main cause of bus delay was studied. A case study application of this method is narrated; focusing on optimize the bus system of Guiyang city, South China. Different methods are used to find out the problem of system and the reason for delay. Moreover, optimization suggestion is proposed according to result. Compared with other methods, the two-factor method has the advantage of locating the reason of delay for each station. The time performance is not superior to other methods. By comparing the situation of adjacent station, the proportion of traffic congestion and overload passenger in bus delay was determined. The two-factor method is applicable for other transit system in different cities which has similar structure as Guiyang. However, for cities with other structure, a feasibility should be made to select an appropriate model.

För att uppskatta influensområdet till följd av skyddsinfiltartion finns ett antal analytiska modeller att tillämpa. Dessa modeller tar hänsyn till parametrar så som hydraulisk konduktivitet och magasinkoefficient, men de följer också med en rad antaganden som i praktiken inte kan uppfyllas. En alternativ tillvägagång för att bestämma influensområdet är därför med hjälp av numeriska modeller, som i större grad kan göras platsspecifika. Numeriska modeller är till följd av detta mer tidskrävande och behöver mer indata. I denna studie undersöktes vilken metod som är bäst lämpad för att bestämma skyddsinfiltrationens influensområden för en fallstudie i Bromstens industriområde, belägen cirka 15 km nordväst om Stockholm centrum. Två numeriska modeller med varierande underlag av platsspecifika data utvecklades över områdets geologi och grundvattenmagasin för att kunna simulera grundvattennivåer med och utan infiltration. Utöver detta beräknades influensområdet med fyra analytiska modeller. Modellerna testades sedan utifrån olika scenarion, där såväl dataupplösning som den platsspecifika kännedomen över området stegvis ökades. Platsspecifika data tillkom till följd av geotekniska undersökningar och hydrogeologiska tester. Studien ämnar även att besvara vilken data som är av störst vikt för att bestämma influensområdet med de analytiska respektive numeriska modellerna samt vilka skillnader som uppstår mellan analytiskt beräknade influensområden och numeriskt simulerade influensområden. Resultaten visar att de numeriska modellerna i huvudsak är känsligast med avseende på den hydrauliska konduktiviteten, samt att den enklare numeriska modellen är känslig för magasinkoefficienten, något som indikerar att denna modell inte uppnår jämvikt i enlighet med vad som observerats i fält. Utöver detta stod det klart att vattenavgivningstalet inte hade någon nämnvärd inverkan på resultaten. Bland de analytiska modellerna råder den största känsligheten i magasinkoefficienten, följt av konduktiviteten. För Sichardts formel, som inte tar hänsyn till magasinkoefficienten var konduktiviteten den känsligaste parametern. Akvifärens mäktighet, vilken reviderades mellan scenario 2 och 3, hade ingen betydande inverkan på de analytiska modellerna. Vidare visade infiltrationstestet på stora skillnader i skyddsinfiltrationens influensområde med avseende på de olika modellerna och dataunderlaget. Den minsta avvikelsen mätt i residualer observerades för den komplexa numeriska modellen under scenario 4, vilket motsvarar det scenario då dataunderlaget var som störst. Trots att detta scenario tillsammans med modell anses vara det dyraste fallet, anses detta vara det bästa och samtidigt mest tillförlitligt metoden för att uppskatta skyddsinfiltrationens influensområde. / To evaluate the area of influence due to artificial infiltration several analytical models are available. Some of the parameters taken into account by these models are the hydraulic conductivity and storage coefficient, but with these models some assumptions, which in reality cannot be fulfilled, are made. An alternative approach to evaluate the area of influence is therefore with numerical models, which in a greater extent account for the site-specific conditions. Due to this, numerical models are more time consuming and require more input data. This project aims to investigate the most effective approaches to evaluate the area of influence due to artificial infiltration for a case study in Bromsten, located 15 kilometers northwest of Stockholm. Two numerical models, with different background data due to the extent of site knowledge, were developed to represent the site's geological settings and groundwater properties to simulate the groundwaterlevels with and without infiltration. Moreover the area of influence were calculated with four analytical models. All of the models were then applied on four different scenarios, in which the data resolution and the site knowledge increased. Site-specific data was added as a result of geological surveys and hydrogeological tests. The study also aims to answer which data is most important in order to determine the area of influence with analytical and numerical models and what differences there are between the analytical solutions compared with the numerical solutions. Among the methods investigated, constructing a more complex model with data from scenario 4, the scenario with the greatest data supply, resulted in the most reliable results and was therefore the best method and the method to choose for this case-study. Other results indicated that the numerical models first of all are sensitive to the conductivity and that the more simpel numerical model is sensitive to the storage coefficient as well. The last result shows that this model does not reach the steady state conditions as observed in field, which highlights the importance of goetechnical investigation for the numerical models. Moreover none of the numerical models were sensitive to the specific yield. Among the analytical models the storage coefficient was the most important followed by the conductivity. For one of the analytical models (Sichardts formula) the conductivity was the most sensitive parameter. The thickness of the aquifer had no significant impact on the analytical models.