The goal of this research is to develop a method of objectively comparing and optimizing the performance of demand-responsive transportation systems in heterogeneous environments. Demand-responsive transportation refers to modes of transportation that do not follow fixed routes or schedules, including taxis, paratransit, deviated-route services, ride sharing as well as other modes. Heterogeneous environments are transportation environments in which streets do not follow regular patterns, passenger behavior is difficult to model, and transit schedules and layouts are non-uniform. An example of a typical heterogeneous environment is a modern suburb with non-linear streets, low pedestrian activity, and infrequent or sparse transit service. The motivation for this research is to determine if demand-responsive transportation can be used to improve customer satisfaction and reduce operating costs in suburban and low-density urban areas where fixed-route transportation may be inefficient. This research extends existing comparison and optimization techniques that are designed to work in homogeneous environments. Homogeneous environments refer to transportation systems where the streets follow regular and repeating patterns, passengers are evenly distributed throughout the system, and the transit system is easily modeled. The performance of systems with these characteristics can be approximated with closed-form analytical expressions representing passenger travel times, vehicle distances traveled, and other performance indicators. However, in the low-density urban areas studied in this research, the street patterns and transit schedules are irregular and passenger behavior is difficult to model. In these areas, analytical solutions cannot be found. Instead, this research develops a simulation-based approach to compare and optimize performance in these heterogeneous environments. Using widely-available route-planning tools, open-source transit schedules, and detailed passenger data, it is possible to simulate the behavior of transit vehicles and passengers to such an exacting degree that analytical solutions are not needed. A major technical contribution of this research is the development of a demand-responsive transportation simulator to analyze performance of demand-responsive systems in heterogeneous environments. The simulator combines several open-source tools for route planning with a custom-built demand-responsive vehicle and passenger-itinerary optimizer to simulate individual vehicles and passengers within a large system. With knowledge of the street network, the transit schedule, passenger locations, and trip request times, the simulator will output exact passenger transit times, passenger travel distances, vehicle travel distance, and other performance indicators for a particular transportation setup in a given area. The simulator is used to develop a method of comparing various demand-responsive and fixed-route systems. By predefining a set of performance indicators, such as passenger travel time and operating cost, the simulator can be used to ascertain the performance of a wide array of transportation systems. Comparing the weighted cost of each type of system permits a transportation engineer or planner to determine what type of system will provide the best results in a given area. The simulator is extended to assist in optimization of the demand-responsive transportation system layout. A key problem that needs to be solved when implementing a demand-responsive system is to determine the size, shape, and location of the demand-responsive coverage areas, i.e., the areas in which passengers are eligible for demand-responsive transportation. Using a particle swarm optimization algorithm and the simulation-based comparison technique, the optimal size and shape for a demand-responsive coverage area can be determined. The efficacy of the comparison and optimization techniques is demonstrated within the city of Atlanta, GA. It is shown that for certain areas of the city of Atlanta, demand-responsive transportation is more efficient than the currently implemented fixed-route system. Depending on the objective of the transportation planner, passenger satisfaction as well as operating costs can be improved by implementing a demand-responsive system in certain low-density areas. The techniques introduced in this research, and the simulation tool developed to implement those techniques, provide a repeatable, accurate, and objective method with which to optimize and compare demand-responsive transportation systems in heterogeneous environments.

Ökad miljömedvetenhet, växande klimatpåverkan och en fortsatt teknologisk utveckling gör att demand responsive transportation (DRT), ses som ett allt mer attraktivt alternativ till traditionell kollektivtrafik för att resa hållbart. Den här uppsatsen undersöker vilka skillnader det finns med DRT-implementationer på landsbygd jämfört med i stadsmiljö, samt vilken roll interaktivitet mellan operatör och resenär kan spela för ett DRT-systems effektivitet.För att svara på detta används en systematisk litteraturstudie och två fallstudier. Resultaten visar att DRT generellt lämpar sig bättre för landsbygd än i stadsmiljö och att DRT fungerar bättre som ett komplement till traditionell kollektivtrafik än som ersättning av den. DRTs potentiella roll i samhället och möjliga framtida forskningsriktningar presenteras och diskuteras. / An increased awareness of environmental issues, climate changes and a continuing technological development makes demand responsive transportation (DRT) a more likely and attractive option for public transportation. This paper examines the differences between various DRT implementations in rural areas compared to cities. It also examines what role interacitivity can play to increase a DRT-systems efficiency.To answer this, a systematic literature review is conducted along with two case studies. The results show that DRT is generally more suited to rural areas compared to cities. It also shows that DRT works better as a complement to public transport, rather than a replacment of it.The potential role of DRT in a society and future research matters are presented and discussed.

With a high reliance on fossil fuels, the need to transition the transportation sector to clean energy is great. Traditional public transportation has been used to reduce congestion and emissions in urban spaces but faces implementation challenges in rural areas from settlements being spread out with low density populations. This study used a survey to look at different groups living in one rural community on Gotland, Stenkyrka, to identify how their needs differ depending on belonging to these groups, to answer the research question: What are the public transportation needs of different groups within Stenkyrka?   The survey received 50 responses, enabling the respondents to be grouped into those who have children, those who do not have children, and by age group, 25-30, 21-40, 41-50, 51-65, and over 65. The results show that people aged 31-40 are most likely to have younger children and appear to need more flexibility and frequent trips in a public transportation system, while people aged 41-50 are more likely to have older children and seem to need shorter trips along with convenience and flexibility. People over 65 value travel time and need flexibility but, while they are more likely to use public transportation than other groups, are not very likely to be drawn to or use more flexible public transportation options such as taxis or dial-a-ride services. Based on these results, it can be said that different groups in Stenkyrka have different needs and look for different solutions in public transportation. Suggestions for how to create a successful public transportation system in Stenkyrka was discussed to provide examples for how resident needs can affect the design of a transit system. In this case, a demand responsive transportation system that uses a variety of transportation options and goes to the places most often used by residents in the community is important to help ensure that all resident’s needs are met.