To date, traditional navigation systems have embedded algorithms that attempt to minimize trip distance and/or travel time. However, many drivers are now becoming increasingly concerned with fuel costs and vehicle emissions that are detrimental to the environment. Therefore, it is desirable to create new “environmentally-friendly” and “energy-friendly” navigation algorithms. Taking advantage of the latest navigation technology, in this dissertation, new navigation techniques have been developed that focus on minimizing energy consumption and vehicle emissions. These methods combine sophisticated mobile-source energy and emission models with route minimization algorithms that are used for navigational purposes. It is also known that different road types can play a significant role in emissions and fuel consumption. As such, a new standalone, high-accuracy road type classification methodology has been developed that only uses a short vehicle velocity trajectory as input, without any external mapping system.

Further, it was found that under chaotic traffic conditions (e.g., those caused by high demand, unexpected road closures, and natural disasters), a shortest-distance route algorithm might suggest a route with unreasonably long travel times, consuming a great deal of energy. On the other hand, under similar chaotic traffic conditions, a shortest-duration routing algorithm might frequently advise a driver to switch routes to avoid congested roadways and maintain reasonable travel time. The number of possible routes varies by the roadway network topology and the location within the network. Thus, it is useful to know how many possible routes exist. Therefore, a new navigational mobility index (NMI) has been developed and justified with an initial focus on freeway networks. NMI can be based on the number of possible routes weighted by shared segments among routes from a source to a destination (referred to as node-to-node NMI). Based on node- to-node NMI, node-NMI and area-NMI are also defined and justified. Different applications of NMI include: 1) measurement of the degree of freedom in which drivers can choose routes from a route choice perspective; 2) determination of the potential effectiveness of navigation systems; 3) determination of the overall connectivity level of an area; and 4) the guidance of the movement of people during an evacuation due to a disaster event.

Based on the proposed NMI concept, a new routing methodology has been developed that is based on maximizing the degree of freedom for re-routing while driving from a known location to a desired destination. Not only is this routing methodology beneficial for dealing with random incidents, it is also useful during major disaster situations when people in an affected area need to be quickly evacuated and relocated to safer areas. A variety of experiments have been carried out to determine the effectiveness of the proposed concept and routing methodology.

The main contribution of this dissertation are as follows: 1) We prove that a shortest-duration and a shortest-distance route are not necessary the most energy efficient route. We have combined a state-of-art energy/emissions model with navigation technologies to develop an environmentally-friendly navigation methodology, which is unique; 2) Because road type plays an important role in vehicle emission and energy consumption, we have developed a highly accurate, low complexity, and stand-alone road-type classification algorithm that only uses a short vehicle speed trajectory as input without external support such as a map system; 3) We have originally proposed and defined a navigational mobility index (NMI) concept specifically for navigational purposes—compared to other existing similar concepts, it has numerous desirable properties and can be used to evaluate the potential effectiveness of a navigation system; 4) Based on the original NMI concept, node-NMI and area-NMI measures have been further defined that can be used to assess the overall degree of freedom of routing in an area; and 5) For emergency evacuation and navigation under chaotic traffic conditions (e.g., those due to high demand or unexpected road closures), drivers can maximize their degree-of-freedom when re-routing. This is highly desirable under emergency evacuation scenarios, in which drivers are more likely to arrive to the safe area using NMI-based navigation than using the traditional shortest-distance or shortest-duration navigation.

To date, traditional navigation systems have embedded algorithms that attempt to minimize trip distance and/or travel time. However, many drivers are now becoming increasingly concerned with fuel costs and vehicle emissions that are detrimental to the environment. Therefore, it is desirable to create new "environmentally-friendly" and "energy-friendly" navigation algorithms. Taking advantage of the latest navigation technology, in this dissertation, we have developed new navigation techniques that focus on minimizing energy consumption and vehicle emissions. These methods combine sophisticated mobile-source energy and emission models with route minimization algorithms that are used for navigational purposes. We know that road type play a great role in emissions and fuel consumption. We further have developed a high accurate and standalone road type classification methodology using only a short vehicle velocity trajectory, without external mapping system.

Under chaotic traffic conditions (highly congested, road closures, natural disasters, etc.), a shortest-distance route might take an unreasonably longer travel time and can consume much more energy. On the other hand, under chaotic traffic conditions, a shortest-duration algorithm might frequently advise a driver to switch routes to avoid congested roadways and maintain a desirable travel time. The number of possible routes varies by roadway network topology and location within the network. It is useful to know how many possible routes exist. Therefore, we have developed and justified a navigational mobility index (NMI) with an initial focus on freeway networks. NMI can be based on the number of possible routes weighted by shared segments among routes from a source to a destination (referred to as a node-to-node NMI). Based on node-to-node NMI, node-NMI and area NMI are also defined and justified. Application of NMI includes: 1) measure the degree of freedom in which drivers can choose routes from a route choice perspective; 2) determine the potential effectiveness of navigation systems; 3) determine the overall connectivity level of an area; and 4) guide the movement of people during evacuation in a disaster event.

Based on the proposed NMI concept, we further develop a new routing methodology that is based on maximizing the degree of freedom for re-routing while driving from a known location to a desired destination. Not only is this routing methodology beneficial for dealing with random incidents, it is also useful during major disaster situations when people in an affected area need to be quickly evacuated and relocated to safer areas. A variety of experiments have been carried out to determine the effectiveness of the proposed concept and routing methodology.

The main contribution of this dissertation is summarized as follows. 1) We prove that a shortest-duration and a shortest-distance routes are not necessary energy efficient routes. We combine a state-of-art CMEM model with navigation technologies to develop an environmentally-friendly navigation methodology, which is unique; 2) Road type plays an important role in vehicle emission and energy consumption. We develop a high accuracy, low complexity, and stand-alone road-type classification algorithm based only on a short vehicle speed trajectory without external support such as a map system. 3) We originally propose and define a navigational mobility index (NMI) concept specifically for navigational purposes. Compared to other existing similar concepts, it has some desirable properties. NMI can be used to evaluate the potential effectiveness of a navigation system. 4) Node-NMI and area-NMI are further defined based on the definition of node-to-node NMI. Node-NMI and area-NMI can be used to assess the overall degree of freedom of an area. Area-NMI is comparable across different areas. 5) For emergency evacuation and navigation under chaotic traffic conditions (highly congested or road link closure), drivers frequently want to switch routes. Based on the defined NMI, we originally propose a navigation methodology giving routes in which drivers have the best degree of freedom for re-routing. This is highly desirable under emergency evacuation scenarios, in which drivers are more likely to arrive to the safe area using NMI-based navigation than using the traditional shortest-distance or shortest-duration navigation.