UC Riverside

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.