**1. The limitations of GPS**

Some of the downsides of GPS are listed in [1]. Among these are several limitations which are relevant to this chapter. The weak intensity signal causes GPS to be less applicable for cases where stable navigating is mandatory or cases where navigating at indoor and covered areas. The low granularity of the signal accuracy makes navigation in crowded cities where landmarks are so close such that GPS is not able to differentiate among them and so is not effective. Furthermore, GPS signal may disperse and change its direction due to interruptions caused

by skyscrapers, trees, geomagnetic storms, etc. The impact of unreliable GPS is huge especially due to the constant growing use of navigation applications such as Google Maps and Waze, which heavily rely on GPS signal. The impact may be more car accidents in cases where required information is missed exactly at the time it is critical and useful for driving continuation. GPS signal is not enough for covering all navigation instances. Local and timely knowledge is required for updated and accurate information to be able to properly react instantly when obstacles appear in the road ahead, for example, whether a deep pit or a flooding road is likely to be or if the road is closed. To reduce the dependency on GPS, several methods and technologies have been proposed, such as detailed map information, data from sensors, vision-based measurements, stop lines, and GPS-fused SLAM technologies.

## **2. GPS and GIS integration**

A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present all types of geographical data. Many electronic navigation systems deliver its road-guiding instructions using just verbal commands referring to the associated electronic map displayed to the user. This approach assumes the user familiarity with street maps and road networks, which sometimes is not so. In addition, there are places where street maps are not commonly used and instead landmarks are used allowing the intuitive navigation by recognizable and memoizable views along the route. The introduction of buildings as landmarks together with corresponding spoken instructions is a step towards a more natural navigation. The integration of GPS and GIS provides this capability. The main problem lies in identifying suitable landmarks and evaluating their usefulness for navigation instructions. Existing databases can help to tackle this problem and be an integrated part of most navigation applications. For example, Brondeel et al. [2] used GPS, GIS, and accelerometer data to collect data of trips and proposed a prediction model for transportation modes with high correction rate. ResZexue et al. [3] developed a logistics distribution manager (LDM) software and a smart machine (SM) system. It is based on fusing GPS, GIS, Big Data, Internet+, and other technologies to effectively apply its attributes and benefits for achieving a robust information management system for the logistics industry. The resulting logistics facility has shorter distribution time, improved operational competitiveness, optimized the workflow of the logistics distribution efficiency, and saved cost. These examples demonstrate the level of improvements we can expect by integrating GPS and GIS as well as the IoT, mobile phones, and other current technologies.

### **3. GPS and mobile phone integration**

GPS positions provided via phone are generated using multiple different methods, resulting in highly variable performance. Performance depends on the smartphone attributes, the cell network, availability of GPS satellites, and line of sight to these satellites. The time from turning on the smartphone to getting GPS coordinates is relatively long. To accelerate it, a variety of techniques got used. Some phones have incomplete GPS hardware, requiring a cell network to function. The quality of the GPS antenna determines the duration until the

**19**

*Expanding Navigation Systems by Integrating It with Advanced Technologies*

hardware, including GLONASS and A-GPS support.

device will get a lock. For example, the S3 Mini device has relatively good GPS

Urban canyons, sky blockage, and multipath errors affect the quality and accuracy of GNSS/GPS. Public transportation in modern cities may have hundreds of routes and thousands of bus stops, exchange points, and busses. These two factors make urban bus systems hard to follow and complex to navigate. Mobile applications provide passengers with transport planning tools and find the optimal route, next bus number, arrival time, and ride duration. More advanced applications provide also micro-navigation-based decisions, such as current position and bus number, the number of stops left till arrival, and exchange to a better route. Micronavigation decisions are highly contextual and depend not just on time and location but also on the user's current transport mode, waiting for a bus or riding on a bus. Emerging technology is where accuracy and robustness are critical requirements for safe guidance and stable control. GNSS accuracy can be significantly improved using several techniques such as differential GNSS (DGNSS), augmented GNSS, and precise positioning services (PPS). These techniques add complexity and additional cost. Multi-constellation GNSS also enhances the accuracy by increasing the number of visible satellites. In dense urban areas where high buildings are common, the geometry of visible satellites often results into high uncertainty in the vehicle's GNSS position estimate resulting in performance in dense urban areas still

**4.1 Bus navigation using embedded Wi-Fi and a smartphone application**

mobile smartphones with Wi-Fi-enabled busses, gaining real-time information about the journey and transport situations of passengers [4]. A key feature of UBN is a semantic bus ride detection that identifies the concrete bus and route the passenger is riding on, providing continuous, just-in-time dynamic rerouting and end-to-end guidance for bus passengers. Technical tests indicate the feasibility of semantic bus ride detection, while user tests revealed recommendations for effective user support with micro-navigation. The system elements include semantic bus ride detection using a Wi-Fi-based recognition system and a dynamic trip tracking. The semantic bus ride detection combined with the phone's GPS is used to monitor the passenger's trip progress. Deviations are immediately recognized and trigger replanning the trip, resulting a new set of navigation instructions for the passenger. The architecture is composed of Wi-Fi for proximity detection of busses by the passenger's mobile phone, a smartphone application for trip planning using macronavigation, a context-aware trip hints using micro-navigation, context sensing, bus

Urban Bus Navigator (UBN) is a system infrastructure that connects passengers'

This urban navigation is based on detecting and mitigating GNSS errors caused

by condensed high buildings interfering signals going through [5]. It is using a map-aided adaptive fusion scheme. The method estimates the current active map segment using dead-reckoning and robust map-matching algorithms modeling the vehicle state history, road geometry, and map topology in a hidden Markov model

*DOI: http://dx.doi.org/10.5772/intechopen.91203*

**4. Urban vehicles navigation**

being challenging.

ride recognition, and trip tracking.

**4.2 GNSS/IMU sensor fusion scheme**

device will get a lock. For example, the S3 Mini device has relatively good GPS hardware, including GLONASS and A-GPS support.
