Pedestrian network map generation approaches and recommendation

This paper explores measures of pedestrian accessibility and network connectivity with a network that includes pedestrian facilities in addition to the street network. Studies that focus on walkability usually use available street networks that do not include pedestrian-only facilities. The effect of missing pedestrian connections where the street network is richer than the pedestrian network has been examined in some studies, but the case of suburban environments with robust pedestrian networks has mostly been ignored. In the current study, various measures of connectivity and accessibility were compared between the pedestrian network and the street network in different suburban settings and for accessibility to different land use activities, such as schools and retail centers. Documenting the degree to which the pedestrian network enhanced pedestrian accessibility over the street network alone was motivated by the desire to inform research and to inform policy. Nine neighborhoods in the city of Davis, California, with typical suburban densities, a variety of street network types, and an extensive system of off-street bicycle and pedestrian facilities were used in the study. A network that included all minor and primary roads in the city plus pedestrian ways was also used. This network included 60 mi of off-street facilities and excluded freeways not open to pedestrians. Households were used as origins and schools and retail centers as destinations to demonstrate the effect of the pedestrian network on connectivity and accessibility in different parts of the city. The results of this study can be used to improve the measurement of built environment in studies of active travel and to increase understanding of the effect of the pedestrian network in the suburban environment.

It is possible for the visually impaired to walk their known area independently by using a white cane. However, in their unknown area, they cannot walk without help of others even if they can use a white cane very well. In such cases, not only a visually impaired person but he/she who assists them receives many stress in dairy activity. Therefore, many types of independent walking assist system for the visually impaired are developing. Our objective of this study is the development of an auto navigation system for the visually impaired which assists independent walking of them. This system measures the position of a visually impaired person (a user) by using GPS (Global Positioning System) and navigates him/her to the destination like as a car navigation system. In our system, a location data is stored in the map database and a position of user are analyzed and optimal route for a user to the destination is calculated. Our system guides a user to the destination along this route, and notifies the user of this route and some information for the safety walking by artificial voice. Three normal subjects were tested with our navigation system. All subjects were blind folded by an eye mask and equipped a navigation system. As a result, our system worked well and all subjects were able to walk to the destination following guidance voice. So, we are developing a new navigation system which is installed on a cellular phone. In Japan, there are some cellular phones that include GPS. And most of application programs for cellular phone are written by JAVA. Therefore, GPS included in a cellular phone will be used and every functions of the navigation system will be written by JAVA. In this paper, we also reports about this new navigation system.

The car navigation service has opened through the global positioning system (GPS). People's lives have changed through car navigation services, and this service is indispensable nowadays. GPS provides reliable location accuracy in open space, but its location accuracy is degraded where multipath and other error factors are present. In particular, since GPS signals must be received in order to calculate the position, the navigation system cannot calculate the position in an indoor space such as underground parking lot, subway or tunnel where GPS signals are cut off. When car navigation is performed in an indoor parking lot where GPS signals are not transmitted, the user cannot calculate the current location, so the user has to go out by relying on the traffic sign because he does not know the current location. Also, when the user leave the parking lot, the car navigation system has problems. In an urban canyon, it is difficult to calculate accurate current position due to multipath. Because the route is calculated through the current location and route, the car navigation system provides the wrong route to the user. In addition, when entering a tunnel while using car navigation, the car navigation system calculates the position using the initial tunnel entry velocity estimated by the GPS. As the speed of the user increases or decreases, the position error also increases. Then the position suddenly changes when passing through the tunnel. And it is difficult to provide a proper guidance at the branch road or exit in the tunnel because of the positioning error. People are demanding a complete car navigation service, but the current navigation service provides the wrong route and position information to the user where the GPS signal quality is poor. In order to solve the above problem, in this paper, we have studied to improve the user's position accuracy by using LTE signal and mobile phone internal sensor. In general, observed time difference of arrival (OTDOA) technique is applied for the LTE based positioning. Because this method gives tens of meters of error, it is difficult to be used for the car navigation service. In our research, we estimate the position using the LTE signal strength. The difference from conventional fingerprinting method is that instead of using only current signal strength, the signal strength pattern accumulated while moving is utilized to estimate the user position. The signal strength increases as the user approach the access point (AP), and decreases as the user move away from the AP. Therefore, when a user moves and stores signal strength with respect to a specific AP, a pattern that could occur only in the corresponding region is saved. There are many similar candidates in the database when using only the current signal strength. However, when using a pattern, similar candidates exist only around the true position. And, when using several LTE signals, the positioning accuracy could be increased. Since the user receives multiple LTE signals, the pattern becomes surface. The surface is compared with the database, and the position with the highest correlation value is estimated as the current position. We call this method surface correlation (SC). In order to verify the performance of the proposed method, experiments were conducted in the Gangnam beltway tunnel in Seoul. The testbed consisted of three tunnel and total distance is almost 11.8 Km. In the tunnel, repeater and several antennas are installed to widen the area of the LTE signal. When an LTE signal is received by the vehicle, the largest signal strength is received every time when the vehicle passes the nearest antenna. As a result, for a specific LTE signal, when the vehicle has passed through the LTE signal region, a unique pattern with multiple peaks is generated. In order to estimate the position, we first estimate the position with the SC technique. Then, the estimated position is corrected by comparing the peak of the currently received pattern with the peak of the database. Through this method, the position was estimated with an error of less than 50meter in the tunnel. Conclusion In this research, the LTE signal strength based positioning technology is presented. Especially, this technology, SC, provides the positioning performance that could be used for the car navigation in the tunnel where GPS signal is blocked. We expected that user would be use the accurate car navigation service through this technology in the GPS challenging environment.

The global positioning system (GPS) trajectory data are extensively utilized in various fields, such as driving behavior analysis, vehicle navigation systems, and traffic management. GPS sensors installed in numerous driving recorders and smartphones facilitate data collection on a large-scale in a high-frequency manner. Therefore, map-matching algorithms are indispensable to identify the GPS trajectories on a road network. Although the local map-matching algorithm reduces computation time, it lacks sufficient accuracy. Conversely, the global map-matching algorithm enhances matching accuracy; however, the computations are time consuming in the case of large-scale data. Therefore, this study proposes a method to improve the accuracy of the local map-matching algorithm without affecting its efficiency. The proposed method first executes the incremental map-matching algorithm. It then identifies the mismatching links in the results based on the connectivity of the links. Finally, the shortest path algorithm and the longest common subsequence are used to correct these error links. An elderly driver’s driving recorder data were used to conduct the experiment to compare the proposed method with four state-of-the-art map-matching algorithms in terms of accuracy and efficiency. The experimental results indicate that the proposed method can significantly increase the accuracy and efficiency of the map-matching process when considering high-frequency and large-scale data. Particularly, compared with the two-benchmark global map-matching algorithms, the proposed method can reduce the error rate of map-matching by nearly half, only consuming 18% and 58% of the computation time of the two global algorithms, respectively.

This paper introduces a new sensor fusion approach for using multiple MEMS sensor-based pedestrian navigation systems (PNSs) to enhance the performance of each individual navigation system. First, we propose a novel single IMU-based PNS which integrates both the inertial navigation system (INS) mechanization and the pedestrian dead reckoning (PDR) mechanization. When two identical PNSs are used by a user at the same time, the output of each PNS is then shared within a Kalman filter (KF) with the state-constrained approach, which, in turn, feeds the state error correction information back to each PNS. Several real experiments are done to assess the proposed methodology for the integration of multiple PNSs. The experimental studies clearly indicate that through applying the proposed state-constrained approach, using motion sensor data from multiple mobile/wearable devices could provide more accurate navigation information for a pedestrian in all indoor and outdoor environments.

Central to every pedestrian network analysis is the mapping of the network. Many studies have used street networks (SNs) to analyze connectivity. Recent studies suggest that analyses based on informal pedestrian networks (e.g., park trails, greenways, or shortcuts) may provide a more robust network representation, however, no studies have examined whether switching to a network consisting of formal pedestrian facilities (FPFs) (e.g., sidewalks and crosswalks) would provide more effective representation of real pedestrian movement and perceptions. Does it yield different outcomes compared to an SN? When does an SN still remain a good proxy, and when should a FPF network (FPFN) be adopted? How worthwhile is it to switch to a FPFN? To address these questions, this paper examines the difference between SNs and FPFNs and how the differences affect the outcomes of network measures. Four U.S. neighborhoods were selected to represent different types of networks. Four classic connectivity measures and four new measures related to crossings were evaluated using both SNs and FPFNs. Comparisons were conducted and the following conclusions drawn: (i) using FPFNs yields greatly different outcomes from using SNs, (ii) an SN can broadly describe the morphological features of a network, especially for a gridiron pattern with narrow streets and small blocks full of sidewalks and crosswalks; however, an FPFN performs much better in describing features which are invisible when using SNs; and (iii) using FPFNs can provide opportunities to develop new measures of performance, such as safety or convenience.

Localization of a vehicle is a key component for driving assistance or autonomous navigation. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches, which rely on some predefined known landmarks or a priori information about the environment, no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3-D information of the scene from image sequences. Relative positions of the camera motion are then estimated by registering the 3-D feature points from two consecutive image frames. Localization of the vehicle is finally given by the reference to its initial position. I. INTRODUCTION Localization and tracking of a vehicle are major compo- nents for providing positions, directions and travel informa- tion to the driver. They also serve as key technologies for building autonomous navigation systems. Many approaches have been proposed for locating a mobile robot or a ve- hicle based on various techniques. The most commonly used methods include dead-reckoning techniques, navigation using active beacons, landmark-based navigation, map-based navigation, global positioning system (GPS), and vision- based positioning. Dead-reckoning is a procedure for determining the present location of a vehicle by advancing some previous position through known path and velocity information over a given period of time. Since an odometer and optical sensors for wheel direction detection are easily installed on a vehicle, dead-reckoning is usually a less expensive method for vehicle localization. However, the integration of incremental motion information over time will lead to accumulation of errors. In the case of long distance travel, accumulation of orientation error will make position errors diverge as the increase of driving time. Active beacons such as laser, sonar or radio can be used as media for vehicle or mobile robot navigation. These approaches use triangulation to measure the distance between a number of beacons and the mobile platform and then deter- mine the current location. The problems associated with this technique are the inaccuracy of the distance measurement caused by time-delay of the signals, and the installation and maintenance cost of a large number of beacons required for an area. Satellite based differential global positioning system (DGPS) is probably the most advanced and accurate method for identifying the position and orientation of an object. However, DGPS does not work well if the satellite signals are blocked. This situation commonly happens in the indoor environments, or urban areas with tall buildings, etc. In this work, we propose a visual positioning system (VPS) for vehicle or mobile robot navigation. Different from general landmark-based or model-based approaches which rely on some predefined known landmarks or ap rioriinfor- mation about the environment (e.g., 3-D models of buildings, objects, etc.), no assumptions on the prior knowledge of the scene are made. A stereo-based vision system is built for both extracting feature correspondences and recovering 3- D information of the scene from image sequences. A robust feature tracking method based on simultaneously considering the inter-frame images from the same camera and the stereo image pair from different cameras at a fixed time instant is developed. The relative position of the camera motion is then estimated by registering the 3-D feature points from two consecutive image frames. Our system includes the following basic modules:

From the precise location data calculated from satellite signals, Global Positioning System makes itself the effective navigation system in the mobile devices yet the cost of which being the higher energy consumption. Being the energy constrained device, the mobile devices thus need the alternative energy efficient navigation system. The pedometer that counts the foot steps taken by the pedestrian, thereby calculating the average distance covered, when integrated with the device's orientation during locomotion, can pave the way for the pedestrian's navigation system. By increasing the efficiency of pedometer using gait analysis and by taking step vectors rather than simply steps, the effective navigation system for pedestrians can be provided. And, this project aims at such energy efficient less GPS dependent navigation system for pedestrians using the mobile sensors-Accelerometer, Magnetometer and Gyroscope for gait analysis and finding orientation.

Car navigation systems (CNS) have become an interesting research and industrial topic due to their wide deployment. However, updating and distribution of the road map database is a complex and high cost process. In this paper, we propose a real-time map generation and updating algorithm, which fulfills or corrects the unknown road area of the conventional road map database by using global positioning system (GPS) information and wireless communication technologies. To generate the road map of unknown roads, the proposed algorithm uses road images, which can be obtained when a driver travels unknown roads. When a driver travels unknown roads, which do not appear on the map of the car navigation system, the car navigation system extracts GPS and image information of the unknown roads. Then, the car navigation system generates the road map for the unknown roads by extracting location and road information, and updates the road map database in the car. After updating the local road map database, the car navigation system updates the master road map database, which is located at the network and is distributed to other car navigation systems. Thus, the road map database provider can update the road map database of other car navigation systems, which share the same master road map database. The proposed algorithm provides an efficient updating process of the road map database with a simplified process and reduced time for unknown road areas.

With the rapid development of computer technology and communication technology, pedestrian navigation system(PNS) began to enter the real practical stage. PNS is a method of track reckoning based on strapdown inertial navigation system. However, inertial navigation system is prone to error accumulation with time, resulting in low positioning accuracy and practicability. Low cost inertial measurement unit(IMU) device has low accuracy, and the inertial navigation system has accumulated error, and the system is easy to diverge. Apart from those above, civil GPS accuracy is not stable, and poor GPS signal in a multipath environment may lead to errors, and GPS signals can not be used indoors. It is necessary to design a kind of navigation method to adapt low cost IMU. In this paper, it is presented that a foot inertial navigation system which is based on low cost MEMS inertial sensors is presented. According to the characteristics of pedestrian zero-velocity during walking steady state, an inertial navigation method based on Zero Velocity Update (ZUPT) is adopted. This paper uses air pressure, GPS to assist ZUPT method to correcting navigation parameters through Kalman filter. Based on the implementation scheme of pedestrian navigation system proposed in this paper, the steady state of walking were experimentally verified through the actual test data. The experimental results show that the proposed method can provide efficient, reliable and continuous position and navigation services.

<div class="htmlview paragraph">A number of reports have been published in recent years concerning research work that is underway in Europe. America and Japan on navigation systems for vehicles. The systems reported on to date employ a variety of position detection sensors, such as geomagnetic sensors, gyrocompasses and loran receivers. However, the literature contains virtually no reports of automobile navigation systems that make use of a Global Positioning System (GPS) as a means of detecting and determining the location a vehicle.</div> <div class="htmlview paragraph">The authors have developed a GPS automobile navigation system that is intended to be one subsystem of a cinorehensive on-board information system. The total system incorporates car telephone data communication capabilities as well as various functions for centralized operation of the vehicle equipment.</div> <div class="htmlview paragraph">This navigation system can accurately locate a vehicle's position using position data received from GPS satellites together with data supplied by geomagnetic sensors. In additions, map data stored in a CD-ROM can be called up for display on a monitor to indicate the vehicle's position on a map.</div> <div class="htmlview paragraph">This paper describes the system and presents the experimental results for position detection accuracy, as well as the author's observations on map data standardisation.</div>

For the guidance, control and navigation of autonomous vehicles, both positioning and orientation are important for mission success and surveillance. Global positioning system (GPS) and strapdown inertial navigation system (SINS) have complementary characteristics to overcome the demerits of each other and can well perform as integrated navigation system. The observability analysis of low-cost SINS/GPS from many researches shown that the heading and yaw rate gyro bias is unobservable in straight motion and with constant acceleration. This paper presents integration of low-cost SINS/GPS aided with odometer and heading from two antennae GPS receiver to overcome this problem and to control the accuracy degradation during GPS outage. The carrier phase measurements are processed to obtain the heading from two antennae position measurement. The obtained heading and position from GPS and velocity measurement from odometer are used as observation input to extended Kalman filter (EKF) for the heading and positioning accuracy improvement of SINS/odometer/GPS integrated system for land vehicle navigation. When GPS signal is unavailable, the filter switches to SINS/odometer integration to control the position and velocity errors growth during this period. The observation model for heading measurement from two antennae is derived for the designed system. A field test was conducted to check the effectiveness using proposed method for heading and positioning accuracy improvement. The test results showed that Low-cost IMU/Odometer/ Two Antennae GPS integrated systems' accuracy both in position and heading is improved significantly.

How to achieve continuous, reliable and accurate positioning performance using low-cost sensors is one of the main challenges for aviation navigation system. Global Positioning System (GPS) can provide the primary means of navigation in a number of aviation navigation applications (e.g., manned and unmanned aircraft vehicle, airport ground vehicle). However, GPS signal deteriorations typically occur due to aircraft itself during maneuvering, ionospheric scintillation, Doppler shift, multipath and so on. Thus, there is a need to research GPS augmentation strategies which can be used in the Communication, Navigation, Surveillance/Air Traffic Management. GPS integration with Inertial Navigation system (INS) is one of the key strategies. But once GPS signal outages, the integrated navigation system works in pure INS, and positioning accuracy deteriorates with time. When using low cost GPS/INS integration, a primary problem is the rapid performance deteriorate during GPS outages. To provide continuous, accurate and reliable positioning information in aviation, discrete grey prediction model (DGPM) aided fusion methodology is proposed. The DGPM provides pseudo-GPS position information for INS during GPS outages. The mathematical model of integrated navigation system is established, including INS error equations, Kalman filter and DGPM. The model works in the update mode when there is no GPS failure, whereas it switches to the prediction mode in case of GPS outages. To verify the feasibility and effectiveness of the proposed methodology, real road test is performed. The comparison results show that accuracy of longitude and latitude are improved by more than 80% and 70%, respectively. The DGPM can effectively provide position corrections for standalone INS during GPS outages.

In order to reduce the dependence of pedestrian navigation on satellite navigation system and wireless communication, a method is studied to construct pedestrian navigation system based on micro inertial technology and human biological kinematics. In the study, the distributed system structure is arranged on human legs instead of feet, and the key technologies, including conception of virtual inertial sensor, initial alignment and error correction, precise gait-phase detection, effect and inhibition of environmental magnetic field are presented. A detailed analysis of the performance characteristics of the pedestrian navigation system is carried out in the environment with or without electromagnetic interference. Experimental results show that, the prototype of virtual inertial sensors works functionally, and the position error with electromagnetic interference accounts for 2.5% of the walking distance. The key technologies proposed in the study can effectively improve the positioning accuracy of pedestrian navigation system, and independently achieve longer time personal navigation in the GNSS and Wireless communication attenuation or invalid environment.

Research examining pedestrian navigation systems that use landmarks to explain routes became popular in the past years. Nevertheless, it is still an open question how many landmarks should be depicted at once. In this paper a user study is presented that evaluates two different indoor navigation system designs that depict either one (N = 63) or four (N = 60) landmarks to guide the user. The time it took the participants to recognize where to go was captured as a dependent variable. Results show that the interface only depicting one landmark leads to faster self-localization. Therefore, it is argued that a pedestrian navigation system should mainly depict one highly salient landmark in a navigation instruction in order to keep navigation efficiency high.