Current status and future plans for digital map databases in Japan

Japan Digital Road Map Association (JDRMA) started to construct the digital road map database for the whole Japan in 1988. The first database was completed in 1989, and consequently the updating started in 1990. There are two kinds of data sources for updating the JDRMA database. One is the newly issued topographic maps, which include both roads and other features but are not updated every year. The other is the information from the road administrators; it is used to update the road networks. Every year the information of the newly opened or improved roads is collected and used to update the road network in the database. Other information is also used to supplement the two main information sources. Documents of transportation facilities other than roads and information from the users are utilized. The investigation to improve the updating procedure is continued. >

This study aimed to develop a machine-learning-based predictive model for winter traffic-accident risk using meteorological variables and analyze the influence of weather factors on accident occurrence. The study area consisted of five cities in Gyeonggi-do, South Korea—Dongducheon, Paju, Yangpyeong, Icheon, and Suwon—where automated surface observing system (ASOS) stations are located. Winter traffic-accident data from 2015 to 2023 were collected from the traffic accident analysis system (TAAS), and meteorological observations were obtained from the Korea Meteorological Administration. The dependent variable was constructed based on accidentoccurrence levels; ten meteorological variables, including temperature, solar radiation, wind speed, humidity, and cloud cover, were the independent variables. Softmax Regression, Random Forest, and XGBoost models were employed for prediction, and their performances were compared in terms of prediction accuracy and feature importance. The results revealed that the Random Forest model demonstrated the highest predictive performance across all regions. In addition, solar radiation, wind speed, and cloud cover significantly influenced accident occurrence beyond the widely assumed impact of temperature alone. These findings highlight the potential of machine-learning models as decision-support tools for winter road-safety management and proactive accident-prevention policies.

SUMMARYNetwork traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. TheNetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposedNetCubetraffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley & Sons, Ltd.

This paper first gives an overview on Vehicle Navigation Systems (VNS), which are — at the time — intensively discussed within technical science in general and geodetic science in particular. Starting with a short description of VNS-components we briefly discuss the various types of systems available nowadays.

To provide safe and secure road space is a very important consideration for road administrators, and road evenness has a great impact on driving safety and riding comfort for road users. Furthermore, frequent and quantitative monitoring of the road evenness is a critical issue to establish PMS (Pavement Management System). In order to promote the PMS in local area, we investigate the evenness in local cities, using a new type of compact profiler and present evenness conditions combining different pieces of road information. In order to conduct the study, we used the Digital Road Map (DRM) and ESRI ArcGIS. Because, the DRM electronic road map has been built for road management and vehicle navigation systems for the purpose of linking road network and geographical information. Different road roughness conditions are shown in IRI, each road category and land use characteristics.

Accurate localization in land vehicle navigation systems is highly dependent on the global navigation satellite system (GNSS). However, GNSS signal outages are common in urban areas due to obstacles such as tall buildings and tunnels. To mitigate these issues, digital road maps and dead reckoning sensors, like odometers, are often integrated to provide continuous vehicle localization. This paper presents a robust estimation method to solve the fusion problem of GNSS, odometer, and digital road map measurements in the presence of GNSS out- ages. The proposed solution utilizes a marginalized particle filter (MPF), which combines the robustness of particle filtering with the efficiency of a Kalman filter to handle the linear and non-linear parts of the state and/or measurement equations, respectively. When GNSS signals are unavailable, the MPF fuses all available pseudo-range data with odometric and map information to enhance vehicle positioning. The effectiveness of the proposed method is demonstrated using real-world data in an urban transportation scenario, highlighting signifi- cant performance improvements and real-time application potential.

The paper focuses on the development of GDF as a standard for digital road maps. GDF is a geographical data format in which the data are defined in three different levels: level O contains the geometry and the topology of the information; level 1 defines real world objects; and on level 2, functionally related level 1 features are grouped into higher order objects. The creation of the European digital road map, which was made possible by the standard and on the expected growth of mass market applications such as car information systems, is also discussed.

The GPS-based navigation systems provide an accurate positioning of land-vehicles but this service is not always continuous in urban areas. These systems are usually enhanced with dead reckoning sensors, like odometers, and the use of a digital road map can improve the vehicle location when GPS fails. In this work, we focus on these GPS outages where the main problem is to combine another available measurements intelligently. We present here a global estimation method to solve the fusion problem of the GPS, odometer and digital road map measurements. It relies on an unscented Kalman filter whose main benefit is to deal with non-linear equations directly. In the case of an urban transport scenario, the comparison with an extended Kalman filter shows that the vehicle positioning is improved without requiring more computing time.

Location based services (LBS) are capturing global spotlights increasingly, in which users tend to pick up navigation service as their favorites. This paper examines the dynamic routing problems based on real-time traffic information in LBS. At first, the overall structure of dynamic navigation system is illustrated, which is composed of four ingredients: traffic data collection, digital road map, static routing and dynamic re-routing. Traffic data collection is the task of traffic information center (TIC), where various sources of data are fused into link travel times that are used as criteria to choose candidates in path determining. Digital road map includes digital road network and historical traffic data, where the topology relationship of road network is elementarily explored and the method to represent statistical traffic data is referred to. Routing with historical traffic data and dynamic re-routing with live traffic data are two main functions of dynamic navigation service. The flow of dynamic re-routing algorithm is exhibited. Lastly, a first-in-first-out (FIFO) time-depend shortest path algorithm is discussed for dynamic routing, which adopts the adaptive A^* algorithm based on binary heap priority queue and RB-tree.

Currently available driver assistance systems (i) warn the driver based on vehicle state sensors (e.g., door open, outside temperature near or below the freezing point), (ii) offer route guidance information (navigation systems based on GPS and digital road maps), or—in some critical situations—(iii) even actively influence vehicle handling under carefully delimited conditions (anti-blocking-system, electronic-stability-program). This contribution reports about investigations to combine passive GPS- and map-based route guidance with model-based machine vision in order to automatically assess or even execute driving maneuvers in inner-city traffic situations. Information provided by todays route guidance systems is treated as a generic description of lane structure. The schematic description of lane structures extractable from commercially available standard digital maps is automatically instantiated by a machine vision approach which interprets video image sequences recorded by cameras from within a driving vehicle. The resulting model of the lane structure in front of the vehicle is subsequently exploited in order to control vehicle maneuvers in real-time as a proof of principal system competences. The machine-vision-based execution of driving maneuvers can at any time be overridden by the driver.

125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Genetic algorithms are proposed as a new learning paradigm for combinatorial optimization that models a natural evolution mechanism. The authors attempt to apply genetic algorithms to the vehicle routing problem. As it is easy to generate the same gene while a generation shift goes on, it is feared that a solution will fall into a local minimum. The authors propose a new method that does not permit overlapping of genes. Some experiments are performed on digital road maps. The authors' results show that the genetic algorithms can effectively find optimum solutions.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

A major challenge in the production and use of geographic information is assessment and control of the quality of the spatial data. The rapid growing number of sources of geospatial data, ranging from high-resolution satellite and airborne sensors, GPS, and derivative geospatial products, pose severe problems for integrating data. Content providers face the problem of continuously ensuring that the information they produce is reliable, accurate and up-to-date. Integrity constraints are able to resolve certain issues in the data, like valid attribute values or relationships between data objects. The main issue is however the consistency of the data with respect to the current “real-world” situation. Today the industry still relies on human operators, who collect and interpret aerial photos and field data to check and correct the current state of the data. Especially for detailed data over large regions, like digital road maps or topographic maps, this is a very labor-intensive and costly process. In addition, human processing is a source of error and inconsistency. Automated detection of change and anomalies in the existing databases using image information can form an essential tool to support quality control and maintenance of spatial information.

Recent years, spatial data and distributed sensing data, such as digital road map, traffic route, vehicle speed, event recording and others, are integrated and stored into the spatial temporal database systems. In this paper, we focus on GIS-based scheduling applications using GPS tracking devices and spatial database for the actual vehicle routing problems. Vehicle Routing Problem (VRP) is the one of important problems in the research fields of transportation. Here, we use two spatial tools, “PhotoTrackr” and “ArcGIS”, which record and analyze trajectories of vehicle and moving objects. Firstly, we compare the actual recorded routes and optimal routes, and discuss the characteristics of routes determined by the day-care center. Next, we consider the problem of “Multi-Agents Scheduling and Routing Problem with Time Windows and Visiting Activities”. We present our algorithm of k-means clustering under the constraints of visiting sequence.

In the age of intelligent communication technology, intelligent mobile phones play a critical role in road accidents. Their effect on driving, resulting in vehicle crashes over the last two decades, has become a significant risk. Speed was identified as an important risk factor for road traffic accidents. By monitoring vehicle speed and position, one can help prevent accidents by sending out alert messages and limiting the consequences for unprotected road users such as pedestrians and cyclists. To manage and control road accidents, the position and speed of the vehicle were communicated to nearby cars, and the excellent circle method was used to calculate the distance between the towing vehicle and the trailing vehicle. This method is based on the zero point of the earth’s equator and GPS. The application was tested in this paper using mobile devices. The experiment used various smartphone modules, such as GPS receivers, digital road maps, and communication systems. A prototype was developed and evaluated using mobile phones in highway and city scenarios with varying speeds and network sizes. As a result of the experiment, location and speed accuracy were determined, and alert messages were generated when the distance between vehicles fell below the standard or government-specified length. The investigation could be expanded further by connecting to the Internet, storing data in the cloud, performing analytics, and involving insurance agents, relatives, and nearby hospitals.KeywordsGPSSpeed monitoringSmart mobileAndroid studioVehicular networkAndroid applications