Real-time Intelligent Map-matching Algorithms for Advanced Transport Telematics Systems (RiMATTS)

各种交通应用和服务，如按驾付费保险计划、导航和路线指引、事故和紧急应变（加强999紧急服务）、巴士站到达信息和车队管理，都需要空间和时间位置和时间信息。这些服务的重要组成部分之一是导航模块，它提供所需的定位数据。许多商用设备可用于支持此类运输系统的导航模块。近年来，大多数商用设备使用GPS技术来获取此类定位数据。由于GPS既有系统误差又有噪声，因此这种设备无法达到许多运输服务所需的定位精度。此外，此类设备不能提供位置解决方案的完整性（置信度），这对于责任和安全关键应用非常重要，例如按驾付费保险计划（由于计费可能不正确）和紧急999呼叫响应。需要采用将位置数据（来自GPS或其他传感器）与空间路网数据相结合的地图匹配算法。地图匹配不仅可以识别车辆的物理位置，而且如果有好的数字地图，还可以提高定位精度。由于固有的限制和约束，目前的地图匹配算法无法在某些操作环境中（特别是在密集的城市地区）支持某些运输系统的导航模块。此外，单一的地图匹配算法无法在不同的操作环境中最优地支持运输系统的导航模块。因此，选择一组具有代表性的映射匹配算法是非常必要的。通过实验对这些算法的详细描述对于评估它们在设计它们的操作环境中的性能和识别它们的局限性至关重要。这一具有代表性的现有地图匹配算法集经过进一步的增强，以及一种新的地图匹配算法，可以考虑到现有地图匹配算法的局限性和约束，可以在大多数操作环境中最佳地支持大多数运输系统的导航模块。因此，本研究项目的主要目标是：(1)从现有算法中确定一套具有代表性的地图匹配算法，(2)开发一种新的地图匹配算法，并解决目标1中在应用和操作环境方面发现的任何差距，(3)开发基于知识的智能地图匹配（iMM）技术，以确定适合操作环境的最佳地图匹配算法（在上述目标1和2中实现）。(4)展示了iMM在不同作战环境中的潜在应用。将定义几个标准，以便与iMM技术一起使用，为给定操作环境中的特定服务选择最佳算法。这些标准将包括操作环境的地理特征（如土地使用、道路网络密度和建筑物高度信息）和其他（如复杂性和成本）（如需要）。本文提出的研究将在两个层面上进行开发：(1)算法；(2)结合算法和导航传感器的实际导航系统。预期与实际导航系统相关的成本将相对较低（每单位500美元的水平）。随着导航传感器芯片和基于MEMS技术的传感器价格的降低，这一数字有望下降。

A variety of transport applications and services such as pay-as-you-drive insurance scheme, navigation and route guidance, accident and emergency responses (enhanced 999 emergency services), bus arrival information at bus stops and fleet management require spatial and temporal location, and time information. One of the important components of such services is the navigation module which provides the required positioning data. Many commercial devices are available to support navigation modules of such transport systems. In recent years, most commercial devices use GPS technology for acquiring such positioning data. Since GPS suffers both systematic errors and noise, the required positioning accuracies of many transport services cannot be achieved by such devices. Moreover, such devices do not provide integrity (the level of confidence) of position solutions which is very important for liability and safety critical applications such as pay-as-you-drive insurance schemes (due to the possibility of billing incorrectly) and responses to emergency 999 calls. A map matching algorithm that integrates the locational data (from GPS or other sensors) with the spatial road network data needs to be employed. Map matching not only enables the physical location of the vehicle to be identified but also improves the positioning accuracy if a good digital map is available. Current map matching algorithms are not capable of supporting the navigation modules of certain transport systems in some operational environments (specifically in dense urban areas) due to the inherent limitations and constraints associated with them. In addition to this, a single map matching algorithm cannot optimally support the navigation module of a transport system in different operational environments. Therefore, there is a distinct need to select a set of representative map matching algorithms. The detailed characterisation of these algorithms through experiments is essential to evaluate their performance in the operational environments for which they were designed and to identify their limitations. This representative set of existing map matching algorithms with further enhancements, along with a new map matching algorithm that can take into account limitations and constraints of existing map matching algorithms, could optimally support the navigation modules of most transport systems in most operational environments. Therefore, the main objectives of this research project are to (1) identify a set of representative map matching algorithms from existing algorithms, (2) develop a new map matching algorithm and to address any gaps identified in objective 1 both in terms of applications and operational environments, (3) develop a knowledge-based intelligent map matching (iMM) technique to identify the best map matching algorithm (achieved in objectives 1 and 2 above) suitable for an operational environment, and (4) demonstrate a potential application of iMM in different operational environments. Several criteria will be defined for use with the iMM technique to select the best algorithm for a particular service in a given operational environment. Such criteria will include the geographic characteristics of the operational environment (such as land-use, road network density, and building height information) and others (such as complexity, and cost) if required. The exploitation of this proposed research would be in two levels: (1) the algorithms, (2) the actual navigation system which incorporates the algorithms and the navigation sensors. The expectation is that the cost associated with the actual navigation system will be relatively low (at the level of 500 per unit). This is expected to fall as the price of navigation sensor chips and MEMS technology-based sensors reduce over time.