Personalized Routing for Car Navigation Systems (original) (raw)
Related papers
Routing Algorithms in Connected Cars Context
2019
Most of the existing navigation solutions compute individual routes based on map topology and traffic data but, without considering the route effect on the entire navigation ecosystem. Traffic data usage and sharing in the context of connected cars is a key element for route planning. Such solutions require efficient implementation and deployment in order to reduce any kind of risk. Following a smart driving methodology, we run different route search algorithms on connected cars traffic scenarios in order to avoid traffic congestion and minimize total driving time on the entire navigation ecosystem. The experiments in this work proved that connected cars data usage and sharing reduce the total driving time of the navigation ecosystem and also that specific routing algorithms are more suitable for specific connected cars scenarios in order to obtain relevant results.
International Journal of Linguistics and Computational Applications, 2015
Traveling is a part of every person's day-today life. With the massive and complicated road network of a modern city or country, finding a good route to travel from one place to another is not a simple task. The knowledge of the actual current state of the road traffic and its short-term and dynamic path evolution for the entire road network is a basic component of ATIS (Advanced Traveler Information Systems) and ATMS Advanced Traffic Management System) applications. In this view the use of real-time Taxi Data (TD), based on traces of GPS positions to gather accurate travel times/speeds in a road network and to improve short-term predictions of travel conditions. GPS-equipped taxis can be regarded as traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge. We mine smart driving directions from the historical GPS trajectories of a large number of taxis, and provide a user with the practically fastest route to a given destination at a given departure time. In our approach, we propose a time-dependent landmark graph, where a node (landmark) is a road segment frequently traversed by taxis, to model the intelligence of taxi drivers and the properties of dynamic road networks. The essential components that will be discussed are a Web-services-based data collection approach then, a Variance-Entropy-Based Clustering approach is devised to estimate the distribution of travel time between two landmarks in different time slots. Based on this graph, we design a two-stage routing algorithm to compute the practically fastest route. In our existing system static (Dynamic)-path and not update the rout.
Personalized Routing Dynamically Adjusted to Avoid Adverse Situations
2021
Routes that are optimal in general for a particular travel need may at times be unsuitable in a specific instance because of various factors such as construction, weather, accidents, rush hour traffic, road surface conditions, presence of crowds, etc. The impact of such factors on a person's travel plans can depend on the specifics of the person's travel circumstances, such as travel mode, vehicle characteristics, etc. This disclosure describes techniques to proactively deliver travel routing guidance personalized to a user, based on user-permitted contextual factors.
2017
More and more drivers use GPS based tools (GNSS) to plan their route in order to choose the optimum route (shorter, faster or the most economical). These GNSS devices can operate offline or online. Off-line, when the GIS database is installed in the device and there is not any information regarding the actual traffic flow, in that case the program always calculates the same route between two places. Online, when the actual traffic flow information is available, in that case there is the possibility of the calculation of the optimal route in real time. Based on the above options, the driver's route-choice decision can be static, dynamic or informed. Static, when the driver always follows the same route: either without using a GNSS device (choosing the usual way) or with an offline device. Dynamic, when the driver chooses the way conforming to only real time visual information on the spot without any navigation tool or external information, and decides according to traffic density. Informed, when the GNSS device calculates the optimal route in line with the current traffic flow information. The aim of this study is the present the influence of the proportion of different route-choice decision modes.
Data management issues for intelligent transportation systems
In this paper we discuss the technical challenges of devising a Data Stream Management System (DSMS) in the intelligent transportation scenario considered in the PEGASUS project, where the final aim is to provide reliable and timely information to improve the safety and the efficiency of vehicles' and goods' flows. The system should collect and integrate the large amounts of geo-located stream items coming from On Board Units (OBUs) installed on vehicles, with the aim of producing real-time maps including traffic and Points Of Interest (POIs) information to be then distributed to OBUs. OBUs' smart navigation engines will exploit these maps to enhance mobility and provide user-targeted information. We propose a two-tiered GIS DSMS architecture where stream items are pulled from the source input stream, processed and stored in a result container to be further pulled by other operators. The system reduces the data acquisition costs by adopting communication-saving policies, supports ad-hoc strategies for reducing the storage management costs (lowering response times and memory consumption), and provides the required data access functionalities through an SQL-like query language enhanced with stream, event, spatial and temporal operators. OBU stream items are also exploited to detect Events Of Interest (EOIs) such as jams and accidents and to support a collaborative mechanism for user-powered POI management and rating. EOIs and POIs are modeled through specific ontologies which allow for a flexible and extensible data management and guarantee data independence from the raw streams.
The use of real-time information in Intelligent Transportation Systems (ITS) in particular Vehicular Ad hoc Networks (VANETs) has the potential to improve traffic conditions and reduce travel delays by facilitating better utilization of available capacity. These systems exploit currently available and emerging computer, communication, and control technologies to monitor, manage, and control the transportation system. They also provide various levels of traffic information and trip advisory service to system users, service providers, so that travellers or drivers can make timely and informed travel decisions. The success of ITS technology deployments is heavily dependent on the availability of timely and accurate estimates of prevailing and emerging traffic conditions. As such, there is a strong need for a "traffic prediction system". The needed system is to utilize advanced traffic models to analyze data, especially real-time traffic data, from different sources to estimate and predict traffic conditions so that proactive strategies can be implemented to meet various traffic control, management, and operation objectives. Vehicular Ad hoc Networks are an envision of the Intelligent Transportation Systems. Vehicles communicate with each other via Inter-Vehicle Communication (IVC) as well as with roadside base stations via Roadside-to-Vehicle Communication (RVC). The optimal goal is that vehicular networks will contribute to safer and more efficient roads in the future by providing timely information to drivers and concerned authorities. The aim of this research was to develop an embedded system that would utilize the real-time information found in Intelligent Transportation Systems through the use of VANETs. This system was simulated using the C++ language and a combinatorial optimization algorithm was used to find cost effective routes from source to destination that would be able to meet the fuel or time constraints. By using these system drivers and other users would be able to make informed decisions to choose the feasible route based on the fuel or time required around the path/road from its current position to destination. The proposed embedded system is simple and can easily be developed and adapted to run in any environment that uses VANETs. This research provided the foundation of developing a future embedded system that can be used in Vehicular Ad Hoc Networks for real road traffic situations.
RouteFinder: Real-time optimum vehicle routing using mobile phone network
TENCON 2015 - 2015 IEEE Region 10 Conference, 2015
Road traffic congestion is a major issue in most mega cities. The traffic jams are often exacerbated by drivers habitually following the same routes. A real-time, optimum vehicle routing system that takes traffic density into account can be a possible solution to this problem. This paper presents RouteFinder, a system of providing real-time traffic density mapping on the driver's smartphone using an in-vehicle module built using inexpensive components that communicates with the existing mobile telephone network, to enable the driver to choose the least congested route to a desired destination. The hardware module contains the essential elements of a cellular handset such as a SIM card. The location of the vehicle is determined through a standard triangulation algorithm performed using signals from the three nearest cellular base stations, and the location information is constantly updated at relevant Home Location Registers (HLRs) / Visitor Location Register (VLRs) at the telecom service provider using an intermediate MySQL database. An Android application developed for the driver's smartphone shows the present locations of all vehicles in all routes from the origin to the selected destination, with colour codes distinguishing between moving and stationary vehicles. We have implemented our device in 10 vehicles in Dhaka city. Our sample calculations have shown significant savings not only in terms of time, but also in fuel consumption.
Dynamic travel time provision for road networks
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems - GIS '08, 2008
The application domain of intelligent transportation is plagued by a shortage of data sources that adequately assess traffic situations. Typically, to provide routing and navigation solutions map attributes in the form of static weights as derived from road categories and speed limits used for road networks. With the advent of Floating Car Data (FCD) and specifically the GPSbased tracking data component, a means was found to derive accurate and up-to-date travel times, i.e., qualitative traffic information. FCD is a by-product in fleet management applications and given a minimum number and uniform distribution of vehicles, this data can be used for accurate traffic assessment and also prediction. This work showcases a system that facilitates the collection of FCD, produces dynamic travel time information, and provides value-added services based on the dynamic travel times. The essential components that will be discussed are a Web-services-based data collection approach, sophisticated map-matching algorithms, a data management architecture and an online visualization platform.