Personalized Routing for Car Navigation Systems

Personalized navigation and way-finding are prominent research areas of location-based service (LBSs). This includes innovative concepts for car navigation. Within this paper, we investigate the idea of providing drivers a routing suggestion which avoids ‘complicated crossings’ in urban areas. Inexperienced drivers include persons who have a driver’s license but, for whatever reason, feel uncomfortable to drive in a city environment. Situations where the inexperienced driver has to depend on a navigation device and reach a destination in an unfamiliar territory may be difficult. Preferences of inexperienced drivers are investigated. ‘Fears’ include driving into ‘complicated crossings’. Therefore, the definition and spatial characteristics of ‘complicated crossings’ are investigated. We use OpenStreetMap as a road dataset for the routing network. Based on the topological characteristics of the dataset, measured by the number of nodes, we identify crossings that are ‘complicated’. The user can choose to compute an alternative route that avoids these complicated crossings. This methodology is one step in building a full ‘inexperienced drivers’ routing system, which includes additional preferences from the user group, for example, as avoiding left turns where no traffic light is present.

Transportation Research Part A: General

Recent technological advances in navigation systems for private vehicles have the capability to provide drivers with highway route information on a dashboard-mounted video display screen. These technological advances, together with two-way radio communication of digital information, automatic measurement of traffic flows, and supercomputer technology, could be combined to provide useful information to drivers concerning expected travel times, best routes, and best departure times. This paper reviews the status of this technology and explores the information and prediction requirements for the computer models required to implement such a system. Research needed to evaluate the potential impact of s&h a system is also described.

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.

TRANSPORT, 2015

Due to changing expectations of characteristics of mobility demands, public transportation users increasingly require a reduction of both the preparation and travel time, an easier and more pleasant travelling experience as well as route plans based on reliable data. Both international and domestic research is widely concerned with route planning optimization. Exemplary assistance applications are already in operation, but they are only semi-occasionally and slightly personalized. Consequently, there is potential for significant research and development in this area. Our developed method and algorithm evaluates the routes based on the personalised user settings and in this way, the ideal routes can be determined. User preferences are represented in evaluation criteria. The algorithm also manages network modifications and often-changing user preferences. The novelty of our algorithm lies in the more realistic evaluation of the routes appreciably considering both the exact physical pr...

2016 IEEE Global Communications Conference (GLOBECOM), 2016

Route guidance and navigation services have been widely attracting researchers and application developers due to the serious problems of traffic congestion and the ceaseless need to improve the driving experience. Motivated by such driving concerns, this paper proposes a real-time, dynamic route guidance system with the main focus on the driver safety and satisfaction. As a unique feature compared to other existing systems, the proposed driver-centric route guidance (DCRG) system considers the driver behavior in the route guidance process for the sake of boosting the safety levels on roads. The system also considers the driver preferences targeting a personalized satisfying driving experience. As most drivers prefer traversing the fastest and healthiest route to their destination, the DCRG system takes into account as well the real-time traffic and road conditions while guiding drivers towards their targeted destinations. Performance evaluation of DCRG shows significant improvements in the travel time, on-road safety, and preference satisfaction levels compared to the shortest and fastest route guidance schemes.

Journal of computational design, 2023

Lecture Notes in Computer Science, 2005

Wayfinding, i.e. getting from some origin to a destination, is one of the prime everyday problems humans encounter. It has received a lot of attention in research and many (commercial) systems propose assistance in this task. We present an approach to route directions based on the idea to adapt route directions to route and environment's characteristics. The lack of such an adaptation is a major drawback of existing systems. Our approach is based on an information-and representation-theoretic analysis of routes and takes into account findings of behavioral research. The resulting systematics is the framework for the optimization process. We discuss the consequences of using an optimization process for generating route directions and outline its algorithmic realization.

2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017

In smart cities, commuters have the opportunities for smart routing that may enable selecting a route with less car accidents, or one that is more scenic, or perhaps a straight and flat route. Such smart personalization requires a data management framework that goes beyond a static road network graph. This paper introduces PreGo, a novel system developed to provide real time personalized routing. The recommended routes by PreGo are smart and personalized in the sense of being (1) adjustable to individual users preferences, (2) subjective to the trip start time, and (3) sensitive to changes of the road conditions. Extensive experimental evaluation using real and synthetic data demonstrates the efficiency of the PreGo system.

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.

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011

As congestion problems become a greater concern and negatively impact society, solutions which alleviate them are needed to improve the performance of the transportation system. Routing systems which take into account the travel-time experienced by the driver have been largely unexplored in the domain of adaptive routing. In this article, we present a system which enables users of smartphones to obtain directions generated using an algorithm which provides an optimal routing policy for reliable on-time arrival; that is, directions which seek to maximize the probability of arriving to the destination within a given time budget, rather than to minimize the travel time based on posted speed limits. Our work leverages the geolocation capabilities of smartphones to provide optimal routing directions along the route dependent on the realized (experienced) travel time. The adaptive routing scheme we implement allows for significant power savings and improved driver safety compared to classical routing algorithms; special attention is paid to minimizing driver distraction by emphasizing aural and graphical components over textual elements during route guidance. Finally, we illustrate system performance and design choices on synthetic examples and real traffic data from the Mobile Millennium system in San Francisco.