Automated route planning consists of using real maps to automatically nd good map routes. Two shortcomings to standard methods are (i) that domain information may be lacking, and (ii) that a \good" route can be hard to de ne. Most on-line map representations do not include information that may be relevant for the purpose of generating good realistic routes, such as tra c patterns, construction,...

We present a set of three new time-dependent models with increasing flexibility for realistic route planning in flight networks. By these means, we obtain small graph sizes while modeling airport procedures in a realistic way. With these graphs, we are able to efficiently compute a set of best connections with multiple criteria over a full day. It even turns out that due to the very limited gra...

For many autonomous agents, such as mobile robots, autonomous vehicles and Computer Generated Forces, route planning in complex terrain is a critical task, as many of the agent’s higher-level goals can only be accomplished if the agent is in the right place at the right time. The route planning problem is often formulated as one of finding a minimum-cost route between two locations in a digitis...

We study the problem of route planning on mobile devices. There are two current approaches to this problem. One option is to have all the routing data on the device, which can then compute routes by itself. This makes it hard to incorporate traffic updates, leading to suboptimal routes. An alternative approach outsources the route computation to a server, which then sends only the route to the ...

The advent of advanced computer a r c h i t e c t u r e s fo r p a r a l l e l and symbolic processing has evolved to the po in t where the technology c u r r e n t l y e x i s t s fo r the development of p ro to type autonomous v e h i c l e s . Contro l o f such devices w i l l r equ i r e communication between knowledgebased subsystems in charge of the v i s i o n , p l ann ing , and c o n f...

Many interesting route planning problems can be solved by computing shortest paths in a suitably modeled, weighted graph representing a transportation network. Such networks are naturally road networks or timetable networks of public transportation. For large networks, the classical Dijkstra algorithm to compute shortest paths is too slow. And therefore have faster algorithms been developed in ...

Utilization of Unmanned Aerial Vehicles (UAVs) in military and civil operations is getting popular. One of the challenges in effectively tasking these expensive vehicles is planning the flight routes to monitor the targets. In this work, we aim to develop an algorithm which produces routing plans for a limited number of UAVs to cover maximum number of targets considering their flight range. The...

Distributed Learning Automata is automata based modelling approach for solving stochastic shortest path problems. The DLA can be applied to road networks to find shortest path that provides a spatial approach to bottom-up modelling of complex geographic systems that are comprised of infrastructure and human objects. Route finding is a popular Geographical Information System (GIS) application un...

Keynotes Dr. James Smithies: Building the Canterbury Earthquakes Digital Archive Senior Lecturer in Digital Humanities, University of Canterbury Dr. Ray Hidayat: Interesting Algorithms in Telogis Route Telogis Route is full of complex algorithms. When planning driver territories, how can we estimate the required driving time quickly? When optimising routes, how can we discourage routes from ove...

Finding a scenic route between two locations is a common trip planning task, in particular for tourists and recreational travelers. For the automated computation of a scenic route in a trip planning system it is necessary to identify which attributes of a route and its surroundings are associated with attractive scenery. This study uses a route choice model, more specifically a Path Size Logit ...