The role of angularity in route choice: an analysis of motorcycle courier GPS traces

The paths of 2425 individual motorcycle trips made in London were analyzed in order to uncover the route choice decisions made by drivers. The paths were derived from global positioning system (GPS) data collected by a courier company for each of their drivers, using algorithms developed for the purpose of this paper. Motorcycle couriers were chosen due to the fact that they both know streets very well and that they do not rely on the GPS to guide their navigation. Each trace was mapped to the underlying road network, and two competing hypotheses for route choice decisions were compared: (a) that riders attempt to minimize the Manhattan distance between locations and (b) that they attempt to minimize the angular distance. In each case, the distance actually traveled was compared to the minimum possible either block or angular distance through the road network. It is usually believed that drivers who know streets well will navigate trips that reduce Manhattan distance; however, here it is shown that angularity appears to play an important role in route choice. 63% of trips made took the minimum possible angular distance between origin and destination, while 51% of trips followed the minimum possible block distance. This implies that impact of turns on cognitive distance plays an important role in decision making, even when a driver has good knowledge of the spatial network

QoE in Pull Based P2P-TV Systems: Overlay Topology Design Tradeoff

Abstract—This paper presents a systematic performance anal-ysis of pull P2P video streaming systems for live applications, providing guidelines for the design of the overlay topology and the chunk scheduling algorithm. The contribution of the paper is threefold: 1) we propose a realistic simulative model of the system that represents the effects of access bandwidth heterogeneity, latencies, peculiar characteristics of the video, while still guaranteeing good scalability properties; 2) we propose a new latency/bandwidth-aware overlay topology design strategy that improves application layer performance while reducing the underlying transport network stress; 3) we investigate the impact of chunk scheduling algorithms that explicitly exploit properties of encoded video. Results show that our proposal jointly improves the actual Quality of Experience of users and reduces the cost the transport network has to support. I

Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018