In this paper, we propose a simple yet effective method of identifying traffic conditions on surface streets given location traces collected from on-road vehicles—this requires only GPS location data, plus infrequent low-bandwidth cellular updates. Unlike other systems, which simply display vehicle speeds on the road, our system characterizes unique traffic patterns on each road segment and identifies unusual traffic states on a segment-by-segment basis. We developed and evaluated the system by applying it to two sets of location traces. Evaluation results show that higher than 90 % accuracy in characterization can be achieved after ten or more traversals are collected on a given road segment. We also show that traffic patterns on a road are very consistent over time, provided that the underlying road conditions do not change. This allows us to use a longer history in identifying traffic conditions with higher accuracy.

In this paper we present a trace-driven framework capable of building realistic mobility models for the simulation studies of mobile systems. With the goal of realism, this framework combines coarse-grained wireless traces, i.e., association data between WiFi users and access points, with an actual map of the space over which the traces were collected. Through a sequence of data processing steps, including filtering the data trace and converting the map to a graph representation, this framework generates a probabilistic mobility model that produces user movement patterns that are representative of real movement. This is done by adopting a set of heuristics that help us infer the paths users take between access points. We describe our experience applying this approach to a college campus, and study a number of properties of the trace data using our framework.

Simulation has become an indispensable tool in the design and evaluation of mobile systems. By using mobility models that describe constituent movement, one can explore large systems, producing repeatable results for comparison between alternatives. In this paper, we show that a large class of mobility models - including all those in which nodal speed and distance or destination are chosen independently - have a transient period in which the average node speed decreases until converging to some long-term average. This speed decay provides an unsound basis for simulation studies that collect results averaged over time, complicating the experimental process. In this paper, we derive a general framework for describing this decay and apply it to a number of cases. Furthermore, this framework allows us to transform a given mobility model into a stationary one by initializing the simulation using the steady-state speed distribution and using the original speed distribution subsequently. This transformation completely eliminates the transient period and the decay in average node speed and, thus, provides sound models for the simulation of mobile systems.

This study examines the random waypoint model widely used in the simulation studies of mobile ad hoc networks. Our findings show that this model fails to provide a steady state in that the average nodal speed consistently decreases over time, and therefore should not be directly used for simulation. We show how unreliable results can be obtained by using this model. In particular, certain ad hoc routing metrics can drop by as much as 40% over the course of a 900-second simulation using the random waypoint model. We give both an intuitive and a formal explanation for this phenomenon. We also propose a simple fix of the problem and discuss a few alternatives. Our modified random waypoint model is able to reach a steady state and simulation results are presented.

Simulation has become an indispensable tool in the construction and evaluation of mobile systems. By using mobility models that describe constituent movement, one can explore large systems, producing repeatable results for comparison between alternatives. Unfortunately, the vast majority of mobility models---including all those in which nodal speed and distance or destination are chosen independently---suffer from decay; average speed decreases until converging to some long-term average. Such decay provides an unsound basis for simulation studies that collect results averaged over time, complicating the experimental process.This paper shows via analysis that such decay is inevitable in a wide variety of mobility models, including the most common in use today. We derive a general framework for describing this decay, and apply it to a number of practical cases. Furthermore, this framework allows us to transform any given mobility model into a stationary one: choose initial speeds from the steady-state distribution, and subsequent speeds from the original. This transformation provides sound models for simulation, eliminating variations in average nodal speed.