Traffic congestion, volumes, origins, destinations, routes, and other road-network performance metrics are typically collected through survey data or via static sensors such as traffic cameras and loop detectors. This information is often out-of-date, difficult to collect and aggregate, difficult to analyze and quantify, or all of the above. In this paper we conduct a case study that demonstrates that it is possible to accurately infer traffic volume through data collected from a roving sensor network of taxi probes that log their locations and speeds at regular intervals. Our model and inference procedures can be used to analyze traffic patterns and conditions from historical data, as well as to infer current patterns and conditions from data collected in real-time. As such, our techniques provide a powerful new sensor network approach for traffic visualization, analysis, and urban planning.
Abstract-We describe an algorithm for stochastic path planning and applications to route planning in the presence of traffic delays. We improve on the prior state of the art by designing, analyzing, implementing, and evaluating data structures that answer approximate stochastic shortest-path queries. For example, our data structures can be used to efficiently compute paths that maximize the probability of arriving at a destination before a given time deadline.Our main theoretical result is an algorithm that, given a directed planar network with edge lengths characterized by expected travel time and variance, pre-computes a data structure in quasi-linear time such that approximate stochastic shortestpath queries can be answered in poly-logarithmic time (actual worst-case bounds depend on the probabilistic model).Our main experimental results are two-fold: (i) we provide methods to extract travel-time distributions from a large set of heterogenous GPS traces and we build a stochastic model of an entire city, and (ii) we adapt our algorithms to work for realworld road networks, we provide an efficient implementation, and we evaluate the performance of our method for the model of the aforementioned city.
In this paper, we present a congestion-aware route planning system. First we learn the congestion model based on real data from a fleet of taxis and loop detectors. Using the learned street-level congestion model, we develop a congestionaware traffic planning system that operates in one of two modes:(1) to achieve the social optimum with respect to travel time over all the drivers in the system or (2) to optimize individual travel times. We evaluate the performance of this system using 10,000+ taxis trips and show that on average our approach improves the total travel time by 15%.
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