A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

On Strategies Improving Accuracy of Speed Prediction from Floating Car Data (FCD)

For smart mobility, speed data extracted from Floating Car Data (FCD) plays an important role in speed predictionaccuracy. However, there are reliability issues for commercial FCD due to processing of individual vehicletracking data, and imposed temporal averaging to compress data size. Furthermore, spatial discretizationsignificantly affects the accuracy of the prediction due to uneven segment lengths and highly variable dataavailability in the network. In this study, these issues are examined in detail, and several strategies to improveaverage speed prediction are proposed. An extensive FCD data from a 75-km long corridor is utilized in thecalculations. Firstly, for data reliability, several filters are applied to clean data, then, a robust algorithm is appliedto smoothen the speed data. Secondly, to investigate and reduce prediction errors due to spatial segmentation, anumber of segmentation approaches are developed, and their effects on the average speed prediction are assessed.Finally, several autoregressive prediction models are implemented and a comprehensive comparison of results ispresented

Data allocation and application for time-dependent vehicle routing in city logistics

In city logistics, service providers have to consider dynamics within logistics processes in order to achieve higher schedule reliability and delivery flexibility. To this end, city logistics routing demands for time-dependent travel time estimates and time-dependent optimization models. We consider the process of allocation and application of empirical traffic data for time-dependent vehicle routing in city logistics with respect to its usage. Telematics based traffic data collection and the conversion from raw empirical traffic data into information models are discussed. A city logistics scenario points out the applicability of the information models provided, which are based on huge amounts of real traffic data (FCD). Thus, the benefits of time-dependent planning in contrast to common static planning methods can be demonstrated

Analysis of Road Safety Speed from Floating Car Data

Intelligent Transportation Systems aims at improving efficiency and safety of the transportation system by acting either on vehicle performances or assisting the driver with information on vehicle and traffic status. Although digital road graphs are available to derive quantitative parameters that describe the road geometry, the information provided usually includes speed limits and repetition of road signs. On the other hand, a huge amount of data is available on individual vehicle speeds and trajectories collected as Floating Car Data (FCD) but they are not combined with road parameters to derive information on how drivers perceive the infrastructure and behave when traveling on it. In the paper, a methodology is presented to evaluate the consistency between drivers' behavior and a theoretical safety speed determined from road geometry. The azimuth profile is progressively built for a road layout, based on the geometry described by a digital graph. Consecutive elements with the constant azimuth variation are identified as circular curves and their radii are computed by circle fitting. The safety speed with respect to longitudinal stability is estimated. The obtained safety speed is then compared to the distribution of speeds observed from about 200 million FCD collected on the regional road network of Latium. The obtained results permit to individuate critical points of the network in terms of road safety