Leveraging Spatial Abstraction in Traffic Analysis and Forecasting with Visual Analytics

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-A spatially abstracted transportation network is a graph where nodes are territory compartments (areas in geographic space) and edges, or links, are abstract constructs, each link representing all possible paths between two neighboring areas. By applying visual analytics techniques to vehicle traffic data from different territories, we discovered that the traffic intensity (a.k.a. traffic flow or traffic flux) and the mean velocity are interrelated in a spatially abstracted transportation network in the same way as at the level of street segments. Moreover, these relationships are consistent across different levels of spatial abstraction of a physical transportation network. Graphical representations of the flux-velocity interdependencies for abstracted links have the same shape as the fundamental diagram of traffic flow through a physical street segment, which is known in transportation science. This key finding substantiates our approach to traffic analysis, forecasting, and simulation leveraging spatial abstraction. Permanent repository linkWe propose a framework in which visual analytics supports three high-level tasks, assess, forecast, and develop options, in application to vehicle traffic. These tasks can be carried out in a coherent workflow, where each next task uses the results of the previous one(s). At the 'assess' stage, vehicle trajectories are used to build a spatially abstracted transportation network and compute the traffic intensities and mean velocities on the abstracted links by time intervals. The interdependencies between the two characteristics of the links are extracted and represented by formal models, which enable the second step of the workflow, 'forecast', involving simulation of vehicle movements under various conditions. The previously derived models allow not only prediction of normal traffic flows conforming to the regular daily and weekly patterns but also simulation of traffic in extraordinary cases, such as road closures, major public events, or mass evacuation due to a disaster. Interactive visual tools support preparation of simulations and analysis of their results. When the simulation forecasts problematic situations, such as major congestions and delays, the analyst proceeds to the step 'develop options' for trying various actions aimed at situation improvement and investigating their consequences. Action execution can be imitated by interactively modifying the input of the simulation model. Specific techniques support comparisons between results of simulating different "what if" scenarios.

Visual Analytics of Mobility and Transportation: State of the Art and Further Research Directions

“…There is a series of works showing how predictive models of vehicle traffic can be derived from historical data consisting of a large number of vehicle trajectories [10][14] [15]. The approach is based on spatial abstraction and aggregation of the trajectory data into collective movements (flows) of the vehicles between territory compartments, as shown in Fig.…”

“…[15] future movements under various conditions but also for spatial planning applications. For example, the system SmartAdP [50] finds suitable locations for billboard placement using taxi trajectories.…”

Visualization Tools for Traffic Bottleneck Analysis

“…Wibisono et al, [20] developed roadway performance maps and charts for several highways in the United Kingdom. Finally, several studies by led by G. Andrienko and N. Andrienko [21,22] developed novel visualizations for identifying congested roadway segments using spatial and temporal abstraction techniques.…”

Leveraging spatial abstraction in traffic analysis and forecasting with visual analytics

“…The topic of this presentation, based on [4], is derivation of traffic forecasting and simulation models from traffic data. Traffic data in the form of trajectories of vehicles are currently collected in great amounts, but their potential remains largely underexploited.…”