RealTE: Real-time Trajectory Estimation Over Large-Scale Road Networks

Real-time traffic estimation focuses on predicting the travel time of one travel path, which is capable of helping drivers selecting an appropriate or favor path. Statistical analysis or neural network approaches have been explored to predict the travel time on a massive volume of traffic data. These methods need to be updated when the traffic varies frequently, which incurs tremendous overhead. We build a system RealTER⁢e⁢a⁢l⁢T⁢E, implemented on a popular and open source streaming system StormS⁢t⁢o⁢r⁢m to quickly deal with high speed trajectory data. In RealTER⁢e⁢a⁢l⁢T⁢E, we propose a locality-sensitive partition and deployment algorithm for a large road network. A histogram estimation approach is adopted to predict the traffic. This approach is general and able to be incremental updated in parallel. Extensive experiments are conducted on six real road networks and the results illustrate RealTE achieves higher throughput and lower prediction error than existing methods. The runtime of a traffic estimation is less than 11 seconds over a large road network and it takes only 619619 microseconds for model updates.

Urban morphogenesis analysis based on geohistorical road data

Road networks result from a subtle balance between geographical coverage and rapid access to strategic points. An understanding of their structure is fundamental when it comes to evaluating and improving territorial accessibility. This study is designed to provide insight into the progressive structuring of territorial patterns by analyzing the evolution of road networks. Studying road network morphogenesis requires geohistorical data, provided here by historical maps from which earlier road networks can be digitized. A hypergraph is constructed from these networks by combining road segments into “ways” on the basis of a method for defining the continuity of road segments. Next, indicators are computed for these ways based on topological and geometrical features. The road patterns of three cities in the Burgundy Franche-Comte region of France (Dijon, Besançon, and Pontarlier) at three historical periods (the 18th, 19th, and twentieth centuries) are then analyzed. In this manner, their topological features and centrality characteristics can be compared from snapshots at different times and places. The innovative method proposed in this paper helps us to read features of the road patterns accurately and to make simple interpretations. It can be applied to any territory for which data is available. The results highlight the underlying structure of the three cities, reveal information about the history and the functioning of the networks, and give preliminary insights into the morphogenesis of those cities. Prospectively this work aims to identify the mechanisms that drive change in road networks. Detecting stability or variation in indicators over time can help in identifying similar behavior, despite geographic and cultural distances, as well as evolution mechanisms linked to specificities of each city. The study of road network morphogenesis can make a major contribution to understanding how road network structure affects accessibility and mobility.

PARP: A Parallel Traffic Condition Driven Route Planning Model on Dynamic Road Networks

The problem of route planning on road network is essential to many Location-Based Services (LBSs). Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Thus, a practical route planning strategy is required to supply the continuous route optimization considering the historic, current, and future traffic condition. However, few existing works comprehensively take into account these various traffic conditions during the route planning. Moreover, the LBSs usually suffer from extensive concurrent route planning requests in rush hours, which imposes a pressing need to handle numerous queries in parallel for reducing the response time of each query. However, this issue is also not involved by most existing solutions. We therefore investigate a parallel traffic condition driven route planning model on a cluster of processors. To embed the future traffic condition into the route planning, we employ a GCN model to periodically predict the travel costs of roads within a specified time period, which facilitates the robustness of the route planning model against the varying traffic condition. To reduce the response time, a Dual-Level Path (DLP) index is proposed to support a parallel route planning algorithm with the filter-and-refine principle. The bottom level of DLP partitions the entire graph into different subgraphs, and the top level is a skeleton graph that consists of all border vertices in all subgraphs. The filter step identifies a global directional path for a given query based on the skeleton graph. In the refine step, the overall route planning for this query is decomposed into multiple sub-optimizations in the subgraphs passed through by the directional path. Since the subgraphs are independently maintained by different processors, the sub-optimizations of extensive queries can be operated in parallel. Finally, extensive evaluations are conducted to confirm the effectiveness and superiority of the proposal.

A New Remote Sensing Index for Assessing Spatial Heterogeneity in Urban Ecoenvironmental-Quality-Associated Road Networks

Although many prior efforts found that road networks significantly affect landscape fragmentation, the spatially heterogeneous effects of road networks on urban ecoenvironments remain poorly understood. A new remote-sensing-based ecological index (RSEI) is proposed to calculate the ecoenvironmental quality, and a local model (geographically weighted regression, GWR) was applied to explore the spatial variations in the relationship between kernel density of roads (KDR) and ecoenvironmental quality and understand the coupling mechanism of road networks and ecoenvironments. The average effect of KDR on the variables of normalized difference vegetation index (NDVI), land surface moisture (LSM), and RSEI was negative, while it was positively associated with the soil index (SI), normalized differential build-up and bare soil index (NDBSI), index-based built-up index (IBI), and land surface temperature (LST). This study shows that rivers and the landscape pattern along rivers exacerbate the impact of road networks on urban ecoenvironments. Moreover, spatial variation in the relationship between road network and ecoenvironment is mainly controlled by the relationship of the road network with vegetation and bare soil. This research can help in better understanding the diversified relationships between road networks and ecoenvironments and offers guidance for urban planners to avoid or mitigate the negative impacts of roads on urban ecoenvironments.

Optimizing The Functional Performance of Road Network using Vulnerability Assessment to Cope with Unforeseen Road Incidents

An Urban Road network is often used for multipurpose trips, due to their transportation functions, such as attractiveness and orientation, as well as social, ecological, and economic features. In Indonesia, road incidents have reportedly increased during the last decade because of a higher frequency of natural hazards, accidents, and on-street mass demonstrations. These incidents are found to degrade or terminate road access, forcing users to utilize alternative routes and decreasing the service performance in adjacent directions. Due to the unexpected occurrences at any location and time, there is a need to investigate the impact of random incidents on road performances. Several accessibility indexes have also been used to evaluate the vulnerability of road networks. However, this is less practical in Indonesia, with the road authority using functional performances as the indicator. This indicates the need for an index to be developed based on road performance parameters. Therefore, this study aims to develop a road performance-based vulnerability index known as the RCI (Road Criticality Index). Combined with a traffic simulation tool, this system is used as an alternative index to assess vulnerabilities, by identifying the road(s) providing worse consequences due to unforeseen incidents. This simulation was conducted by using the PTV Visum, assuming a road section is closed due to the worst incident scenarios. The result showed that the RCI offered a more comprehensive assessment than the existing indicator (volume capacity ratio). The RCI included travel speed and mobility components for evaluating both local and global road performances. With the knowledge of the most vulnerable locations and their consequences, road authorities can prioritize maintenance and development strategies based on the criticality index. Also, preventive measures should be conducted to mitigate risk under a constrained budget. This methodology can be applied to sustainably enhance the resilience of urban road networks.