MultiParameter Estimation of Average Speed in Road Networks Using Fuzzy Control Average speed is crucial for calculating link travel time to find the fastest path in a road network. However, readily available data sources like OpenStreetMap (OSM) often lack information about the average speed of a road. However, OSM contains other road information which enables an estimation of average speed in rural regions. In this paper, we develop a Fuzzy Framework for Speed Estimation (FuzzyFSE) that employs fuzzy control to estimate average speed based on the parameters road class, road slope, road surface and link length. The OSM road network and, optionally, a digital elevation model (DEM) serve as freetouse and worldwide available input data. The FuzzyFSE consists of two parts: (a) a rule and knowledge base which decides on the output membership functions and (b) multiple Fuzzy Control Systems which calculate the output average speeds. The FuzzyFSE is applied exemplary and evaluated for the BioBio and Maule region in central Chile and for the north of New South Wales in Australia. Results demonstrate that, even using only OSM data, the FuzzyFSE performs better than existing methods such as fixed speed profiles. Compared to these methods, the FuzzyFSE improves the speed estimation between 2% to 12%. In future work, we will investigate the potential of datadriven machine learning methods to estimate average speed. The applied datasets and the source code of the FuzzyFSE are available via GitHub

Boosted Trajectory Calibration for Traffic State Estimation Traffic state estimation is among the most critical issues in intelligent transport systems, and it has been widely explored for years considering its significance for diverse industrial applications such as vehicle dispatch and route planning. The availability of vehicle trajectories has provided a promising data source for traffic estimation, as it shed light on traffic status and characterizes human transition activates that are otherwise extremely hard to track. However, there are major challenges in incorporating trajectories in traffic estimation largely because of its inherent uncertainty from data sparsity and semantic ambiguity. Entangled in complicated road topology and spatiotemporal traffic dynamics, properly embedding trajectories in modeling is especially difficult. To address the aforementioned challenges, in this paper, we propose a Boosted Trajectory Calibration (BTRAC) framework to model traffic states of complex road networks that effectively integrates trajectory information. In the framework, we first use a deep neural network to predict the traffic states of each road individually from historical traffic information, along with the prediction uncertainty. Then we refine the predictions by an iterative boosting calibration procedure with embedded trajectories. We conduct extensive largescale empirical studies on two cities and demonstrate the effectiveness of the proposed approach.