Obtaining urban-scale vehicle trajectories is essential to understand urban mobility and benefits various downstream applications. The mobility knowledge obtained from existing vehicle trajectory sensing techniques is typically incomplete. To fill the gap, we propose$F^{3}VeTrac$, an efficient deep-learning-based vehicle trajectory recovery system that utilizes complementary characteristics of the Camera Surveillance System and the Vehicle Tracking System to obtain fine-grained, fully-road-covered, and fully-individual-penetrative ($F^{3}$) trajectories.$F^{3}VeTrac$utilizes five well-designed modules to model the co-occurrence relationships hidden in both coarse-grained and fine-grained trajectories from the two complementary sensing systems and fuse them to recover the coarse-grained trajectories. We implement and evaluate$F^{3}VeTrac$with two real-world datasets from over 100 million regular vehicle trajectories and 16 million commercial vehicle trajectories in two cities of China, together with an on-field case study based on 251 regular vehicle trajectories collected by 17 volunteers, demonstrating its great advantages over six state-of-the-art alternative schemes. Moreover, we present a downstream application of$F^{3}VeTrac$for traffic condition estimation, which obtains obvious performance gains.

Green Light Optimized Speed Advisory (GLOSA) systems have emerged to allow drivers to pass traffic lights during a green interval. However, various adaptive and intelligent traffic light control approaches have been adopted in many cities, resulting in the development of current GLOSA technologies lagging behind that of traffic light technologies. When taking diverse dynamic traffic lights into account, it is difficult to model the interactions between vehicles and traffic lights, which is further exacerbated by the hybrid control strategies of traffic lights. To this end, we design a new GLOSA systemSpeedAdvto provide optimal speed advisory for addressing diverse traffic lights. We formulate the problem as a Multi-Agent Markov Decision Process (MAMDP) with an implicit common goal and propose a heterogeneous-agent collaborative framework based on reinforcement learning. Three main modules are used in the system: i) a spatio-temporal relation reasoning module based on the phase-aware attention mechanism pays more attention to the traffic rules and traffic flow diversion of adjacent intersections to predict traffic conditions for a few seconds later; ii) a behavior approximating module based on imitation learning is introduced to approximate the phases of diverse traffic lights; iii) a speed advisory module provides the optimal speed advisory based on policy gradient reinforcement learning relying on the above two modules and other information collected by vehicles. We implement and evaluateSpeedAdvwith a real-world trajectory dataset, together with a field test based on a prototype system, demonstrating thatSpeedAdvimproves the overall performance by at least 24.1% in terms of travel time, energy consumption, safety, and comfort compared to the state-of-the-artGreenDrivemethod.

This paper presents a novel approach to the on-line signature verification using local shape analysis. First, segment the input signature into several segments using HMM (hidden Markov model). Then, combine two adjacent segments to form a long segment and get its spectral and tremor information using FFT (fast Fourier transformation). At last, accept it or reject it based on the similarity between the spectral and its prototype. In addition, we proposed a novel initialization algorithm to avoid the local optimal of the HMM's re-estimation and a novel algorithm to avoid losing the important information at cusps in preprocessing. Combining the local shape analysis with the local time-based comparison, we get promising experimental results.

This paper describes on-line signature verification using a HMM (hidden Markov model). The system is based on combining local time-based and shape-based comparison between the input signature and the reference model. We proposed a novel initialization algorithm to avoid the local optimal of the HMM's re-estimation. To get reliable segmentation using the HMM, we adopt a meliorated HMM re-estimation algorithm during training. In addition, we use a novel algorithm to avoid losing important information at the cusps in preprocessing. Tests based on databases and real cases reveal the feasibility of the proposed method.