Towards data-driven car-following models

Car following models have been studied with many diverse approaches for decades. Nowadays, technological advances have significantly improved our traffic data collection capabilities. Conventional car following models rely on mathematical formulas and are derived from traffic flow theory; a property that often makes them more restrictive. On the other hand, data-driven approaches are more flexible and allow the incorporation of additional information to the model; however, they may not provide as much insight into traffic flow theory as the traditional models. In this research, an innovative methodological framework based on a data-driven approach is proposed for the estimation of car-following models, suitable for incorporation into microscopic traffic simulation models. An existing technique, i.e. locally weighted regression (loess), is defined through an optimization problem and is employed in a novel way. The proposed methodology is demonstrated using data collected from a sequence of instrumented vehicles in Naples, Italy. Gipps’ model, one of the most extensively used car-following models, is calibrated against the same data and used as a reference benchmark. Optimization issues are raised in both cases. The obtained results suggest that data-driven car-following models could be a promising research direction.     

Analysis of three independent real-world driving studies: A data driven and expert analysis approach to determining parameters affecting fuel economy

It is well established that individual variations in driving style have a significant impact on vehicle energy efficiency. The literature shows certain parameters have been linked to good fuel economy, specifically acceleration, throttle use, number of stop/starts and gear change behaviours. The primary aim of this study was to examine what driving parameters are specifically related to good fuel economy using a non-homogeneous extended data set of vehicles and drivers over real-world driving scenarios spanning two countries. The analysis presented in this paper shows how three completely independent studies looking at the same factor (i.e., the influence of driver behaviour on fuel efficiency) can be evaluated, and, despite their notable differences in location, environment, route, vehicle and drivers, can be compared on broadly similar terms. The data from the three studies were analysed in two ways; firstly, using expert analysis and the second a purely data driven approach. The various models and experts concurred that a combination of at least one factor from the each of the categories of vehicle speed, engine speed, acceleration and throttle position were required to accurately predict the impact on fuel economy. The identification of standard deviation of speed as the primary contributing factor to fuel economy, as identified by both the expert and data driven analysis, is also an important finding. Finally, this study has illustrated how various seemingly independent studies can be brought together, analysed as a whole and meaningful conclusions extracted from the combined data set.     

基于数据驱动的船用冷藏集装箱智能故障诊方法研究

采用数据驱动方法,避开船用冷藏集装箱复杂的控制策略,建立系统或部件模型用于智能故障诊断。根据正常运行状态的实验数据“训练”冷凝器模型,利用该模型预测冷凝器的运行状态,并与实际的故障数据进行比较。结果表明,数据驱动模型具有较高的预测精度,能够分辨故障状态,实现智能故障报警。     

基于运行模态分析的船舶推进轴系状态监测

论文提出一种基于运行模态分析(OMA)的新的船舶推进轴系状态监测方法。论文以船舶推进轴系试验台为试验对象,获取轴系运行时不同加载工况下的扭振信号,利用基于数据的随机子空间法(DD-SSI)识别扭振的固有频率,并与已知的试验模态分析(EMA)识别的轴系静态时同一加载工况下的结果进行对比,验证运行模态分析识别结果的准确性,并研究不同加载工况下轴系扭振固有频率随加载工况的变化规律。试验结果表明,运行模态分析能够准确识别轴系的扭振固有频率,且扭振固有频率的增量与加载量呈正相关,因而运行模态分析可以用作一种新的船舶推进轴系状态监测方法。     

Data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. A statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions.     

An autonomous system for maintenance scheduling data-rich complex infrastructure: Fusing the railways’ condition,planning and cost

National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment.Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain.Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value.A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.     

Data-driven linear decision rule approach for distributionally robust optimization of on-line signal control

We propose a two-stage, on-line signal control strategy for dynamic networks using a linear decision rule (LDR) approach and a distributionally robust optimization (DRO) technique. The first (off-line) stage formulates a LDR that maps real-time traffic data to optimal signal control policies. A DRO problem is solved to optimize the on-line performance of the LDR in the presence of uncertainties associated with the observed traffic states and ambiguity in their underlying distribution functions. We employ a data-driven calibration of the uncertainty set, which takes into account historical traffic data. The second (on-line) stage implements a very efficient linear decision rule whose performance is guaranteed by the off-line computation. We test the proposed signal control procedure in a simulation environment that is informed by actual traffic data obtained in Glasgow, and demonstrate its full potential in on-line operation and deployability on realistic networks, as well as its effectiveness in improving traffic.     

Prediction of seasonal maximum wave height for unevenly spaced time series by Black Widow Optimization algorithm

The present study aimed to predict the maximum seasonal wave height by new integrative data driven methods. For this purpose, two data-driven techniques, that are, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Regression (SVR), were applied, and a BWO algorithm was used as an integrated method (ANFIS-BWO and SVR-BWO). In addition, the Particle Swarm Optimization (PSO) algorithm was used as a method integrated with SVR and ANFIS (SVR-PSO and ANFIS-PSO) to compare the performance of the newly developed methods (ANFIS-BWO and SVR-BWO). The wave data were collected in different seasons by a buoy station deployed in the southern Baltic Sea by the Institute of Hydro-Engineering of the Polish Academy of Sciences. Seasonal simulations were performed to investigate the effect of seasons on the maximum wave height. The wave data constituted an unevenly spaced time series. The maximum wave height was modeled using the maximum wave height period (T_max), the significant wave height (H_s), the significant wave period (T_s), and time steps (Δt). The results showed that the application of BWO and PSO algorithms increased the accuracy of ANFIS and SVR by about 18.45%. Moreover, the results show that PSO increased the accuracy of ANFIS and SVR by about 17.98% and 21.59%, respectively. The results of different runs indicated that the BWO is more stable to reach the global solution than PSO. The results also show that show that SVR-BWO is the most accurate model.     

A simple nonparametric car-following model driven by field data

Car-following models are always of great interest of traffic engineers and researchers. In the age of mass data, this paper proposes a nonparametric car-following model driven by field data. Different from most of the existing car-following models, neither driver’s behaviour parameters nor fundamental diagrams are assumed in the data-driven model. The model is proposed based on the simple k-nearest neighbour, which outputs the average of the most similar cases, i.e., the most likely driving behaviour under the current circumstance. The inputs and outputs are selected, and the determination of the only parameter k is introduced. Three simulation scenarios are conducted to test the model. The first scenario is to simulate platoons following real leaders, where traffic waves with constant speed and the detailed trajectories are observed to be consistent with the empirical data. Driver’s rubbernecking behaviour and driving errors are simulated in the second and third scenarios, respectively. The time–space diagrams of the simulated trajectories are presented and explicitly analysed. It is demonstrated that the model is able to well replicate periodic traffic oscillations from the precursor stage to the decay stage. Without making any assumption, the fundamental diagrams for the simulated scenario coincide with the empirical fundamental diagrams. These all validate that the model can well reproduce the traffic characteristics contained by the field data. The nonparametric car-following model exhibits traffic dynamics in a simple and parsimonious manner.     

Automated vision inspection of rail surface cracks: A double-layer data-driven framework

A double-layer data-driven framework for the automated vision inspection of the rail surface cracks is proposed in this paper. Based on images of rails, the proposed framework is capable to detect the location of cracks firstly and next automatically obtain the boundary of cracks via a feature-based linear iterative crack aggregation (FLICA). Extended Haar-like features are applied to develop significant features for identifying cracks in images. Built on extended Haar-like features, a cascading classifier ensemble integrating three single cascading classifiers with a major voting scheme is proposed to decide the presence of cracks in the image. Each single cascading classifier is composed of a sequence of stage classifiers trained by the LogitBoost algorithm. A scalable sliding window carrying the cascading classifier ensemble is applied to scan images of rail tracks, which is identified by the Otsu’s method, and detect cracks. After completing the crack registration in the first layer, the FLICA is developed to discover boundaries of cracks. The effectiveness of the proposed data-driven framework for identifying rail surface cracks is validated with the rail images provided by the China Railway Corporation and Hong Kong Mass Transit Railway (MRT). Six benchmarking methods, the Otsu’s method, mean shift, the visual detection system, the geometrical approach, fully convolutional networks and the U-net, are utilized to prove advantages of the proposed framework. Results of the validation and comparative analyses demonstrate that the proposed framework is most effective in the rail surface crack detection.