An improved cellular automaton with axis information for microscopic traffic simulation

Cellular Automaton (CA), an efficient dynamic modeling method that is widely used in traffic engineering, is newly introduced for traffic load modeling. This modeling method significantly addresses the modest traffic loads for long-span bridges. It does, however, require improvement to calculate precise load effects. This paper proposed an improved cellular automaton with axis information, defined as the Multi-axle Single-cell Cellular Automaton (MSCA), for the precise micro-simulation of random traffic loads on bridges. Four main ingredients of lattice, cells’ states, neighborhoods and transition rules are redefined in MSCA to generate microscopic vehicle sequences with detailed vehicle axle positions, user-defined cell sizes and time steps. The simulation methodology of MSCA is then proposed. Finally, MSCA is carefully calibrated and validated using site-specific WIM data. The results indicate: (1) the relative errors (REs) for the traffic parameters, such as volumes, speeds, weights, and headways, from MSCA are basically no more than ±10% of those of WIM data; (2) the load effects of three typical influence lines (ILs) with varied lengths of 50, 200 and 1000 m are also confidently comparable, both of which validate the rationality and precision of MSCA. Furthermore, the accurate vehicle parameters and gaps generated from MSCA can be applied not only for precise traffic loading on infrastructures but also for the accurate estimation of vehicle dynamics and safety. Hence, wide application of MSCA can potentially be expected.     

汽车油管式动态称重的仿真与分析

介绍了油管式汽车动态称重系统。运用VPG技术、利用动态显式非线性有限元分析软件LS-DYNA对某两轴货车满载驶过油管式称重仪的全过程进行了数值模拟。分析单/双胎、车速、油管内初始油压和管径等因素对传感器输出特性的影响。仿真分析结果与实车试验结果趋势一致,验证了仿真分析方法的正确性。     

油管式汽车动态称重仪材料参数反演

为了确定油管式汽车动态称重仪的仿真材料参数,根据油管的主要作用采用VPG技术中的简化方法,将钢丝/橡胶复合层视为弹性材料,该弹性材料的待定参数为弹性模量E和泊松比μ。依据反演的基本原理设计了获取观测数据的试验方案,将有限元法与人工神经网络两种计算方法相结合,对参数E和μ进行了反演。最后将反演结果代入有限元模型中进行计算,其结果与实测数据吻合较好。     

Improving truck safety: Potential of weigh-in-motion technology

Trucks exceeding the legal mass limits increase the risk of traffic accidents and damage to the infrastructure. They also result in unfair competition between transport modes and companies. It is therefore important to ensure truck compliance to weight regulation. New technologies are being developed for more efficient overload screening and enforcement. Weigh-in-Motion (WIM) technologies allow trucks to be weighed in the traffic flow, without any disruption to operations. Much progress has been made recently to improve and implement WIM systems, which can contribute to safer and more efficient operation of trucks.     

Effectiveness of vehicle weight enforcement in a developing country using weigh-in-motion sorting system considering vehicle by-pass and enforcement capability

Vehicle overloading has been identified as one of the major contributors to road pavement damage in Malaysia. In this study, the weigh-in-motion (WIM) system has been used to function as a vehicle weight sorting tool to complement the exsiting static weigh bridge enforcement station. Data collected from the developed system is used to explore the effectiveness of using WIM system in terms of generating more accurate data for enforcement purposes and at the same time improving safety and reducing the number of vehicle weight violations on the roads. This study specifically focus on the effect of vehicle by-pass and static weigh station enforcement capability on the overall effectiveness of vehicle weight enforcement system in a developing country. Results from this study suggest that the WIM system will significantly enhance the effectiveness and efficiency of the current vehicle weight enforcement, thus generating substantial revenue that would greatly off-set the current road maintenance budget that comes from tax payers money. If there is substantial reduction in overloaded vehicles, the public will still gain through reduction in road maintenance budget, less accident risks involving heavy trucks, and lesser greenhouse gases (GHGs) emissions.     

Long distance truck tracking from advanced point detectors using a selective weighted Bayesian model

Truck flow patterns are known to vary by season and time-of-day, and to have important implications for freight modeling, highway infrastructure design and operation, and energy and environmental impacts. However, such variations cannot be captured by current truck data sources such as surveys or point detectors. To facilitate development of detailed truck flow pattern data, this paper describes a new truck tracking algorithm that was developed to estimate path flows of trucks by adopting a linear data fusion method utilizing weigh-in-motion (WIM) and inductive loop point detectors. A Selective Weighted Bayesian Model (SWBM) was developed to match individual vehicles between two detector locations using truck physical attributes and inductive waveform signatures. Key feature variables were identified and weighted via Bayesian modeling to improve vehicle matching performance. Data for model development were collected from two WIM sites spanning 26 miles in California where only 11 percent of trucks observed at the downstream site traversed the whole corridor. The tracking model showed 81 percent of correct matching rate to the trucks declared as through trucks from the algorithm. This high accuracy showed that the tracking model is capable of not only correctly matching through vehicles but also successfully filtering out non-through vehicles on this relatively long distance corridor. In addition, the results showed that a Bayesian approach with full integration of two complementary detector data types could successfully track trucks over long distances by minimizing the impacts of measurement variations or errors from the detection systems employed in the tracking process. In a separate case study, the algorithm was implemented over an even longer 65-mile freeway section and demonstrated that the proposed algorithm is capable of providing valuable insights into truck travel patterns and industrial affiliation to yield a comprehensive truck activity data source.     

Improving estimates of transportation emissions: Modeling hourly truck traffic using period-based car volume data

We estimate hourly truck traffic using period-based car volumes that are usually available from travel demand models. Due to the lack of local or regional data, default vehicle-miles traveled mix by vehicle class in mobile emission inventory models is usually used in transportation emissions inventory estimates. Results from such practice, however, are often far from accurate. Heavy-duty trucks generate orders of magnitudes higher emission rates than light duty vehicles. Vehicle classification data collected from weigh-in-motion stations in California are used to examine the performance of various forms of the method across days of week and geographic areas. We find that the models identified provide satisfactory and statistically robust estimates of truck traffic.     

Comparisons of the environmental footprint of heavy vehicles in the UK and Switzerland

The European Union project Eureka Logchain Footprint is an ongoing project to identify road and rail vehicles by means of their environmental footprint as characterised by dynamic load, noise, ground borne vibrations and gaseous emissions induced by the vehicle. Part of the project involves the installation of road and rail footprint monitoring stations throughout Europe. This paper presents results of the road stations in Switzerland and the UK. Individual vehicle data from weigh-in-motion and noise are compared. The results indicate that a significant number of vehicles surpass the limits set in both countries. It was shown that the UK sites are generating higher noise levels than their Swiss counterparts; in part due to the much coarser aggregate embedded in the running course of the pavement employed in the UK. Such data can be used to create an incentive for vehicle types with a low footprint and a penalty for vehicles with a large footprint.