多轴车桥耦合振动模型的Fourier级数求解方法分析

文章以某高速公路40m简支T梁为研究对象,基于结构动力学原理推导了多轴车桥耦合振动系统的系统振动微分方程,并求解出微分方程时变激振力的Fourier级数表达式,同时基于模态叠加法得出结构动力响应的解析表达式,对该简支T梁在不同过桥车速、不同车辆车轴布置以及不同车辆车轴超重情况下的结构动力响应进行参数分析。     

Vehicle identification sensor models for origin–destination estimation

The traditional approach to origin–destination (OD) estimation based on data surveys is highly expensive. Therefore, researchers have attempted to develop reasonable low-cost approaches to estimating the OD vector, such as OD estimation based on traffic sensor data. In this estimation approach, the location problem for the sensors is critical. One type of sensor that can be used for this purpose, on which this paper focuses, is vehicle identification sensors. The information collected by these sensors that can be employed for OD estimation is discussed in this paper. We use data gathered by vehicle identification sensors that include an ID for each vehicle and the time at which the sensor detected it. Based on these data, the subset of sensors that detected a given vehicle and the order in which they detected it are available. In this paper, four location models are proposed, all of which consider the order of the sensors. The first model always yields the minimum number of sensors to ensure the uniqueness of path flows. The second model yields the maximum number of uniquely observed paths given a budget constraint on the sensors. The third model always yields the minimum number of sensors to ensure the uniqueness of OD flows. Finally, the fourth model yields the maximum number of uniquely observed OD flows given a budget constraint on the sensors. For several numerical examples, these four models were solved using the GAMS software. These numerical examples include several medium-sized examples, including an example of a real-world large-scale transportation network in Mashhad.     

Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors

A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capacity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection.     

Development of a sensor system for traffic data collection

Although many types of traffic sensors are currently in use, all have some drawbacks, and widespread deployment of such sensor systems has been difficult due to high costs. Due to these deficiencies, there is a need to design and evaluate a low cost sensor system that measures both vehicle speed and counts. Fulfilling this need is the primary objective of this research. Compared to the many existing infrared-based concepts that have been developed for traffic data collection, the proposed method uses a transmission-based type of optical sensor rather than a reflection-based type. Vehicles passing between sensors block transmission of the infrared signal, thus indicating the presence of a vehicle. Vehicle speeds are then determined using the known distance between multiple pairs of sensors. A prototype of the sensor system, which uses laser diode and photo detector pairs with the laser directly projected onto the photo detector, was first developed and tested in the laboratory. Subsequently this experimental prototype was implemented for field testing. The traffic flow data collected were compared to manually collected vehicle speed and traffic counts and a statistical analysis was done to evaluate the accuracy of the sensor system. The analysis found no significant difference between the data generated by the sensor system and the data collected manually at a 95% confidence interval. However, the testing scenarios were limited and so further analysis is necessary to determine the applicability in more congested urban areas. The proposed sensor system, with its simple technology and low cost, will be suitable for saturated deployment to form a densely distributed sensor network and can provide unique support for efficient traffic incident management. Additionally, because it may be quickly installed in the field without the need of elaborate fixtures, it may be deployed for use in temporary traffic management applications such as traffic management in road work zones or during special events.     

The vehicle emissions and performance monitoring system: analysis of tailpipe emissions and vehicle performance

This paper describes tailpipe emission results generated by the Vehicle Performance and Emissions Monitoring system (VPEMS). VPEMS integrates on‐board emissions and vehicle/driver performance measurements with positioning and communications technologies, to transmit a coherent spatio‐temporally referenced dataset to a central base station in near real time. These results focus on relationships between tailpipe emissions of CO, CO₂, NO_x and speed and acceleration. Emissions produced by different driving modes are also presented. Results are generally as one would expect, showing variation between vehicle speed, vehicle acceleration and emissions. Data is based upon a test run in central London on urban streets with speeds not exceeding about 65 km/h. The results presented demonstrate the capabilities of the system. Various issues remain with regard to validation of the data and expansion of the system capability to obtain additional vehicle performance data.     

Autonomous vehicle control systems for safe crossroads

This article presents a cooperative manoeuvre among three dual mode cars – vehicles equipped with sensors and actuators, and that can be driven either manually or autonomously. One vehicle is driven autonomously and the other two are driven manually. The main objective is to test two decision algorithms for priority conflict resolution at intersections so that a vehicle autonomously driven can take their own decision about crossing an intersection mingling with manually driven cars without the need for infrastructure modifications. To do this, the system needs the position, speeds, and turning intentions of the rest of the cars involved in the manoeuvre. This information is acquired via communications, but other methods are also viable, such as artificial vision. The idea of the experiments was to adjust the speed of the manually driven vehicles to force a situation where all three vehicles arrive at an intersection at the same time.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.     

Developing link-based particle number emission models for diesel transit buses using engine and vehicle parameters

To better assess health impacts from diesel transportation sources, particle number emissions can be modeled on a road network using traffic operating parameters. In this work, real-time particle number emissions rates from two diesel transit buses were aggregated to the roadway link-level and modeled using engine parameters and then vehicle parameters. Modern statistical methods were used to identify appropriate predictor variables in the presence of multicollinearity, and controlled for correlated emission measurements made on the same day and testing route. Factor analysis helped to reduce the number of potential engine parameters to engine load, engine speed, and exhaust temperature. These parameters were incorporated in a linear mixed model that was shown to explain the variation attributable to link-characteristics. Vehicle specific power and speed were identified as two surrogate vehicle travel variables that can be used in the absence of engine parameters, although with a loss in predictive power compared to the engine parameter model. If vehicle speed is the only operating input available, including road grades in the model can significantly improve particle number emission estimates even for links with mild grade. Although the data used are specific to the buses tested, the approach can be applied to modeling emissions from other vehicle models with different engine types, exhaust systems, and engine retrofit technologies.     

Proposed collision warning system for right-turning vehicles at two-way stop-controlled rural intersections

At two-way stop-controlled (TWSC) rural intersections, a right-turning driver who is departing the minor road may select an improper gap and subsequently may be involved in a rear-end collision with another vehicle approaching on the rightmost lane on the major road. This paper provides perceptual framework and algorithm design of a proposed infrastructure-based collision warning system that has the potential to aid unprotected right-turning drivers at TWSC rural intersections. The proposed system utilizes a radar sensor that measures the location, speed, and acceleration of the approaching vehicle on the major road. Based on these measurements, the system’s algorithm determines if there will be any potential conflict between the approaching and the turning vehicles and warns the driver of the latter vehicle if such a conflict is found. The algorithm is based on realistic acceleration profile of the turning vehicle to estimate its acceleration rates at different times so that the system can accurately estimate the time and distance needed for the departing vehicle to accelerate to the same speed as for the approaching vehicle. That realistic acceleration profile is established using actual experimental data collected by a Global Positioning System (GPS) data logger device that was used to record the positions and instantaneous speeds of different right-turning vehicles at 1-s intervals. The algorithm also gives consideration to the time needed by the driver of the departing vehicle to perceive the message displayed by the system and react to it (to start departure) where it was found that 95% of drivers have a perception–reaction time of 1.89 s or less. A methodology is also illustrated to select the maximum measurement errors suggested for the detectors in measuring the locations of the approaching vehicle on the major road where it was found that the accuracy of the system significantly deteriorates if the errors in measuring the distance and the azimuth angle exceed 0.1 m and 0.2°, respectively. An application example is provided to illustrate the algorithm used by the proposed system.     

Diesel-engined vehicle nitric oxide and soot emissions during the European light-duty driving cycle using a transient mapping approach

An engine mapping-based methodology is developed to gain a first approximation of a vehicle’s performance and emissions during a light-duty cycle. The procedure is based on a steady-state experimental investigation of the engine with an appropriate vehicle drivetrain model applied so that the cycle vehicle speed data can be transformed into engine speed and torque. Correction analysis is then applied based on transient experimentation to account for the transient discrepancies during real driving. The developed algorithm is applied for the case of a diesel-engined vehicle running on the European light-duty cycle. A comparative analysis is performed for each section of the cycle revealing its individual transient characteristics.     

Point-to-point speed enforcement systems: Speed limits design criteria and analysis of drivers’ compliance

Point-to-point (P2P) speed enforcement is a relatively new approach to traffic law enforcement. Its technology allows vehicles whose average speed exceeds the speed limit over the controlled section to be fined. It therefore encourages compliance over distances longer than those where spot enforcement policies have been in place.In this paper, a procedure for consistently setting speed limits with such enforcement systems is proposed. Such a method has been applied to design the speed limits on two motorways in the district of Naples, Italy, where P2P enforcement systems became operational in 2009 and 2010. The speed limits, which were set using the Italian geometric design standard to assess vehicle stability and stopping sight distance, have been compared with those provided by using well-known international standards.The impact of the newly designed speed limits and of the P2P enforcement system on drivers’ speeding behaviour has been quantified for each highway section and vehicle type. In fact, accurate measurements of the average travel speeds of each vehicle crossing the enforced sections, before and after the activation of the system, were available. The migration from the old speed limits with spot speed enforcement to the new approach resulted in a notable increase in drivers’ compliance to the speed limits with a remarkable decrease in both the average of individual speeds and in their standard deviation.In addition, the analysis of 3 years of data shows that a gradual adaptation of drivers’ behaviour to the system took place. In particular, a decreasing compliance to the speed limits points to a non-optimal system management. Finally, the results of a revealed preference survey allowed us to make a behavioural interpretation regarding the significantly different impacts measured on the two motorways.     

Virtual 3D city model as a priori information source for vehicle localization system

This paper aims at demonstrating the usefulness of integrating virtual 3D models in vehicle localization systems. Usually, vehicle localization algorithms are based on multi-sensor data fusion. Global Navigation Satellite Systems GNSS, as Global Positioning System GPS, are used to provide measurements of the geographic location. Nevertheless, GNSS solutions suffer from signal attenuation and masking, multipath phenomena and lack of visibility, especially in urban areas. That leads to degradation or even a total loss of the positioning information and then unsatisfactory performances. Dead-reckoning and inertial sensors are then often added to back up GPS in case of inaccurate or unavailable measurements or if high frequency location estimation is required. However, the dead-reckoning localization may drift in the long term due to error accumulation. To back up GPS and compensate the drift of the dead reckoning sensors based localization, two approaches integrating a virtual 3D model are proposed in registered with respect to the scene perceived by an on-board sensor. From the real/virtual scenes matching, the transformation (rotation and translation) between the real sensor and the virtual sensor (whose position and orientation are known) can be computed. These two approaches lead to determine the pose of the real sensor embedded on the vehicle. In the first approach, the considered perception sensor is a camera and in the second approach, it is a laser scanner. The first approach is based on image matching between the virtual image extracted from the 3D city model and the real image acquired by the camera. The two major parts are: 1. Detection and matching of feature points in real and virtual images (three features points are compared: Harris corner detector, SIFT and SURF). 2. Pose computation using POSIT algorithm. The second approach is based on the on–board horizontal laser scanner that provides a set of distances between it and the environment. This set of distances is matched with depth information (virtual laser scan data), provided by the virtual 3D city model. The pose estimation provided by these two approaches can be integrated in data fusion formalism. In this paper the result of the first approach is integrated in IMM UKF data fusion formalism. Experimental results obtained using real data illustrate the feasibility and the performances of the proposed approaches.     

Heterogeneous traffic flow modelling for an arterial using grid based approach

A grid based modelling approach akin to cellular automata (CA) is adopted for heterogeneous traffic flow simulation. The road space is divided into a grid of equally sized cells. Moreover, each vehicle type occupies one or more cell as per its size unlike CA traffic flow model where each vehicle is represented by a single cell. Model needs inputs such as vehicle size, its maximum speed, acceleration, deceleration, probability constants, and arrival pattern. The position and speed of the vehicles are assumed to be discrete. The speed of each vehicle changes according to its interactions with other vehicles, following some stochastic rules depending on the circumstances. The model is calibrated and validated using real data and VISSIM. The results indicate that grid based model can reasonably well simulate complex heterogeneous traffic as well as offers higher computational efficiency needed for real time application.     

Noise prediction for highways in Thailand

A model of highway traffic noise is formulated based on vehicle types. The data were collected from local highways in Thailand with free-flow traffic conditions. First, data on vehicle noise was collected from individual vehicles using sound level meters placed at a reference distance. Simultaneously, measurements were made of vehicles’ spot speeds. Secondly, are data for building the highway traffic noise model. This consists of traffic noise levels, traffic volumes by vehicle classification, average spot speeds by vehicle type, and the geometric dimension of highway sections. The free-flow traffic noise model is generated from this database. A reference energy mean emission level (the basic noise) level for each type of vehicles is developed based on direct measurement of Leq (10 s) from the real running condition of each type of vehicles. Modification of terms and parameters are used to make the model fit highway traffic characteristics and different types of vehicle.     

Longitudinal control of a vehicle

Generally applicable formulae for the gain constants in a proportional plus integral controller required for stable control of the speed of any vehicle in terms of natural frequency, damping ratio, vehicle mass, and thruster time constant are derived. An example, based on a simulation of the controller and vehicle, is given. The theory shows that only speed and position feedback are needed. Acceleration feedback is unnecessary.     

Automatic vehicle classification system with range sensors

Traffic management systems use inductive loop detectors and more recently video cameras to detect vehicles. Loop detectors are expensive to maintain and video-based systems are sensitive to environmental conditions and do not perform well in vehicle classification. Cameras based upon range sensors are not sensitive to lighting and may be less sensitive to other environmental conditions. In addition, range imagery should provide data to form a good basis for vehicle classification applications. In this paper, we describe methods for processing range imagery and performing vehicle detection and classification. A vehicle classification rate of over 92% accuracy was obtained in classifying vehicles into different vehicle classes.     

Pedestrian crossing detection based on evidential fusion of video-sensors

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.     

Enhanced cooperative car-following traffic model with the combination of V2V and V2I communication

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication are emerging components of intelligent transport systems (ITS) based on which vehicles can drive in a cooperative way and, hence, significantly improve traffic flow efficiency. However, due to the high vehicle mobility, the unreliable vehicular communications such as packet loss and transmission delay can impair the performance of the cooperative driving system (CDS). In addition, the downstream traffic information collected by roadside sensors in the V2I communication may introduce measurement errors, which also affect the performance of the CDS. The goal of this paper is to bridge the gap between traffic flow modelling and communication approaches in order to build up better cooperative traffic systems. To this end, we aim to develop an enhanced cooperative microscopic (car-following) traffic model considering V2V and V2I communication (or V2X for short), and investigate how vehicular communications affect the vehicle cooperative driving, especially in traffic disturbance scenarios. For these purposes, we design a novel consensus-based vehicle control algorithm for the CDS, in which not only the local traffic flow stability is guaranteed, but also the shock waves are supposed to be smoothed. The IEEE 802.11p, the defacto vehicular networking standard, is selected as the communication protocols, and the roadside sensors are deployed to collect the average speed in the targeted area as the downstream traffic reference. Specifically, the imperfections of vehicular communication as well as the measured information noise are taken into account. Numerical results show the efficiency of the proposed scheme. This paper attempts to theoretically investigate the relationship between vehicular communications and cooperative driving, which is needed for the future deployment of both connected vehicles and infrastructure (i.e. V2X).     

Connectivity-based optimization of vehicle route and speed for improved fuel economy

Traditionally, vehicle route planning problem focuses on route optimization based on traffic data and surrounding environment. This paper proposes a novel extended vehicle route planning problem, called vehicle macroscopic motion planning (VMMP) problem, to optimize vehicle route and speed simultaneously using both traffic data and vehicle characteristics to improve fuel economy for a given expected trip time. The required traffic data and neighbouring vehicle dynamic parameters can be collected through the vehicle connectivity (e.g. vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-cloud, etc.) developed rapidly in recent years. A genetic algorithm based co-optimization method, along with an adaptive real-time optimization strategy, is proposed to solve the proposed VMMP problem. It is able to provide the fuel economic route and reference speed for drivers or automated vehicles to improve the vehicle fuel economy. A co-simulation model, combining a traffic model based on SUMO (Simulation of Urban MObility) with a Simulink powertrain model, is developed to validate the proposed VMMP method. Four simulation studies, based on a real traffic network, are conducted for validating the proposed VMMP: (1) ideal traffic environment without traffic light and jam for studying the fuel economy improvement, (2) traffic environment with traffic light for validating the proposed traffic light penalty model, (3) traffic environment with traffic light and jam for validating the proposed adaptive real-time optimization strategy, and (4) investigating the effect of different powertrain platforms to fuel economy using two different vehicle platforms. Simulation results show that the proposed VMMP method is able to improve vehicle fuel economy significantly. For instance, comparing with the fastest route, the fuel economy using the proposed VMMP method is improved by up to 15%.     

Vehicle reidentification and travel time measurement across freeway junctions using the existing detector infrastructure

Conventional vehicle detectors are capable of monitoring discrete points along the freeway but do not provide information about conditions on the link between detectors. Knowledge of conditions on the link is useful to operating agencies for enabling timely decisions in response to various delay causing events and hence to reduce the resulting congestion of the freeway system. This paper presents an approach that matches vehicle measurements between detector stations to provide information on the conditions over the link between the detectors rather than relying strictly on the aggregate point measurements from the detectors. In particular this work reidentifies measurements from distinct vehicles using the existing loop detector infrastructure. Here the distinct vehicles are the long vehicles, but depending on the vehicle population or type of detector used, one might chose to use some other reproducible feature.This new methodology represents an important advancement over preceding loop based vehicle reidentification, as illustrated herein, it enables vehicle reidentification across a major diverge and a major merge. The examples include a case where the reidentification algorithm responded to delay between two detector stations an hour before the delay was locally observable at either of the stations used for reidentification. While previous loop based reidentification work was limited to dual loop detectors, the present effort also extends the methodology to single loop detectors; thereby making it more widely applicable. Although the research uses loop detector data, the algorithm would be equally applicable to data obtained from many other traffic detectors that provide reproducible vehicle features.