Urban road transport navigation: performance of the global positioning system after selective availability

Satellite navigation systems have a potential to support multi-modal transport navigation requirements. In road transport, the global positioning system (GPS) is currently supporting a wide variety of in-car navigation and transport telematics systems. The performance of GPS has in the past been limited by the artificial degradation of the signal through the process of selective availability (SA). With SA operational, the instantaneous horizontal positional accuracy was 100 m 95% of the time. Additional infrastructure was used with the differential concept (where range errors are determined at a known location and transmitted to users) to improve this to the level of a few metres. The US Government on 1 May 2000 removed SA. This paper presents the results of a study to assess and characterise the post-SA performance of GPS for positioning vehicles in urban areas. This is an important functionality of advanced transport telematics systems that aim to address everyday problems associated with road transport, particularly in urban areas. The performance assessment addresses, in varying levels of detail, the issues of service coverage, positioning accuracy, integrity and availability of service. Comparison is made with the results of a previous study conducted when SA was turned on. The results show an improvement in stand-alone navigation accuracy without SA compared to the period when SA was operational. Furthermore, no significant difference is seen between the level of accuracy achievable with differential positioning and post-SA stand-alone navigation. The parameters that characterise the performance of GPS determined at the analysis stage have been used to specify an architecture for a local navigation system for urban areas.     

Incorporating e-Technology to Advantage in a Greener Taxi Industry and its Impact on Driving Performance and Safety

Abstract

New technologies, especially advances in telecommunications, have had profound impacts on everyday life and brought even greater changes to some business models. Taxis represent one of the major modes of transportation in urban areas but they face a number of problems, including their environmental impacts. The status of the taxi industry in Taiwan is revealed and analyzed in this study. A new business model for the extensive and popular use of smart phones is proposed. However, it is important to know whether their use is both effective and safe. Two field experiments were executed to examine the effects on taxi drivers of using a navigation system installed in a GPS PDA phone. The results revealed that the efficiency of drivers using such a portable navigation system was better than those relying on paper maps in unfamiliar urban areas. Furthermore, performance in terms of safety was also better than those who used an on-board navigation system. In summary, incorporating e-technology can promote a taxi company's competitiveness and a driver's performance and safety as well as offer environmental benefits.     

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.     

A new strategy for minimum usage of external yaw moment in vehicle dynamic control system

Due to the loss of vehicle directional stability in emergency maneuvers, a new complete desired model for vehicle handling based on the linear two-degrees-of-freedom (2DOF) model and tire/road conditions is presented to be tracked by the direct yaw moment control (DYC) system. In order to maintain the vehicle actual motions, yaw rate and side-slip angle, close to the proposed desired responses without excessively large external yaw moment, a complete linear quadratic (LQ) optimal problem is formulated and its analytical solution is obtained. Here, the derived control law is evaluated and its different versions are discussed. It is shown that the side-slip tracking by DYC is more effective than the yaw rate control to stabilize vehicle motions in nonlinear regimes. Also, optimal property of the control law provides the possibility of reducing the external yaw moment as low as possible, at the cost of some admissible tracking errors. Simulation studies of vehicle handling, with and without control, have been conducted using a full nonlinear vehicle dynamic model. The results, obtained during various maneuvers, indicate that when the proposed optimal controller is engaged with the model, improvements in the handling performance through a reduced external yaw moment can be acquired.     

A driving cycle detection approach using map service API

Following advancements in smartphone and portable global positioning system (GPS) data collection, wearable GPS data have realized extensive use in transportation surveys and studies. The task of detecting driving cycles (driving or car-mode trajectory segments) from wearable GPS data has been the subject of much research. Specifically, distinguishing driving cycles from other motorized trips (such as taking a bus) is the main research problem in this paper. Many mode detection methods only focus on raw GPS speed data while some studies apply additional information, such as geographic information system (GIS) data, to obtain better detection performance. Procuring and maintaining dedicated road GIS data are costly and not trivial, whereas the technical maturity and broad use of map service application program interface (API) queries offers opportunities for mode detection tasks. The proposed driving cycle detection method takes advantage of map service APIs to obtain high-quality car-mode API route information and uses a trajectory segmentation algorithm to find the best-matched API route. The car-mode API route data combined with the actual route information, including the actual mode information, are used to train a logistic regression machine learning model, which estimates car modes and non-car modes with probability rates. The experimental results show promise for the proposed method’s ability to detect vehicle mode accurately.     

Estimating vehicle speed with embedded inertial sensors

Pavements were instrumented with inertial sensors, and the possibility of estimating the speed of a passing vehicle was investigated numerically and experimentally from the measurements of two embedded accelerometers. The sensors were spaced apart in the travel direction, and subsequently the speed was directly related to the time delay between the received signals. No assumption was made regarding the vehicle and pavement properties. Model accelerations were presented, studied, and contrasted against field measurements; the latter were shown to be dominated by random vibration sources. Two calculation techniques were offered and applied to handle the noisy data. The first was based on time-centroids, and the second was based on cross-correlation with kernel presmoothing. The overall concept is deemed promising not only for inferring speeds but also for extracting additional traffic characteristics such as axle spacing and relative axle load distributions.     

A robust method for estimating transit passenger trajectories using automated data

Development of an origin-destination demand matrix is crucial for transit planning. The development process is facilitated by automated transit smart card data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specification (GTFS) data to infer the most likely trajectory of individual transit passengers. The method relaxes the assumptions on various parameters used in the existing trip chaining algorithms such as transfer walking distance threshold, buffer distance for selecting the boarding location, time window for selecting the vehicle trip, etc. The method also resolves issues related to errors in GPS location recorded by AFC systems or selection of incorrect sub-route from GTFS data. The proposed trip chaining method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). Based on the consecutive tags of the passenger, the proposed algorithm is also modified for pay-exit cases. The method is compared to previous methods developed by the researchers and shows improvement in the number of inferred cases. Finally, results are visualized to understand the route ridership and geographical pattern of trips.     

Classifying the purpose of stopped truck events: An application of entropy to GPS data

This paper applies the concept of entropy to mine large volumes of global positioning system (GPS) data in order to determine the purpose of stopped truck events. Typical GPS data does not provide detailed activity information for a given stop or vehicle movement. We categorize stop events into two types: (1) primary stops where goods are transferred and (2) secondary stops where vehicle and driver needs are met, such as rest stations. The proposed entropy technique measures the diversity of truck carriers with trucks that dwell for 15 min or longer at a given location. Larger entropy arises from a greater variety of carriers and an even distribution of stop events among these carriers. An analysis confirms our initial hypothesis that the stop locations used for secondary purposes such as fuel refills and rest breaks tend to have higher entropy, reflecting the diversity of trucks and carriers that use these facilities. Conversely, primary shipping depots and other locations where goods are transferred tend to have lower entropy due to the lower variety of carriers that utilize such locations.     

Estimating traffic volumes for signalized intersections using connected vehicle data

Recently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9–12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.     

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.     

Optimization of nonlinear control strategy for anti-lock braking system with improvement of vehicle directional stability on split-μ roads

In a hard braking on a split-μ road, the achievement of shorter stopping distance while maintaining the vehicle in the straight line are of great importance. In this paper, to achieve these conflicting aims, an optimal nonlinear algorithm based on the prediction of vehicle responses is presented to distribute the wheel braking forces properly. The base of this algorithm is reducing the maximum achievable braking forces of one side wheels, as low as possible, so that the minimum stabilizing yaw moment is produced. The optimal property of the proposed control method makes it possible to get a trade-off between the shorter stopping distance and the less deviation of the vehicle heading from the straight line. The special case of this algorithm leads to the conventional anti-lock braking system (ABS) which generates the maximum braking forces for all wheels to attain the minimum stopping distance. However, the ABS cannot control the vehicle directional stability directly. The simulation results carried out using a nonlinear 8-DOF vehicle model demonstrate that the designed control system has a suitable performance to attain the desired purposes compared with the convectional ABS.     

On-line estimation of state-of-charge of Li-ion batteries in electric vehicle using the resampling particle filter

Accurate battery state-of-charge (SOC) estimation is important for ensuring reliable operation of electric vehicle (EV). Since a nonlinear feature exists in the battery system and particle filter (PF) performs well in solving nonlinear or non-Gaussian problems, this paper proposes a new PF-based method for estimating SOC. Firstly, the relationships between the battery characteristics and SOC are analyzed, then the suitable battery model is developed and the unknown parameters in the battery model are on-line identified using the recursive least square with forgetting factors. The proposed battery model is considered as the state space model of PF and then SOC is estimated. All experimental data are collected from the running EVs in Beijing. The experimental errors of SOC estimation based on PF are less than 0.05 V, which confirms the good estimation performance. Moreover, the contrastive results of three nonlinear filters show PF has the same computational complexity as extend Kalman filter (EKF) and unscented Kalman filter (UKF) for low dimensional state vector, but PF have significantly better estimation accuracy in SOC estimation.     

管道惯性测绘系统研制及应用

针对管道中心线地理坐标信息的测量需求,基于惯性导航原理,研制了以管道内检测器为载体的管道中心线测绘内检测设备,并将其与漏磁内检测器结合进行工程应用。选取了5处已知点对测绘结果进行了验证,测绘结果和实际验证结果表明:研制的惯性导航内检测器能够准确测绘管道中心线轨迹坐标,在校验点间距约为2 km时,5个距校验点约1 km的已知点的平均测绘误差约为1.8 m;通过将惯性导航检测器与漏磁检测器的组合使用,可有效获取管道缺陷的地理坐标信息,提高了管道内检测缺陷检测定位的精度。     

内河航标遥控遥测管理初探

文章结合航标遥控遥测技术在贵梧Ⅱ级航道工建设中的应用实践,分析了航标遥控遥测应用管理遇到的主要问题,并提出建立有效的运行管理制度、加强人才的培养和引进、做好系统软硬件的维护管理、加强实时监控的技术管理和深化应用管理等充分发挥航标遥控遥测技术效能的有效措施。     

A dynamic lane-changing trajectory planning model for automated vehicles

This paper focuses on the lane-changing trajectory planning (LTP) process in the automatic driving technologies. Existing studies on the LTP algorithms are primarily the static planning method in which the states of the surrounding vehicles of a lane-changing vehicle are assumed to keep unchanged in the whole lane-changing process. However, in real-world traffic, the velocities of the surrounding vehicles change dynamically, and the lane-changing vehicle needs to adjust its velocity and positions correspondingly in real-time to maintain safety. To address such limitations, the dynamic lane-changing trajectory planning (DLTP) model is proposed in the limited literature. This paper proposes a novel DLTP model consisting of the lane-changing starting-point determination module, trajectory decision module and trajectory generation module. The model adopts a time-independent polynomial trajectory curve to avoid the unrealistic assumptions on lane-changing velocities and accelerations in the existing DLTP model. Moreover, a rollover-avoidance algorithm and a collision-avoidance algorithm containing a reaction time are presented to guarantee the lane-changing safety of automated vehicles, even in an emergent braking situation. The field lane-changing data from NGSIM data are used to construct a real traffic environment for lane-changing vehicles and verify the effectiveness of the proposed model, and CarSim is applied to investigate the traceability of the planned lane-changing trajectories using the proposed model. The results indicate that an automated vehicle can complete the lane-changing process smoothly, efficiently and safely following the trajectory planned by the proposed model, and the planned velocity and trajectory can be well-tracked by automated vehicles.     

A practice-ready relocation model for free-floating carsharing systems with electric vehicles – Mesoscopic approach and field trial results

This paper introduces a relocation model for free-floating Carsharing (FFCS) systems with conventional and electric vehicles (EVs). In case of imbalances caused by one-way trips, the approach recommends profit maximizing vehicle relocations. Unlike existing approaches, two types of relocations are distinguished: inter zone relocations moving vehicles between defined macroscopic zones of the operating area and intra zone relocations moving vehicles within such zones. Relocations are combined with the unplugging and recharging of EVs and the refueling of conventional vehicles. In addition, remaining pure service trips are suggested. A historical data analysis and zone categorization module enables the calculation of target vehicle distributions. Unlike existing approaches, macroscopic optimization steps are supplemented by microscopic rule-based steps. This enables relocation recommendations on the individual vehicle level with the exact GPS coordinates of the relocation end positions. The approach is practice-ready with low computational times even for large-scale scenarios.To assess the impact of relocations on the system’s operation, the model is applied to a FFCS system in Munich, Germany within three real world field tests. Test three shows the highest degree of automation and represents the final version of the model. Its evaluation shows very promising results. Most importantly, the profit is increased by 5.8% and the sales per vehicle by up to 10%. The mean idle time per trip end is decreased by 4%.     

基于两步抗差法的桥梁施工GPS控制网研究

施工控制网的建立对于保证桥梁施工质量和施工过程安全具有非常重要的意义。本文根据高程数据异常拟合模型参数估计,提出了两步抗差估计GPS高程拟合方法,提高了GPS控制网中高程拟合精度,并在实际工程中进行了应用。通过与采用曲面法、函数法的计算结果对比表明,两步抗差法对于处理粗差有着明显效果,尤其对于含有多个粗差的情况;同时两步抗差法使用简单,具有明显技术经济性。     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

西江航道实行船舶定线制的可行性分析

船舶定线制是国际上通行的实行水上船舶交通管理的一种形式,它对于规范船舶的航路,避免船舶过多交叉航行,减少船舶交通安全事故具有重要意义。文章以西江实行船舶分边通航为例,对实行船舶定线进行了可行性分析,建议通过分流和分道来防止或减少船舶发生碰撞、搁浅、触礁等水上交通事故。