Travel mode identification using bluetooth technology

Bluetooth technology has been widely used in transportation studies to collect traffic data. Bluetooth media access control (MAC) readers can be installed along roadways to collect Bluetooth-based data. This data is commonly used to measure traffic performance. One of the advantages of using Bluetooth technology to measure traffic performance is that travel time can be measured directly with a certain level of error instead of by estimation. However, travel time outliers can commonly be observed due to different travel mode on arterials. Since travel mode information cannot be directly obtained from the raw Bluetooth-based data, a mathematical methodology is in need to identify travel mode. In this study, a genetic algorithm and neural network (GANN)-based model was developed to identify travel mode. GPS-enabled devices were used to collect ground truth travel time. In order to additionally compare the model performance, K nearest neighbor (KNN) and support vector machine (SVM) were also implemented. N-fold cross validation was applied to statistically assess the models’ results. Since the model performances depend on the model inputs, seven collections of model inputs were tested in order to achieve the best travel mode identification performance. An arterial segment with four consecutive links and three intersections was selected to be the study segment. The results suggested that correctly identifying the three travel modes successfully every time was not possible, although the GANN based model had low misidentification rates. In our study, 6.12% of autos were misidentified as bikes and 10.53% of bikes were misidentified as autos using three links.     

基于群决策理论和双层规划模型的交通信号控制优化

基于干道延误时间最小和路段行程速度最大的设计理念,利用智能优化算法和群决策理论,建立了一种双层规划模型下的城市干道交通信号控制方法。算法结合了智能优化策略中的遗传算法和丰富的群体专家意见,并采用模糊数的描述方式实现对不同控制目标的分析评价,给出了一套完善的干道交通信号配时优化方案,使交通问题的分析得以更加客观和实际。最后给出一个实际主干道问题的算例分析,运用MATLAB和Visual C++编程计算对控制方案进行模拟。仿真结果表明,这一方法能有效地改善延误和路段行程速度。     

Modeling travel time of car with varying demand on an urban midblock section

The present study analyzes the stochastic nature of travel time distribution under the uncertainty of traffic volume and the proportion of cars in the traffic stream. Stochastic response surface method (SRSM) is adopted for modeling the travel time variation under the influence of traffic composition and traffic volume. This model is applied to an uninterrupted urban arterial corridor of 1.7 km length in New Delhi. Video graphic data were collected for 2 days during morning hours between 8 AM and 12 noon and evening hours of 3–7 PM. License plate matching technique was used for measuring the travel time in the study area. This study focused on travel time variation of cars with varying traffic volume and proportion of car in the traffic stream. Linear regression analysis was carried out initially to know the functional relation and significance relation between the input and output variables, and then SRSM analysis was performed. Artificial neural network (ANN) is also considered to map the relation among travel time, traffic volume and composition of traffic stream. A comparative evaluation is made among ANN, SRSM and regression analysis. Results indicate that apart from traffic volume, the influence of car population is more on travel time variation than motorized two-wheelers. It is attributed to the smaller size and comparability better operating condition of motorized two-wheelers. Also, the ANN and SRSM models are more efficient for analyzing the stochastic relation between the response and uncertain explanatory variable than the regression model.     

基于RFID和视频监测的城市交通运行状态模糊判别

为了确定城市道路网路的交通状态,为主动的交通管理、交通诱导及控制提供支持,提出了一种基于无线射频识别(RFID)交通检测系统和视频监控系统的交通运行状态模糊判别方法.在该方法中,交通运行状态由从RFID系统获得的车辆行驶时间和从视频监控系统中获得的车辆速度决定.由于实际的交通状态可以从视频中直接观测,因此实际交通运行状态的阈值可以根据视频来校准,用以评估本文所提出方法的性能.基于安装于南京的RFID和视频交通检测系统进行实证分析,结果表明本文所提出的方法是可行的.下一步工作可推进交通数据,特别是 RFID数据在交通管理中的应用.      

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.     

Performance Evaluation of Handoff-Based Cellular Traffic Monitoring Systems Using Combined Wireless and Traffic Simulation Platform

Handoff-based cellular probe technologies have been proven to be a cost-effective solution for traffic surveillance due to their low cost, large sample size, and intensive spatial coverage. At the same time, the development of reliable simulation tools that can be used to conduct a feasibility study, performance evaluation, and estimation model testing for cellular probe technologies, especially in the arterial environment, is still limited. In this study, by simulating the real-world signal delaying and wireless signal communication protocols, we establish a simulation platform for evaluating cellular probe system. The simulation platform eliminates unrealistic assumption on static and regular cell boundaries in the existing simulation tools and can provide simulation results similar to field observations. Based on the platform, we compared the performance of cellular probe system under both freeway and arterial environment. We analyzed the impact of traffic conditions, the duration of cellphone calls, and penetration rate on the handoff efficiency, handoff link speed error, and information-disseminating link speed estimation error. In addition, we also provide a detailed evaluation of the accuracy of the intersection delay estimation and the potential error sources and mechanism. The results show that the handoff-based cellular probe technologies attain good performance in traffic monitoring and the proposed simulation platform can reflect the real-world condition on an acceptable level.     

城乡结合部路段中心式双向左转车道设计研究

目前国内存在大量路侧土地开发强度高、交通量大、接入道和交叉口密度大、道路用地紧张的城市双向四车道路段。由于现有条件的限制,这些道路的交通压力已经不可能通过拓宽道路、增加车道来缓解,将其改造为含双向左转车道（TWLTL）的三车道道路是一种有效方案。国内外的研究成果表明双向左转车道能在保证道路服务水平不降低的基础上减少事故、缓解交通压力,使用效果良好。结合TSIS仿真模拟,对双向左转车道设计与应用进行了探讨分析。     

基于数据驱动的城市快速路段拥堵状态辨识及诱导对象选择方法

交通诱导实施效果不佳的主要原因之一是具有差异性出行特征的出行者无法接受单一的诱导方案。针对城市快速路高峰时段拥堵问题, 研究了考虑车辆出行特征差异的交通诱导对象精准识别方法, 以保障诱导方案的实施效果。利用高德路况数据提取拥堵路段, 根据拥堵路段与相邻路段交通状态的相关性提出拥堵源路段识别方法; 利用车牌识别数据提取使用快速路车辆的出行特征, 包括快速路出行强度、地面道路出行强度、快速路出发时刻离散度和快速路路径选择多样性; 采用K-means++算法对车辆出行特征进行聚类, 识别出显著影响道路交通状态的出行者, 并为出行者推荐适合其出行特征的错峰或绕行诱导方案。以苏州快速路为例, 研究发现: 针对拥堵源路段的交通诱导能有效改善拥堵路段的交通状态; 类型3车辆(高频出行且易绕行)占单月工作日早高峰所有使用快速路车辆总数的14%, 却占单日早高峰总交通量的51%, 是重点诱导对象; 通过精准识别, 可推荐诱导车辆数占总车辆数的47%。      

Towards lane-level traffic monitoring in urban environment using precise probe vehicle data derived from three-dimensional map aided differential GNSS

Today's urban road transport systems experience increasing congestion that threatens the environment and transport efficiency. Global Navigation Satellite System (GNSS)-based vehicle probe technology has been proposed as an effective means for monitoring the traffic situation and can be used for future city development. More specifically, lane-level traffic analysis is expected to provide an effective solution for traffic control. However, GNSS positioning technologies suffer from multipath and Non-Line-Of-Sight (NLOS) propagations in urban environments. The multipath and NLOS propagations severely degrade the accuracy of probe vehicle data. Recently, a three-dimensional (3D) city map became available on the market. We propose to use the 3D building map and differential correction information to simulate the reflecting path of satellite signal transmission and improve the results of the commercial GNSS single-frequency receiver, technically named 3D map-aided Differential GNSS (3D-DGNSS). In this paper, the innovative 3D-DGNSS is employed for the acquisition of precise probe vehicle data. In addition, this paper also utilizes accelerometer-based lane change detection to improve the positioning accuracy of probe vehicle data. By benefitting from the proposed method, the lane-level position, vehicle speed, and stop state of vehicles were estimated. Finally, a series of experiments and evaluations were conducted on probe data collected in one of the most challenging urban cities, Tokyo. The experimental results show that the proposed method has a correct lane localization rate of 87% and achieves sub-meter accuracy with respect to the position and speed error means. The accurate positioning data provided by the 3D-DGNSS result in a correct detection rate of the stop state of vehicles of 92%.     

Urban travel time reliability at different traffic conditions

The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is.     

基于纵向平等的干道BRT信号优先方法

在定时式协调信号控制的背景下，以加快BRT车辆运行速度，降低信号优先给社会车辆造成的负面影响为目标，以实现绿灯时间再分配的纵向平等性为基本要求，提出了一种新颖的干道BRT主动信号优先方法。在BRT专用道沿线布设3类检测器，采集BRT车辆的到达时刻。定义了绿灯延长、相位插入、绿灯早启3类优先请求时间窗，有条件地生成和删除不同类型的优先请求，有节制地实施相位插入。给出信号优先贡献和补偿的混合作用方式以及协调方向的社会车辆连续行进的保障措施。遵循纵向平等性的要求，建立信号优先贡献算法和信号优先补偿算法。在高负荷机动车交通需求下进行仿真试验，给出该方法的最佳参数取值建议；BRT车辆的行程时间降幅超过28%，协调方向社会车辆的行程时间增幅不足5%的结果验证该方法的有效性；BRT车辆的行程时间降幅超过19%、社会车辆的车均延误差异不足1%的结果验证该方法相较于传统方法的优越性。     

公路网管理与应急处置服务技术研究

随着公路网规模逐步延伸庞大,公众跨区域出行和物资流动迅猛发展、交通突发事件呈现递增现象,建设公路网管理、应急处置和信息服务系统为必然趋势。分析了公路网日常管理、突发事件应急处置、出行信息服务及基础数据支撑的系统功能需求,以省域公路网为研究对象,基于逻辑框架方法,设计了融合日常监控、突发事件预警监测、资源调配与指挥、多方信息联动等多功能的综合性集成平台系统方案,并给出了突发交通应急事件处置流程。研究表明,所构建系统能够掌握实时全面交通信息,实现全路网的协调管理,有效提升应急处置能力和服务水平。     

A technical note on evaluating the effectiveness of bus rapid transit with transit signal priority

Transit Signal Priority (TSP) and Bus Rapid Transit (BRT) are innovative Intelligent Transportation System (ITS) tools that can reduce travel times for buses. Combining TSP and BRT can significantly improve bus travel, but can negatively impact network traffic operations. Although TSP has been implemented worldwide, few previous studies holistically examined the effects of using various conditional and unconditional TSP strategies with or without a BRT system. This research simulates multiple TSP and BRT combination scenarios to understand their impact on traffic operations, including crossing street traffic. A test bed along International Drive (I-Drive) in Orlando, Florida, was chosen as the simulation area. Field data collected for this test bed, which included traffic volumes, bus travel times, and traffic signal control data, were used to develop, calibrate, and validate the simulation model. Results showed that BRT with Conditional TSP 3 minutes behind significantly improved travel times, average speed, and average total delay per vehicle for the main through movements compared with no BRT or TSP, with only minor effects on crossing street delays. BRT with Unconditional TSP resulted in significant crossing street delays, especially at major intersections with high traffic demand, indicating that this scenario is impractical for implementation. The simulation suggests that BRT and TSP will be most effective when used in areas where crossing street volumes are low. However, it is unknown how these ITS tools affect pedestrian traffic. Using optimization methods can determine the best strategy to balance transit and pedestrian traffic.     

1种基于浮动车数据的多车车速融合算法

在浮动车处理技术中,多浮动车样本车速的融合是整个计算的最后1个环节,算法的好坏直接影响到动态交通信息的准确性。从多权重系数和多种路况状态的角度构建了1种新的基于浮动车数据的多车车速融合算法,该算法从浮动车行驶特征等角度,综合考量在表征实时路况时浮动车多车样本间的共性与个性差异去融合多车车速,提高了实时路况的准确性,并且可根据实际交通环境快速调整相关参数。最后通过实证分析对其准确性进行了评估验证,结果表明能有效提高动态交通信息的准确性,具有良好的实用性。     

Fusion de données pour l'estimation des temps de parcours via la théorie de l'évidence

The emergence of new information technologies and the transformation that has occurred in traffic management have both increased drivers' already considerable need for road traffic information. The travel time is one of the forms in which this information is presented, and a number of systems are based on its dissemination. In this context, this indicator is used as a measure of the impedance (or cost) of routes on the network and/or a congestion indicator. This raises the problem of estimating travel times with an acceptable degree of accuracy, which is a particularly difficult task in urban areas as a result of difficultes of a theoretical, technical and methodological nature. Thus, in order to find out the traffic conditions that prevail on an urban road, the traffic sensors that are usually used to measure traffic conditions are ineffective under certain circumstances. New measurement devices (cameras, GPS or cellphone tracking, etc.) mean that other sources of data are increasingly used in order to supplement the information provided by conventional measurement techniques and improve the accuracy of travel) time estimates. As a result, travel time estimation becomes a typical data fusion problem. This study deals with a multisource estimate of journey times and attempts to provide a comprehensive framework for the utilization of multiple data and demonstrate the feasibility of a travel time estimation system based on the fusion of data of several different types. In this case two types of data are involved, data from conventional induction loop sensors (essentially flow and occupancy measurements) and data from probe vehicles. The selected modelling framework is the Dempster-Shafer Evidence Theory, which has the advantage of being able to take account of both the imprecision and uncertainty of the data. The implementation of this methodology has demonstrated that, in each case, better results are achieved with fusion than with methods based on a single source of data and that the quality of the information, as measured by correctly classified rates, improves as the degree of precision required of the estimate is increased.     

Safety performance functions for low-volume rural minor collector two-lane roadways

The Fixing America's Surface Transportation Act (FAST Act) highlights a data-driven method to improve traffic safety on all public paved roads in the U.S. The first edition of the Highway Safety Manual (HSM) is a widely used tool that provides crash predictive models in the form of safety performance functions (SPFs). There are no specific SPFs for low-volume roadways in the HSM. It is important to know that low-volume roadways are the major roadway types in terms of total mileage. This study used 2015–2019 crash data from Texas, incorporating with other relevant geometric and traffic variables, to develop SPFs for a specific low-volume roadway type (rural minor collector two-lane roadways). This study proposed a rules-based SPF developed approach that makes the prediction accuracies higher compared to the full model. The R² values range from 0.18 to 0.22 for all data (without splitting) for different injury level models. The prediction accuracies are improved in the decision tree-based models. For different class specific models (based on injury levels), the R² values range from 0.25 to 0.41. Three SPF groups are developed based on crash injury types. The SPFs can provide guidance in refining the prediction accuracies of rural minor collectors.     

动态输入下路段行程时间研究

以元胞传输模型(LWR模型的离散形式)作为分析工具,以行程时间为研究对象,研究了单车道路段没有出入口的基本路段受交通信号控制影响下的动态行程时间.考虑到路段上车辆密度对车辆速度的影响,文章定义了路段加权密度来表征车辆进入路段时路段的状态.分析结果表明,动态行程时间和车辆进入路段时的流量基本上没有关系;当车辆进入路段时刻一定时,路段加权密度和车辆的动态行程时间成线性关系.     

Vissim在常州站综合客运枢纽交通仿真中的应用

依附铁路而建、集多种交通方式于一体的综合客运枢纽随着我国铁路大建设而大量涌现,其规模、布局等的规划设计方案需通过交通仿真来检验、评估和优化。文中结合仿真软件Vissim,对沪宁城际常州站综合客运枢纽进行了交通仿真,结合定量分析法,对机动车进出站车流密度、机动车道通行能力、乘客通道客流密度、乘客通道通行能力等进行了综合分析,并提出了优化建议。     

Real-Time Transportation Mode Detection Using Smartphones and Artificial Neural Networks: Performance Comparisons Between Smartphones and Conventional Global Positioning System Sensors

Traditionally, traffic monitoring requires data from traffic cameras, loop detectors, or probe vehicles that are usually operated by dedicated employees. In efforts to reduce the capital and operational costs associated with traffic monitoring, departments of transportation have explored the feasibility of using global positioning system (GPS) data loggers on their probe vehicles that are postprocessed for analyzing the traffic patterns on desired routes. Furthermore, most cell phones are equipped with embedded assisted-GPS (AGPS) chips, and if the mode of transportation the phone is in can be anonymously identified, the phones can be treated as if they are probe vehicles that are voluntarily hovering throughout the city, at minimal additional costs. Emerging cell phones known as “smartphones” are equipped with additional sensors including an accelerometer and magnetometer. The accelerometer can directly measure the acceleration values, as opposed to having acceleration values derived from speed values in conventional GPS sensors. The magnetometer can measure mode-specific electromagnetic levels. Smartphones are subscribed with roadside Internet data plans that can provide an essential platform for real-time traffic monitoring. In this article, neural network-based artificial intelligence is used to identify the mode of transportation by detecting the patterns of distinct physical profile of each mode that consists of speed, acceleration, number of satellites in view, and electromagnetic levels. Results show that newly available values in smartphones improve the mode detection rates when compared with using conventional GPS data loggers. When smartphones are in known orientations, they can provide three-dimensional (3-D) acceleration values that can further improve mode detection accuracies.     

一种新的城市快速路行程时间预测技术

根据城市快速路交通诱导和监控系统的实际需要,提出了基于宏观动态流体力学模型的行程时间预测技术,可以动态预测稳定流和非稳定流状况下城市快速路网上任意两点间行程时间.