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.     

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.     

A cluster analysis approach for differentiating transportation modes using Bluetooth sensor data

A variety of automatic data collection technologies have been used to gather road and highway system data. The majority of these automatic data collection technologies are designed to collect vehicle-based data and either do not have the capability to collect other travel mode data (e.g., bicycles and pedestrians), or may need to be deployed differently to support this capability.

One type of wireless-based data collection system that has been deployed recently is based on Bluetooth technology. A key feature of Bluetooth-based data collection systems that makes travel mode identification feasible is that the Bluetooth-enabled devices within vehicles are also present on bicyclists and pedestrians. This research explores the effectiveness of applying cluster analysis methods when processing data collected via Bluetooth technology from vehicles, bicyclists, and pedestrians to automatically identify the associated travel modes. The results of several experiments utilizing multiple Bluetooth-based data collection units arranged linearly and in relatively close proximity on a simulated intersection demonstrate the potential of cluster analysis to accurately differentiate transportation modes from the collected data.     

基于元胞自动机的动态路段走行时间计算模型

为建立合理的动态交通网络中路段走行时间模型,分析了动态路段走行时间函数的一般形式,对比国内外常用的几种离散型动态路段走行时间函数,基于元胞自动机交通流模型,建立了动态路段走行时间模型。模型可以根据实际路段驶入率、驶出率,推算出任意时刻进入路段车辆的走行时间,并利用M atlab对模型进行求解和数值分析。结果表明,车辆进入路段后的交通状态是动态路段走行时间的主要影响因素;根据累积驶入驶出车辆数曲线可以直接求出动态路段走行时间,能够为动态交通网络中路径走行时间求解奠定基础。      

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.     

基于蓝牙的道路行程时间检测设备设计及测试

以嵌入式技术为基础,采用单片机及串口通信技术,开发基于蓝牙的路网平均行程时间检测器,实现自动采集车载蓝牙M AC地址并配时和数据存储的功能。通过场地测试确定了设备漏检率的平均大小、与速度的关系及初次检测点位置的分布规律。测定了设备的检测精度,并对平均速度、检测数、配对数等因素进行了分析;通过 t检验、曼惠特尼U检验及沃尔德沃尔福威茨(W-W )检验验证了设备数据的可靠性;通过实地测试验证了设备的可用性和实用性,并根据测试结果,提出了设备存在的一些不足以及后期改进的建议,为蓝牙检测器的后续研发奠定了基础。      

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.     

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high-frequency data sources from specialist data providers, which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using “readily available” vehicle trajectory data from moving sensors such as buses, taxis, and logistical vehicles equipped with GPS in “near” real time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds, and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates, and time window lengths. Global positioning system (GPS) data from Interstate I-880 (California) for a total of 73 vehicles over 6 h were obtained from the University of California Berkeley's Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links, and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5-min time window length and for a GPS sampling frequency of 60 s.     

A framework for travel time variability analysis using urban traffic incident data

This study aims to develop a framework to estimate travel time variability caused by traffic incidents using integrated traffic, road geometry, incident, and weather data. We develop a series of robust regression models based on the data from a stretch in California's highway system during a two-year period. The models estimate highway clearance time and percent changes in speed for both downstream and upstream sections of the incident bottleneck. The results indicate that highway shoulder and lane width factor adversely impact downstream highway clearance time. Next, travel time variability is estimated based on the proposed speed change models. The results of the split-sample validation show the effectiveness of the proposed models in estimating the travel time variability. Application of the model is examined using a micro-simulation, which demonstrates that equipping travelers with the estimated travel time variability in case of an incident can improve the total travel time by almost 60%. The contribution of this research is to bring several datasets together, which can be advantageous to Traffic Incident Management.     

Platoon recognition using connected vehicle technology

This article presents a mathematical model for real-time platoon recognition using the connected vehicle (CV) technology. Platoon information is a crucial part of traffic signal coordination and is difficult to obtain with traditional technologies such as loop detectors. The past work on platoon recognition using CV is very limited and lacked verification on the applicable range or evaluation of the performance of algorithms. The proposed algorithm is focused on estimating platoon characteristics for signal coordination and adaptive signal control with CV's vehicle-to-vehicle communication and an onboard GPS device. First, the detected platoon is identified by a modified critical time-headway. Then, platoon size and starting and ending times are estimated. Lastly, the filtering process for “qualified” detected platoon is proposed to optimize detectability. The results show that the proposed algorithm can estimate well in various traffic conditions and under both fixed-time and actuated signal control without the need for recalibration. Furthermore, two analytical models to estimate the detection rate are proposed and shown to be close to the numerical results and can be used to estimate the required market penetration ratio for the application without field experiments or microscopic simulation. The accuracy of both the recognition algorithm and detection rate estimation is obtained without relying on inputs that are hard to obtain in practice. Accordingly, the proposed algorithm can be an important part of adaptive signal control focusing on real-time coordination in CV environment.     

Plate scanning tools to obtain travel times in traffic networks

The management of vehicle travel times has been shown to be fundamental to traffic network analysis. To collect travel time measurement, some methods focus solely on isolated links or highway segments, and where two measurement points, at the beginning and at the end of a section, are deemed sufficient to evaluate users' travel time. However, in many cases, transport studies involve networks in which the problem is more complex. This article takes advantage of the plate scanning technique to propose an algorithm that minimizes the required number of registering devices and their location in order to identify vehicles candidates to compute the travel times of a given set of routes (or subroutes). The merits of the proposed method are explained using simple examples and are illustrated by its application to the real network of Ciudad Real.     

基于改进SWF神经网络多因素行程时间预测模型与算法

行程时间预测一直是交通领域研究的重点问题之一,道路系统的复杂性使预测工作变得困难。将影响路段行程时间的多种因素和改进后的样条权函数神经网络结合起来,根据机动车运行特点,建立行程时间预测模型,可以刻划道路运行的多种状态,能较准确的估计出路段的行程时间,也继承了样条权函数神经网络算法的各种优点。     

新技术时代城市交通管理与服务研究发展展望

随着我国互联网通信技术、计算机技术以及车辆技术的交叉融合发展,当前我国城市交通管理领域车联网技术的应用日益普及,通过车联网能够将司机、乘客、行人、道路、车辆与调度员之间的信息进行联通,并形成城市交通管理大数据网络,实时监控城市道路交通相关信息.除此之外,在新技术发展的背景下,共享出行等新型出行方式出现,一定程度上改变了...     

基于少量探测车的城市路段平均行程时间估计

与传统的固定式采集系统(感应线圈等)比较,探测车系统具有直接采集行程时间、时空覆盖范围广等优点.研究少量探测车情况下的路段行程时间估计问题对降低探测车系统的运营费用具有重要意义.在发现停车组和非停车组的行程时间均值、非停车组所占百分比等3个参数之间关联关系的基础上,提出了在极小样本情况下估计城市路段平均行程时间的方法.基于微观交通仿真的比较分析显示,该方法优于通过样本均值估计平均路段行程时间的方法,特别是当交通状况处于拥挤情况下其优势更为明显.     

基于动态清空距离的特殊车辆与CAVs混合车道控制

特殊车辆的优先通行是道路交通管理的一项重要工作,而目前相关控制措施存在实施难度较大、道路空间利用率低和道路通行能力下降等问题。为解决这些问题,结合智能网联汽车（CAVs）技术特点,提出考虑特殊车辆优先通行的CAVs专用车道控制方法,按应急车辆、一般优先级车辆和CAVs的优先通行顺序设计车辆通行规则。通过预测特殊车辆到达下游交叉口时的路口排队长度,建立“满足不同优先级特殊车辆通行需求”的动态清空距离模型,其中应急车辆以速度损失最小化为优化目标,一般优先级车辆以均衡车辆通行需求为优化目标。针对CAVs在专用道上可能成为其他车辆通行障碍的情况,考虑换道安全和不同换道动机,设计CAVs进入和离开专用道的规则,建立换道决策控制模型;在此基础上,提出适用于不同优先级车辆的专用车道通行控制策略。通过仿真实验对所提方法的控制效果予以分析验证。实验结果表明:与不考虑特殊车辆优先通行的控制方法相比,虽然该方法的车均出行时间和人均出行时间分别增加了3.9%和2.8%,但特殊车辆的车均延误时间减少了59.6%以上;与IBL控制方法相比,该方法的车均出行时间和人均出行时间分别减少16.7%和14.6%,特殊车辆的车均延误时间减少13.5%,专用车道利用率提高36.3%以上,并且在CAVs渗透率大于0.4时获得最佳控制效果。该控制方法在特殊车辆优先通行方面,减少了单一控制策略的局限性,为交通控制和管理提供理论支撑。      

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。     

考虑共享停车的用户出行选择研究

为探讨共享停车的出现对用户出行选择的影响,将出行者划分为共享停车和普通停车两种用户类型,分别构建停车阻抗函数,并基于分层Logit(NL)建立停车和路径联合选择结构模型.进一步提出带有两种停车方式的随机用户均衡模型,并改进相继平均算法对模型进行求解,通过算例分析发现预约费用从4元变化到12元时,共享停车需求量从378辆...     

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.     

基于自适应遗传算法的相位差优化模型研究

通过分析车辆在城市交通干线交叉口的延误规律,从相对相位差的约束条件出发,以干线的总延误最小为目标,建立了城市交通干线的相位差优化模型。模型中考虑到了车辆在干线相邻交叉口的双向延误情况,并采用自适应遗传算法在Matlab下编程实现了相位差优化模型的算例计算。结果表明该优化模型具有较好的有效性,能较大地提高城市交通干线协调控制的性能。     

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.