Development of behavioral context addressable loggers in the shell for travel-activity analysis

This study attempts to develop a small, portable travel-activity measuring instrument that requires no entry from respondents. Conventional surveys have collected identification information such as facility type, transport mode, and activity content through the operation of instruments, questionnaires, etc. However, these complicated surveys burden the respondents and rely on their memory, often leading to recording omissions or incorrect records. We propose a method for estimating behavioral contexts using BCALs (Behavioral Context Addressable Loggers in the Shell), a wearable, behavioral context information-measuring instrument, for re-estimating label information such as facility type and transport mode from ecological and environmental sensors based on learning models. The numerical values observed by these sensors differed greatly among locations or means of transportation, revealing the high possibility of automatic identification of locations and means of transportation using BCALs.     

船载GPS在内河海事监管中的应用研究

文章阐述了百色辖区内河的通航环境、渡口渡工特点和营运渡船状况,分析了远洋航行定位技术在百色右江客渡船舶上运用的可行性,并提出了利用船载GPS设备进行内河海事监管的具体措施,为提升海事监管能力提供有效的技术支持。     

水平定向钻数据采集及远程监控系统研究

文中针对油气长输管道水平定向钻施工,研发了数据采集及远程监控系统。通过数据采集装置将钻机施工中的关键数据进行实时采集、处理和远程无线传输,由手机APP进行远程监控,实现了工程项目的远程管理,预测后续施工中可能出现的问题,降低施工风险,提高施工效率。     

Mining missing train logs from Smart Card data

This paper shows how to recover the arrival times of trains from the gate times of metro passengers from Smart Card data. Such technique is essential when a log, the set of records indicating the actual arrival and departure time of each bus or train at each station and also a critical component in reliability analysis of a transportation system, is missing partially or entirely. The procedure reconstructs each train as a sequence of the earliest exit times, called S-epochs, among its alighting passengers at each stations. The procedure first constructs a set of passengers, also known as reference passengers, whose routing choices are easily identifiable. The procedure then computes, from the exit times of the reference passengers, a set of tentative S-epochs based on a detection measure whose validity relies on an extreme-value characteristic of the platform-to-gate movement of alighting passengers. The tentative S-epochs are then finalized to be a true one, or rejected, based on their consistencies with bounds and/or interpolation from prescribed S-epochs of adjacent trains and stations. Tested on 12 daily sets of trains, with varying degrees of missing logs, from three entire metro lines, the method restored the arrival times of 95% of trains within the error of 24 s even when 100% of logs was missing. The mining procedure can also be applied to trains operating under special strategies such as short-turning and skip-stop. The recovered log seems precise enough for the current reliability analysis performed by the city of Seoul.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

Short-term speed predictions exploiting big data on large urban road networks

Big data from floating cars supply a frequent, ubiquitous sampling of traffic conditions on the road network and provide great opportunities for enhanced short-term traffic predictions based on real-time information on the whole network. Two network-based machine learning models, a Bayesian network and a neural network, are formulated with a double star framework that reflects time and space correlation among traffic variables and because of its modular structure is suitable for an automatic implementation on large road networks. Among different mono-dimensional time-series models, a seasonal autoregressive moving average model (SARMA) is selected for comparison. The time-series model is also used in a hybrid modeling framework to provide the Bayesian network with an a priori estimation of the predicted speed, which is then corrected exploiting the information collected on other links. A large floating car data set on a sub-area of the road network of Rome is used for validation. To account for the variable accuracy of the speed estimated from floating car data, a new error indicator is introduced that relates accuracy of prediction to accuracy of measure. Validation results highlighted that the spatial architecture of the Bayesian network is advantageous in standard conditions, where a priori knowledge is more significant, while mono-dimensional time series revealed to be more valuable in the few cases of non-recurrent congestion conditions observed in the data set. The results obtained suggested introducing a supervisor framework that selects the most suitable prediction depending on the detected traffic regimes.     

Flow-based accessibility measurement: The Place Rank approach

In this paper, we empirically test the viability of a flow-based approach as an alternative to transport accessibility measurement. To track where commuters travel from and to (but not commute times), we use transactional smartcard data from residents in Singapore to construct the (daily) spatial network of trips generated. We use the Place Rank method to demonstrate the viability of the flow-based approach to study accessibility. We compute the Place Rank of each of 44 planning areas in Singapore. Interestingly, even though the spatial network is constructed using only origin–destination information, we find that the travel time of the trips out of each planning area generally decreases as the area’s Place Rank increases. The same is also the case for in-vehicle time, number of transfers in the network and transfer time. This shows that a flow-based approach can be used to measure the notion of accessibility, which is traditionally assessed using travel time information in the system. We also compare Place Rank with other indicators, namely, bus stop density, eigenvector centrality, clustering coefficient and typographical coefficient to evaluate an area’s accessibility. The results show that these indicators are not as effective as the Place Rank method.     

基于GPS和GSM的清管器智能跟踪系统

为了在清管施工中实时跟踪清管器,设计一种基于GPS系统和GSM通信系统的智能清管器跟踪系统,系统包括发射机、中继器和监控系统。发射机搭载在清管器上持续发射电磁信号和静磁信号,中继器预先埋设在设定的跟踪点接收和转发通过信息;清管器通过跟踪点时,中继器接收发射机发出的信号,精确识别出清管器并将通过时间和跟踪点GPS位置信息通过GSM网络发送至监控系统;监控系统接收到信息后,在监控界面地图上实时显示通过位置和通过时间。清管器卡堵时,系统可及时在地图上指示卡堵区间和到达卡堵现场路径。现场应用表明:该系统实现了清管器智能跟踪,通过时间和通过位置记录精确,降低清管施工中安全风险,节约成本。     

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.