Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SSRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.     

On the use of mathematical programming in the evaluation of transport policies

This paper focuses on the evaluation processes by which decisions regarding transportation alternatives can be assisted. A multidimensional approach usually called multiple criteria decision making is required to represent the complexity of transportation policy and systems.

The multiple criteria decision making techniques can be divided into two groups. The first is based on a ranking scheme approach and the second on a mathematical programming approach.

A multiple objective mathematical programming procedure known as Goal Programming is presented. The authors examined the use of that procedure in real transportation problems.

The results suggest that multiple objective mathematical programming techniques in general do not appear to be appropriate in transportation policy analysis involving mutually exclusive alternatives. Their use can be limited to special cases in the private sector.     

中小城市交通生成预测模型研究

城市交通生成预测是城市交通规划的基础，如何客观、科学地进行交通生成预测，是当前我国城市交通规划中共同面临的课题。一般来说，不同的土地利用布局、不同的土地利用性质和不同的土地利用强度，对应着不同的交通生成。因此，建立土地利用与交通生成的相互关系模型，实现土地利用-出行生成相关因素-交通生成的三步式预测方法向土地利用-交通生成两步式预测方法的过渡，将在大大简化交通需求预测过程的同时提高预测结果的可靠性。     

A Comparative Study of Three Multivariate Short-Term Freeway Traffic Flow Forecasting Methods With Missing Data

Short-term traffic flow forecasting is a critical function in advanced traffic management systems (ATMS) and advanced traveler information systems (ATIS). Accurate forecasting results are useful to indicate future traffic conditions and assist traffic managers in seeking solutions to congestion problems on urban freeways and surface streets. There is new research interest in short-term traffic flow forecasting due to recent developments in intelligent transportation systems (ITS) technologies. Previous research involves technologies in multiple areas, and a significant number of forecasting methods exist in the literature. However, most studies used univariate forecasting methods, and they have limited forecasting abilities when part of the data is missing or erroneous. While the historical average (HA) method is often applied to deal with this issue, the forecasting accuracy cannot be guaranteed. This article makes use of the spatial relationship of traffic flow at nearby locations and builds up two multivariate forecasting approaches: the vector autoregression (VAR) and the general regression neural network (GRNN) based forecasting models. Traffic data collected from U.S. Highway 290 in Houston, TX, were used to test the model performance. Comparison of performances of the three models (HA, VAR, and GRNN) in different missing ratios and forecasting time intervals indicates that the accuracy of the VAR model is more sensitive to the missing ratio, while on average the GRNN model gives more robust and accurate forecasting with missing data, particularly when the missing data ratio is high.     

TSP超前地质预报在中天山隧道1#斜井雁行式断裂带探测中的应用

TSP超前地质预报系统是利用接收入工地震波来完成地下工程的超前探测。以发育在中天山隧道1#斜井的雁行式断裂带为研究对象,介绍了TSP超前地质预报系统的应用。重点介绍了解译过程中将前期地质工作和地质物探资料有机结合在一起,确定超前地质预报目标体的特征及其与中天山隧道的关系,从而使得TSP超前地质预报探测有了明确的目标,保证TSP超前地质预报的质量。     

Predicting travel time reliability using mobile phone GPS data

Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability and can be used for risk-averse routing, for reporting travel time reliability to a user, or as a component of fleet vehicle decision-support systems. Many of these uses (such as those for mapping services like Bing or Google Maps) require predictions for routes in the road network, at arbitrary times; the highest-volume source of data for this purpose is GPS data from mobile phones. We introduce a method (TRIP) to predict the probability distribution of travel time on an arbitrary route in a road network at an arbitrary time, using GPS data from mobile phones or other probe vehicles. TRIP captures weekly cycles in congestion levels, gives informed predictions for parts of the road network with little data, and is computationally efficient, even for very large road networks and datasets. We apply TRIP to predict travel time on the road network of the Seattle metropolitan region, based on large volumes of GPS data from Windows phones. TRIP provides improved interval predictions (forecast ranges for travel time) relative to Microsoft’s engine for travel time prediction as used in Bing Maps. It also provides deterministic predictions that are as accurate as Bing Maps predictions, despite using fewer explanatory variables, and differing from the observed travel times by only 10.1% on average over 35,190 test trips. To our knowledge TRIP is the first method to provide accurate predictions of travel time reliability for complete, large-scale road networks.     

动态交通流量预测方法研究

随着智能运输系统的广泛应用,实时交通流量预测的重要性也日益显著。本文介绍了预测模型发展过程中比较重要的几个模型,并由此引出人工神经网络。介绍误差逆传播(BP)模型的相关理论。指出传统BP神经网络的缺陷,并提出提高预测精度的措施引进高阶神经网络。建立普通BP神经网络的预测模型,利用误差反传播算法实现这些影响因素到输出变量的复杂映射,再用高阶神经网络构建另一预测模型。利用交叉口实测数据进行预测,并用实际数据进行比较验证。     

基于内在因素提取的内河航运量神经网络预测方法

内河航运量预测是内河航道网规划的依据,利用主成分分析法提取影响内河航运量的内在因素,利用内在影响因素与内河航运量之间的联系,结合BP神经网络建立模型对内河航运量进行预测。算例表明,该模型可以提高预测精度。     

Forecasting demand for high speed rail

It is sometimes argued that standard state-of-practice logit-based models cannot forecast the demand for substantially reduced travel times, for instance due to High Speed Rail (HSR). The present paper investigates this issue by reviewing the literature on travel time elasticities for long distance rail travel and comparing these with elasticities observed when new HSR lines have opened. This paper also validates the Swedish long distance model, Sampers, and its forecast demand for a proposed new HSR, using aggregate data revealing how the air–rail modal split varies with the difference in generalized travel time between rail and air. The Sampers long distance model is also compared to a newly developed model applying Box–Cox transformations. The paper contributes to the empirical literature on long distance travel, long distance elasticities and HSR passenger demand forecasts. Results indicate that the Sampers model is indeed able to predict the demand for HSR reasonably well. The new non-linear model has even better model fit and also slightly higher elasticities.     

基于灰色模型及月度比例系数法的铁路客流预测方法

文章针对铁路客流变化的影响因素及特点,提出了基于灰色模型及月度比例系数法的铁路客流预测方法,并通过实例分析,证明了该方法预测误差小、精度高、计算简便、可操作性强,可为铁路车站客运计划的制定及日常客运工作组织提供准确、可靠的数据。