A graph deep learning method for short‐term traffic forecasting on large road networks

Short‐term traffic flow prediction on a large‐scale road network is challenging due to the complex spatial–temporal dependencies, the directed network topology, and the high computational cost. To address the challenges, this article develops a graph deep learning framework to predict large‐scale network traffic flow with high accuracy and efficiency. Specifically, we model the dynamics of the traffic flow on a road network as an irreducible and aperiodic Markov chain on a directed graph. Based on the representation, a novel spatial–temporal graph inception residual network (STGI‐ResNet) is developed for network‐based traffic prediction. This model integrates multiple spatial–temporal graph convolution (STGC) operators, residual learning, and the inception structure. The proposed STGC operators can adaptively extract spatial–temporal features from multiple traffic periodicities while preserving the topology information of the road network. The proposed STGI‐ResNet inherits the advantages of residual learning and inception structure to improve prediction accuracy, accelerate the model training process, and reduce difficult parameter tuning efforts. The computational complexity is linearly related to the number of road links, which enables citywide short‐term traffic prediction. Experiments using a car‐hailing traffic data set at 10‐, 30‐, and 60‐min intervals for a large road network in a Chinese city shows that the proposed model outperformed various state‐of‐the‐art baselines for short‐term network traffic flow prediction.     

Simulation model based on Monte Carlo method for traffic assignment in local area road network

For a local area road network, the available traffic data of traveling are the flow volumes in the key intersections, not the complete OD matrix. Considering the circumstance characteristic and the data availability of a local area road network, a new model for traffic assignment based on Monte Carlo simulation of intersection turning movement is provided in this paper. For good stability in temporal sequence, turning ratio is adopted as the important parameter of this model. The formulation for local area road network assignment problems is proposed on the assumption of random turning behavior. The traffic assignment model based on the Monte Carlo method has been used in traffic analysis for an actual urban road network. The results comparing surveying traffic flow data and determining flow data by the previous model verify the applicability and validity of the proposed methodology.     

An approach for modelling CO2 emissions from road traffic in urban areas

An approach that incorporates three modelling components has been developed to estimate road traffic CO2 emissions for an urban area with street level resolution. The first component enables the determination of the road traffic characteristics using the SATURN (Simulation and Assignment of Traffic in Urban Road Networks) model. The output from this component is then analysed using MATrix LABoratory (MATLAB) programming to provide estimates of CO2 emissions for the urban area. Finally, ArcGIS is used to illustrate the model output. The three components are integrated using a Loose-Coupling approach in which the individual components each load the necessary data to give an independent output. The model structure is discussed in the current paper and the modelling results for a small city (Norwich, UK) are presented.     

Network‐level scheduling of road construction projects considering user and business impacts

Construction projects are often associated with partial or full road closures, which result in user costs and community disruptions in terms of reduced business productivity. A number of studies have addressed the problem of scheduling construction projects based on a variety of stakeholder objectives. Yet still, there seems to exist a few gaps regarding (1) possible tradeoffs between road user cost reduction and business cost reduction associated with optimal scheduling, (2) role of the project type (rehabilitation and capacity expansion) on the solution methodology, and (3) lack of solution algorithm to address the problem complexity by deriving the optimal solution. In addressing these gaps, this article adopts a novel approach for developing an optimal project schedule for multiple road projects within a construction horizon. The goal is to minimize the overall cost of the projects to road users and adjacent businesses over the construction horizon. The project scheduling problem is formulated as a mixed‐integer nonlinear program. We solve the problem using a local decomposition method. The methodology is demonstrated using the Sioux Falls city network with two project types: capacity expansion and rehabilitation. The results of the numerical experiment suggest that (1) the solution algorithm converges to optimal solution in finite iterations and (2) a network‐wide scheduling of urban road projects using explicit optimization can yield a significant reduction in business disruption costs while incurring a relatively smaller increase in system travel time, and overall, is superior to a schedule developed only considering the total system travel time.     

Real‐Time Estimation of Pollution Emissions and Dispersion from Highway Traffic

Traffic‐related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals' exposure to traffic‐related air pollution remains a challenge. Obtaining high‐resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions, and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the emission sources responsible for these hot spots, which in turn makes it difficult to reduce the uncertainty of health risk estimates for communities and to develop policies that mitigate these risks. We present a novel air pollution estimation method that models the highway traffic state, highway traffic‐induced air pollution emissions, and pollution dispersion, and describe a prototype implementation for the San Francisco Bay Area. Our model is based on the availability of real‐time traffic estimates on highways, which we obtain using a traffic dynamics model and an estimation algorithm that augments real‐time data from both fixed sensors and probe vehicles. These traffic estimates combined with local weather conditions are used as inputs to an emission model that estimates pollutant levels for multiple gases and particulates in real‐time. Finally, a dispersion model is used to assess the spread of these pollutants away from the highway source. Maps generated using the output of the dispersion model allow users to easily analyze the evolution of individual pollutants over time, and provides transportation engineers and public health officials with valuable information that can be used to minimize health risks.     

曹妃甸生态城交通和土地利用整合规划

交通问题与城市交通拥堵．能源消耗和温室气体排放关系密切,只有将交通运输规如与城市土地利用相结合,减少交通量,并组织良好的交通运输才能使交通问题得到有效解决,曹妃甸生态城的交通规划与土地利用相结合,以交通导向发展和位置策略的规划理念为基础．在城市系统中采用了一系列综合的可持续发展的交通战略,大力发展便捷舒适的公共交通和非机动运输网络,减少机动车出行比例。     

End‐to‐End Deep Learning Methodology for Real‐Time Traffic Network Management

This article presents a novel real‐time traffic network management system using an end‐to‐end deep learning (E2EDL) methodology. A computational learning model is trained, which allows the system to identify the time‐varying traffic congestion pattern in the network, and recommend integrated traffic management schemes to reduce this congestion. The proposed model structure captures the temporal and spatial congestion pattern correlations exhibited in the network, and associates these patterns with efficient traffic management schemes. The E2EDL traffic management system is trained using a laboratory‐generated data set consisting of pairings of prevailing traffic network conditions and efficient traffic management schemes designed to cope with these conditions. The system is applied for the US‐75 corridor in Dallas, Texas. Several experiments are conducted to examine the system performance under different traffic operational conditions. The results show that the E2EDL system achieves travel time savings comparable to those recorded for an optimization‐based traffic management system.     

公交优先下的道路规划设计探讨

公交专用车道是一种新生事物,是城市“治堵”的重要举措,关系着城市交通的成败。剖析城市道路交通的渠化和公交站位置的合理设置,探讨有利于适应公交优先的道路渠化及合理的公交站设置方案,将有利于公交优先功能的高效发挥。     

基于低碳交通理念的城市规划策略研究

该文从城市规划策略的角度,提出了通过构建紧凑型多中心的城市结构和有效混合用地功能以减少交通出行、通过建设网络化的城市道路系统提高机动车出行效率、通过优化公共交通设施降低小汽车的使用、通过建设环境友好的非机动车交通网络来促进步行和自行车出行,从而实现城市交通节能减排的目标。     

基于浮动车的城市道路行程时间预测算法研究

装载GPS的浮动车在社会交通流中比重越来越高,已成为主要的行程时间采集手段。研究了基于浮动车的城市道路路段行程时间预测算法,输入数据包括静态空间属性数据、行程时间历史备份数据和基于GPS采集的动态交通行程时间数据,并以5 min为预测间隔进行20 min短时行程时间预测。最后经2.1 km含3个交叉口的路段预测验证,表明该算法单方向单次最大误差23.0%;经过滚动预测,单方向平均绝对误差为5%~6%,精度满足用于城市道路路段的信息发布要求。     

Measuring traffic lane‐changing by converting video into space–time still images

Empirical data is needed in order to extend our knowledge of traffic behavior. Video recordings are used to enrich typical data from loop detectors. In this context, data extraction from videos becomes a challenging task. Setting automatic video processing systems is costly, complex, and the accuracy achieved is usually not enough to improve traffic flow models. In contrast “visual” data extraction by watching the recordings requires extensive human intervention. A semiautomatic video processing methodology to count lane‐changing in freeways is proposed. The method allows counting lane changes faster than with the visual procedure without falling into the complexities and errors of full automation. The method is based on converting the video into a set of space–time still images, from where to visually count. This methodology has been tested at several freeway locations near Barcelona (Spain) with good results. A user‐friendly implementation of the method is available on http://bit.ly/2yUi08M .     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

Rural road networks as barriers to gene flow for amphibians: Species-dependent mitigation by traffic calming

Population structuring based on mitochondrial DNA variation along the rural landscape of the Trubia valley in North Iberia revealed significant association between road density and genetic distance between populations of two amphibian species, the midwife toad Alytes obstetricans and the palmate newt Lissotriton helveticus. Traffic calming (concentration of flows on minor rural roads at a few highways to decrease volumes and speeds) near urban settlements mitigates the population fragmentation of L. helveticus but not that of A. obstetricans, indicating that even small roads with low-intensity traffic act as barriers for the latter species. We suggest that the construction of passages for amphibians across rural roads would potentially mitigate population fragmentation of endangered species like the anuran A. obstetricans.     

交通意外事件紧急疏导配流方案生成方法研究

城市交通意外事件易造成交通拥挤、诱发新的交通意外事件,严重影响城市道路网的正常运行,首要之策是分析评估交通意外事件造成的影响范围,实施交通紧急控制与诱导。因此,本文提出交通意外事件紧急疏导配流方案生成方法。该方法由交通意外事件疏导范围确定和多目标交通疏导配流模型两部分组成,通过寻找合理的交通区域进行交通流的二次分配,根据事发前后区域出行总时间的变化寻找合理的疏导范围;在确定的疏导范围内,应用多目标交通疏导配流模型对交通流实施动态分配。该方法在方案生成过程中,兼顾疏导区域、总行驶时间、道路交通流量、道路交通流增量等诸多因素,形成交通意外事件紧急疏导配流方案,保障疏导范围内配流方案的可行性、平稳性和安全性。     

城市交通需求管理的国际借鉴与策略建议

交通需求管理是解决当前我国城市交通拥堵与高碳排放问题的当务之急。城市交通需求管理的根本理念是引导人们采取科学的交通行为、理智地使用道路交通设施及其有限资源。结合各国的实践,城市交通需求管理的主要策略有:坚持公交优先、引导小汽车合理使用、发展自行车交通、土地使用与交通用地联合规划、推行步行化模式等。     

Freeway Travel Time Prediction Using Takagi–Sugeno–Kang Fuzzy Neural Network

This article presents a Takagi–Sugeno–Kang Fuzzy Neural Network (TSKFNN) approach to predict freeway corridor travel time with an online computing algorithm. TSKFNN, a combination of a Takagi–Sugeno–Kang (TSK) type fuzzy logic system and a neural network, produces strong prediction performance because of its high accuracy and quick convergence. Real world data collected from US‐290 in Houston, Texas are used to train and validate the network. The prediction performance of the TSKFNN is investigated with different combinations of traffic count, occupancy, and speed as input options. The comparison between online TSKFNN, offline TSKFNN, the back propagation neural network (BPNN) and the time series model (ARIMA) is made to evaluate the performance of TSKFNN. The results show that using count, speed, and occupancy together as input produces the best TSKFNN predictions. The online TSKFNN outperforms other commonly used models and is a promising tool for reliable travel time prediction on a freeway corridor.     

Wavelet‐Based Denoising for Traffic Volume Time Series Forecasting with Self‐Organizing Neural Networks

Abstract: In their goal to effectively manage the use of existing infrastructures, intelligent transportation systems require precise forecasting of near‐term traffic volumes to feed real‐time analytical models and traffic surveillance tools that alert of network links reaching their capacity. This article proposes a new methodological approach for short‐term predictions of time series of volume data at isolated cross sections. The originality in the computational modeling stems from the fit of threshold values used in the stationary wavelet‐based denoising process applied on the time series, and from the determination of patterns that characterize the evolution of its samples over a fixed prediction horizon. A self‐organizing fuzzy neural network is optimized in its configuration parameters for learning and recognition of these patterns. Four real‐world data sets from three interstate roads are considered for evaluating the performance of the proposed model. A quantitative comparison made with the results obtained by four other relevant prediction models shows a favorable outcome.     

Optimal allocation of area in hierarchical road networks

This paper deals with the hierarchical road network design using a continuous model. The model is based on a grid road network where roads are classified into three types: access roads, minor arterial roads, and major arterial roads. Using a continuous approximation in which the distance is measured as the rectilinear distance, we obtain a simple approximation for the total travel time. We then find the optimal allocation of area taken up by roads at each level of the hierarchy so as to minimize the sum of the travel and construction costs. The result demonstrates how the total traffic volume, the traffic composition, and the unit construction cost affect the optimal road area. The optimal area of major arterial roads increases with the total traffic volume and the proportions of inward, outward, and through traffic and decreases with the unit construction cost.     

楚雄市城市道路交通现状及对策研究

基于文献阅读、资料查询、实地调查等,对道路网现状、车辆保有量现状、道路交通流构成对楚雄市城市道路交通现状进行了研究,研究表明目前楚雄市城市道路交通存在人车混行严重,车多路窄、行车困难等问题,最后针对这些问题提出了相应的对策。     

Short‐Term Traffic Speed Prediction for an Urban Corridor

Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.