Cycleways and footpaths: What separation is needed for equivalent air pollution dose between travel modes?

Cycling and walking are being promoted in many urban areas as alternatives to motorised transport for health, environmental, and financial reasons. The reduced congestion and resulting decrease in the overall amount of pollution reduced can be expected to result in health benefits for the community. However, active commuters, due to their increased respiration rates and often increased travel times can expect to receive larger doses of air pollution compared with those using motorised forms of transport. However, given the large dropoff in concentrations away from a road, it can be expected that significant reductions can be achieved even with relatively small increases in separation between the path of cyclists/pedestrians and motor vehicles.This study presents a simple methodology for calculating the separation needed for cyclists and pedestrians to experience the same air pollution dose as car commuters. An example is given based on carbon monoxide (CO) data collected in a field campaign consisting of a car driver, a cyclist and a pedestrian travelling on a 2600 metre loop of road in Auckland. For this case study, the estimated distance from the centreline needed for cyclists and pedestrians to receive an equivalent dose of CO as motorists was found to range from 5.8 to 14.2 m depending on the commuting mode and the dispersion state of the atmosphere at the site. This was equal to a CO concentration reduction of 0.1–0.14 ppm per metre. Recommendations on facility modifications and route selections have been made to make active mode commuting safer.     

A novel variable selection method based on frequent pattern tree for real-time traffic accident risk prediction

With the availability of large volumes of real-time traffic flow data along with traffic accident information, there is a renewed interest in the development of models for the real-time prediction of traffic accident risk. One challenge, however, is that the available data are usually complex, noisy, and even misleading. This raises the question of how to select the most important explanatory variables to achieve an acceptable level of accuracy for real-time traffic accident risk prediction. To address this, the present paper proposes a novel Frequent Pattern tree (FP tree) based variable selection method. The method works by first identifying all the frequent patterns in the traffic accident dataset. Next, for each frequent pattern, we introduce a new metric, herein referred to as the Relative Object Purity Ratio (ROPR). The ROPR is then used to calculate the importance score of each explanatory variable which in turn can be used for ranking and selecting the variables that contribute most to explaining the accident patterns. To demonstrate the advantages of the proposed variable selection method, the study develops two traffic accident risk prediction models, based on accident data collected on interstate highway I-64 in Virginia, namely a k-nearest neighbor model and a Bayesian network. Prior to model development, two variable selection methods are utilized: (1) the FP tree based method proposed in this paper; and (2) the random forest method, a widely used variable selection method, which is used as the base case for comparison. The results show that the FP tree based accident risk prediction models perform better than the random forest based models, regardless of the type of prediction models (i.e. k-nearest neighbor or Bayesian network), the settings of their parameters, and the types of datasets used for model training and testing. The best model found is a FP tree based Bayesian network model that can predict 61.11% of accidents while having a false alarm rate of 38.16%. These results compare very favorably with other accident prediction models reported in the literature.     

The impact of extreme weather conditions on long distance travel behaviour

This paper examines traveller attitudes and responses towards disruption from weather and natural events. An internet-based travel behaviour survey was conducted with more than 2000 respondents in London and Glasgow. Of these respondents, 740 reported information on over 1000 long distance trips affected by extreme weather and natural events over the previous three years. Results show respondents are generally cautious towards travelling during extreme weather events. For a slight majority in the case of air and public transport, and a greater one in the case of car, travellers did not considerably alter their travel plan following the disruption. This was explained not only by less disruptive weather conditions (with heavy snow and volcanic ash being the most disruptive) and impact, but also by the relative importance of their trips. Differences between transport modes were not substantial. Business trips sometimes appeared to give travellers more flexibility, some other times not. Origin and destination did have an impact on reaction, as well as the presence of children whilst travelling. Mixed results were obtained about socio-economic and attitudinal variables. Age in particular did not appear to have a significant effect. Whilst most respondents did acknowledge no external influence in their decision, results showed an important contribution of transport organisation staff, as well as home and mobile internet technology. A limited but still considerable number of respondents indicated their closest friends/relatives as the main influence of their decisions. The results will help planners deploy strategies to mitigate the negative effects of weather related disruptions.     

Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai

The impacts of the built environment characteristics in residential neighborhoods on commuting behavior are explored in the literature. Scant evidence, however, is provided to scrutinize the role of the built environment characteristics at job locations. Studies also overlooked the potential error correlations between commuting mode and commuting distance due to the unobserved factors that influence both variables. We examined the impacts of the built environment characteristics at both residential and job locations on commuting mode and distance, by applying a discrete-continuous copula-based model on 857 workers in Shanghai. In contrast with studies of Western countries, we showed residential built environment characteristics are more influential on commute behavior than the built environment characteristics at job locations. This suggests the importance of local specificity in policymaking process. We also found the proportion of four-way intersections, road density, and population density in residential areas are negatively associated with driving probability, with elasticity amounts of −1.00, −0.23, and −0.08, respectively. Hence, dense and pedestrian- and cyclist-oriented development help to reduce travel distance and encourage walking, biking, and transit modes of travel.     

A universal distribution law of network detour ratios

Using trajectory data of normal taxis and ride-sourcing vehicles for 10 cities with various sizes in China, we analyze trip distance characteristics by examining the distribution of network detour ratios. The detour ratio for a specific ride is the ratio of the actual driving distance to the corresponding Euclidean (straight-line) distance. We find that, in spite of their different sizes and geographical features, the various cities exhibit an amazingly similar distribution law of network detour ratios: the mean of the detour ratios is inversely proportional to the Euclidean distance with an intercept. We further verify our findings with extensive simulation experiments for a hypothetical circular city with a directional grid street network. Our finding of this universal distribution law of network detour ratios contrasts sharply with the traditional wisdom of modeling throughout the past 50 years that have typically assumed a constant road detour ratio or factor within the range of 1.25–1.41. Our finding in the urban context also has far-reaching implications for fundamental research in many fields such as human mobility, human geography, facility location problems, logistic distribution networks and urban transportation planning.     

厦门轨道交通3号线跨海段盾构滚刀磨损预测

为有效控制海底隧道盾构刀具更换风险,提高盾构施工效率,针对厦门轨道交通3号线跨海段复杂地层,通过开展不同类型岩石的缩尺滚刀磨损试验和岩石磨蚀性试验,揭示滚刀材料磨损速率与岩石磨蚀性指标CAI值呈幂指数关系,建立通过测定拟建工程岩样CAI值预测工程刀具消耗的方法。利用建立的预测方法,对厦门轨道交通3号线中微风化花岗岩地层的滚刀批量换刀距离进行预测,得到该地层下边滚刀的批量换刀距离为50 m,正滚刀的批量换刀距离为215 m; 并在此基础上给出滚刀更换位置与换刀工法建议,为该工程与类似工程施工提供参考。     

高速公路中央分隔带横净距问题及对策

结合我国相关公路路线设计规范以及湖南省高速公路设计与管理实践,对高速公路中央分隔带弯道路段横净距进行计算分析,结果表明,若按照现有路线设计规范进行最小平曲线设计,高速公路局部弯道路段中央分隔带横净距不足以满足停车视距。然后,针对这一问题,综合考虑经济可行性和技术可行性,笔者提出了若干工程对策,为高速公路中央分隔带设计和运营管理提供参考。     

海上交通安全监管救助飞机配置规模

根据海上交通安全特点和需求,对海上监管救助飞机类型进行了划分,并确定了不同飞机的功能定位、适用水域和性能参数.通过分析不同类型飞机执行监管救助任务时的飞行路线,确定了飞机探测距离和有效监管救助水域面积,基于适用海域范围建立了不同飞机的配置规模数学模型.对全国沿海水域监管救助飞机配置规模做了案例分析,可作为规划海上交通安全监管救助飞机布局的理论基础.     

关于对技规第66条修改的建议

建议对技规第66条中以列车速度来确定两架不满足制动距离的信号机间的显示关系的方法进行修改,从条款、图解等各方面进行说明,并论证了其可行性和安全性。期望征求各种不同的意见。     

基于单目机器视觉的船舶测距定位原理研究

利用透视投影几何关系和摄像机内部参数,计算船舶与各陆标之间的距离,提出一种基于单目机器视觉的船舶测距定位方法。仿真结果表明,该方法与传统的陆标定位方法相比,具有简易、实时、受海况影响小的优势,可满足全封闭舰船陆标定位要求。