Prioritizing bicycle paths in Belo Horizonte City,Brazil: Analysis based on user preferences and willingness considering individual heterogeneity

Using bicycles as a commuting mode has proven to be beneficial to both urban traffic conditions and travelers’ health. In order to efficiently design facilities and policies that will stimulate bicycle use, it is necessary to first understand people’s attitudes towards bicycle use, and the factors that may influence their preferences. Such an understanding will enable reliable predictions of bicycle use willingness level, based on which cycling facility construction can be reasonably prioritized.As people often have different perceptions on exercising, green transportation, and traffic conditions, effects of potentially influencing factors on people’s willingness of using bicycles tend to be highly heterogeneous. This paper uses a random parameter ordered probit model to analyze how travelers’ willingness of using bicycles is influenced by various socio-economic factors in Belo Horizonte City, Brazil, with the consideration of individual heterogeneity. The data was collected through the 2010 bicycle use survey in Belo Horizonte City. Results show that, first, the willingness of using bicycle is favored by middle income class household, and negatively related with commuting time. Second, people who rent apartments tend to be more willing to use bicycles. Third, if a person is currently walking a long time to work, he/she would be most willing to commute with a bicycle in the future. Those currently commuting a relatively short distance by motorcycle and bus follow this group in terms of willingness to commute by bicycle in the future. Car users seem to be difficult to convert to bicycle users. Moreover, the estimation shows clear evidence that significant individual heterogeneity indeed exists, especially for education level, necessitating the consideration of such an effect. With the calibrated model, residents’ willingness of using bicycle commuting is then estimated for the entire Belo Horizonte City using the 2010 Census and the 2012 O/D survey data. The results are cross validated using the bicycle path preference information, also obtained from the 2010 bicycle use survey.     

Safety modeling for two‐way left‐turn lanes and a procedure to identify two‐way left‐turn lane segments with safety concerns

In the past, two‐way left‐turn lane (TWLTL) median treatments have been frequently used in Florida to inexpensively improve traffic and safety performances. In order to identify factors that may have significant impacts on safety operations in TWLTL sections and to identify TWLTL locations that present existing and future safety concerns, a research project was carried out and results are summarized in the paper. In the research, a three‐year crash history database with crashes and section characteristics from a total of 1688 TWLTL sections all over Florida was developed and used. A negative binomial regression model was developed to determine the statistical relationship between the number of crashes per mile per year and several variables such as traffic volume, access density, posted speed, and number of lanes. In regard to the methodology, in order to identify locations with safety concerns, several steps are needed: development of real crash data distribution, determination of statistical distribution models that better represent the actual crash data, determination of percentile values for the average number of crashes, estimation of crash rates for sections with the same characteristics, estimation of critical values for the variables corresponding to the percentile values for average number of crashes, calculation of tables of critical average annual daily traffic values, and generation of a list of TWLTL locations with critical safety concerns. Results presented in the paper have been used in real applications. Copyright © 2012 John Wiley & Sons, Ltd.     

旅客购票模式与渠道关联选择行为的建模分析

为分析多元化售票渠道下旅客购票选择行为的影响因素及交互特征，解析了多渠道情境下旅客购票过程的 3个阶段，辨识了旅客购票的 3类典型模式；运用协同进化分析思想，建立了购票模式与渠道关联选择的协同进化 Logit模型；基于道路客运联网售票信息系统数据，进行了模型的实证应用，分析了特征变量对旅客购票选择行为的影响，揭示了渠道与模式间关联选择行为特征.研究表明：性别，年龄，提前购票时间，出行时间，出行里程，发车频率等因素对旅客购票行为有显著影响；旅客购票模式和渠道选择间存在相关性，某一决策选择受另一决策结果的影响；考虑两者的交互作用有助于提高分析预测精度，2类决策模型的精度分别提高了5.98%和5.72%.     

Adapt-Traf: An adaptive multiagent road traffic management system based on hybrid ant-hierarchical fuzzy model

Usually, road networks are characterized by their great dynamics including different entities in interactions. This leads to more complex road traffic management. This paper proposes an adaptive multiagent system based on the ant colony behavior and the hierarchical fuzzy model. This system allows adjusting efficiently the road traffic according to the real-time changes in road networks by the integration of an adaptive vehicle route guidance system. The proposed system is implemented and simulated under a multiagent platform in order to discuss the improvement of the global road traffic quality in terms of time, fluidity and adaptivity.     

Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: Application to intended purchase of electric cars

Recently, policy makers’ expectations about the role of electric cars in reducing emissions have risen substantially. In parallel, academic research on purchase intentions has dramatically increased. Originally, most studies have focused on utility attributes and price. More recently, several hybrid choice models have been estimated to include the impact of attitudes on choice probabilities. In addition, a few studies have caught the attention to social influence. In contributing to this line of research, this paper reports the results of an expanded hybrid choice, which simultaneously estimated all these different effects in a single integrated model of purchase intention. Results indicate that the model performs well. Costs considerations contribute most to the utility of electric cars. Social influence is less important, but there is also evidence that people tend to take it into consideration when there are positive public opinions about electric cars and the market share becomes almost half of friends of their social network. The intention to purchase an electric car is also influenced by attitudes about environmental concerns and technology acceptance.     

基于OpenGL选择机制的模型变换组件的实现

为了便于用鼠标控制模型的平移、缩放和旋转变换,构建了三种模型变换组件,将3D场景中对模型的多种变换命令用几何图元进行可视化表示,并利用OpenGL选择机制,实现了各种模型变换命令的快速鼠标选择与切换,能够使用鼠标灵活地进行多种模型变换.这些组件已经应用到所开发的虚拟环境及仿真软件中,方便了用户操作,精简了用户界面.     

機車騎士未戴安全帽辨識之研究

臺灣地區機車車輛數眾多,但在應用在機車騎士執法上這一層面較少討論到。國人普遍駕駛習慣不良,而道路交通事故的發生常常就是因爲用路人不守法的行爲所致,近年來未戴安全帽而招舉報的案件上昇10萬多件,使得交通安全的問題日益嚴重。文中使用監視攝影機,透過模糊類神經演算法的運算,篩選出未戴安全帽者,並可結合顔色特徵作進一步分析,提高其辨識率,進行未戴安全帽的辨識工作。這一研究經簡單測試有九成的準確率,在智慧型執法系統中有輔助的效應,也殿立了後續發展的基礎。     

降雨量对OD 行程时间可靠性影响分析

为详细研究降雨量对OD行程时间可靠性的影响，基于Uber 出行共享的3 年美国波士顿10 对OD行程时间数据及WeatherUnderground 网站提供的小时历史天气，构建了OD行程时间高斯混合模型(GMM). 模型参数利用EM法进行求解，K 值根据K-S 检验后的P 值 (大于0.500 0)进行确定，模型分位数利用二分法进行求解. 提出一种基于缓冲指数(BI)的新指标——缓冲指数变化率(BIVR)作为定量评估指标. 结果表明：降雨会降低总体OD行程时间可靠性，降低效果随降雨量提高而增强，但增强效果并不明显；尽管可能性较低，但当降雨处于次要影响因素时可能提高可靠性；小雨天气可视为正常天气；雨天可靠性显著低于正常天气，居民在雨天(除小雨外)出行应预留更多时间.     

基于驾驶人特性的双车道跟驰模型稳定性分析

为了研究交通拥堵问题，了解交通拥挤形成的过程及驾驶员自身特性对双车道交通流稳定性的影响.本文基于复杂网络聚类同步理论，对一类基于驾驶员特性的双车道跟驰模型的稳定性进行研究. 通过设计适当的控制器使得基于驾驶人特性的双车道车辆跟驰模型趋于稳定，并得到了模型稳定性的条件.此外，在双车道上的车辆受到随机外部扰动的情形下，利用具有外部扰动的复杂网络自适应H∞ 聚类同步理论，研究了外部扰动情形下基于驾驶人特性的双车道车辆跟驰模型的稳定性.最后，采用MATLAB仿真技术进行数值模拟，验证所设计控制器对双车道交通流稳定性的影响及不同的驾驶员性格特性对交通流运行的影响.     

Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.