Road traffic congestion measurement considering impacts on travelers

The article intends to find a method to quantify traffic congestion＇s impacts on travelers to help transportation planners and policy decision makers well understand congestion situations. Three new congestion indicators, including transportation environment satisfaction （TES）, travel time satisfaction （TTS）, and traffic congestion frequency and feeling （TCFF）, are defined to estimate urban traffic congestion based on travelers＇ feelings. Data of travelers＇ attitude about congestion and trip information were collected from a survey in Shanghai, China. Based on the survey data, we estimated the value of the three indi- cators. Then, the principal components analysis was used to derive a small number of linear combinations of a set of variables to estimate the whole congestion status. A linear regression model was used to find out the significant variables which impact respondents＇ feelings. Two ordered logit models were used to select significant variables of TES and TTS. Attitudinal factor variables were also used in these models. The results show that attitudinal factor variables and cluster category variables are as important as sociodemographic variables in the models. Using the three congestion indicators, the government can collect travelers＇ feeling about traffic congestion and estimate the transportation policy that might be applied to cope with traffic congestion.

Comparisons on methods for identifying accident black spots using vehicle kinetic parameters collected from road experiments

Identification of accident black spots has gained tremendous popularity among road agencies and safety specialists for evaluating and subsequently enhancing road traffic safety.However,there is still limited understanding of the internal relationship between black spots and microscopic vehicle kinetic parameters.To address this gap,this paper describes a project that was undertaken using the real-time tire force data(kinetic response)obtained from road experiments on Wenli Expressway.First,factor analysis was applied to extracted three independent indicators(power-braking,handling stability,and ride comfort)from seven original kinetic indicators with multiple collinearities.Afterward,the main indicators were given vehicle kinetic meaning by analyzing the characteristics of original indicators associated with them.A compelling correlation was established among kinetic parameters,vehicle running qualities,and accident risk.Additionally,an integrated evaluation framework was established to identify accident black spots based on applying ordered logit models and PLS-entropy-TOPSIS approaches.The recognition results exhibited that the overall recognition accuracy obtained by the latter was found to be comparable to that achieved using the previous one.The compound evaluation model proposed in this paper has been proven to present many advantages for black spot identification.It is evidently clear from the findings that the vehicle kinetic parameters have significant correlations with road accident risk.This paper could provide some insightful knowledge for identifying and preventing the black spots from ameliorating traffic safety.