Accurate short-term traffic flow prediction is essential for real-time traffic control. A linear hybrid method and a nonlinear hybrid method for short-term traffic flow prediction are proposed with vehicle type as one concern. Traffic flow data are divided into the similar, volatile, and irregular parts. The selected methods are the autoregressive integrated moving average and generalized autoregressive conditional heteroscedasticity (ARIMA-GARCH) model, the Markov model with state membership degree, and the wavelet neural network. The ARIMA-GARCH model is used to predict the similar and volatile parts, and the other methods are adopted to predict the irregular part. This paper aims at providing better prediction methods for short-term traffic flow, and comparing the advantages and disadvantages of the linear and nonlinear hybrid methods. Additionally, the impacts of vehicle type on the predicted values are analyzed. The proposed methods are tested using field data from Dalian, China, and Hefei, China. The results suggest that the developed nonlinear hybrid method should be used with vehicle type and sampling interval as concerns.

Lane changes are particularly frequent on urban streets, which not only impact traffic operations, but also cause negative effects on safety. This study uses vehicle trajectory data collected from Southwest Road in Dalian, China, to investigate the influential factors of discretionary lane changing in an urban road environment. Both the standard logit model and mixed logit model were fitted to the data, which evidently identified several key factors. The mixed logit model outperformed the standard logit model in terms of model fit. The results suggest that driver heterogeneity is present on the speed differentials of the subject vehicle and the leading vehicles, and the distance gap in the target lane. A sensitivity analysis was further conducted to quantify the degree of influence of the statistically significant variables. The findings support the notion and purpose of discretionary lane changing, i.e. seeking a speed advantage and/or a more satisfactory driving environment.

Housing price is one of the most popular topics in urban studies. Among all the determinants of housing price, transportation accessibility is an essential factor that people often consider when purchasing a house. Using a semilogarithmic hedonic price model, this paper discusses the effects of structural attributes and accessibility of neighboring destinations on housing prices. The study area targets the core urban area of Dalian city, China. The data used in this study consist of sales prices of commercial apartments and houses and the geographic coordinates of corresponding residential areas. Estimation results of the hedonic price model show that structural attributes and accessibility to neighboring destinations significantly affect housing prices. The most influential factors to housing prices are the location of apartment or house, the nearby quality of higher education resources, and proximity to public transit service. These findings are significant for real estate developers, urban planners, and transportation planners to better understand the real estate market and to promote transit-oriented development.

At an isolated signalized intersection, the capacity of the lane group with a short left-turn lane heavily depends on the short lane length and the effective green time allocated for the lane group, as does the capacity of the entire intersection. In this paper, three optimization models are formulated and solved to optimally allocate the lane space and green splits of an isolated signalized intersection with short left-turn lanes. Two intersection performance indexes are proposed to compare the performance of the three models and study its sensitivity to model parameters. The results indicate that all three models can produce satisfactory capacity and delay performances, but it is laborious to find a suitable parameter set when using the objective functions of maximizing intersection capacity and minimizing the weighted sum of the normalized intersection capacity and delay; it is easy when using the objective functions of minimizing intersection delay, maximizing capacity-to-delay ratio, and minimizing delay-to-capacity ratio. Moreover, many sets of noninferior solutions can be obtained by minimizing the delay-to-capacity ratio and adjusting some parameters. Finally, three variations of the formulated models are recommended for use, and the procedure for using these models is given for practical applications.