Urban arterial performance evaluation has been broadly studied, with the major focus on average travel time estimation. However, in view of the stochastic nature of interrupted flow, the ability to capture the characteristics of travel time variability has become a critical step in determining arterial level of service (LOS). This article first presents a stochastic approach that integrates classic cumulative curves and probability theories in order to investigate delay variability at signalized intersections, as a dominant part of the link travel time variability. This serves as a basis for arterial travel time estimation, which can be obtained through a convolution of individual link travel time distributions. The proposed approach is then applied in the estimation of travel time along one arterial in Shanghai, China, with abundant automatic vehicle identification (AVI) data sources. The travel time variability is evaluated thoroughly at 30-min intervals, with promising results achieved in comparison to the field measurements. In addition, the estimated travel time distributions are utilized to illustrate the probability of multiple LOS ranges, namely, reliability LOS. The results provide insights into how we might achieve a more reliable and informative understanding of arterial performance. 相似文献
This paper studies the impact of removing the level crossing, which constitutes traffic hazard to the society, on house prices by conducting a quasi-natural experiment using the Level Crossing Removal Project (LXRP) implemented by the Victoria state government in Australia since 2015. Using a difference-in-differences method, we analyzed the changes in housing prices due to the improvement of transportation infrastructure, gauging the LXRP’s impact on house and unit submarkets separately. We found that the prices for house and unit markets increased significantly after the removal of level crossings, with the value uplift decreasing with distance from the removal site. This paper contributes to the existing literature by adding an empirical study related to the enhancement of infrastructure aiming to improve the traffic safety in the urban context. Unlike previous studies, this study examines the effect of improvement projects for existing infrastructure and provides relevant implications to improve the efficiency of investing public resources in infrastructure improvement.
The modeling of travel decision making has been a popular topic in transportation planning. Previous studies focused on random-utility discrete choice models and machine learning methods. This paper proposes a new modeling approach that utilizes a mixed Bayesian network (BN) for travel decision inference. The authors use a predetermined BN structure and calculate priori and posterior probability distributions of the decision alternatives based on the observed explanatory variables. As a “utility-free” decision inference method, the BN model releases the linear structure in the utility function but assumes the traffic level of service variables follow multivariate Gaussian distribution conditional on the choice variable. A real-world case study is conducted by using the regional travel survey data for a two-dimensional decision modeling of both departure time choice and travel mode choice. The results indicate that a two-dimensional mixed BN provides better accuracy than decision tree models and nested logit models. In addition, one can derive continuous elasticity with respect to each continuous explanatory variable for sensitivity analysis. This new approach addresses a research gap in probabilistic travel decision making modeling as well as two-dimensional travel decision modeling. 相似文献
