Journal of Transportation Engineering, Part A: Systems

The aim of this paper is to design rail pad stiffness in terms of a general desirability range and to suggest a classification system. The design is based on an analysis of the effect of rail pad stiffness on key track parameters, such as the track modulus and the characteristic length, and on the dynamic load attenuation. The rail pad stiffness is expressed in terms of the stiffness of the track in question. Both recommended and minimum rail pad stiffnesses are formulated. Finally, a classification system for rail pads is suggested.

This study aims at developing a robust Bayesian statistical approach to determine the speed threshold (ST) for detecting a traffic breakdown event using traffic flow parameters. Data collected from a freeway section of I-295 in Jacksonville, Florida was used as a case study segment. The approach particularly is based on the change-point regression, in which two models—the Student-$t$ and Gaussian residual distributed regressions—were developed and compared. The study found promising results in detecting the ST value when verified using the hypothesis test and simulated data. Moreover, it was found that the Student-$t$ regression can significantly improve the goodness-of-fit compared with the Gaussian residual distributed regression. The methodology described in the current study can be used in the procedures of analyzing the breakdown process, stochastic roadway capacity analysis, congestion duration, the dynamic evolution of recurring traffic conditions, and clustering different traffic conditions. The results from these analyses provide useful information required in developing advanced traffic management strategies for highway operations.

The cell transmission model (CTM) is important for solving macroscopic traffic problems. Although some studies have been conducted for multiclass CTM, a simplified CTM for traffic flow mixed with connected and autonomous vehicles (CAVs) is still needed. This paper extends the homogenous CTM to form a mixed CTM under different CAV market penetration rates. To deal with this, the mixed triangular fundamental diagram is derived with different proportions of various vehicle types. The maximum capacity and backward wave speed of the mixed traffic flow are described by vehicle class proportions. Based on the mixed triangular fundamental diagram, the mixed CTM is proposed for different CAV penetration rates. An example application to validate the usefulness of the proposed mixed CTM is carried out. The mixed traffic flow in the application consists of cooperative adaptive cruise control (CACC), adaptive cruise control (ACC), and human vehicles with random vehicle orders. It is defined that CAVs travel under ACC when following a human vehicle without vehicle-to-vehicle (V2V) communication. Otherwise, the CACC function applies. The expected proportions of the CACC, ACC, and human vehicles in the random mixed traffic flow are described by the CAV penetration rate. Mixed CTM numerical experiments are conducted to evaluate the impacts of traffic accidents under different CAV penetration rates. Moreover, car-following simulations are performed to show consistency between macroscopic CTM numerical experiments and microscopic car-following simulations. The example application reveals that the proposed mixed CTM is simple and practical as applied to solve the traffic problem under different CAV penetration rates.

Autonomous vehicles (AVs) offer new technologies that could revolutionize travel, such as greater link capacity and innovative intersection controls. One such traveler behavior is the potential for empty repositioning trips, in which a vehicle travels without any passengers. Repositioning trips could allow travelers to avoid parking costs or make their vehicle available to other household members. However, empty repositioning trips increase the demand for personal vehicle travel. A previous study using static traffic assignment on home-to-work trips showed that repositioning trips still resulted in a net increase in congestion even when link capacity improvements for AVs were modeled. This raises the question of whether empty repositioning trips should be permitted. However, a key characteristic of repositioning trips is that they depart after the traveler has been dropped off. This could reduce the concentration of demand at any point in time. By using dynamic traffic assignment with a more realistic model of link flow on the downtown Austin network, we showed that when repositioning trips encourage travelers to switch to AVs, the resulting improvement from those AVs could decrease congestion. Furthermore, even if all vehicles are AVs, the congestion resulting from empty repositioning is still less than current conditions. Therefore, allowing empty repositioning trips could be beneficial for the traffic network.

In this paper, a fundamental diagram is calibrated for observed traffic data on a freeway segment using triangular regression analysis and the fixed capacity of the freeway is derived. Stochastic capacity analysis is then conducted to investigate the nature of the breakdown phenomenon and its effect on freeway capacity. The Weibull distribution function as a generalized extreme value distribution model is fit to the data. For both deterministic and stochastic capacity analysis, the influence of the weather is evaluated for four types of weather conditions that include clear, rainy, snowy, and low visibility. The statistical analysis results show that weather conditions have a significant effect on both the fixed and stochastic value of freeway capacity. One of the other important findings of this study is that jam density is shown to be significantly affected by weather conditions and needs to be incorporated when developing advanced freeway control and management strategies such as queue detection and management schemes.

This study used a time-varying Markov chain (TMC) assumption to develop an empirical probabilistic model that evaluates the influence of rainy weather and traffic volume on the dynamic transition of traffic conditions. The 2015 traffic and precipitation data for the I-295 freeway in Jacksonville, Florida, were used in the analysis. Using the Gaussian mixture model, speed thresholds for free-flow regimes during the morning and evening peak periods were determined to be 101.4 and $103.0\mathrm{km}/\mathrm{h}$ (63 and $64\mathrm{mi}/\mathrm{h}$), respectively. The results from the TMC model suggested that precipitation and traffic flow rate significantly influence the stochastic dynamic transition of traffic conditions at a 95% Bayesian credible interval. The presence of rain was observed to significantly increase the breakdown process compared with the state of remaining in the congested regime. Similarly, the probability of breakdown was observed to increase more than the probability of remaining in a congested regime state when traffic flow increased. These findings are expected to enhance the understanding of the transition process of different traffic conditions over time, which in turn will facilitate developing effective congestion solutions.

For both travelers and traffic operation centers, especially under extremely large traffic volumes, full-coverage traffic state monitoring of a major corridor is urgently needed. In the present paper, a traffic speed estimation method is proposed using a big data and deep learning approach under extreme traffic conditions. Particularly, a geospatial mapping method is proposed in this paper. This method ensures the scalability and easy-deployment, extracts phone speed (PSP) and phone count (PC) from raw cellular data, and estimates the traffic speed using a deep long short-term memory (DLSTM) neural network. The proposed method is used to estimate traffic speed for a major expressway in China that is installed with limited roadside equipment. The field test, which gives a promising performance, was performed during the Golden Week, the Chinese national holiday in 2014 (00:00 October 1 to 23:59 October 7) on the nearly 250-km-long busy freeway, G42, for both directions. The results suggest that the proposed cellular-based system can be an alternative and supplement solution for monitoring various practical traffic states, especially when only limited conventional roadside equipment is installed.