The time headway of vehicles is an important microscopic traffic flow parameter which affects the safety and capacity of highway facilities such as freeways and multi-lane highways. The present paper intends to provide a report on the results of a study aimed at investigating the effect of the lane position on time headway distributions within the high levels of traffic flow. The main issue of this study is to assess the driver's behavior at different highway lanes based on a headway distribution analysis. The study was conducted in the city of Isfahan, Iran. Shahid Kharrazi six-lane highway was selected for collecting the field headway data. The under-study lanes consisted of passing and middle lanes. The appropriate models of headway distributions were selected using a methodology based on Chi-Square test for each lane. Using the selected models, the headway distribution diagrams were predicted for high levels of traffic flow in both the passing and middle lanes and the relationship between statistical criteria of the models and the driver's behaviors were analyzed. The results certify that the appropriate model for the passing lane is different than the one for the middle lane. This is because of a different behavioral operation of drivers which is affected by specific conditions of each lane. Through car-following conditions in the passing lane, a large number of drivers adopt unsafe headways. This shows high risk-ability of driver population which led to considerably differences in capacities and statistical distribution models of two lanes.

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Train marshalling is the process of reordering the railcars of a train in such a way that the railcars with the same destination appear consecutively in the final, reassembled train. The process takes place in the shunting yard by means of a number of classification tracks. In the Train Marshalling Problem (TMP), the objective is to perform this rearrangement of the railcars with the use of as few classification tracks as possible. The problem has been shown to be NP-hard, and several exact and approximation algorithms have been developed for it. In this paper, we propose a novel exact dynamic programming (DP) algorithm for the TMP. The worst-case time complexity of this algorithm (which is exponential in the number of destinations and linear in the number of railcars) is lower than that of the best presently available algorithm for the problem, which is an inclusion-exclusion-based DP algorithm. In practice, the proposed algorithm can provide a substantially improved performance compared to its inclusion-exclusion-based counterpart, as demonstrated by the experimental results.