International Journal of Transportation Engineering
Volume:1 Issue: 4, Spring 2014

The first step in improving traffic safety is identifying hazardous situations. Based on traffic accidents’ data, identifying hazardous situations in roads and the network is possible. However, in small areas such as intersections, especially in maneuvers resolution, identifying hazardous situations is impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflict indicator and kernel density estimation (KDE) method have been used to identify hazardous situations. KDE applies smooth function on critical TTC value events, this surface indicates risk changes. The maximum quantity of this function represents the hazardous situations. To assess and implement the presented method, left-turn on unsignalized intersection has been chosen. TTC data are determined by automated video analysis and coordinating TTC smaller than threshold value was used as input data in KDE method. Hazardous situations have been identified and the factors that caused them have been recognized using these results and performing safety audit. Two countermeasures are proposed to improve safety of left-turn in study location.

Road traffic volumes in intercity roads are generally estimated by probability functions, statistical techniques or meta-heuristic approaches such as artificial neural networks. As the road traffic volumes depend on input variables and mainly road geometrical design, weather conditions, day or night time, weekend or national holidays and so on, these are also estimated by pattern recognition techniques. The main purpose of this research work is to check the using chaotic pattern of daily traffic volume and the performance of chaos theory for estimating daily traffic. In this paper, the existing chaotic behavior in daily traffic volume in intercity roads has been examined and also the performance of chaos theory is discussed and compared to probability functions. The ratio between the minimum and maximum of daily traffic volume is defined as chaos factor, and data, gathered through installed automatic traffic counters over one year, have been used in analytical process. Results revealed that daily traffic volumes have chaotic behavior with defined twenty-four hour time span. They also show that the application of chaos theory is better than uniform distribution function, while weaker than normal distribution function for estimating daily traffic volume.

Random utility models have been widely used in many diverse fields. Considering utility as a random variable opened many new analytical doors to researchers in explaining behavioral phenomena. Introducing and incorporating the random error term into the utility function had several reasons, including accounting for unobserved variables. This paper incorporates fuzziness into random utility models to account for the imprecision of data intrinsic in human perception and statement. Fuzzy variables are contrasted with random variables, and a model is presented of relationships among real, perceived, and stated/reported conditions. The proposed fuzzy approach is applied to modeling telecommuting suitability, using data gathered from 242 employees in Tehran, Iran to construct fuzzy membership functions of job-tasks to the fuzzy set of telecommuting suitability. The resulting utility function can be viewed as representing the global wisdom of respondents. The enhancement in the fuzzy random utility model results, although modest, is promising and sets the stage for further research in the field of fuzzy logit models.

Due to an anticipated increase in air traffic during the next decade, air traffic control in busy airports is one of the main challenges confronting the controllers in the near future. Since the runway is often a bottleneck in an airport system, there is a great interest in optimizing the use of the runway. The most important factors in aircraft landing modeling are time and cost. For this reason, Aircraft Landing Scheduling Problem (ASLP) is a typical hard multi-constraint optimization problem and finding its efficient solution would be very difficult. So in real applications finding the best solution is not the most important issue and providing a feasible landing schedule in an acceptable time would be the preferred requirement. In this study a three objectives formulation of the problem proposed as a mathematical programming model on a runway in static mode. Problem is solved by multi-objective genetic algorithm (NSGA-II) and multi-objective Particle Swarm Optimization Algorithm (MOPSO). Considering a group of 20 aircrafts, this problem is solved and landing sequence determined and we are shown the obtained sequence does not follow First Come First Serve law for sequencing as well. Finally by comparing results, conclusion and suggestions are proposed.

The present article involves in evaluation and engineering judgment of various geometric configurations for highway interchanges by considering substantial parameters over the discretion process. The geometric, economical and architectural criteria as the fundamental indicators are divided into related sub-indicators and the total combinations of such sub-elements from the general criterion for establishment of decision making process. Hence, this article deals with geometric configuration analysis of interchanges as a complex decision problem by the use of analytic hierarchy process (AHP) which is a structured technique to analyze complicated engineering systems. By considering an interchange as a case study in north of Tehran, the capital of Iran, the performance of the proposed method has been examined in order to select the most suitable type of interchange by forming the evaluation process of AHP and taking into account the given design and construction data. In order to find a reasonable decision making approach for the optimum geometric configuration of highway interchange before construction stage, an interactive methodology has been reported in this study to facilitate the decision process, once a wide range of notorious criteria and desired prerequisites are available. Owing to established the AHP model and perform the decision-making method, the Expert Choice analytical software has been utilized. Only four options are considered for geometric shape of the interchange in the case study and the functionality of the process examined by reporting the general quantities for any option. The evaluation results are determined in terms of priorities for various options and their decision weights in the case study. However the presented model is able to be applied for other cases and different alternatives. As a tentative finding, using directional pattern for the case example of current work has been the optimum variant rather than parallel alternatives i.e. semi-directional and loop schemes.

The fixed-charge Capacitated Multi-commodity Network Design (CMND) is a well-known problem of both practical and theoretical significance. Network design models represent a wide variety of planning and operation management issues in transportation telecommunication, logistics, production and distribution. In this paper, Ant Colony Optimization (ACO) based neighborhoods are proposed for CMND problem. In the proposed neighborhoods, first, an open arc based on the incumbent solution is closed; then, by using an ant colony optimization algorithm called Ant Colony System (ACS), a new solution is generated by constructing new paths for the demands delivered on the closed arc. An algorithm is presented to construct new paths by using ACS algorithm for demands with continuous volume. A sub mixed integer programming (MIP) model is then created by joining the ACS and incumbent solutions. The generated sub-MIP is solved by using an MIP solver and its solution is considered as a neighborhood. In order to evaluate the proposed neighborhoods, an algorithm is developed. The algorithm parameters are tuned by using design of experiments. To assess the algorithm, several benchmark problems with different sizes are used. The statistical analysis shows the efficiency and effectiveness of the proposed algorithm compared to the best approaches found in the literature.

A One-Dimensional Binary Integer Programming Problem (1DB-IPP) is concerned with selecting a subset from a set of k items in budget constraint to optimize a goal function. In this problem a dominant solution is defined as a feasible selection to which no further item could be added in budget constraint. This paper presents a simple algorithm for Enumeration of Dominant Solutions (EDS) and investigates its functionality. The algorithm is then applied on the formulation of the Network Design Problem (NDP) with fixed travel-time links. The problem is a case study of 1DB-IPPs in the transportation planning literature which arises in the networks where the link travel-times are not sensitive to the amount of flow. The results are reported in detail for three illustrative examples and compared with the results of the Branch-and-Bound (B&B) algorithm. These examples suggest that in lower budget levels up to 40.2, 40.3 and 27.1 percentages the EDS algorithm outperforms the B&B algorithm. However, the overall performance of the B&B algorithm is notably faster in higher budget levels.