Journal of Transportation Engineering
Year:2019 | Volume:11 | Issue:1 (42) |Papers Count:13

The purpose of this study is modeling the effects of geometric factors geometric design variables such as characteristics of horizontal and vertical curve, longitudinal slope of road and environmental conditions like as land uses which are around the road according to region types (residential area, farms), types of topography (flat, rolling, mountain), kinds of access (protected or not), and access number (U-turn and access to subsidiary), simultaneously. The data are gathered by using plan, longitudinal profile, and access maps around the four accident-prone axes in the pieces with a length of 5 km. in Kerman province, where is the largest province in Iran and has the third highest stage at the number of road accidents. After correlation analysis, linear regression, generalized linear regression, Poisson and negative binomial models are fitted on information and finally, the most appropriate models are identified. Finally, Poisson generalized linear model with more fitting index and less percent relative error than the linear regression has been chosen. In Poisson generalized linear model, Characteristics of vertical curves factor with variables such as (number of vertical curves, average slope difference of vertical curves and, average difference of total slope) and the type of topography with variables like (mountains Existence, average Longitudinal of land slope and hill region due to the average slope) have played a major part as the most effective variables in increasing the number of accidents. Also, access variables including (the number of access and U-turn, the number of unprotected access and, the existence of residential area) have had correlate with the number of accidents. Reduction of slope difference between axes in curves, correct design for vertical curves and, avoiding large longitudinal slope could be an effective step in reducing the number of accidents, in road design for mountainous topography in the future.

In all educational systems, selection of appropriate locations for schools in the city and allocation of students to these schools are part of the basic decisions. Also, finding the optimal route for the transportation of students is very necessary. In order to daily presence of students in schools, the traveling population in streets increases significantly. Thus, the required time for travelling a street increases. Also, stochastic events such as accidents and traffics can affect the travel time between two regions. It is obvious that with increase in the population flow in the street, probabilities of occurrence of these events increase. In the provided model, contrary to existing models in this field, the impact of population travelling and stochastic events on the location of schools, the allocation of students to the schools and routing are considered simultaneously. Generally, the aim is to determine appropriate locations as schools locations, allocate the existing students in each region to schools and find the movement path of each student to reach its corresponding school by considering direct impact of the stochastic factors and the population flow on the probabilistic travel times so that the total expected transportation time is minimized. Here, it is assumed that schools and streets have limited capacities for accepting the population. First, a function to compute the population-dependent travel times is defined and then, considering stochastic factors, a mixed-intiger nonlinear programming model is provided. To solve large problems, a hybrid algorithm incorporating genetic algorithm and simulated annealing algorithm is introduced. To validate the proposed model, a sample problem is considered and analyzed. Comparative numerical results demonstrate the potential effectiveness of the presented algorithms.

In the past few years, with the advances made in the production of geosynthetic products, the use of these products in the pavement reinforcement has increased dramatically. Among the geosynthetic products, geocell improves the performance of the pavement due to the proper 3D confining of aggregates materials inside its cells. In order to investigate the effect of base layer reinforced with the geocell, six different geometries with reinforced and unreinforced base layer were simulated in ABAQUS finite element software. The behaviour of aggregate base and subbase layers were consirdered nonlinear which is implemented in ABAQUS by a FORTRAN subroutine. In the next stage, the results of the ABAQUS program as well as FORTRAN subroutine were validated using the results of KENLAYER and MICHPAVE programs as well as a full scale test. The results showed that the results of the ABAQUS program are consistent with results of other programs and also with the results of the fieled test. According to this research, by reinforcement of base layer using geocell, the surface deflection responses decreases betwen 5 to 10. 28%, the tensile strain responses at the bottom of the asphalt decreases between 7. 15 to 12. 24% and the vertical compression strain on the top of subgrade decreases between 5. 1to 9. 19%. Also, the damage analysis was conducted based on the Asphalt Institute relations and results showed that the reinforcement of base layer increase asphalt fatigue life by 21. 68-34. 94% and subgrade rutting life by 20. 89-35%.

In this paper, an effective approach for risk minimizing vehicle routing with time window for valuable commodities transportation is proposed. The proposed model pursues two objective functions including distance minimization and risk minimization. The risk minimization objective depends on the size of goods transported, the probability of an armed terrorist attack, and the probability of the success of robbery. The probability of a terrorist attack is estimated using game theory approach for different strategies, in such a way that a two-player, zero-sum game is played between the terrorist and the valuable goods carrier. The equilibrium of such a game is a strategy combining set of player's strategies. Moreover, the probability of success is evaluated using multi-criteria decision-making, and in order to increase the security of valuable commodities transportation, distribution routes will change on a daily basis. An effective multi-objective hybrid genetic algorithm is designed to tackle the proposed model. The efficiency and effectiveness of the algorithm are also examined through standard data sets, and the results indicate the effectiveness of the proposed solution algorithm.

The origin-destination matrix, which is usually estimated by transport experts using mathematical and statistical methods as well as optimization techniques, is one of the most important elements in transport planning. The wide-range usage of electronic technologies in transportation helps authorities on tracking vehicles over the road network for many purposes, so a two-stage mathematical model has been developed to estimate origin-destination matrix for vehicle fleets. The proposed model is based on the possibility of reading license plates over the path as well as selecting the shortest path from origin to destination by drivers. Two different definitions of possibility including the number of cameras in each O-D pairs comparing to its distance and the maximum required number of existing cameras are considered as criteria for developing the mathematical model. In order to evaluate the efficiency of the proposed model, the road network of the eastern provinces of Iran including Khorasan Razavi, North Khorasan, South Khorasan, Golestan, Kerman, Sistan and Baluchestan and Semnan, where there are 310 nodes and 356 one-way edges (712 two-way edges), has been selected as case study. All O-D pairs have also been collected, using electronic documents on 1395 in lunar calendar. Using the well-known software of GAMS, results revealed that the model is capable to determine the best edges for installing license plate reading cameras to cover high portion of vehicles fleets those passing over the road network.

Presently, the world faces two challenges of fuel shortage and the reduction of environmental emissions due to the growing population and consequently increased fuel consumption in the manufacturing and transportation systems. Hence, the adoption of solutions that can reduce fuel consumption in these organizations is unavoidable. This paper presents a new mathematical model for integrating scheduling and routing problems in a two-stage supply chain. Vehicles can distribute the demands between customers in a multi-trip manner in which each vehicle has to be used multiple times. Vehicles, in this supply chain, are considered as multi-trip. In other words, in each period, any vehicle can be used several times. The model considers the effect of vehicle weight, speed and technical characteristic on its fuel consumption. The goal of the problem is to determine the optimal sequence of production, routing, and determination of the transmission speed in each distribution path by vehicles in order to minimize fuel consumption costs and lateness and earliness costs. A mathematical model for this problem is presented and an innovative heuristic solution algorithm is also introduced. The efficiency of the solution algorithm is examined through extensive numerical experiments. The results indicate that the production sequence, customer order distribution and vehicle speed can have a significant impact on fuel consumption. Finally, the results of the analysis of numerical examples and managerial insights are presented.

Various methods exist to improve the performance of asphalt mixtures against various damages, one of which is the most common use of alternatives to aggregate, filler or bitumen modifier. Considering the use of fillers such as cement and their negative economic and environmental impacts, this study attempts to investigate the effect of Gilsonite additive as an alternative to aggregate filler aggregates of 0, 20, 40, 60 and 80% filler will be dealt with. Then, the effect of this additive on performance parameters of asphalt mixture is investigated. In this regard, Marshall mix design was first implemented for control samples. Then, fatigue tests using indirect tensile strength, rutting potential with dynamic creep test and resilient modulus with repeated loading tests were used. Results show that the use of Gilsonite has increased the resilient modulus of asphalt mixtures. Also, the fatigue life of asphalt mixture samples has improved as a result of using Gilsonite. The results of the dynamic creep test also show that the amount of permanent deformation of the modified mixed in the various loading cycles has been shown to be the lowest. In this way, it can be expected that using Gilsonite additive can improve the mechanical properties of asphalt mixtures.

Nowadays, transportation and logistics are considered as the drivers of economic development in the countries due to their impacts on the main variables of the country's economy such as production, employment, price, and cost of living Index. Statistics indicate that fuel consumption is a significant part of transportation costs. On the other hand, vehicles traffic is also one of the main factors affecting the travel time of vehicles between demand points in a supply chain, increasing fuel consumption and, consequently, damaging effects of greenhouse gases. In this paper, a novel model of vehicle routing problem (VRP) is presented with respect to different urban traffic conditions (no traffic, light traffic, semi-heavy traffic, heavy traffic) and fuel consumption, according to the time windows of services for perishable products. In order to solve the problem and validate the proposed model, CPLEX solver of GAMS software is used. Finally, a case study with a sensitivity analysis is solved and analyzed in order to investigate the applicability of the proposed model, and the optimal management policies are described.

Due to interactions between land-use and transportation and potential synergies of integrated urban land-use and transit planning, in this paper an integrated land-use allocation and transit network design problem is modelled as a multi-objective optimization problem. The objectives of the model include maximizing population and activity coverage of the transit corridor, optimizing land-use diversity and minimizing total costs of transit corridor implementation. Decision variables include land-use and density allocated to each block and the location of the stations and links of the transit corridor. Furthermore, several constraints for environmental issues and maximum allowable population and activity for each neighbourhood have also been incorporated into the model. The resultant multi-objective mixed integer non-linear model is transformed into a single objective mixed integer linear programming model using goal attainment approach and a number of linearization techniques. The resultant approach is applied to a real case study in Tehran’ s 22nd region and the results are analysed. The results show that the proposed model on average improves population coverage by 12. 2 percent, activity coverage by 20. 6 percent and land-use diversity by 10. 2 percent while mainaing its cost competitiveness (with an average 2. 5 percent increase in total costs).

Today, the simultaneous reduction of location costs and transportation costs in the establishment of urban facilities is of critical importance. This becomes extremely sensitive when the area under consideration is in crisis situations and the inevitable transit of vehicles and inland transportation is difficult in those circumstances and may cause financial and irreparable damages. In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points. Regarding to the bi-objective mathematical model, the ε-constraint and GAMS software are applied. According to the application of the proposed model for the large-scale problems and The NP-Hard structure of the problem, the NSGA-II is applied and evaluated. In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points. According to the two-objective mathematical model, the ε-constraint method and the GAMS 24. 1. 2 software are used. Due to the possibility of using the model in the large dimensional applications as well as the NP-hardness of the model, the NSGA-II meta-algorithm is applied and the efficiency of the proposed approach is examined and evaluated.

At the onset of the yellow phase, drivers often come across a dilemma situation where they are unable to stop comfortably before the stop line or clear the intersection (without excessive acceleration) prior to the onset of the red signal phase. yellow time is designed to inform drivers about passing time and preventing extreme changes in cars' speed in timing of intersections with traffic lights. However, studies have confirmed that drivers face high level of uncertainty during yellow time. drivers visually sample their surroundings while driving so they are able to change their behavior based on other vehicles’ movements, the roadway environment and traffic signal data. This implies that drivers’ behaviors are affected by surrounding factors such as other vehicles’ headway or intersection conditions. In the dilemma zone, drivers’ decisions are influenced not only by their own condition (e. g., distance to the stop line, speed, red time)but also by the surrounding environment at an intersection. The primary goal of the research described here was to develop a comprehensive knowledge of the stopping characteristics of dilemma zone drivers at signalized intersections. Physical, traffic, timing and phasing of intersections and weather conditions are assessed factors. The research performed here involved macroscopic evaluation of driver behavior; thus, characteristics of individual drivers were not investigated as it was not feasible to determine information such as age, experience, route familiarity, and sex of each driver. This study investigates actual data of traffic cameras and central smart program in four intersections in Qazvin in which traffic lights are set up. Peak, normal sunny and rainy conditions and drivers' behavior in yellow and red times are studied using binary and mixed logit model. A field study was performed using a video-based data collection system to record several attributes related to the behavior of the last vehicle to go through and the first vehicle to stop in each lane during each yellow interval. The researchers concluded that a driver’ s decision to stop or go through when presented with a yellow indication is complex but can be predicted reasonably well based on several factors. Pedestrians in streets and headway are the most effective factors on drivers' pauses in yellow or red phase. High speed of cars and also waiting time (red phaseare the most influential factors on drivers motion in yellow or red phases. The results of model show binary logit model has a higher accuracy than the combined logit model for assessing driver behavior.

This paper examines the relationship of investment in transport infrastructure and economy of Iran from 1959 to 2015 using VAR and VECM model. The results of research using Cointegration tase and Wald test (Considering the significance of the coefficients) in short and long term showed that: Economic growth has a meaningful reaction to investment fluctuations in transport infrastructure in short and long term, and investment in transport infrastructure has a meaningful reaction to economic growth fluctuations in long term and short term. so, investment in transport infrastructure and economic growth fluctuations in long and short term has two-way communication and that is the Progress in each one leads to another growth.

During recent years, various vehicle routing problem (VRP) models are developed for reducing supply chain transportation costs. Increasing commodities distribution costs and its environmental effects leads to develop the green VRP models. Incorporating electric vehicles in the distribution fleet could result in reduced pollution effects. The main restrictions of these vehicles are battery’ s low charging capacity, long recharging time and availability of recharging stations. On the other hand, the battery consumption is dependent on the fleet weight and therefore, vehicles loading should be considered during creating optimal routing plan. In this paper, vehicles loading weight between customers’ nodes and availability of recharging stations are considered for reducing fuel consumption. The two-stage heuristic algorithm and simulated annealing (SA) algorithm are developed for solving this problem. Computational results illustrate the efficiency of heuristic algorithm for solving problems within reasonable running time and could be applied for solving large scale problems.