پژوهشنامه حمل و نقل
سال ششم شماره 3 (پیاپی 20، پاییز 1388)

Gravity structures are used for waterfront quaywalls where the seabed soil condition is appropriate. Some gravity walls are built behind a cofferdam in the dry but most walls are constructed in water by a method used only in maritime works, in which large pre-cast units are lifted or floated into position and installed on a prepared bed under water. It is usual to use rubble or a free-draining granular fill immediately behind a quay wall so that the effects of tidal lag are minimized and earth pressure is reduced. Gravity quay walls can be classified into different types such as caissons, L-shaped blocks, rectangular concrete blocks, cellular concrete blocks and cast in-place concrete. Optimum design of blockwork gravity type quaywalls with precast concrete blocks are the object of the present investigation. The advantages of these quaywalls are: simple construction technology, preferred costs and satisfactory durability.The external and environmental loads acting on these type structures are Surcharge, Deadweight of the wall, Earth pressure, Residual water pressure, Buoyancy, Seismic forces, Dynamic water pressure during an earthquake and Tractive forces of vessels. The principal modes for failure of this gravity structure are: sliding, overturning, deep slip and foundation failure. Therefore in the stability calculations the following items should be examined in general: Settlement, circular slip, bearing capacity of the foundation, sliding and overturning at all horizontal surfaces between blocks. To study the behavior of a quay wall and to check the stability against probable different failure modes, a computer program has been developed. This program can easily consider the effects of different parameters such as section geometry of quaywall, material property and loading condition in design.In common designs, designers often select an accepted sketch with their experiences, and cannot review different sketches and present the best one. Sometimes the final drawing may be uneconomical and also the transport and placing of blocks may be very difficult and sometimes impossible. Therefore, adopting an optimization procedure for design of these structures is required. In this paper, a procedure for optimization of cross section of a blockwork gravity type quaywall has been introduced and a numerical program for this procedure has been developed. After reviewing design and construction considerations for such quaywalls, available methods for optimum design of these structures are discussed and objective function, constrains, design variables are considered. The main constrains of the optimization problem in the present study is the safety factor in various mode of failures. As the relation between safety factor and design variables is unknown, therefore, a proper method should first be used for approximating the objective function and constraints, according to design variables. Then, an efficient method should be selected for formulation of mathematical optimization of the objective function under existing constraints. For this purpose, the optimization of the cross section is accomplished using Sequential Quadratic Programming (SQP) method in the present work. Results indicate that the cross section of a blockwork quay wall has an important role on stability of the structure and one can reduce costs of such structures by optimizing the cross section. Finally, some recommendations for optimum design of this type of quay wall are presented.

Public transportation systems such as bus and railway transportation play the main role in transferring passengers and reduce problems in large cities due to low costs of traveling, high flexibility and ability in passengers transferring.Increasing user's satisfaction and optimal usage of existing facilities without planning is impossible. Vehicle assignment to routes, preparing time tables of trips, monitoring and implementation of schedules are the most important activities in public transportation planning.Common process of planning in public transportation systems include four steps: route design, generation timetables of trips, schedule of vehicles and schedule of crews.In this process, short term activities at the beginning of each month or optimistically, at the beginning of each day, based on historical data set are planned. Hence, this type of planning is referred to as static planning.In most transportation systems such as Tehran bus transportation system, static planning, in which plans are fixed in different months, is used. However, recently, Intelligent Transportation System (ITS) and Advanced Traffic Management System (ATMS) have been extended in public transportation systems. These systems prepare proper data set from location and state of vehicles and traffic variables and convert static planning to dynamic planning. Time of traveling is the most important factor in both static and dynamic planning. Public transportation systems planning without any information about bus travel time between stations is impossible and lead to unreal plans.In addition, estimation of travel time can be used for managing passenger’s demand and traffic via information systems as well as planning the other public transportation systems such as subway, taxi and so on.In the previous research works, probability distributions are used to estimate the bus travel time in short period of time. Continuous changes of factors over travel time such as changing in traffic conditions in a route affect on probability distribution. Hence, a fixed probability distribution with known parameters cannot be considered for time travel variable during a day. Development of proper forecasting models should be considered to estimate travel time in all time periods during a day precisely. In dynamic planning, one line data sets are available in addition to the historical data set available in static planning. Forecasting methods with different performances can be designed and implemented to estimate travel time of public vehicles in both static and dynamic planning approach.In this paper, proper variables are defined to measure the effect of time, traffic, and location factors on the travel time of public vehicles in a route. The effect of the mentioned variables is investigated on travel time of bus system in Tehran.Two forecasting models are designed based on the identified variables to estimate travel time of public vehicle between two successive stations in a route.First, a model based on historical data set and multiple linear regression is designed in which the relationship between travel time variable and the variables affect it is described.A feed forward artificial neural network is developed to estimate travel time based on one line and historical data set. This model can be used in dynamic planning approach.Correlation coefficient between real and predicted data in regression and artificial neural network models are about 75 and 94 percent, respectively, based on the data set from Tehran public transportation system.

becomes an important subject addressed by many researchers. A supply chain represents all stages that have added value to a product.Integration and synchronization of information and material flows of manufacturing sites belonging to a supply chain (SC) has become more practical and have attracted the attention by both industry practitioners and academic researchers. In this study after presenting previous works on scheduling in supply chain, the problem is described and mathematical model of the problem is presented. Then a genetic algorithm is proposed for solving the problem that has chromosomes with variable structures. Finally we provide concluding remarks and some scopes for future researches.This paper studies a 3-stage supply chain scheduling problem in which the first stage is composed of multiple suppliers with different production speeds that produce parts ordered by a manufacturing company at third stage. In the second stage vehicles with variable speeds and variable capacities convey the jobs from the suppliers to the manufacturing company at third stage. The main focus of this study is on the integration of the production and the transportation scheduling.For simplicity, it is assumed that all suppliers are in one geographical zone and the transportation times between them are negligible in comparison with the transportation time from the suppliers to the manufacturing company. However, in some realistic situations the suppliers may be located in multiple geographical zones, in sake of reducing complexity, it can be assumed that original problem can be divided in multiple sub-problems whereas each group of suppliers are located in one geographical zone and sharing the vehicles between the suppliers in different sub-problems are not allowed. Each vehicle after delivering a batch to the manufacturing company back haul empty to the suppliers’ zone for the next dispatching. The objective function of the problem is to minimize delivery time of a set of jobs to the manufacturing company that herein we address it as minimizing maximum completion time of all jobs, i.e, makespan.Since such problems have NP-hard structure, thus Genetic algorithm can be mentioned as an approach that frequently used for solving them. In this study a genetic algorithm named dynamic genetic algorithm (DGA) that has chromosomes with different structure is developed.DGA has six parameters as follows: 1) population size (popsize), 2) crossover rate (percross), 3) mutation rate (permut), 4) percentage of the best chromosomes are selected to the next population (best), 5) number of times with no improvement in fitness function for terminating the algorithm (termination) and 6) a parameter in crossover operation (r).After solving various test problems we empirically have found that values of 100 for popsize, 0.6 for percross, 0.8 for permute,0.7 for best,10 for termination and 0.7 for r may lead to better solutions.According to the author's knowledge, this problem has not been studied previously. Since there is no algorithm to compare with dynamic genetic algorithm in the literature, we compared the results of DGA with those of two algorithms: a random search algorithm and an adapted algorithm based on the nearest problem in the literature to our problem (namely H1).In sake of comparisons, many test problems are produced randomly according to a defined structure and solved by DGA, the random search approach and H1. In order to compare DGA with the random search approach 4 critoria were used as follows: 1) Mean of solutions of DGA, 2) Mean of solutions of the random search, 3) Percentage of runs that DGA get better result than the random search and 4) Percentage of runs that DGA get equal result to the random search. Results show that DGA outperforms the random search approach in all cases. Also with increasing the number of jobs mean of solutions increased. Increasing the number of suppliers causes mean of solutions decreased but when there exist a bottleneck in this stage mean of solutions increased. Also by decreasing the process times of jobs in the first stage, the mean of solutions decreases. Increasing the vehicle's capacities causes the mean of solutions to decrease. DGA is also compared with an adapted algorithm based on the nearest problem in the literature to our problem, namely H1, proposed by Chang and Lee (2004).Their scheduling problem is the same as problem considered in this paper but they assumed that only one vehicle exists in the transportation stage while in our problem transportation fleet is composed of l vehicles with different speeds and capacities. Also they considered at most two suppliers with identical production speed but in our problem m suppliers exist with different production speeds. For the case of two suppliers, they proposed a heuristic algorithm and proved that it could cause at most 100% error under the worst-case with the bound being tight. In order to compare DGA with H1 four criteria for both algorithms are used as follows: 1) Mean of solutions, 2) Percentage of runs that an algorithm gets better result than the another one (PBR), 3) Percentage of runs that an algorithm get equal result to the another one (PER) and 4) Mean of solving time. Experimental results show DGA performance is much better than that of H1.

Nowadays one of the major problems that public transit system planners are facing is lack of enough availability of the system for private vehicles. So providing reliable and efficient public transportation for this group is an effective way for overcoming car dependency. One of the alternatives that can reduce this problem is Park-and-ride that has been used since 1930s as a travel demand management measure.For the effective use of park-and-ride facilities, lots of elements should be considered. Park-and-ride facilities in the U.S. and Japan are being well utilized and one of the major factors in theses countries for the successful operations is the location of facilities.Given this, this paper is devoted to the development of a GIS based two-parts method for locating park and ride facilities based on a defined set of criteria, which will include such things as demand coverage, access to major roads, using existing parking sites, high performance transit priority as compared to bus system, proximity of each site to CBD, distance between sites, limited capacity for each site, locating sites beyond the congested area and etc.Past researches have identified criteria that are relevant to optimal park-and-ride location, but much of them do not address all of the above objectives simultaneously. And much of them have not assigned weight relationships to these criteria. For instance, various criterions may carry more or less significance than each other.In this paper a method for integrating these factors is presented simultaneously and a benefit based multi-objective optimization model including demand coverage, access to major roads, using existing parking sites, high performance transit priority as compared to bus system, proximity of each site to CBD in term of benefit(rials) is introduced. At the end of the research for showing the developed approach performance, the method is applied for sitting park-and-ride facilities in Mashhad, Khorasan Razavi, Iran.Steps in a case study are as: first we locate potential sites according to some of the objectives in the GIS environment and then with exported file as model Input data from GIS we will solving the introduced model using Genetic Algorithm and find the optimum location set among the potential sites.Using GIS is advantageous due to its vast information, storage, and graphical capabilities, and because of its major impact on the field of Location Science in terms of model application and model development is used in this research, and Genetic Algorithm was chosen for solving the model because of its ability to find the overall optimum solution in a spread and nonintegrated search space with lots of local optimum. The combination of these tools via an MATLAB interface allowed us to use geographical features of the urban network in our location process.Application results show the usefulness of the developed method in an urban region. One of the features of the approach developed here is that capacity of each site is limited at park-and-ride lots. While much of the existing methods and models do not address this objective. Besides in this research the objectives like coverage, access to major roads, proximity to CBD and construction of new sites are incorporated in a model simultaneously in term of cost, and the method can be used for sitting park-and-ride facilities in the context of an existing park and ride system. This could help transit agencies across the Iran (and abroad) aspiring to increase transit rider ship reach sound location decisions for their park-and-ride systems. As was shown in the paper, the model was applied for finding a compromise sitting configuration for park-and-ride facilities in Mashhad, Khorasan Razavi, Iran, from many available location set as a case study and the result of the model for the case that the target is sitting 5 locations in the city is showed.Solving the developed model with constant weighting method resulted in a unique solution, and using of weighting method for calibrating the model were useful for visualizing the effect of placing more importance on one objective relative to another. Moreover using weighting method is important for decision makers as they allow them to take the appropriate course of action(s).Issues for Further Study:During implementing this method as a case study, several issues inside and outside its original scope were identified. Many were addressed in this research; however, two issues should be further discussed as part of ongoing efforts and updates to this research.• finding or introducing a standardized approach to estimate benefits of objectives such as coverage, accessibility, proximity, transit modes priority and etc to get better model output.• Better understanding the park and ride facilities usage by Iranian private vehicles in order to modify the predicted capacity.

In this paper, a statistical model has been offered to predict accident number in the segments of Iranian urban roads. The model was constructed on the base of traffic, geometric, environmental data and also the accident number for one hundred sixty five segments of urban road in Zahedan city, one of the biggest cities in Iran. Producing of a general model for several urban road types for evaluation of safety performance was the final aim of the applying of the model in this area. For production of the model a new methodology have been used. The most important features of this methodology are: selecting type of model on the base of characteristics statistical of data, applying of integral functions for selecting of variables functional form, the optimization on the base of improved gradient vector method, the goodness of fit models on the base of plot of cumulative residuals, and also no using of software statistical for running of modeling processes. On the other hand, this methodology can be extended to the other engineering subjects.The results showed that the independent variables, on the base of their character, imitate special functional form related to their data characteristics. The functional form can be liner, nonlinear or occasionally complex of them. Although the generalized linear models (GLMs) commonly use in develop accident prediction models, are not able to model probability of nonlinear relationship between dependent variables and independent variables, and on the other hand they usually divide independent variables and study their relationships separately. In this study following results related to road safety in Iran have obtaineda. Traffic flow is the strongest variable for increasing or decreasing the accidents. The relationships between this variable and number of accidents is nonlinear in the form of and the coefficients is about 1, which means for each 10000 transport means in day the accidents number increases to 15- 20 percent and show reduction process in high variations. b. Apart from traffic flow, there are some different variables can effect on the accident. On the base of Iranian cities condition, these variables are: percentage of curb parking using, the number of minor roads and junction’s number or access point density, and the lane numbers, one/two- way traffic and also lane width. The weight of these variables is different. In this study the percentage of curb parking show the biggest effect and being or inbeing of median have the little effects on accidents. c. The other variables such as road surface quality, being or not being of lane, speed and land use have a little effect or even ineffective on accident numbers.

Nowadays one of the problems of big cities is increased number of vehicles for transportation of people and goods. Under this circumstances, congestion might be gently increased in urban transportation networks. Construction new roads and other urban transportation facilities are costly and time-consuming. On the other hand, the available urban transportation infrastructures cannot be used effectively, because drivers (especially private vehicles) are unfamiliar with various alternative routes and so they often choose longer ones. As a result, trips complexity is ever increasing. Therefore, one of the main challenges of traffic networks is guiding vehicles under dynamic traffic condition to their destination to reduce travel time using available network capacity more effectively. In order to reply to these problems, dynamic route guidance system (DRGS) is apparently an effective approach. It is among important sections of intelligent transportation systems (ITS). This system increases favorability of available infrastructures in transportation network and helps control of congestion on accessible time and space.The main core of dynamic route guidance system is computation of shortest path based on real-time information. This system can find the shortest path from starting node or zone to destination for drivers based on current situation and present to vehicles. Efficiency of this duty depends on shortest path algorithms. Also, using real-time information is possible by video or camera systems, magnetic loops and other traffic sensors on routes of transportation network.In transportation application especially in urban traffic networks, travel time from links depends on traffic volume, since static algorithms faces weakness in real-time conditions. Therefore, dynamic network condition must be examined for computation shortest path. Because of various approaches, solving dynamic network problems are more complex and Np-hard. In this kind of problems, choosing the optimized route is not easy but is a policy.Generally, with regard to necessity of research accomplishment, the general purpose of this research has defined in case of extension the robust routing strategy for route guidance systems under dynamic environment condition. In this paper the research methodology is combination of conceptual and quantitative models. At first vehicles route guidance in traffic networks remark to conceptual model, then the mechanism of working intelligent agents in network like quantitative model additive to solution problem. In this regard, this paper can be expressed as a route guidance conceptual framework based on decentralized routing structure by combining the e route guidance system and artificial intelligence. Thus we apply the concept of Agent-Oriented techniques emphasized on reinforcement learning (RL) as one of the alternatives toward overcoming uncertainty of vehicles routing in traffic networks. The main preference of reinforcement learning is ability of agent to learn for reaching a target, due to communicating control actions and affecting on the environment without exact model. In this regard we assume that some intelligent agents have been set in considered nodes and in fact, nodes of network become intelligent. These agents learn in addition to collecting information, and so they use previous experiences. Generally this research aims at traffic controlling through execution of an intelligent learning model on nodes of dynamic transportation network whose duty is guiding vehicles in route to target. The important results of this research are ability of learning models in presenting of route guidance strategy in adjustment to dynamic traffic condition and presentation of several route alteratives for drivers to reduce vehicle travel time criteria. Variable message signs usually inform drivers. This can be done by monitors inside the vehicles.In this paper the solution of research enquairy has been presented for grid networks and then for a small segment of Tehran traffic network as a real transportation network.

Traffic accidents in urban signalized intersections cause great human and economic losses. Intersections especially in urban areas are one of the most critical hazardous locations with high risk of accidents. But in Iran, it does not seem any research work being implemented for studying safety at intersections. Therefore, optimum allocation of scarce resources for intersection safety promotion is difficult in this situation.The purpose of this study is to develop a statistical model to predict the frequency of injury accidents at 4-armed intersections and to diagnose contributory attributes which affect the accident frequency occurrences. Then, the ultimate purpose of the study would be to find cost-effective remedial measures to reduce the injury accidents at such intersections. In previous studies for intersection safety modeling, both poison and negative binomial distributions have been used. Important limitation related to poison distribution is that the ratio of variance to mean is one, which rarely is justified for traffic accident data. To compensate this limitation, negative binomial distribution is used. In this study a statistical forecasting model based on negative binomial distribution for injury accident frequency in 4-armed signalized intersections in the city of Mashhad, is developed. To obtain a reliable model, it is necessary that independent variables would be true representatives of real geometric, traffic and control attributes of the intersection an its approaches. These include the number of traffic lanes in each approach, the width of traffic lanes, existence of protected left and/or right turn lane(s) and its (their) number, width and length, the existence of median, the distance of bus-stop(s) to intersection, one-way or two-way the approaches, the angle between major and minor approaches, the number of phases in each cycle, the existence of control camera(s), Type of intersection control system, the volumes of left and right turnings and the total of traffic volume. Most of geometry related variables were taken from 1:2000 scales Mashhad map, and traffic related variables were obtained from Mashhad transportation and traffic organization. From 110 existed 4-armed intersections in the city of Mashhad, 50 of them were selected for this analysis, and related variables for the Iranian year 1385 (21st March 2006 – 20th March 2007) were collected. The statistical package Genmod in software SAS version 9.0 was used and the suitability of negative binomial distribution based on Deviance and Pearson x2 tests was controlled.The developed injury frequency accident model is as following: The model parameters are fully introduced in the paper manuscript as following:Y`TIA= Total of predicted Injury Accidents at the Intersection, xNL= Number of traffic Lanes at the intersection approaches, xM= the existence of Median at the intersection, xNP= Number of phases per cycle, xST =Type of control system intersection, xSC= the existence of Surveillance Camera at the intersection, and xTV= Total traffic Volume at the intersection approaches. To calibrate the developed model and to examine its suitability, R2 was calculated. Validation of the model, by using a new set of data and employing paired samples t-test method was controlled. Developed model showed a strong relationship exists between frequency of injury accidents and independent variables of road geometry, the type of control system and traffic characteristics. The results of this study show that six of independent variables considerably affect the safety of signalized intersections. These are: approach lane numbers, the existence of median, number of phases per cycle, Type of intersection control system, control camera and approach traffic volume.