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Logit models are one of the most important discrete choice models and they play an important role in

describing decision makers’ choices among alternatives. In this paper the Multi-Nominal Logit models has been used

in mode choice modeling of Isfahan. Despite the availability of different mathematical computer programs there are

not so many programs available for estimating discrete choice models. Most of these programs use optimization

methods that may fail to optimize these models properly. Even when they do converge, there is no assurance that they

have found the global optimum, and it just might be a good approximation of the global minimum. In this research a

heuristic optimization algorithm, simulated annealing (S.A), has been tested for estimating the parameters of a Logit

model for a mode choice problem that had 17 parameters for the city of Isfahan and has been compared with the same

model calculated using GAUSS that uses common and conventional algorithms. Simulated annealing is and algorithm

capable of finding the global optimum and also it’s less likely to fail on difficult functions because it is a very robust

algorithm and by writing the computer program in MATLAB the estimation time has been decreased significantly. In

this paper, this problem has been briefly discussed and a new approach based on the simulated annealing algorithm

to solve that is discussed and also a new path for using this technique for estimating Nested Logit models is opened

for future research by the authors. For showing the advantages of this method over other methods explained above a

case study on the mode choice of Isfahan has been done.

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 Investigations on vibration behaviors of railway track systems were attempted in this research. This was made by conducting a comprehensive field investigation into the free vibration of track systems and response of tracks to train moving loads. In-situ modal analysis was used in a railway track field as an efficient method of investigating dynamic properties of railway track systems. Natural frequencies and mode shapes of the track system in different insitu track conditions were obtained for the fist time. The sensitivity of the natural frequencies of the track to the types of sleepers, fastening systems, ballast conditions, and rail joints were studied. Efficiency of rail welded joints in CWR tracks and the effects of replacing timber sleepers with concrete sleepers on dynamic behavior of a track were investigated. Advantages of flexible sleeper fastening system from the aspects of serviceability and passenger riding comfort were discussed. The effects of the track accumulative loading as a main indicator of ballast degradation on track dynamic behavior were studied. Rail deflections were calculated by using auto-spectra obtained from vibrations of the track under trainloads, leading to the development of a new mathematical expression for the calculation of the rail dynamic amplification factor.

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The congestion pricing has been discussed as a practical tool for traffic management on urban transport networks. The traffic congestion is defined as an external diseconomy on the network in transport economics. It has been proposed that the congestion pricing would be used to reduce the traffic on the network. This paper investigates the cordon-based second-best congestion-pricing problems on road networks, including optimal selection of both toll levels and toll locations. A road network is viewed as a directed graph and the cutest concept in graph theory is used to describe the mathematical properties of a toll cordon by examining the incidence matrix of the network. Maximization of social welfare is sought subject to the elastic-demand traffic equilibrium constraint. A mathematical programming model with mixed (integer and continuous) variables is formulated and solved by use of two genetic algorithms for simultaneous determination of the toll levels and cordon location on the networks. The model and algorithm are demonstrated in the road network of Mashhad CBD.

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In spite of significant advances in highways safety, a lot of crashes in high severities still occur in highways. Investigation of influential factors on crashes enables engineers to carry out calculations in order to reduce crash severity. Therefore, this paper deals with the models to illustrate the simultaneous influence of human factors, road, vehicle, weather conditions and traffic features including traffic volume and flow speed on the crash severity in urban highways. This study uses a series of artificial neural networks to model and estimate crash severity and to identify significant crash-related factors in urban highways. Applying artificial neural networks in engineering science has been proved in recent years. It is capable to predict and present desired results in spite of limited data sets, which is the remarkable feature of the artificial neural networks models. Obtained results illustrate that the variables such as highway width, head-on collision, type of vehicle at fault, ignoring lateral clearance, following distance, inability to control the vehicle, violating the permissible velocity and deviation to left by drivers are most significant factors that increase crash severity in urban highways.

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Magnetic levitation (maglev) is amongst the most advanced technologies that are available to the transportation industries. It

has already been noticed by decision makers in many countries around the globe. Contrary to such high levels of interest, there

are no practical algorithms available to the engineers and/or managers to assist them in analyzing economics of the maglev

systems. Therefore, it has been the purpose of this research to find appropriate answers to such vital questions and also investigate

feasibility for practical use of maglev technology in rapid transit systems. The life cycle costs (LCC) for the maglev system

including the cost of initiating such projects are included in this survey and are evaluated. To serve the purpose, an algorithm is

presented that facilitates the technical and economical analyses of maglev systems. The proposal for a long distance maglev

system, Mashhad-Tehran (M-T), is used as a case study by using the proposed algorithm. Moreover, the cost of establishing and

operating M-T project is estimated by two other different approaches. These include the already established mathematically based

cost estimating method, and the cost estimations based on the international norms and standards. These standards are based on

statistical (or provided) data. Such cost estimations assist verification of the proposed algorithm. Comparisons between outcomes

of the three methods prove close agreement for the cost estimation by all of them. It is concluded that the proposed algorithm for

implementation and operation of maglev route is practical.

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Present study is an extension of earlier work carried out on two-lane two way roads in the two provinces of Pakistan i.e. N-25,

N-55 and N-5 regarding the measure of operating speed and development of operating speed prediction models. Curved sections

of two-lane rural highways are the main location of run-off road accidents. In addition to that the road alignment having

combination of geometric elements may be more harmful to the drivers than the successive features with adequate separation.

This study is carried out on two-lane two- way road along N-65 (from Sibi to Quetta). Three sections are selected for study with

thirty three horizontal curves. Continuous speed profile data was recorded with the help of VBox (GPS based device) which was

attached with a vehicle to detect vehicle position through satellite signals. VBox is new equipment with modern technology in this

field and it helps in recording continuous speed profile and saving of this information on the computer as a permanent record.

Through the regression analysis, models were developed for estimation of operating speed on horizontal curves and on tangent,

and estimation of maximum speed reduction from tangent to curve. The validation of developed model shows compatibility with

the experimental data.

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In this paper, a location allocation (LA) problem in construction and demolition (C&D) waste management (WM) is studied. A bi-level model for this problem under a fuzzy random environment is presented where the upper level is the governments who sets up the processing centers, and the lower level are the administrators of different construction projects who control C&D waste and the after treatment materials supply. This model using an improved particle swarm optimization program based on a fuzzy random simulation (IPSO-based FRS) is able to handle practical issues. A case study is presented to illustrate the effectiveness of the proposed approach. Conclusions and future research directions are discussed.

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In this paper, a new approach was presented for bus network design which took the effects of three out of four stages of the bus planning process into account. The presented model consisted of three majors steps 1- Network Design Procedure (NDP), 2- Frequency Determination and Assignment Procedure (FDAP), and 3- Network Evaluation Procedure (NEP). Genetic Algorithm (GA) was utilized to solve this problem since it was capable of solving large and complex problems. Optimization of bus assignment at depots is another important issue in bus system planning process which was considered in the presented model. In fact, the present model was tested on Mandl’s bus network which was a benchmark in Swiss network and was initially employed by Mandl and later by Baaj, Mahmassani, Kidwai, Chakroborty and Zhao. Several comparisons indicated that the model presented in this paper was superior to the previous models. Meanwhile, none of the previous approaches optimized depots assignment. Afterwards, sensitivity analysis on GA parameters was done and calculation times were presented. Subsequently the proposed model was evaluated thus, Mashhad bus network was designed using the methodology of the presented model.

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Since the 1990’s, network reliability has been considered as a new index for evaluating transportation networks under uncertainty. A large number of studies have been revealed in the literature in this field, which are mostly dedicated to developing relevant measures that can be utilized for the evaluation of vulnerable networks under different sources of uncertainty, such as daily traffic flow fluctuations, natural disasters, weather conditions, and so fourth. This paper addresses the resource allocation problem in vulnerable transportation networks, in which multiple performance reliability measures should be met at their desired levels, while the overall cost of upgrading links’ performances should be minimized simultaneously. For this purpose, a new approach has been considered to formulate the two well-known performance measures, connectivity and capacity reliability, along with their application in a bi-objective nonlinear mixed integer goal programming model. In order to take into account the uncertain conditions of supply, links’ capacities have been assumed to be random variables and follow normal distribution functions. A computationally efficient method has been developed that allows calculating the network-wise performance indices simply by means of a set of functions of links’ performance reliabilities. Using this approach, as the performance reliability of links are themselves functions of the random links’ capacities, they can be simply calculated through numerical integration. To achieve desirable levels for both connectivity reliability and capacity reliability (as network-wise performance reliability measures) two distinct objectives have been considered. One of the objectives seeks to maximize each of the measures regardless of what is happening to the other objective function which minimizes the budget. Since optimization models with two conflicting objectives cannot be solved directly, the well-known goal attainment multi-objective decision-making (MODM) approach has been adapted to formulate the model as a single objective model. Then the resultant single objective model has been solved through the generalized gradient method, which is a straightforward solution algorithm coded in existing commercial software such as MATLAB programming software. To show the applicability of the proposed model, numerical results are provided for a simple network. Also, to show the sensitiveness of the model to decision maker’s direction weights, the results of sensitivity analysis are presented..

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Delay is one of the principal measures of performance used to determine the Level of Service (LOS) at signalized intersections and several methods have been widely used to estimate vehicular delay. Very few studies only have been carried out to estimate delay at signalized intersections under mixed traffic conditions prevailing in developing countries like India. In the present study, various problems associated with delay estimation under mixed traffic conditions in a developing country (India) and the methods to over come them were discussed and an attempt was made to improve the accuracy estimating the same. Five isolated signalized intersections from a fast developing industrial city located in TamilNadu, India were chosen for the study. Site specific PCU values were developed considering the static and dynamic characteristics of vehicles. Saturation flow was also directly measured in the field for the prevailing roadway, traffic and signalized conditions and expressed in PCU/h. Control delay was also measured following HCM 2000 guidelines. Later, this was compared with that estimated from the theoretical delay model. Even after taking several measures, good correlation between observed and predicted delay could not be obtained. Therefore, in the present scenario field measured control delay was taken into account to define LOS. A new criteria for Indian cities recently published in the literature was used to assign LOS grades of study intersections and found to be better reflecting the field conditions.

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This paper proposes a new hybrid method namely SA-IP including simulated annealing and interior point algorithms to find the optimal toll prices based on level of service (LOS) in order to maximize the mobility in urban network. By considering six fuzzy LOS for flows, the tolls of congested links can be derived by a bi-level fuzzy programming problem. The objective function of the upper level problem is to minimize the difference between current LOS and desired LOS of links. In this level, to find optimal toll, a simulated annealing algorithm is used. The lower level problem is a fuzzy flow estimator model with fuzzy link costs. Applying a famous defuzzification function, a real-valued multi-commodity flow problem can be obtained. Then a polynomial time interior point algorithm is proposed to find the optimal solution regarding to the estimated flows. In pricing process, by imposing cost on some links with LOS F or E, users incline to use other links with better LOS and less cost. During the iteration of SA algorithm, the LOS of a lot of links gradually closes to their desired values and so the algorithm decreases the number of links with LOS worse than desirable LOS. Sioux Falls network is considered to illustrate the performance of SA-IP method on congestion pricing based on different LOS. In this pilot, after toll pricing, the number of links with LOS D, E and F are reduced and LOS of a great number of links becomes C. Also the value of objective function improves 65.97% after toll pricing process. It is shown optimal toll for considerable network is 5 dollar and by imposing higher toll, objective function will be worse.

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In this paper, a bi-level decision making model is proposed for a vehicle routing problem with multiple decision-makers (VRPMD) in a fuzzy random environment. In our model, the objective of the leader is to minimize total costs by deciding the customer sets, while the follower is trying to minimize routing costs by choosing routes for each vehicle. Demand for each item has considerable uncertainty, so customer demand is considered a fuzzy random factor in this paper. After setting up the bi-level programming model for VRPMD, a bi-level global-local-neighbor particle swarm optimization with fuzzy random simulation (bglnPSO-frs) is developed to solve the bi-level fuzzy random model. Finally, the proposed model and method are applied to construction material transportation in the Yalong River Hydropower Base in China to illustrate its effectiveness.

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Short-term traffic flow forecasting plays a significant role in the Intelligent Transportation Systems (ITS), especially for the traffic signal control and the transportation planning research. Two mainly problems restrict the forecasting of urban freeway traffic parameters. One is the freeway traffic changes non-regularly under the heterogeneous traffic conditions, and the other is the successful predictability decreases sharply in multiple-steps-ahead prediction. In this paper, we present a novel pattern-based short-term traffic forecasting approach based on the integration of multi-phase traffic flow theory and time series analysis methods. For the purpose of prediction, the historical traffic data are classified by the dynamic flow-density relation into three traffic patterns (free flow, synchronized and congested pattern), and then different predict models are built respectively according to the classified traffic patterns. With the current traffic data, the future traffic state can be online predicted by means of pattern matching to identify traffic patterns. Finally, a comparative study in a section of the Third-Loop Freeway, LIULIQIAO, Beijing city, shows that the proposed approach represents more accurately the anticipated traffic flow when compared to the classical time series models that without integration with the traffic flow theory.

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Road transportation by way of automobiles is a very convenient means of transportation. Today, the most detrimental consequence of developing transportation systems in a country is traffic accident that places a huge financial burden on society. This paper investigates the role of information systems in transportation safety that leads to improved planning and operation of the transportation system through the application of new technologies. Current methods for identification of segments of roads with high potential of accident are based on statistical approaches. Since there are not accident records for newly built roads, these methods cannot be used for regional roads that are recently built. This paper presents a GIS based Neuro-Fuzzy modeling for identification of road hazardous zones. The results of proposed approach are compared with statistical methods. It is shown that this method is a cheaper but at the same time robust means of analyzing the level of hazard associated with each road segment under consideration, specially when data are uncertain and incomplete.

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A structural model for urban arterial road is proposed. It describes the road traffic dynamics in a disaggregated way. The structural model mainly includes: (1) a link traffic model that tracks the traffic waves cyclically. Traffic waves within each cycle are captured by three characteristic points. These points are formed by the encounter of different traffic waves. (2) a proportional line model which is used to split the overall outflow into different turning flow. The model is derived directly from first-in-first-out (FIFO) principle. (3) a spillover component that deals with channelized section queue overflow and (4) a traffic flow performance index component that outputs macroscopic and microscopic level indexes. These indexes include delay, stops, queue length and vehicle trajectory, travel time. The former three can be used in traffic flow optimization and the latter two are valuable in vehicle emission evaluation. Simulation results show that with the increasing of numerical resolution, traditional CTM model gradually converges to our model.

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The previous studies show that a high percentage of traffic accidents take place in two-lane rural highways and most of which happen at horizontal curves. Meanwhile the horizontal alignment is often subject to hard topographic conditions where because of economic aspects designers are forced to design horizontal curves at grades. Vertical angle of longitudinal slope reduces the normal force of vehicle on road and friction force in tire-pavement surface will decrease. This leads to a lack of sufficient driver control over the vehicle especially if the curve with small radius is located at downgrade. In this paper, the suitability of operating speed and lateral friction coefficient as geometric design criteria for horizontal curves in downgrades are studied with regard to traffic safety and vehicle stability. The investigation of speed reduction of the vehicles running on a horizontal curve at downgrade as a response of driver behavior and the use of friction ellipse theory give the available friction coefficient. Whereas the dynamic analysis of forces applied on the vehicle in curve which is located at downgrade if combined with operating speed results in the required coefficient of lateral friction. Finally, a comparison of these two parameters based on safety evaluation criteria gives an estimation of actual safety level in designing horizontal curve at downgrades with regard to AASHTO’s data in horizontal curve design.

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We consider the problem of continuous dynamic berth allocation to containerships in a tidal seaport. In some container ports, low water depth in coastal area causes many restrictions on providing vessel's services. Therefore, berth allocation planning for relatively large vessels with high draft is subject to tidal conditions when the vessels are in the access channel as from anchorage area to the quay. Tidal conditions sometimes have a significant effect on possibility of entrance and departure of these ships to or from ports. Shahid Rajaee Port Complex, Iran's largest container seaport and the case study of this research, located at northern coast of Persian Gulf and has low water depth in its area. Historical data of seaside operations in this port is applied to the proposed model. This model also takes into account the variations of water depth in different berths. Simultaneous programming for two or more container terminals and exertion of priority and precedency coefficients based on vessel size and voyage type altogether are other attributes of this model. Here, genetic algorithm in combination with pattern search algorithm was used for solving the problem. Computational experiments have indicated that the proposed heuristic is relatively effective just for small size instances.

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Abstract: Gridlock is an extreme traffic state where vehicle cannot move at all. This research studies the development of gridlock by theoretical and numerical analysis. It is shown that the development of gridlock can be divided into several stages. The core of the development is the evolution of congestion loop. A congestion loop is comprised of a number of consecutively connected spillover links. The evolution of a congestion loop always tends to be stable, i.e. the state of all related links tends to be identical.. Under the stable condition, traffic states of all links are identical. A novel concept, “virtual signal” is proposed to describe the queue propagation and spillover during the stabilization. Simulation results show that congestion propagates in an accelerated way. The prevention of the first congestion loop is crucial. The achieved results have potential use for future network traffic control design and field applications

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This paper presents a novel approach to solve the double-track railway rescheduling problem, when an incident occurs into one of the block sections of the railway. The approach restricts the effects of an incident to a specific time, based on which the trains are divided into rescheduled and unchanged ones, so that the latter retain their original time-table after the incident. The main contribution of this approach is the simultaneous consideration of three rescheduling policies: cancelling, delaying and re-ordering. A mixed-integer optimization model is developed to find optimal conflict-free time-table compatible with the proposed approach. The objective function minimizes two cost parts: the cost of deviation from the primary time-table and the cost of train cancellation. The model is solved by CPLEX 11 software which automatically generates the optimal solution of a problem. Also, a meta-heuristic solution method based on simulated annealing algorithm is proposed for tackling the large-scale problems. The results of an experimental analysis on two double-track railways of the Iranian network show an appropriate capability of the model and solution method for handling the simultaneous train rescheduling. The results indicate that the proposed solution method can provide good solutions in much shorter time, compared with the time taken to solve the mathematical model by CPLEX software.

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This study proposes a prediction model about the trajectories a vehicle, in isolated conditions, along a curve of a road. As we know, the road environment induces stress on users and, under certain conditions, influences driving behavior. It is of advantage then, to isolate and identify those conditions from among the numerous variables, which are actually the most significant so as to prevent or mitigate the occurrence of dangerous maneuvers. On the basis of an experiment performed using an instrumented vehicle, we collected a data base to which we subsequently applied Neuro-Fuzzy techniques for the selection of the most representative variables. We then used these data to prepare a nonlinear dynamic Hammerstein-Wiener’s model able to predict the track paths along curves. The findings were encouraging since almost all the results obtained from the validation checks proved satisfactory. This research is the first step in the identification of complex systems and could be applied in road safety measures and design of new and existing roads.