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This paper specifies the relationship among various factors contributing to road accidents including geometrical design characteristics, environmental and traffic specifications, by multiple regression analysis. The main objective of this paper is identification of problems associated with the safety issue of road networks by application of accident prediction models. Data from previous accidents were used to develop the models. Results of this study showed that the rate of road accidents is to a large extent dependent on the rate of traffic volume. Type of road and land-use are other important factors influencing the number and intensity of accidents. The mountainous roads in this respect require special attention regarding their safety factors. The quantitative rate of road safety upgrading has also been specified by adding traffic lanes in road networks.

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This paper presents a hybrid approach to developing a short-term traffic flow prediction model. In this

approach a primary model is synthesized based on Neural Networks and then the model structure is optimized through

Genetic Algorithm. The proposed approach is applied to a rural highway, Ghazvin-Rasht Road in Iran. The obtained

results are acceptable and indicate that the proposed approach can improve model accuracy while reducing model

structure complexity. Minimum achieved prediction r2 is 0.73 and number of connection links at least reduced 20%

as a result of optimization.

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The paper discusses the current state of research concerning railway network timetabling and traffic

management. Timetable effectiveness is governed by frequency, regularity, accurate running, recovery and layover

times, as well as minimal headway, buffer times and waiting times. Analytic (queuing) models and stochastic microsimulation

are predominantly used for estimation of waiting times and capacity consumption anlong corridors and in

stations, while combinatorial models and stability analysis are suitable for network timetable optimisation. Efficient

traffic management can be achieved by real-time monitoring, fusion, analysis and rescheduling of railway traffic in

case of disturbances. Real-time simulation, optimisation and impact evaluation of dispatching measures can improve

the effectiveness of rescheduling and traffic management. The display of dynamic signal and track occupancy data in

driver cabins, as RouteLint developed by ProRail, can support anticipative actions of the driver in order to reduce

knock-on delays and increase throughput.

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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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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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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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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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Harvesting of timber using ground based machinery is still a common practice around the world. Track and road building, and movement of machinery during harvesting operations cause soil disturbance. Therefore the aim of this study was to investigate the change in soil properties after logging operation on skid trails (2 years and 7 years after logging) and compare disturbed soil properties with control sampling (undisturbed soil). For this purpose, soil samples were collected from the skid trail and undisturbed area. Electrical conductivity, pH, organic carbon, moisture equivalent, moisture, total porosity and bulk density were determined on the skid trail and undisturbed area. Soil characteristics were examined in two ages (2 years and 7 years skid trail). There were crucial differences in the values of electrical conductivity, organic carbon, moisture, total porosity and bulk density from skid trail and undisturbed area in 2 years skid trail (p<0.05). But on 7 years skid trail, there were no significant differences in values of mentioned factors from skid trail and undisturbed area (p>0.05) except bulk density (p=0). It has been concluded that 7 years after logging, all soil properties except bulk density were completely recovered on skid trail. These findings have important implications for assessing the impact of skidders traffic and recovery time in skid trails.

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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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The geometric design of a road based on consistency implies that this should not violate driver expectations. Although there are different methods for estimating consistency, the most used have been those based on the operating speed of vehicles. This is due to its relationship with accidents. Road alignments which cause marked differences in vehicle speeds favour a greater accident rate. In this research local approaches to evaluate the consistency of the alignments (tangents, curves) versus global approaches to evaluate an entire stretch of road have been analyzed. Different models have been used to estimate operating speed of vehicles. The study has focused from a practical point of view using two applications for the evaluation of consistency of a stretch of road. The results show the influence of the choice of the speed model in the level of consistency. In addition, practical issues about how some variables, such as desired speed, posted speed and design speed, can influence the results are presented.

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Sufficient literature is available on approaches to deal with heterogeneous traffic on mid-blocks in developing countries, but not much work is done on roundabouts. The estimation of passenger car unit (PCU) for different vehicles to convert heterogeneous traffic into homogeneous traffic is a well-accepted procedure. But the parameters used for mid-blocks may not be helpful on roundabouts as traffic flow characteristics on the two locations are different. Suggested PCU values on roundabouts from developing countries are not recent, and needs a relook. It is also not clear whether to use static or dynamic PCU values on account of possible temporal and spatial variations across locations. This paper presents an estimation approach for PCUs on roundabouts, as well as, suggests using static value instead of dynamic. The problem to deal with re-estimation of PCU values at different locations, due to possible traffic flow variations, is dealt with by proposing a Heterogeneity Equivalency Factor (H-Factor). The factor is multiplicative and converts heterogeneous traffic (veh/h) into homogeneous traffic (pcu/h).

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The gap acceptance behavior of drivers at U-turn median openings is studied by considering the critical gap which cannot be obtained directly by field measurements. A thorough investigation on critical gap estimation is carried out by collecting the data sets from median openings at 4-lane, 6-lane and 8-lane roads of Hyderabad City, India. Wide difference (10% - 42%) among the critical gap values estimated by the application of existing methods shows the limitation to consider for the mixed traffic situations. In order to address this issue, recently developed method Influence Area For Gap Acceptance (INAFOGA) which is based on clearing behavior of drivers at unsignalised intersections is modified and applied considering the merging behavior of U-turn vehicles at median openings and named as ‘Modified INAFOGA’ method. Modified INAFOGA method is compared with probability equilibrium method through paired-sample hypothesis (t-test) and result revealed that difference in mean values 0.009 signifies that both methods are comparable. Difference in critical gap values obtained from the box plots and radar charts indicates that Probability equilibrium method is not suitable to address the behavior of U-turn vehicles at median openings under mixed traffic conditions. These observations coupled with higher critical gap values validate the fact that ‘modified INAFOGA’ method is indeed appropriate under mixed traffic conditions.